The discrete-time GeomG1 queue with multiple adaptive vacations and server SetupClosedown t

eulerancestraldiscretescheduler用法-概述说明以及解释1.引言1.1 概述概述部分的内容可以描述以下内容：引言部分是文章的开篇，主要目的是引起读者的兴趣并介绍文章的主题和重要性。

本文的主题是“eulerancestraldiscretescheduler用法”，它是一种离散的调度器工具，该工具可以用于实际系统中的任务调度和资源管理。

在当前快速发展的信息技术时代，任务调度和资源管理对于提高系统性能和效率至关重要。

传统的调度器在处理大量任务时往往效率低下，无法充分利用系统资源。

而eulerancestraldiscretescheduler的出现，为解决这一问题提供了一种新的解决方案。

eulerancestraldiscretescheduler采用了一种基于离散数学和遗传算法的调度策略，通过优化任务的分配和资源的利用，提高系统的调度效率和性能。

本文将首先介绍eulerancestraldiscretescheduler的定义和背景，包括其工作原理和相关理论基础。

其次，将详细阐述eulerancestraldiscretescheduler的特点，包括其优势和适用领域。

最后，将详细介绍eulerancestraldiscretescheduler的使用方法，包括软件安装和配置等方面。

通过深入了解eulerancestraldiscretescheduler的用法，读者将能够更好地理解和应用该工具，从而提高任务调度和资源管理的效率。

同时，本文还将探讨eulerancestraldiscretescheduler的优势和应用前景，总结文章的主要观点，并对未来对该领域的研究进行展望。

综上所述，本文的主要目的是介绍eulerancestraldiscretescheduler 的用法，帮助读者更好地理解和应用该工具，从而提高系统的调度效率和性能。

1.2 文章结构文章结构是指文章整体的组织和呈现方式，它是文章的骨架，能够使读者快速了解整篇文章的内容和论述逻辑。

Which function is used for starting a process?Which is not one of the major differences between user-level threads+ ? When a process changes state, which of the following conversion will not occur ?-*If there are five processes in the system, how many processes at most may be under the waiting state at the same time.-Which one of the followings is not a method for communication between processes.A.sharing memoryB.message bufferC. pipeD.semaphore operations wait and signalith indirect communication, the messages are sent to and received fromA.supplyB.chanelC. pipeD.mailbox-*Which information is not included in PCBA.page sizeB.memory addressC.priorityD.process ID+-*One process may include many threads. Which one of the followings+-In banch system, job scheduling program chooses multiple jobs from the job queue and put them intoJWhich one is wrong with operating system?+Tn computer system, the technology that allows multiple programs enter the memory and execute at the same time is called+*Which kind of operating systems has well-defined, fixed time*What is not the major activity of an operating system?+In user program, operating system services are requested byich of the following performs interrupt responseich element is not part of a microkernel?*Which one can identify the existence and state of a process+-Which one of the following is not a feature of process+^Which environment considers memory, process, and device and file management from a global viewpoint?On designing batch system, we should first consider+In multiprogramming system, operating system allocates resources in*In multiprogramming system, in order to achieve the full utilization of+-*If a process is waiting for printer to continue executing, it is under which sate?+-A process may be in one of the following states, except+-*In operating system, when a process turns to ready from running, it indicatesD.time-slice is used outWhat state is a process in when it can not run because it needs a resource to become available?+-Which is set in program state word (PSW) to avoid the user program executing special instructions。

【LOJ2461】「2018集训队互测Day1」完美的队列（分块+双指针）⼤致题意：让你维护n个有限定长度的队列，每次区间往队列⾥加数，求每次加完后的队列⾥剩余元素种类数。

核⼼思路这道题可以⽤分块+双指针去搞。

考虑求出每个操作插⼊的元素在队列中被全部弹完所需要的时间Max_i，最后差分即可求出答案。

我们可以O(\sqrt n)枚举块，然后O(m)枚举询问，从⽽统计每⼀个块对每⼀个询问的贡献值。

因此，我们主要要考虑的，就是分别对于整块与⾮整块，如何求出其对于⼀个询问的贡献。

整块对询问的贡献整块对询问的贡献应该是⽐较好统计的。

考虑记录⼀个tot表⽰整个块被完全覆盖的次数，并开⼀个数组t表⽰每个队列还需要被覆盖多少次才能把当前数弹掉（初始化：t_i=a_i）。

然后，我们⽤⼀个变量Mx维护t的最⼤值，则对于⼀个覆盖整块的操作，它会被全部弹完，当且仅当Mx\le tot。

由于这个被全部弹完的时间显然是递增的，因此我们可以⽤双指针来维护，即固定当前正在处理的操作为左端点，然后移动右端点使得Mx\le tot。

则对于⼀个整块操作，若移动右端点，则我们将tot加1，反之减1。

对于⼀个⾮整块操作，若移动右端点，则我们将操作范围内的t_i减1，反之加1，同时重新求⼀遍Mx。

移动完后，我们将当前左端点的Max值向当前右端点的位置取max即可。

这样，我们就处理完了整块对询问的贡献。

⾮整块对询问的贡献这就略微⿇烦了。

为了处理这个，我们要先在前⾯处理整块的时候维护⼀些信息：Sum_i：维护在前i个操作中有多少个覆盖整块的操作。

Cov_i：记录第i个覆盖整块的操作的时间。

NCov_i：记录第i个没有覆盖整块但与这个整块有交集的操作的时间。

Pos_i：记录第i个没有覆盖整块的操作的上⼀个覆盖整块操作的时间。

接下来，考虑枚举块内的每⼀个位置，然后枚举每⼀个⾮整块操作，统计贡献。

让我们来思考⼀下，如何求出某⼀操作在什么时候会被弹完。

跨时钟域问题的解决2⽉18⽇跨时钟域问题(Clock Domain Crossing) –同两个时钟域打交道！引⾔：设计者有时候需要将处于两个不同时钟域的系统对接，由于接⼝处是异步（会产⽣setuptime 和holdtime violation，亚稳态以及不可靠的数据传输）的，因此处理起来较同步逻辑更棘⼿，需要寻求特殊处理来进⾏接⼝界⾯的设计。

任意的两个系统如果满⾜以下条件之⼀，就可称其为异步的：(1)⼯作在不同的时钟频率上；(2)⼯作频率相同，但是相位不相同；处理跨时钟域的数据传输，有两种实现⽅案：（1）采⽤握⼿信号来交互（2）以异步FIFO来实现1.1、以握⼿信号交互：假设系统A以这种⽅式向系统B传递数据，握⼿信号分别为req和ack。

握⼿协议：Transmitter asserts the req (request) signal, asking the receiver to accept the data on the data bus.Receiver asserts the ack (acknowledge) signal, asserting that it has accepted the data.这种处理跨时钟域的⽅式很直接，但是也最容易产⽣亚稳态，由于系统A发送的req信号需要系统B中的时钟去sample，⽽系统B发出的ack信号⼜需要系统A中的时钟去sample，这样两边都存在着setup time和hold time violation的问题。

为了避免由于setup time和hold time vilation所造成的亚稳态，通常我们可以将异步时钟域交互的信号⽤local system的时钟打两级甚⾄三级寄存器，以此来消除亚稳态的影响。

下图以系统A发送到系统B的req信号⽰例消除亚稳态的⽅法：当然，这种处理⽅式是以损失传输速率为代价的，加⼊两到三级寄存器同步异步时钟域的信号，会有许多时钟周期浪费在了系统的“握⼿”。

Discovery软件其实就是一个工作平台，也就是将传统的地质制图工作作成了一体化的平台，如果你是初学者，那么，你可以多跑软件流程，同时，结合你具体的工区（project），来作练习。

如果你有较长的工作时间（油田研究院或者采油厂地质研究所），那么，在你加载完基本数据后，你就可以作很多基础的工作了。

比如，砂体厚度（储层）平面图，地层厚度、物性（孔隙度，渗透率）等等，这样会比传统的工作方法（手工）效率高很多。

关于具体的工作流程，Discovery软件中在建立工区时有个Workflow,Discovery 软件包括地震解释（SV）、测井解释（Prizm）、平面图（Goatlas）剖面图（Xsection），简单的叠后处理（Pstax），正演（GMAPlus），以及坐标系统，井数据库等等模块，但确实没有反演（Inversion）模块。

1、可是现在我的断层文件中只有Inline,Crossline，Faultname,time四列，没有X,Y坐标信息，还能加入马？我看你给的头文件中也没有提到Inline,Crossline。

其他的比如解释者等信息可以不加马？SV中加载断层文件确实比较复杂，在SV中加载断层文件（Fault Trace）必须要有X Y 坐标，反而没有InLine和CRLIne却是可以的。

这与一般的地震解释和反演软件有所区别。

也可以说是SV的一个缺点。

解决只有InLine和CrLine而没有X，Y的断层文件（Fault Trace）的办法有两个：（1）重新让解释人员给你输出，呵呵，这个办法最简单了：）（2）自己转换线道号为XY坐标，自己或者照别人编一个就行了，实在不会的话，在Excell里也可以转化的，转换的办法就是简单的集合运算，呵呵，我就不详细解释了：）提示：最好的办法还是按照我上次说的用默认格式输入，要不，你的断层（Fault segment）可能会出现混乱！2、层位和断层ＡＳＣ码文件格式是：断层：fycWX1002 262 650 2062 7 1层位； 363（Inline） 415（Crossline） 1992(Time）断层和层位都没有X,Y坐标在Seisvision－》Horizon—》Horizon import中需要设置那些参数才能导入层位和断层。

1Discrete-time MPC for Beginners1.1IntroductionIn this chapter,we will introduce the basic ideas and terms about model pre-dictive control.In Section1.2,a single-input and single-output state-space model with an embedded integrator is introduced,which is used in the design of discrete-time predictive controllers with integral action in this book.In Sec-tion1.3,we examine the design of predictive control within one optimization window.This is demonstrated by simple analytical examples.With the results obtained from the optimization,in Section1.4,we discuss the ideas of reced-ing horizon control,and state feedback gain matrices,and the closed-loop configuration of the predictive control system.The results are extended to multi-input and multi-output systems in Section1.5.In a general framework of state-space design,an observer is needed in the implementation,and this is discussed in Section1.6.With a combination of estimated state variables and the predictive controller,in Section1.7,we present state estimate predictive control.1.1.1Day-to-day Application Example of Predictive ControlThe general design objective of model predictive control is to compute a tra-jectory of a future manipulated variable u to optimize the future behaviour of the plant output y.The optimization is performed within a limited time window by giving plant information at the start of the time window.To help understand the basic ideas that have been used in the design of predictive control,we examine a typical planning activity of our day-to-day work.The day begins at9o’clock in the morning.We are,as a team,going to complete the tasks of design and implementation of a model predictive control system for a liquid vessel.The rule of the game is that we always plan our activities for the next8hours work,however,we only implement the plan for thefirst hour.This planning activity is repeated for every fresh hour until the tasks are completed.21Discrete-time MPC for BeginnersGiven the amount of background work that we have completed for9o’clock,we plan ahead for the next8hours.Assume that the work tasks are divided into modelling,design,simulation and pleting these tasks will be a function of various factors,such as how much effort we will put in,how well we will work as a team and whether we will get some additional help from others.These are the manipulated variables in the planning problem.Also,we have our limitations,such as our ability to understand the design problem,and whether we have good skills of computer hardware and software engineering. These are the hard and soft constraints in the planning.The background information we have already acquired is paramount for this planning work.After everything is considered,we determine the design tasks for the next 8hours as functions of the manipulated variables.Then we calculate hour-by-hour what we need to do in order to complete the tasks.In this calculation, based on the background information,we will take our limitations into con-sideration as the constraints,andfind the best way to achieve the goal.The end result of this planning gives us our projected activities from9o’clock to 5o’clock.Then we start working by implementing the activities for thefirst hour of our plan.At10o’clock,we check how much we have actually done for thefirst hour.This information is used for the planning of our next phase of activities. Maybe we have done less than we planned because we could not get the correct model or because one of the key members went for an emergency meeting.Nevertheless,at10o’clock,we make an assessment on what we have achieved,and use this updated information for planning our activities for the next8hours.Our objective may remain the same or may change.The length of time for the planning remains the same(8hours).We repeat the same planning process as it was at9o’clock,which then gives us the new projected activities for the next8hours.We implement thefirst hour of activities at 10o’clock.Again at11o’clock,we assess what we have achieved again and use the updated information for the plan of work for the next8hours.The planning and implementation process is repeated every hour until the original objective is achieved.There are three key elements required in the planning.Thefirst is the way of predicting what might happen(model);the second is the instrument of assessing our current activities(measurement);and the third is the instrument of implementing the planned activities(realization of control).The key issues in the planning exercise are:1.the time window for the planning isfixed at8hours;2.we need to know our current status before the planning;3.we take the best approach for the8hours work by taking the constraintsinto consideration,and the optimization is performed in real-time with a moving horizon time window and with the latest information available. The planning activity described here involves the principle of MPC.In this example,it is described by the terms that are to be used frequently in the1.1Introduction3 following:the moving horizon window,prediction horizon,receding horizon control,and control objective.They are introduced as below.1.Moving horizon window:the time-dependent window from an arbitrarytime t i to t i+T p.The length of the window T p remains constant.In this example,the planning activity is performed within an8-hour window, thus T p=8,with the measurement taken every hour.However,t i,which defines the beginning of the optimization window,increases on an hourly basis,starting with t i=9.2.Prediction horizon:dictates how‘far’we wish the future to be predictedfor.This parameter equals the length of the moving horizon window,T p.3.Receding horizon control:although the optimal trajectory of future controlsignal is completely described within the moving horizon window,the actual control input to the plant only takes thefirst sample of the control signal,while neglecting the rest of the trajectory.4.In the planning process,we need the information at time t i in order topredict the future.This information is denoted as x(t i)which is a vec-tor containing many relevant factors,and is either directly measured or estimated.5.A given model that will describe the dynamics of the system is paramountin predictive control.A good dynamic model will give a consistent and accurate prediction of the future.6.In order to make the best decision,a criterion is needed to reflect the ob-jective.The objective is related to an error function based on the difference between the desired and the actual responses.This objective function is often called the cost function J,and the optimal control action is found by minimizing this cost function within the optimization window.1.1.2Models Used in the DesignThere are three general approaches to predictive control design.Each ap-proach uses a unique model structure.In the earlier formulation of model predictive control,finite impulse response(FIR)models and step response models were favoured.FIR model/step response model based design algo-rithms include dynamic matrix control(DMC)(Cutler and Ramaker,1979) and the quadratic DMC formulation of Garcia and Morshedi(1986).The FIR type of models are appealing to process engineers because the model structure gives a transparent description of process time delay,response time and gain.However,they are limited to stable plants and often require large model orders.This model structure typically requires30to60impulse re-sponse coefficients depending on the process dynamics and choice of sampling intervals.Transfer function models give a more parsimonious description of process dynamics and are applicable to both stable and unstable plants.Rep-resentatives of transfer function model-based predictive control include the predictive control algorithm of Peterka(Peterka,1984)and the generalized41Discrete-time MPC for Beginnerspredictive control(GPC)algorithm of Clarke and colleagues(Clarke et al., 1987).The transfer function model-based predictive control is often considered to be less effective in handling multivariable plants.A state-space formulation of GPC has been presented in Ordys and Clarke(1993).Recent years have seen the growing popularity of predictive control de-sign using state-space design methods(Ricker,1991,Rawlings and Muske, 1993,Rawlings,2000,Maciejowski,2002).In this book,we will use state-space models,both in continuous time and discrete time for simplicity of the design framework and the direct link to the classical linear quadratic regulators. 1.2State-space Models with Embedded IntegratorModel predictive control systems are designed based on a mathematical model of the plant.The model to be used in the control system design is taken to be a state-space model.By using a state-space model,the current information required for predicting ahead is represented by the state variable at the current time.1.2.1Single-input and Single-output SystemFor simplicity,we begin our study by assuming that the underlying plant is a single-input and single-output system,described by:x m(k+1)=A m x m(k)+B m u(k),(1.1) y(k)=C m x m(k),(1.2)where u is the manipulated variable or input variable;y is the process output; and x m is the state variable vector with assumed dimension n1.Note that this plant model has u(k)as its input.Thus,we need to change the model to suit our design purpose in which an integrator is embedded.Note that a general formulation of a state-space model has a direct term from the input signal u(k)to the output y(k)asy(k)=C m x m(k)+D m u(k).However,due to the principle of receding horizon control,where a current information of the plant is required for prediction and control,we have im-plicitly assumed that the input u(k)cannot affect the output y(k)at the same time.Thus,D m=0in the plant model.Taking a difference operation on both sides of(1.1),we obtain thatx m(k+1)−x m(k)=A m(x m(k)−x m(k−1))+B m(u(k)−u(k−1)).1.2State-space Models with Embedded Integrator 5Let us denote the difference of the state variable byΔx m (k +1)=x m (k +1)−x m (k );Δx m (k )=x m (k )−x m (k −1),and the difference of the control variable byΔu (k )=u (k )−u (k −1).These are the increments of the variables x m (k )and u (k ).With this transfor-mation,the difference of the state-space equation is:Δx m (k +1)=A m Δx m (k )+B m Δu (k ).(1.3)Note that the input to the state-space model is Δu (k ).The next step is to connect Δx m (k )to the output y (k ).To do so,a new state variable vector is chosen to be x (k )= Δx m (k )T y (k ) T ,where superscript T indicates matrix transpose.Note thaty (k +1)−y (k )=C m (x m (k +1)−x m (k ))=C m Δx m (k +1)=C m A m Δx m (k )+C m B m Δu (k ).(1.4)Putting together (1.3)with (1.4)leads to the following state-space model:x (k +1) Δx m (k +1)y (k +1) =A A m o T m C m A m 1 x (k ) Δx m (k )y (k ) +B B m C m B mΔu (k )y (k )=C o m 1 Δx m (k )y (k ),(1.5)where o m =n 1 00...0 .The triplet (A,B,C )is called the augmented model,which will be used in the design of predictive control.Example 1.1.Consider a discrete-time model in the following form:x m (k +1)=A m x m (k )+B m u (k )y (k )=C m x m (k )(1.6)where the system matrices areA m = 1101 ;B m = 0.51;C m = 10 .Find the triplet matrices (A,B,C )in the augmented model (1.5)and calcu-late the eigenvalues of the system matrix,A ,of the augmented model.61Discrete-time MPC for BeginnersSolution.From (1.5),n 1=2and o m =[00].The augmented model for this plant is given byx (k +1)=Ax (k )+BΔu (k )y (k )=Cx (k ),(1.7)where the augmented system matrices are A = A m o T m C m A m 1 =⎡⎣110010111⎤⎦;B = B m C m B m =⎡⎣0.510.5⎤⎦;C = o m 1 = 001 .The characteristic equation of matrix A is given by ρ(λ)=det(λI −A )=det λI −A m o T m −C m A m (λ−1)=(λ−1)det(λI −A m )=(λ−1)3.(1.8)Therefore,the augmented state-space model has three eigenvalues at λ=1.Among them,two are from the original integrator plant,and one is from the augmentation of the plant model.1.2.2MATLAB Tutorial:Augmented Design ModelTutorial 1.1.The objective of this tutorial is to demonstrate how to obtain a discrete-time state-space model from a continuous-time state-space model,and form the augmented discrete-time state-space model.Consider a continuous-time system has the state-space model:˙x m (t )=⎡⎣010301010⎤⎦x m (t )+⎡⎣113⎤⎦u (t )y (t )= 010 x m (t ).(1.9)Step by Step1.Create a new file called extmodel.m.We form a continuous-time state vari-able model;then this continuous-time model is discretized using MATLAB function ‘c2dm’with specified sampling interval Δt .2.Enter the following program into the file:Ac =[010;301;010];Bc=[1;1;3];Cc=[010];Dc=zeros(1,1);Delta_t=1;[Ad,Bd,Cd,Dd]=c2dm(Ac,Bc,Cc,Dc,Delta_t);1.3Predictive Control within One Optimization Window7 3.The dimensions of the system matrices are determined to discover thenumbers of states,inputs and outputs.The augmented state-space model is produced.Continue entering the following program into thefile: [m1,n1]=size(Cd);[n1,n_in]=size(Bd);A_e=eye(n1+m1,n1+m1);A_e(1:n1,1:n1)=Ad;A_e(n1+1:n1+m1,1:n1)=Cd*Ad;B_e=zeros(n1+m1,n_in);B_e(1:n1,:)=Bd;B_e(n1+1:n1+m1,:)=Cd*Bd;C_e=zeros(m1,n1+m1);C_e(:,n1+1:n1+m1)=eye(m1,m1);4.Run this program to produce the augmented state variable model for thedesign of predictive control.1.3Predictive Control within One Optimization Window Upon formulation of the mathematical model,the next step in the design of a predictive control system is to calculate the predicted plant output with the future control signal as the adjustable variables.This prediction is described within an optimization window.This section will examine in detail the opti-mization carried out within this window.Here,we assume that the current time is k i and the length of the optimization window is N p as the number of samples.For simplicity,the case of single-input and single-output systems is consideredfirst,then the results are extended to multi-input and multi-output systems.1.3.1Prediction of State and Output VariablesAssuming that at the sampling instant k i,k i>0,the state variable vector x(k i)is available through measurement,the state x(k i)provides the current plant information.The more general situation where the state is not directly measured will be discussed later.The future control trajectory is denoted by Δu(k i),Δu(k i+1),...,Δu(k i+N c−1),where N c is called the control horizon dictating the number of parameters used to capture the future control trajectory.With given information x(k i), the future state variables are predicted for N p number of samples,where N p is called the prediction horizon.N p is also the length of the optimization window.We denote the future state variables asx(k i+1|k i),x(k i+2|k i),...,x(k i+m|k i),...,x(k i+N p|k i),81Discrete-time MPC for Beginnerswhere x (k i +m |k i )is the predicted state variable at k i +m with given current plant information x (k i ).The control horizon N c is chosen to be less than (or equal to)the prediction horizon N p .Based on the state-space model (A,B,C ),the future state variables are calculated sequentially using the set of future control parameters:x (k i +1|k i )=Ax (k i )+BΔu (k i )x (k i +2|k i )=Ax (k i +1|k i )+BΔu (k i +1)=A 2x (k i )+ABΔu (k i )+BΔu (k i +1)...x (k i +N p |k i )=A N p x (k i )+A N p −1BΔu (k i )+A N p −2BΔu (k i +1)+...+A N p −N c BΔu (k i +N c −1).From the predicted state variables,the predicted output variables are,by substitutiony (k i +1|k i )=CAx (k i )+CBΔu (k i )(1.10)y (k i +2|k i )=CA 2x (k i )+CABΔu (k i )+CBΔu (k i +1)y (k i +3|k i )=CA 3x (k i )+CA 2BΔu (k i )+CABΔu (k i +1)+CBΔu (k i +2)...y (k i +N p |k i )=CA N p x (k i )+CA N p −1BΔu (k i )+CA N p −2BΔu (k i +1)+...+CA N p −N c BΔu (k i +N c −1).(1.11)Note that all predicted variables are formulated in terms of current state variable information x (k i )and the future control movement Δu (k i +j ),where j =0,1,...N c −1.Define vectors Y = y (k i +1|k i )y (k i +2|k i )y (k i +3|k i )...y (k i +N p |k i )T ΔU = Δu (k i )Δu (k i +1)Δu (k i +2)...Δu (k i +N c −1) T ,where in the single-input and single-output case,the dimension of Y is N p and the dimension of ΔU is N c .We collect (1.10)and (1.11)together in a compact matrix form asY =F x (k i )+ΦΔU,(1.12)where F =⎡⎢⎢⎢⎢⎢⎣CACA 2CA 3...CA N p ⎤⎥⎥⎥⎥⎥⎦;Φ=⎡⎢⎢⎢⎢⎢⎣CB 00...0CAB CB 0...0CA 2B CAB CB ...0...CA N p −1B CA N p −2B CA N p −3B ...CA N p −N c B⎤⎥⎥⎥⎥⎥⎦.1.3Predictive Control within One Optimization Window9 1.3.2OptimizationFor a given set-point signal r(k i)at sample time k i,within a prediction horizon the objective of the predictive control system is to bring the predicted output as close as possible to the set-point signal,where we assume that the set-point signal remains constant in the optimization window.This objective is then translated into a design tofind the‘best’control parameter vectorΔU such that an error function between the set-point and the predicted output is minimized.Assuming that the data vector that contains the set-point information isR T s=N p11 (1)r(k i),we define the cost function J that reflects the control objective asJ=(R s−Y)T(R s−Y)+ΔU T¯RΔU,(1.13) where thefirst term is linked to the objective of minimizing the errors between the predicted output and the set-point signal while the second term reflects the consideration given to the size ofΔU when the objective function J is made to be as small as possible.¯R is a diagonal matrix in the form that¯R=rw I N c×N c(r w≥0)where r w is used as a tuning parameter for the desired closed-loop performance.For the case that r w=0,the cost function (1.13)is interpreted as the situation where we would not want to pay any attention to how large theΔU might be and our goal would be solely to make the error(R s−Y)T(R s−Y)as small as possible.For the case of large r w,the cost function(1.13)is interpreted as the situation where we would carefully consider how large theΔU might be and cautiously reduce the error (R s−Y)T(R s−Y).Tofind the optimalΔU that will minimize J,by using(1.12),J is ex-pressed asJ=(R s−F x(k i))T(R s−F x(k i))−2ΔU TΦT(R s−F x(k i))+ΔU T(ΦTΦ+¯R)ΔU.(1.14) From thefirst derivative of the cost function J:∂J∂ΔU=−2ΦT(R s−F x(k i))+2(ΦTΦ+¯R)ΔU,(1.15) the necessary condition of the minimum J is obtained as∂J∂ΔU=0,from which wefind the optimal solution for the control signal asΔU=(ΦTΦ+¯R)−1ΦT(R s−F x(k i)),(1.16)101Discrete-time MPC for Beginnerswith the assumption that (ΦT Φ+¯R)−1exists.The matrix (ΦT Φ+¯R )−1is called the Hessian matrix in the optimization literature.Note that R s is a data vector that contains the set-point information expressed asR s =N p [111...1]T r (k i )=¯Rs r (k i ),where¯Rs =N p [111...1]T .The optimal solution of the control signal is linked to the set-point signal r (k i )and the state variable x (k i )via the following equation:ΔU =(ΦT Φ+¯R )−1ΦT (¯R s r (k i )−F x (k i )).(1.17)Example 1.2.Suppose that a first-order system is described by the state equa-tion:x m (k +1)=ax m (k )+bu (k )y (k )=x m (k ),(1.18)where a =0.8and b =0.1are scalars.Find the augmented state-space model.Assuming a prediction horizon N p =10and control horizon N c =4,calcu-late the components that form the prediction of future output Y ,and the quantities ΦT Φ,ΦT F and ΦT ¯Rs .Assuming that at a time k i (k i =10for this example),r (k i )=1and the state vector x (k i )=[0.10.2]T ,find the optimal solution ΔU with respect to the cases where r w =0and r w =10,and compare the results.Solution.The augmented state-space equation is Δx m (k +1)y (k +1) = a 0a 1 Δx m (k )y (k ) + b b Δu (k )y (k )= 01 Δx m (k )y (k ).(1.19)Based on (1.12),the F and Φmatrices take the following forms:F =⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣CA CA 2CA 3CA 4CA 5CA 6CA 7CA 8CA 9CA 10⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦;Φ=⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣CB 000CAB CB 00CA 2B CAB CB 0CA 3B CA 2B CAB CB CA 4B CA 3B CA 2B CAB CA 5B CA 4B CA 3B CA 2B CA 6B CA 5B CA 4B CA 3B CA 7B CA 6B CA 5B CA 4B CA 8B CA 7B CA 6B CA 5B CA 9B CA 8B CA 7B CA 6B ⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦.1.3Predictive Control within One Optimization Window11 The coefficients in the F andΦmatrices are calculated as follows:CA=s11CA2=s21CA3=s31... CA k=s k1,(1.20)where s1=a,s2=a2+s1,...,s k=a k+s k−1,andCB=g0=bCAB=g1=ab+g0CA2B=g2=a2b+g1...CA k−1B=g k−1=a k−1b+g k−2CA k B=g k=a k b+g k−1.(1.21) With the plant parameters a=0.8and b=0.1,N p=10and N c=4,we calculate the quantitiesΦTΦ=⎡⎢⎢⎣1.15411.04070.91160.7726 1.04070.95490.84750.7259 0.91160.84750.76750.6674 0.77260.72590.66740.5943⎤⎥⎥⎦ΦT F=⎡⎢⎢⎣9.23253.21478.32592.76847.29272.33556.18111.9194⎤⎥⎥⎦;ΦT¯R s=⎡⎢⎢⎣3.21472.76842.33551.9194⎤⎥⎥⎦.Note that the vectorΦT¯R s is identical to the last column in the matrixΦT F. This is because the last column of F matrix is identical to¯R s.At time k i=10,the state vector x(k i)=[0.10.2]T.In thefirst case,the error between predicted Y and R s is reduced without any consideration to the magnitude of control ly,r w=0.Then,the optimalΔU is found through the calculationΔU=(ΦTΦ)−1(ΦT R s−ΦT F x(k i))=7.2−6.400T.We note that without weighting on the incremental control,the last two ele-mentsΔu(k i+2)=0andΔu(k i+3)=0,while thefirst two elements have a rather large magnitude.Figure1.1a shows the changes of the state variables where we can see that the predicted output y has reached the desired set-point121Discrete-time MPC for Beginners(a)State variables with no weight onΔu(b)State variables with weight on Δuparison of optimal solutions.Key:line (1)Δx m ;line (2)y 1while the Δx m decays to zero.To examine the effect of the weight r w on the optimal solution of the control,we let r w =10.The optimal solution of ΔU is given below,where I is a 4×4identity matrix,ΔU =(ΦT Φ+10×I )−1(ΦT R s −ΦT F x (k i ))(1.22)= 0.12690.10340.08290.065 T .With this choice,the magnitude of the first two control increments is signifi-cantly reduced,also the last two components are no longer zero.Figure 1.1b shows the optimal state variables.It is seen that the output y did not reach the set-point value of 1,however,the Δx m approaches zero.An observation follows from the comparison study.It seems that if we want the control to move cautiously,then it takes longer for the control signal to reach its steady state (i.e.,the values in ΔU decrease more slowly),because the optimal control energy is distributed over the longer period of future time.We can verify this by increasing N c to 9,while maintaining r w =10.The result shows that the magnitude of the elements in ΔU is reducing,but they are significant for the first 8elements:ΔU T = 0.12270.09930.07900.06140.04630.03340.02270.01390.0072 .In comparison with the case where N c =4,we note that when N c =9,the first four parameters in ΔU are slightly different from the previous case.Example 1.3.There is an alternative way to find the minimum of the cost function via completing the squares.This is an intuitive approach,also the minimum of the cost function becomes a by-product of the approach.Find the optimal solution for ΔU by completing the squares of the cost func-tion J (1.14).1.3Predictive Control within One Optimization Window 13Solution.From (1.14),by adding and subtracting the term(R s −F x (k i ))T Φ(ΦT Φ+¯R)−1ΦT (R s −F x (k i ))to the original cost function J ,its value remains unchanged.This leads toJ =(R s −F x (k i ))T (R s −F x (k i )) −2ΔU T ΦT (R s −F x (k i ))+ΔU T (ΦT Φ+¯R )ΔU + (R s −F x (k i ))T Φ(ΦT Φ+¯R)−1ΦT (R s −F x (k i ))−(R s −F x (k i ))T Φ(ΦT Φ+¯R)−1ΦT (R s −F x (k i )),(1.23)where the quantities under the .are the completed ‘squares’:J 0= ΔU −(ΦT Φ+¯R )−1ΦT (R s −F x (k i )) T ×(ΦT Φ+¯R ) ΔU −(ΦT Φ+¯R )−1ΦT (R s −F x (k i )) .(1.24)This can be easily verified by opening the squares.Since the first and last terms in (1.23)are independent of the variable ΔU (sometimes,we call this a decision variable),and (ΦT Φ+¯R)is assumed to be positive definite,then the minimum of the cost function J is achieved if the quantity J 0equals zero,i.e.,ΔU =(ΦT Φ+¯R)−1ΦT (R s −F x (k i )).(1.25)This is the optimal control solution.By substituting this optimal solution into the cost function (1.23),we obtain the minimum of the cost asJ min =(R s −F x (k i ))T (R s −F x (k i ))−(R s −F x (k i ))T Φ(ΦT Φ+¯R)−1ΦT (R s −F x (k i )).1.3.3MATLAB Tutorial:Computation of MPC GainsTutorial 1.2.The objective of this tutorial is to produce a MATLAB function for calculating ΦT Φ,ΦT F ,ΦT ¯Rs .The key here is to create F and Φmatrices.Φmatrix is a Toeplitz matrix,which is created by defining its first column,and the next column is obtained through shifting the previous column.Step by Step1.Create a new file called mpcgain.m.2.The first step is to create the augmented model for MPC design.The input parameters to the function are the state-space model (A p ,B p ,C p ),prediction horizon N p and control horizon N c .Enter the following program into the file:141Discrete-time MPC for Beginnersfunction[Phi_Phi,Phi_F,Phi_R,A_e,B_e,C_e]=mpcgain(Ap,Bp,Cp,Nc,Np);[m1,n1]=size(Cp);[n1,n_in]=size(Bp);A_e=eye(n1+m1,n1+m1);A_e(1:n1,1:n1)=Ap;A_e(n1+1:n1+m1,1:n1)=Cp*Ap;B_e=zeros(n1+m1,n_in);B_e(1:n1,:)=Bp;B_e(n1+1:n1+m1,:)=Cp*Bp;C_e=zeros(m1,n1+m1);C_e(:,n1+1:n1+m1)=eye(m1,m1);3.Note that the F and P hi matrices have special forms.By taking advantageof the special structure,we obtain the matrices.4.Continue entering the program into thefile:n=n1+m1;h(1,:)=C_e;F(1,:)=C_e*A_e;for kk=2:Nph(kk,:)=h(kk-1,:)*A_e;F(kk,:)=F(kk-1,:)*A_e;endv=h*B_e;Phi=zeros(Np,Nc);%declare the dimension of PhiPhi(:,1)=v;%first column of Phifor i=2:NcPhi(:,i)=[zeros(i-1,1);v(1:Np-i+1,1)];%Toeplitz matrixendBarRs=ones(Np,1);Phi_Phi=Phi’*Phi;Phi_F=Phi’*F;Phi_R=Phi’*BarRs;5.Type into the MATLAB Work Space with Ap=0.8,Bp=0.1,Cp=1,Nc=4and Np=10.Run this MATLAB function by typing[Phi_Phi,Phi_F,Phi_R,A_e,B_e,C_e]=mpcgain(Ap,Bp,Cp,Nc,Np);paring the results with the answers from Example1.2.If it is identicalto what was presented there,then your program is correct.7.Varying the prediction horizon and control horizon,observe the changesin these matrices.8.CalculateΔU by assuming the information of initial condition on x andr.The inverse of matrix M is calculated in MATLAB as inv(M).9.Validate the results in Example1.2.1.4Receding Horizon Control 151.4Receding Horizon ControlAlthough the optimal parameter vector ΔU contains the controls Δu (k i ),Δu (k i +1),Δu (k i +2),...,Δu (k i +N c −1),with the receding horizon control principle,we only implement the first sample of this sequence,i.e.,Δu (k i ),while ignoring the rest of the sequence.When the next sample period arrives,the more recent measurement is taken to form the state vector x (k i +1)for calculation of the new sequence of control signal.This procedure is repeated in real time to give the receding horizon control law.Example 1.4.We illustrate this procedure by continuing Example 1.2,where a first-order system with the state-space descriptionx m (k +1)=0.8x m (k )+0.1u (k )is used in the computation.We will consider the case r w =0.The initial conditions are x (10)=[0.10.2]T and u (9)=0.Solution.At sample time k i =10,the optimal control was previously com-puted as Δu (10)=7.2.Assuming that u (9)=0,then the control signal to the plant is u (10)=u (9)+Δu (10)=7.2and with x m (10)=y (10)=0.2,we calculate the next simulated plant state variablex m (11)=0.8x m (10)+0.1u (10)=0.88.(1.26)At k i =11,the new plant information is Δx m (11)=0.88−0.2=0.68and y (11)=0.88,which forms x (11)= 0.680.88 T .Then we obtainΔU =(ΦT Φ)−1(ΦT R s −ΦT F x (11))= −4.24−0.960.00000.0000 T .This leads to the optimal control u (11)=u (10)+Δu (11)=2.96.This new control is implemented to obtainx m (12)=0.8x m (11)+0.1u (11)=1.(1.27)At k i =12,the new plant information is Δx m (12)=1−0.88=0.12and y (12)=1,which forms x (12)= 0.121 .We obtainΔU =(ΦT Φ)−1(ΦT R s −ΦT F x (11))= −0.960.0000.00000.0000 T .This leads to the control at k i =12as u (12)=u (11)−0.96=2.By imple-menting this control,we obtain the next plant output asx m (13)=ax m (12)+bu (12)=1.(1.28)The new plant information is Δx m (13)=1−1=0and y (13)=1.From this information,we obtain。

Discrete-Time Integrator/p/1903691379执行离散时间信号的整合或累积即离散时间积分离散的离散时间积分器模块的功能您可以使用Discrete-Time Integrator模块，以取代Integrator块来创建一个纯粹的离散系统。

随着Discrete-Time Integrator块，您可以：•定义块对话框或输入到块的初始条件。

•定义输入增益（K）值。

•输出块的状态。

•定义的积分的上限和下限。

•复位状态，取决于一个额外的复位输入。

整合和积累方法该块可以整合或累积使用向前欧拉，向后欧拉，梯形方法。

假设u为输入，y是输出，x是的状态。

对于一个给定的步骤n，Simulink的更新y(n)和x(n+1)。

在积分模式中，T是块采样时间（ΔT的情况下，触发采样时间）。

在积累模式下，T = 1;块的采样时间确定时，计算输出，但不输出值。

K为增益值。

超出所值根据上限或下限剪辑。

•向前欧拉方法（默认），也被称为正向矩形，或左手逼近。

对于这种方法，1/s近似为T/(z-1).块的n步输出是由此产生的的表达式为：y(n) = y(n-1) + K*T*u(n-1)让x(n+1) = x(n) + K*T*u(n). 块使用以下步骤来计算其输出：步骤 0: y(0) = x(0) = IC (剪辑如果必要的)x(1) = y(0) + K*T*u(0)步骤 1: y(1) = x(1)x(2) = x(1) + K*T*u(1)步骤 n: y(n) = x(n)x(n+1) = x(n) + K*T*u(n) (剪辑如果必要的) 使用这种方法，输入端口1不具有直接馈通。

•向后Euler方法，也被称为向后矩形或近似右手。

对于这种方法，1/s近似为T*z/(z-1)块n步的输出是由此产生的的表达式为y(n) = y(n-1) + K*T*u(n)让x(n) = y(n-1). 块使用以下步骤来计算其输出步骤 0: y(0) = x(0) = IC (剪辑如果必要的)x(1) = y(0)或者，根据Use initial condition as initial and reset value for参数:步骤 0: x(0) = IC (剪辑如果必要的)x(1) = y(0) = x(0) + K*T*u(0)步骤 1: y(1) = x(1) + K*T*u(1)x(2) = y(1)步骤 n: y(n) = x(n) + K*T*u(n)x(n+1) = y(n)使用这种方法，输入端口1具有直接馈通。

迪杰斯特拉算法最短路径迪杰斯特拉算法（Dijkstra's algorithm）是一种用于计算图中最短路径的算法。

它是由荷兰计算机科学家艾兹赫尔·迪杰斯特拉（Edsger Wybe Dijkstra）于1956年提出的，并且被广泛应用于网络路由和地图导航等领域。

迪杰斯特拉算法可以解决的问题是，给定一个带有非负权重的有向图和一个起始节点，找出从起始节点到其他所有节点的最短路径。

该算法采用了贪心的策略，即每次选择当前离起始节点最近的节点进行扩展，直到扩展到目标节点为止。

算法的具体步骤如下：1.初始化：将起始节点的距离设置为0，其他节点的距离设置为无穷大。

2.创建一个优先队列（通常是最小堆），用于存储待扩展的节点。

将起始节点加入队列。

3.循环以下步骤直到队列为空：-从队列中取出距离起始节点最近的节点，记为当前节点。

-如果当前节点已被访问过，则跳过该节点。

-更新与当前节点相邻节点的距离。

如果经过当前节点到达某个相邻节点的路径比之前计算的路径短，则更新这个节点的距离。

-将未访问过的相邻节点加入队列。

4.循环结束后，所有节点的最短路径已被计算出。

迪杰斯特拉算法的核心思想是不断扩展距离起始节点最近的节点，通过更新节点的距离，逐步获取最短路径。

算法的时间复杂度为O(V^2)，其中V是图中的节点数量。

这是因为每次循环需要查找距离起始节点最近的节点，而在最坏情况下，这个操作需要遍历所有节点。

以下是一个简单的例子来说明迪杰斯特拉算法的使用：假设有一个有向图，如下所示：```A ->B (1)A -> C (4)B ->C (2)B -> D (5)C ->D (1)C -> E (3)D ->E (4)```起始节点为A，我们希望找到到达其他节点的最短路径。

首先，初始化距离：A到A的距离为0，A到B/C/D/E的距离均为无穷大。

然后，将A加入优先队列。

从队列中取出A，更新A的邻居节点的距离。

quarter警告信息Quartus警告分析 warning1.Found clock-sensitive change during active clock edge at time on register ""原因:vector source file中时钟敏感信号(如:数据,允许端,清零,同步加载等)在时钟的边缘同时变化.而时钟敏感信号是不能在时钟边沿变化的.其后果为导致结果不正确.措施:编辑vector source file2.Verilog HDL assignment warning at : truncated with size to match size of target (原因:在HDL设计中对目标的位数进行了设定,如:reg[4:0] a;而默认为32位, 将位数裁定到合适的大小措施:如果结果正确,无须加以修正,如果不想看到这个警告,可以改变设定的位数3.All reachable assignments to data_out(10) assign '0', register removed by optimization原因:经过综合器优化后,输出端口已经不起作用了4.Following 9 pins have nothing, GND, or VCC driving datain port -- changes to this connectivity may change fitting results 原因:第9脚,空或接地或接上了电源措施:有时候定义了输出端口,但输出端直接赋‘0’,便会被接地,赋‘1’接电源. 如果你的设计中这些端口就是这样用的,那便可以不理会这些warning5.Found pins ing as undefined clocks and/or memory enables原因:是你作为时钟的PIN没有约束信息.可以对相应的PIN做一下设定就行了. 主要是指你的某些管脚在电路当中起到了时钟管脚的作用,比如flip-flop的clk 管脚,而此管脚没有时钟约束,因此QuartusII把“clk”作为未定义的时钟. 措施:如果clk不是时钟,可以加“not clock”的约束;如果是,可以在clock setting当中加入;在某些对时钟要求不很高的情况下,可以忽略此警告或在这里改:Assignments>Timinganalysissettings...>Individual clocks...>...6.Timing characteristics of device EPM570T144C5 are preliminary原因:因为MAXII 是比較新的元件在 QuartusII 中的時序并不是正式版的,要等 Service Pack措施:只影响 Quartus 的 Waveform7.Warning: Clock latency analysis for PLL offsets is supported for the current device family, but is not enabled措施:将setting中的timing Requirements&Option-->More TimingSetting-->setting-->Enable Clock Latency中的on改成OFF8.Found clock high time violation at 14.8 ns on register"|counter|lpm_counter:count1_rtl_0|dffs[11]"原因:违反了steup/hold时间,应该是后仿真,看看波形设置是否和时钟沿符合steup/hold时间措施:在中间加个寄存器可能可以解决问题9.warning: circuit may not operate.detected 46 non-operational paths clocked by clock clk44 with clock skew larger than data delay原因:时钟抖动大于数据延时,当时钟很快,而if等类的层次过多就会出现这种问题,但这个问题多是在器件的最高频率中才会出现措施:setting-->timing Requirements&Options-->Default required fmax 改小一些,如改到50MHZ10.Design contains input pin(s) that do not drive logic原因:输入引脚没有驱动逻辑(驱动其他引脚),所有的输入引脚需要有输入逻辑措施:如果这种情况是故意的,无须理会,如果非故意,输入逻辑驱动.11.Warning:Found clock high time violation at 8.9ns on node 'TEST3.CLK' 原因:FF中输入的PLS的保持时间过短措施:在FF中设置较高的时钟频率12.Warning: Found 10 node(s) in clock paths which may be acting as ripple and/or gated clocks -- node(s) analyzed as buffer(s) resulting in clock skew原因:如果你用的 CPLD 只有一组全局时钟时,用全局时钟分频产生的另一个时钟在布线中当作信号处理,不能保证低的时钟歪斜(SKEW).会造成在这个时钟上工作的时序电路不可靠,甚至每次布线产生的问题都不一样.措施:如果用有两组以上全局时钟的 FPGA 芯片,可以把第二个全局时钟作为另一个时钟用,可以解决这个问题.13.Critical Warning: Timing requirements were not met. See Report window for details.原因:时序要求未满足,措施:双击Compilation Report-->Time Analyzer-->红色部分(如clock setup:'clk'等)-->左键单击list path,查看fmax的SLACK REPORT再根据提示解决,有可能是程序的算法问题14.Can't achieve minimum setup and hold requirement along path(s). See Report window for details.原因:时序分析发现一定数量的路径违背了最小的建立和保持时间,与时钟歪斜有关,一般是由于多时钟引起的措施:利用Compilation Report-->Time Analyzer-->红色部分(如clock hold:'clk'等),在slack中观察是hold time为负值还是setup time 为负值, 然后在:Assignment-->AssignmentEditor-->To中增加时钟名(fromnode finder),Assignment Name中增加和多时钟有关的Multicycle 和Multicycle Hold选项,如hold time为负,可使Multicycle hold的值>multicycle,如设为2和1.15: Can't analyze file -- file E://quartusii/*/*.v is missing原因:试图编译一个不存在的文件,该文件可能被改名或者删除了措施:不管他,没什么影响16.Warning: Can't find signal in vector source file for input pin whole|clk10m原因:因为你的波形仿真文件( vector source file )中并没有把所有的输入信号(input pin)加进去,对于每一个输入都需要有激励源的17.Error: Can't name logic scfifo0 of instance "inst" has same name as current design file原因:模块的名字和project的名字重名了措施:把两个名字之一改一下,一般改模块的名字18.Warning: Using design file lpm_fifo0.v, which is not specified as a design file for the current project, but contains definitions for 1 design units and 1 entities in project Info: Found entity 1: lpm_fifo0原因:模块不是在本项目生成的,而是直接copy了别的项目的原理图和源程序而生成的,而不是用QUARTUS将文件添加进本项目措施:无须理会,不影响使用19.Timing characteristics of device are preliminary原因:目前版本的QuartusII只对该器件提供初步的时序特征分析措施:如果坚持用目前的器件,无须理会该警告.关于进一步的时序特征分析会在后续版本的Quartus得到完善.20.Timing Analysis does not support the analysis of latches as synchronous elements for the currently selected device family 原因:用analyze_latches_as_synchronous_elements setting可以让 Quaruts II来分析同步锁存,但目前的器件不支持这个特性措施:无须理会.时序分析可能将锁存器分析成回路.但并不一定分析正确.其后果可能会导致显示提醒用户:改变设计来消除锁存器21.Warning:Found xx output pins without output pin load capacitance assignment(网友:gucheng82提供)原因:没有给输出管教指定负载电容措施:该功能用于估算TCO和功耗,可以不理会,也可以在Assignment Editor 中为相应的输出管脚指定负载电容,以消除警告22.Warning: Found 6 node(s) in clock paths which may be acting as ripple and/or gated clocks -- node(s) analyzed as buffer(s) resulting in clock skew原因:使用了行波时钟或门控时钟,把触发器的输出当时钟用就会报行波时钟, 将组合逻辑的输出当时钟用就会报门控时钟措施:不要把触发器的输出当时钟,不要将组合逻辑的输出当时钟,如果本身如此设计,则无须理会该警告23.Warning (10268): Verilog HDL information at lcd7106.v(63): Always Construct contains both blocking and non-blocking assignments原因: 一个always模块中同时有阻塞和非阻塞的赋值在QuartusII下进行编译和仿真的时候,会出现一堆warning,有的可以忽略,有的却需要注意,虽然按F1可以了解关于该警告的帮助,但有时候帮助解释的仍然不清楚,大家群策群力,把自己知道和了解的一些关于警告的问题都说出来讨论一下,免得后来的人走弯路.下面是我收集整理的一些,有些是自己的经验,有些是网友的,希望能给大家一点帮助,如有不对的地方,请指正,如果觉得好,请版主给点威望吧,谢谢1.Found clock-sensitive change during active clock edge at time on register ""原因：vector source file中时钟敏感信号（如：数据，允许端，清零，同步加载等）在时钟的边缘同时变化。

[警告错误信息]【错误和警告信息汇总】（此贴为复件，请勿回复）[复制链接]zsq-w管理员CIO仿真币33975阅读权限255 电梯直达1#发表于 2009-5-7 17:08:16 |只看该作者|倒序浏览本帖最后由 zsq-w 于 2009-6-2 17:11 编辑*************************错误与警告信息汇总*************************－－－－－－－－－－－－－－简称《错误汇总》***ERROR***WARNING***二次开发%%%%%%%%%%%%%%% @@@ 布局@@@ &&&&&&&&&&&&&&&&&&&&&&常见错误信息常见警告信息网格扭曲cdst udio斑竹总结的fortran二次开发的错误表%%%%%%%%%%%%%%%%% @@@@@@ &&&&&&&&&&&&&&&&&&&&&&&&&模型不能算或不收敛，都需要去monitor，msg文件查看原因，如何分析这些信息呢？这个需要具体问题具体分析，但是也存在一些共性。

这里只是尝试做一个一般性的大概的总结。

如果你看见此贴就认为你的warning以为迎刃而解了，那恐怕令你失望了。

不收敛的问题千奇万状，往往需要头疼医脚。

接触、单元类型、边界条件、网格质量以及它们的组合能产生许多千奇百怪的警告信息。

企图凭一个警告信息就知道问题所在，那就只有神仙有这个本事了。

一个warning出现十次能有一回参考这个汇总而得到解决了，我们就颇为欣慰了。

3GPP TS 36.331 V13.2.0 (2016-06)Technical Specification3rd Generation Partnership Project;Technical Specification Group Radio Access Network;Evolved Universal Terrestrial Radio Access (E-UTRA);Radio Resource Control (RRC);Protocol specification(Release 13)The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP. The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification. Specifications and reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.KeywordsUMTS, radio3GPPPostal address3GPP support office address650 Route des Lucioles - Sophia AntipolisValbonne - FRANCETel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16InternetCopyright NotificationNo part may be reproduced except as authorized by written permission.The copyright and the foregoing restriction extend to reproduction in all media.© 2016, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TSDSI, TTA, TTC).All rights reserved.UMTS™ is a Trade Mark of ETSI registered for the benefit of its members3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational PartnersLTE™ is a Trade Mark of ETSI currently being registered for the benefit of its Members and of the 3GPP Organizational Partners GSM® and the GSM logo are registered and owned by the GSM AssociationBluetooth® is a Trade Mark of the Bluetooth SIG registered for the benefit of its membersContentsForeword (18)1Scope (19)2References (19)3Definitions, symbols and abbreviations (22)3.1Definitions (22)3.2Abbreviations (24)4General (27)4.1Introduction (27)4.2Architecture (28)4.2.1UE states and state transitions including inter RAT (28)4.2.2Signalling radio bearers (29)4.3Services (30)4.3.1Services provided to upper layers (30)4.3.2Services expected from lower layers (30)4.4Functions (30)5Procedures (32)5.1General (32)5.1.1Introduction (32)5.1.2General requirements (32)5.2System information (33)5.2.1Introduction (33)5.2.1.1General (33)5.2.1.2Scheduling (34)5.2.1.2a Scheduling for NB-IoT (34)5.2.1.3System information validity and notification of changes (35)5.2.1.4Indication of ETWS notification (36)5.2.1.5Indication of CMAS notification (37)5.2.1.6Notification of EAB parameters change (37)5.2.1.7Access Barring parameters change in NB-IoT (37)5.2.2System information acquisition (38)5.2.2.1General (38)5.2.2.2Initiation (38)5.2.2.3System information required by the UE (38)5.2.2.4System information acquisition by the UE (39)5.2.2.5Essential system information missing (42)5.2.2.6Actions upon reception of the MasterInformationBlock message (42)5.2.2.7Actions upon reception of the SystemInformationBlockType1 message (42)5.2.2.8Actions upon reception of SystemInformation messages (44)5.2.2.9Actions upon reception of SystemInformationBlockType2 (44)5.2.2.10Actions upon reception of SystemInformationBlockType3 (45)5.2.2.11Actions upon reception of SystemInformationBlockType4 (45)5.2.2.12Actions upon reception of SystemInformationBlockType5 (45)5.2.2.13Actions upon reception of SystemInformationBlockType6 (45)5.2.2.14Actions upon reception of SystemInformationBlockType7 (45)5.2.2.15Actions upon reception of SystemInformationBlockType8 (45)5.2.2.16Actions upon reception of SystemInformationBlockType9 (46)5.2.2.17Actions upon reception of SystemInformationBlockType10 (46)5.2.2.18Actions upon reception of SystemInformationBlockType11 (46)5.2.2.19Actions upon reception of SystemInformationBlockType12 (47)5.2.2.20Actions upon reception of SystemInformationBlockType13 (48)5.2.2.21Actions upon reception of SystemInformationBlockType14 (48)5.2.2.22Actions upon reception of SystemInformationBlockType15 (48)5.2.2.23Actions upon reception of SystemInformationBlockType16 (48)5.2.2.24Actions upon reception of SystemInformationBlockType17 (48)5.2.2.25Actions upon reception of SystemInformationBlockType18 (48)5.2.2.26Actions upon reception of SystemInformationBlockType19 (49)5.2.3Acquisition of an SI message (49)5.2.3a Acquisition of an SI message by BL UE or UE in CE or a NB-IoT UE (50)5.3Connection control (50)5.3.1Introduction (50)5.3.1.1RRC connection control (50)5.3.1.2Security (52)5.3.1.2a RN security (53)5.3.1.3Connected mode mobility (53)5.3.1.4Connection control in NB-IoT (54)5.3.2Paging (55)5.3.2.1General (55)5.3.2.2Initiation (55)5.3.2.3Reception of the Paging message by the UE (55)5.3.3RRC connection establishment (56)5.3.3.1General (56)5.3.3.1a Conditions for establishing RRC Connection for sidelink communication/ discovery (58)5.3.3.2Initiation (59)5.3.3.3Actions related to transmission of RRCConnectionRequest message (63)5.3.3.3a Actions related to transmission of RRCConnectionResumeRequest message (64)5.3.3.4Reception of the RRCConnectionSetup by the UE (64)5.3.3.4a Reception of the RRCConnectionResume by the UE (66)5.3.3.5Cell re-selection while T300, T302, T303, T305, T306, or T308 is running (68)5.3.3.6T300 expiry (68)5.3.3.7T302, T303, T305, T306, or T308 expiry or stop (69)5.3.3.8Reception of the RRCConnectionReject by the UE (70)5.3.3.9Abortion of RRC connection establishment (71)5.3.3.10Handling of SSAC related parameters (71)5.3.3.11Access barring check (72)5.3.3.12EAB check (73)5.3.3.13Access barring check for ACDC (73)5.3.3.14Access Barring check for NB-IoT (74)5.3.4Initial security activation (75)5.3.4.1General (75)5.3.4.2Initiation (76)5.3.4.3Reception of the SecurityModeCommand by the UE (76)5.3.5RRC connection reconfiguration (77)5.3.5.1General (77)5.3.5.2Initiation (77)5.3.5.3Reception of an RRCConnectionReconfiguration not including the mobilityControlInfo by theUE (77)5.3.5.4Reception of an RRCConnectionReconfiguration including the mobilityControlInfo by the UE(handover) (79)5.3.5.5Reconfiguration failure (83)5.3.5.6T304 expiry (handover failure) (83)5.3.5.7Void (84)5.3.5.7a T307 expiry (SCG change failure) (84)5.3.5.8Radio Configuration involving full configuration option (84)5.3.6Counter check (86)5.3.6.1General (86)5.3.6.2Initiation (86)5.3.6.3Reception of the CounterCheck message by the UE (86)5.3.7RRC connection re-establishment (87)5.3.7.1General (87)5.3.7.2Initiation (87)5.3.7.3Actions following cell selection while T311 is running (88)5.3.7.4Actions related to transmission of RRCConnectionReestablishmentRequest message (89)5.3.7.5Reception of the RRCConnectionReestablishment by the UE (89)5.3.7.6T311 expiry (91)5.3.7.7T301 expiry or selected cell no longer suitable (91)5.3.7.8Reception of RRCConnectionReestablishmentReject by the UE (91)5.3.8RRC connection release (92)5.3.8.1General (92)5.3.8.2Initiation (92)5.3.8.3Reception of the RRCConnectionRelease by the UE (92)5.3.8.4T320 expiry (93)5.3.9RRC connection release requested by upper layers (93)5.3.9.1General (93)5.3.9.2Initiation (93)5.3.10Radio resource configuration (93)5.3.10.0General (93)5.3.10.1SRB addition/ modification (94)5.3.10.2DRB release (95)5.3.10.3DRB addition/ modification (95)5.3.10.3a1DC specific DRB addition or reconfiguration (96)5.3.10.3a2LWA specific DRB addition or reconfiguration (98)5.3.10.3a3LWIP specific DRB addition or reconfiguration (98)5.3.10.3a SCell release (99)5.3.10.3b SCell addition/ modification (99)5.3.10.3c PSCell addition or modification (99)5.3.10.4MAC main reconfiguration (99)5.3.10.5Semi-persistent scheduling reconfiguration (100)5.3.10.6Physical channel reconfiguration (100)5.3.10.7Radio Link Failure Timers and Constants reconfiguration (101)5.3.10.8Time domain measurement resource restriction for serving cell (101)5.3.10.9Other configuration (102)5.3.10.10SCG reconfiguration (103)5.3.10.11SCG dedicated resource configuration (104)5.3.10.12Reconfiguration SCG or split DRB by drb-ToAddModList (105)5.3.10.13Neighbour cell information reconfiguration (105)5.3.10.14Void (105)5.3.10.15Sidelink dedicated configuration (105)5.3.10.16T370 expiry (106)5.3.11Radio link failure related actions (107)5.3.11.1Detection of physical layer problems in RRC_CONNECTED (107)5.3.11.2Recovery of physical layer problems (107)5.3.11.3Detection of radio link failure (107)5.3.12UE actions upon leaving RRC_CONNECTED (109)5.3.13UE actions upon PUCCH/ SRS release request (110)5.3.14Proximity indication (110)5.3.14.1General (110)5.3.14.2Initiation (111)5.3.14.3Actions related to transmission of ProximityIndication message (111)5.3.15Void (111)5.4Inter-RAT mobility (111)5.4.1Introduction (111)5.4.2Handover to E-UTRA (112)5.4.2.1General (112)5.4.2.2Initiation (112)5.4.2.3Reception of the RRCConnectionReconfiguration by the UE (112)5.4.2.4Reconfiguration failure (114)5.4.2.5T304 expiry (handover to E-UTRA failure) (114)5.4.3Mobility from E-UTRA (114)5.4.3.1General (114)5.4.3.2Initiation (115)5.4.3.3Reception of the MobilityFromEUTRACommand by the UE (115)5.4.3.4Successful completion of the mobility from E-UTRA (116)5.4.3.5Mobility from E-UTRA failure (117)5.4.4Handover from E-UTRA preparation request (CDMA2000) (117)5.4.4.1General (117)5.4.4.2Initiation (118)5.4.4.3Reception of the HandoverFromEUTRAPreparationRequest by the UE (118)5.4.5UL handover preparation transfer (CDMA2000) (118)5.4.5.1General (118)5.4.5.2Initiation (118)5.4.5.3Actions related to transmission of the ULHandoverPreparationTransfer message (119)5.4.5.4Failure to deliver the ULHandoverPreparationTransfer message (119)5.4.6Inter-RAT cell change order to E-UTRAN (119)5.4.6.1General (119)5.4.6.2Initiation (119)5.4.6.3UE fails to complete an inter-RAT cell change order (119)5.5Measurements (120)5.5.1Introduction (120)5.5.2Measurement configuration (121)5.5.2.1General (121)5.5.2.2Measurement identity removal (122)5.5.2.2a Measurement identity autonomous removal (122)5.5.2.3Measurement identity addition/ modification (123)5.5.2.4Measurement object removal (124)5.5.2.5Measurement object addition/ modification (124)5.5.2.6Reporting configuration removal (126)5.5.2.7Reporting configuration addition/ modification (127)5.5.2.8Quantity configuration (127)5.5.2.9Measurement gap configuration (127)5.5.2.10Discovery signals measurement timing configuration (128)5.5.2.11RSSI measurement timing configuration (128)5.5.3Performing measurements (128)5.5.3.1General (128)5.5.3.2Layer 3 filtering (131)5.5.4Measurement report triggering (131)5.5.4.1General (131)5.5.4.2Event A1 (Serving becomes better than threshold) (135)5.5.4.3Event A2 (Serving becomes worse than threshold) (136)5.5.4.4Event A3 (Neighbour becomes offset better than PCell/ PSCell) (136)5.5.4.5Event A4 (Neighbour becomes better than threshold) (137)5.5.4.6Event A5 (PCell/ PSCell becomes worse than threshold1 and neighbour becomes better thanthreshold2) (138)5.5.4.6a Event A6 (Neighbour becomes offset better than SCell) (139)5.5.4.7Event B1 (Inter RAT neighbour becomes better than threshold) (139)5.5.4.8Event B2 (PCell becomes worse than threshold1 and inter RAT neighbour becomes better thanthreshold2) (140)5.5.4.9Event C1 (CSI-RS resource becomes better than threshold) (141)5.5.4.10Event C2 (CSI-RS resource becomes offset better than reference CSI-RS resource) (141)5.5.4.11Event W1 (WLAN becomes better than a threshold) (142)5.5.4.12Event W2 (All WLAN inside WLAN mobility set becomes worse than threshold1 and a WLANoutside WLAN mobility set becomes better than threshold2) (142)5.5.4.13Event W3 (All WLAN inside WLAN mobility set becomes worse than a threshold) (143)5.5.5Measurement reporting (144)5.5.6Measurement related actions (148)5.5.6.1Actions upon handover and re-establishment (148)5.5.6.2Speed dependant scaling of measurement related parameters (149)5.5.7Inter-frequency RSTD measurement indication (149)5.5.7.1General (149)5.5.7.2Initiation (150)5.5.7.3Actions related to transmission of InterFreqRSTDMeasurementIndication message (150)5.6Other (150)5.6.0General (150)5.6.1DL information transfer (151)5.6.1.1General (151)5.6.1.2Initiation (151)5.6.1.3Reception of the DLInformationTransfer by the UE (151)5.6.2UL information transfer (151)5.6.2.1General (151)5.6.2.2Initiation (151)5.6.2.3Actions related to transmission of ULInformationTransfer message (152)5.6.2.4Failure to deliver ULInformationTransfer message (152)5.6.3UE capability transfer (152)5.6.3.1General (152)5.6.3.2Initiation (153)5.6.3.3Reception of the UECapabilityEnquiry by the UE (153)5.6.4CSFB to 1x Parameter transfer (157)5.6.4.1General (157)5.6.4.2Initiation (157)5.6.4.3Actions related to transmission of CSFBParametersRequestCDMA2000 message (157)5.6.4.4Reception of the CSFBParametersResponseCDMA2000 message (157)5.6.5UE Information (158)5.6.5.1General (158)5.6.5.2Initiation (158)5.6.5.3Reception of the UEInformationRequest message (158)5.6.6 Logged Measurement Configuration (159)5.6.6.1General (159)5.6.6.2Initiation (160)5.6.6.3Reception of the LoggedMeasurementConfiguration by the UE (160)5.6.6.4T330 expiry (160)5.6.7 Release of Logged Measurement Configuration (160)5.6.7.1General (160)5.6.7.2Initiation (160)5.6.8 Measurements logging (161)5.6.8.1General (161)5.6.8.2Initiation (161)5.6.9In-device coexistence indication (163)5.6.9.1General (163)5.6.9.2Initiation (164)5.6.9.3Actions related to transmission of InDeviceCoexIndication message (164)5.6.10UE Assistance Information (165)5.6.10.1General (165)5.6.10.2Initiation (166)5.6.10.3Actions related to transmission of UEAssistanceInformation message (166)5.6.11 Mobility history information (166)5.6.11.1General (166)5.6.11.2Initiation (166)5.6.12RAN-assisted WLAN interworking (167)5.6.12.1General (167)5.6.12.2Dedicated WLAN offload configuration (167)5.6.12.3WLAN offload RAN evaluation (167)5.6.12.4T350 expiry or stop (167)5.6.12.5Cell selection/ re-selection while T350 is running (168)5.6.13SCG failure information (168)5.6.13.1General (168)5.6.13.2Initiation (168)5.6.13.3Actions related to transmission of SCGFailureInformation message (168)5.6.14LTE-WLAN Aggregation (169)5.6.14.1Introduction (169)5.6.14.2Reception of LWA configuration (169)5.6.14.3Release of LWA configuration (170)5.6.15WLAN connection management (170)5.6.15.1Introduction (170)5.6.15.2WLAN connection status reporting (170)5.6.15.2.1General (170)5.6.15.2.2Initiation (171)5.6.15.2.3Actions related to transmission of WLANConnectionStatusReport message (171)5.6.15.3T351 Expiry (WLAN connection attempt timeout) (171)5.6.15.4WLAN status monitoring (171)5.6.16RAN controlled LTE-WLAN interworking (172)5.6.16.1General (172)5.6.16.2WLAN traffic steering command (172)5.6.17LTE-WLAN aggregation with IPsec tunnel (173)5.6.17.1General (173)5.7Generic error handling (174)5.7.1General (174)5.7.2ASN.1 violation or encoding error (174)5.7.3Field set to a not comprehended value (174)5.7.4Mandatory field missing (174)5.7.5Not comprehended field (176)5.8MBMS (176)5.8.1Introduction (176)5.8.1.1General (176)5.8.1.2Scheduling (176)5.8.1.3MCCH information validity and notification of changes (176)5.8.2MCCH information acquisition (178)5.8.2.1General (178)5.8.2.2Initiation (178)5.8.2.3MCCH information acquisition by the UE (178)5.8.2.4Actions upon reception of the MBSFNAreaConfiguration message (178)5.8.2.5Actions upon reception of the MBMSCountingRequest message (179)5.8.3MBMS PTM radio bearer configuration (179)5.8.3.1General (179)5.8.3.2Initiation (179)5.8.3.3MRB establishment (179)5.8.3.4MRB release (179)5.8.4MBMS Counting Procedure (179)5.8.4.1General (179)5.8.4.2Initiation (180)5.8.4.3Reception of the MBMSCountingRequest message by the UE (180)5.8.5MBMS interest indication (181)5.8.5.1General (181)5.8.5.2Initiation (181)5.8.5.3Determine MBMS frequencies of interest (182)5.8.5.4Actions related to transmission of MBMSInterestIndication message (183)5.8a SC-PTM (183)5.8a.1Introduction (183)5.8a.1.1General (183)5.8a.1.2SC-MCCH scheduling (183)5.8a.1.3SC-MCCH information validity and notification of changes (183)5.8a.1.4Procedures (184)5.8a.2SC-MCCH information acquisition (184)5.8a.2.1General (184)5.8a.2.2Initiation (184)5.8a.2.3SC-MCCH information acquisition by the UE (184)5.8a.2.4Actions upon reception of the SCPTMConfiguration message (185)5.8a.3SC-PTM radio bearer configuration (185)5.8a.3.1General (185)5.8a.3.2Initiation (185)5.8a.3.3SC-MRB establishment (185)5.8a.3.4SC-MRB release (185)5.9RN procedures (186)5.9.1RN reconfiguration (186)5.9.1.1General (186)5.9.1.2Initiation (186)5.9.1.3Reception of the RNReconfiguration by the RN (186)5.10Sidelink (186)5.10.1Introduction (186)5.10.1a Conditions for sidelink communication operation (187)5.10.2Sidelink UE information (188)5.10.2.1General (188)5.10.2.2Initiation (189)5.10.2.3Actions related to transmission of SidelinkUEInformation message (193)5.10.3Sidelink communication monitoring (195)5.10.6Sidelink discovery announcement (198)5.10.6a Sidelink discovery announcement pool selection (201)5.10.6b Sidelink discovery announcement reference carrier selection (201)5.10.7Sidelink synchronisation information transmission (202)5.10.7.1General (202)5.10.7.2Initiation (203)5.10.7.3Transmission of SLSS (204)5.10.7.4Transmission of MasterInformationBlock-SL message (205)5.10.7.5Void (206)5.10.8Sidelink synchronisation reference (206)5.10.8.1General (206)5.10.8.2Selection and reselection of synchronisation reference UE (SyncRef UE) (206)5.10.9Sidelink common control information (207)5.10.9.1General (207)5.10.9.2Actions related to reception of MasterInformationBlock-SL message (207)5.10.10Sidelink relay UE operation (207)5.10.10.1General (207)5.10.10.2AS-conditions for relay related sidelink communication transmission by sidelink relay UE (207)5.10.10.3AS-conditions for relay PS related sidelink discovery transmission by sidelink relay UE (208)5.10.10.4Sidelink relay UE threshold conditions (208)5.10.11Sidelink remote UE operation (208)5.10.11.1General (208)5.10.11.2AS-conditions for relay related sidelink communication transmission by sidelink remote UE (208)5.10.11.3AS-conditions for relay PS related sidelink discovery transmission by sidelink remote UE (209)5.10.11.4Selection and reselection of sidelink relay UE (209)5.10.11.5Sidelink remote UE threshold conditions (210)6Protocol data units, formats and parameters (tabular & ASN.1) (210)6.1General (210)6.2RRC messages (212)6.2.1General message structure (212)–EUTRA-RRC-Definitions (212)–BCCH-BCH-Message (212)–BCCH-DL-SCH-Message (212)–BCCH-DL-SCH-Message-BR (213)–MCCH-Message (213)–PCCH-Message (213)–DL-CCCH-Message (214)–DL-DCCH-Message (214)–UL-CCCH-Message (214)–UL-DCCH-Message (215)–SC-MCCH-Message (215)6.2.2Message definitions (216)–CounterCheck (216)–CounterCheckResponse (217)–CSFBParametersRequestCDMA2000 (217)–CSFBParametersResponseCDMA2000 (218)–DLInformationTransfer (218)–HandoverFromEUTRAPreparationRequest (CDMA2000) (219)–InDeviceCoexIndication (220)–InterFreqRSTDMeasurementIndication (222)–LoggedMeasurementConfiguration (223)–MasterInformationBlock (225)–MBMSCountingRequest (226)–MBMSCountingResponse (226)–MBMSInterestIndication (227)–MBSFNAreaConfiguration (228)–MeasurementReport (228)–MobilityFromEUTRACommand (229)–Paging (232)–ProximityIndication (233)–RNReconfiguration (234)–RNReconfigurationComplete (234)–RRCConnectionReconfiguration (235)–RRCConnectionReconfigurationComplete (240)–RRCConnectionReestablishment (241)–RRCConnectionReestablishmentComplete (241)–RRCConnectionReestablishmentReject (242)–RRCConnectionReestablishmentRequest (243)–RRCConnectionReject (243)–RRCConnectionRelease (244)–RRCConnectionResume (248)–RRCConnectionResumeComplete (249)–RRCConnectionResumeRequest (250)–RRCConnectionRequest (250)–RRCConnectionSetup (251)–RRCConnectionSetupComplete (252)–SCGFailureInformation (253)–SCPTMConfiguration (254)–SecurityModeCommand (255)–SecurityModeComplete (255)–SecurityModeFailure (256)–SidelinkUEInformation (256)–SystemInformation (258)–SystemInformationBlockType1 (259)–UEAssistanceInformation (264)–UECapabilityEnquiry (265)–UECapabilityInformation (266)–UEInformationRequest (267)–UEInformationResponse (267)–ULHandoverPreparationTransfer (CDMA2000) (273)–ULInformationTransfer (274)–WLANConnectionStatusReport (274)6.3RRC information elements (275)6.3.1System information blocks (275)–SystemInformationBlockType2 (275)–SystemInformationBlockType3 (279)–SystemInformationBlockType4 (282)–SystemInformationBlockType5 (283)–SystemInformationBlockType6 (287)–SystemInformationBlockType7 (289)–SystemInformationBlockType8 (290)–SystemInformationBlockType9 (295)–SystemInformationBlockType10 (295)–SystemInformationBlockType11 (296)–SystemInformationBlockType12 (297)–SystemInformationBlockType13 (297)–SystemInformationBlockType14 (298)–SystemInformationBlockType15 (298)–SystemInformationBlockType16 (299)–SystemInformationBlockType17 (300)–SystemInformationBlockType18 (301)–SystemInformationBlockType19 (301)–SystemInformationBlockType20 (304)6.3.2Radio resource control information elements (304)–AntennaInfo (304)–AntennaInfoUL (306)–CQI-ReportConfig (307)–CQI-ReportPeriodicProcExtId (314)–CrossCarrierSchedulingConfig (314)–CSI-IM-Config (315)–CSI-IM-ConfigId (315)–CSI-RS-Config (317)–CSI-RS-ConfigEMIMO (318)–CSI-RS-ConfigNZP (319)–CSI-RS-ConfigNZPId (320)–CSI-RS-ConfigZP (321)–CSI-RS-ConfigZPId (321)–DMRS-Config (321)–DRB-Identity (322)–EPDCCH-Config (322)–EIMTA-MainConfig (324)–LogicalChannelConfig (325)–LWA-Configuration (326)–LWIP-Configuration (326)–RCLWI-Configuration (327)–MAC-MainConfig (327)–P-C-AndCBSR (332)–PDCCH-ConfigSCell (333)–PDCP-Config (334)–PDSCH-Config (337)–PDSCH-RE-MappingQCL-ConfigId (339)–PHICH-Config (339)–PhysicalConfigDedicated (339)–P-Max (344)–PRACH-Config (344)–PresenceAntennaPort1 (346)–PUCCH-Config (347)–PUSCH-Config (351)–RACH-ConfigCommon (355)–RACH-ConfigDedicated (357)–RadioResourceConfigCommon (358)–RadioResourceConfigDedicated (362)–RLC-Config (367)–RLF-TimersAndConstants (369)–RN-SubframeConfig (370)–SchedulingRequestConfig (371)–SoundingRS-UL-Config (372)–SPS-Config (375)–TDD-Config (376)–TimeAlignmentTimer (377)–TPC-PDCCH-Config (377)–TunnelConfigLWIP (378)–UplinkPowerControl (379)–WLAN-Id-List (382)–WLAN-MobilityConfig (382)6.3.3Security control information elements (382)–NextHopChainingCount (382)–SecurityAlgorithmConfig (383)–ShortMAC-I (383)6.3.4Mobility control information elements (383)–AdditionalSpectrumEmission (383)–ARFCN-ValueCDMA2000 (383)–ARFCN-ValueEUTRA (384)–ARFCN-ValueGERAN (384)–ARFCN-ValueUTRA (384)–BandclassCDMA2000 (384)–BandIndicatorGERAN (385)–CarrierFreqCDMA2000 (385)–CarrierFreqGERAN (385)–CellIndexList (387)–CellReselectionPriority (387)–CellSelectionInfoCE (387)–CellReselectionSubPriority (388)–CSFB-RegistrationParam1XRTT (388)–CellGlobalIdEUTRA (389)–CellGlobalIdUTRA (389)–CellGlobalIdGERAN (390)–CellGlobalIdCDMA2000 (390)–CellSelectionInfoNFreq (391)–CSG-Identity (391)–FreqBandIndicator (391)–MobilityControlInfo (391)–MobilityParametersCDMA2000 (1xRTT) (393)–MobilityStateParameters (394)–MultiBandInfoList (394)–NS-PmaxList (394)–PhysCellId (395)–PhysCellIdRange (395)–PhysCellIdRangeUTRA-FDDList (395)–PhysCellIdCDMA2000 (396)–PhysCellIdGERAN (396)–PhysCellIdUTRA-FDD (396)–PhysCellIdUTRA-TDD (396)–PLMN-Identity (397)–PLMN-IdentityList3 (397)–PreRegistrationInfoHRPD (397)–Q-QualMin (398)–Q-RxLevMin (398)–Q-OffsetRange (398)–Q-OffsetRangeInterRAT (399)–ReselectionThreshold (399)–ReselectionThresholdQ (399)–SCellIndex (399)–ServCellIndex (400)–SpeedStateScaleFactors (400)–SystemInfoListGERAN (400)–SystemTimeInfoCDMA2000 (401)–TrackingAreaCode (401)–T-Reselection (402)–T-ReselectionEUTRA-CE (402)6.3.5Measurement information elements (402)–AllowedMeasBandwidth (402)–CSI-RSRP-Range (402)–Hysteresis (402)–LocationInfo (403)–MBSFN-RSRQ-Range (403)–MeasConfig (404)–MeasDS-Config (405)–MeasGapConfig (406)–MeasId (407)–MeasIdToAddModList (407)–MeasObjectCDMA2000 (408)–MeasObjectEUTRA (408)–MeasObjectGERAN (412)–MeasObjectId (412)–MeasObjectToAddModList (412)–MeasObjectUTRA (413)–ReportConfigEUTRA (422)–ReportConfigId (425)–ReportConfigInterRAT (425)–ReportConfigToAddModList (428)–ReportInterval (429)–RSRP-Range (429)–RSRQ-Range (430)–RSRQ-Type (430)–RS-SINR-Range (430)–RSSI-Range-r13 (431)–TimeToTrigger (431)–UL-DelayConfig (431)–WLAN-CarrierInfo (431)–WLAN-RSSI-Range (432)–WLAN-Status (432)6.3.6Other information elements (433)–AbsoluteTimeInfo (433)–AreaConfiguration (433)–C-RNTI (433)–DedicatedInfoCDMA2000 (434)–DedicatedInfoNAS (434)–FilterCoefficient (434)–LoggingDuration (434)–LoggingInterval (435)–MeasSubframePattern (435)–MMEC (435)–NeighCellConfig (435)–OtherConfig (436)–RAND-CDMA2000 (1xRTT) (437)–RAT-Type (437)–ResumeIdentity (437)–RRC-TransactionIdentifier (438)–S-TMSI (438)–TraceReference (438)–UE-CapabilityRAT-ContainerList (438)–UE-EUTRA-Capability (439)–UE-RadioPagingInfo (469)–UE-TimersAndConstants (469)–VisitedCellInfoList (470)–WLAN-OffloadConfig (470)6.3.7MBMS information elements (472)–MBMS-NotificationConfig (472)–MBMS-ServiceList (473)–MBSFN-AreaId (473)–MBSFN-AreaInfoList (473)–MBSFN-SubframeConfig (474)–PMCH-InfoList (475)6.3.7a SC-PTM information elements (476)–SC-MTCH-InfoList (476)–SCPTM-NeighbourCellList (478)6.3.8Sidelink information elements (478)–SL-CommConfig (478)–SL-CommResourcePool (479)–SL-CP-Len (480)–SL-DiscConfig (481)–SL-DiscResourcePool (483)–SL-DiscTxPowerInfo (485)–SL-GapConfig (485)。

slurm 分区、节点、队列的概念理论说明以及概述1. 引言1.1 概述在科学计算和高性能计算领域，对于并行任务的调度和管理是至关重要的。

Slurm（Simple Linux Utility for Resource Management）作为一种流行且强大的集群管理系统，被广泛应用于各种规模的高性能计算环境中。

Slurm通过将资源分配与任务调度相结合，实现了高效地利用计算集群资源的目标。

而为了更好地组织和管理这些资源，Slurm引入了三个重要概念：分区、节点和队列。

1.2 文章结构本文将详细介绍Slurm分区、节点、队列的概念、理论说明以及配置与管理方法。

首先，将对Slurm分区进行阐述，包括其定义以及在集群中的作用。

接着，将对Slurm节点进行详细解释，包括其定义、属性与特性，以及相关的配置与管理方法。

然后，将探讨Slurm队列的概念理论说明，包括定义与类型、优先级与调度策略，以及队列配置与管理方法。

最后，在结论部分总结了Slurm分区、节点、队列在集群管理中的重要性和作用，并对未来发展趋势进行展望或提出建议。

1.3 目的本文的目的是全面介绍和解释Slurm分区、节点、队列的概念及其相关理论。

通过对这些概念的深入了解，读者可以更好地理解和应用Slurm集群管理系统，提高任务调度和资源利用效率。

同时，本文旨在为科学计算和高性能计算领域的从业人员提供一个全面而系统的参考资料，以便他们能够更好地使用和管理Slurm系统。

通过阅读本文，读者将对Slurm分区、节点和队列有一个清晰的理论基础，从而更好地应用于实际工作中。

2. Slurm 分区的概念理论说明2.1 Slurm 分区的定义Slurm 分区是指将计算集群中的计算节点按照一定的规则进行划分和分类，使得不同的任务可以在不同的分区中进行管理和调度。

每个分区都具有独立的资源配额和调度策略，这样可以更好地满足不同用户或应用程序对计算资源的需求。

2.2 Slurm 分区的作用Slurm 分区的主要作用是实现对计算资源的有效管理和分配。

学校代码：10255学号：2130854单机下最小化最大延迟问题的滚动调度方法研究ROLLING HORIZON ALGORITHMS FOR THESINGLE MACHINE MAXIMAL DELAY TIME SCHEDULING PROBLEM WITH RELEASE DATES学科专业：物流工程作者姓名：王鸽指导教师：王长军答辩日期：2015.5.14单机下最小化最大延迟问题的滚动调度方法研究摘要生产调度问题从上世纪七十年代起就被广泛关注，至今依然是研究难点，归根结底为其NP难的属性。

虽然目前遗传、禁忌等邻域算法，基于工件数目、基于时间长度等滚动算法已有大量研究，但是从现实和学术研究角度来说此类问题依然有研究的价值。

本文从单机调度入手，研究在动态到达的环境下，如何获得最小的最大延迟时间的调度。

该问题是NP难问题，同时也是研究现实调度情景的基础问题。

在工件动态到达和现有滚动调度尚有不足的基础上，提出了一种基于冲突窗口（Collision-Window Based，CWB）的滚动调度方法，这种调度策略，使用了一种新的分解问题的方法，使得子问题不再是NP难问题、调度性能不再受工件到达紧密程度影响。

本文给出了CWB滚动调度的具体算法步骤，并且利用C语言编程将其和两种EDD算法、两种传统滚动算法进行大规模仿真比较，证明了在不同工件到达密度和不同工件数目下，新算法求解结果更加稳定、更加优秀。

同时由于子问题的多项式解也使得新算法的求解速度相对于两种传统的滚动算法来说更加快速。

关键词：单机调度、滚动调度、动态到达、到达密度、冲突窗口ROLLING HORIZON ALGORITHMS FOR THE SINGLE MACHINE MAXIMAL DELAY TIME SCHEDULING PROBLEM WITH RELEASE DATESABSTRACTProduction scheduling problem is widely concerned since the 1970s, and until now still is the research challenge. In the final analysis is for its NP-hard attributes. Although genetic, taboos and other neighborhood algorithm, Job-based, Time-based and other rolling algorithm has a number of studies, this problems still have research value from the angle of realistic and academic research.This paper is from the perspective of the single machine scheduling problem. And discuss how to get the minimum of maximal delay time scheduling in a dynamic environment. The problem is a NP hard and it is the basis for the scheduling problem. On the basis of the job dynamic arrival and shortage of the existing rolling scheduling, this paper proposes a Collision-Window Based rolling scheduling method. This scheduling strategy uses a new decomposition problem method to make the sub-problem to be a un NP-hard problem, and the scheduling performance is no longer affected by the job release density.This paper proposes the specific algorithm steps of CWB rolling scheduling algorithm. It is to prove that the new algorithm result is more stable and more outstanding under different job release density and different job number, comparing the new algorithm with EDD, 2 kinds of traditional rolling algorithms and other algorithms using C language programming and large-scale simulation. At the same time the computation speed of the new algorithm is faster compared with the two traditional rolling algorithms due to the polynomial solution of the sub-problem.KEY WORDS:single machine, rolling scheduling, dynamic, release density, collision-window目录摘要 (I)ABSTRACT................................................... I I 本文涉及到的参数符号定义 (V)1绪论 (1)1.1研究背景 (1)1.2研究目的及意义 (2)1.3论文组织结构 (4)2生产调度建模与方法概述 (6)2.1生产调度模型介绍 (6)2.2非滚动式的生产调度方法 (7)2.2.1单机调度 (7)2.2.2并行机与Job shop调度方法 (9)2.3滚动调度调度方法 (10)2.3.1单机滚动调度方法 (11)2.3.2并行机、Job Shop滚动调度方法 (12)2.4滚动调度方法在其他领域的应用 (13)3单机下最大延迟时间问题的EDD及滚动调度算法概述 (15)3.1单机下最大延迟时间动态达到问题的EDD算法概述 (15)3.1.1 EDD算法及其示例 (15)3.1.2 EDD算法特点 (17)3.2单机下最大延迟时间动态达到问题的滚动调度算法概述 (17)3.2.1两种常见的滚动调度算法 (17)3.2.3两种滚动调度算法子问题的常用求解算法 (18)3.2.4两种常见的滚动调度算法示例 (23)4单机下最大延迟时间问题基于冲突窗口的滚动调度算法 (28)4.1冲突窗口定义 (28)4.2子问题的多项式求解 (29)4.2.1基本假设 (29)4.2.2子问题的多项式求解理论推导 (30)4.2.3子问题的多项式求解算法 (32)4.3单机下最大延迟时间问题下基于冲突窗口的滚动调度算法流程 . 33 5仿真比较与分析 (36)5.1随机数据产生 (36)5.1.1 工件到达密度含义 (36)5.1.2 随机数产生规则 (36)5.2单机下最大延迟时间问题的四种不同算法仿真问题结果及分析 . 385.2.2不同工件数下的仿真结果对比分析 (38)5.2.3 不同到达密度下的仿真结果对比分析 (41)5.3考虑带淡旺季的仿真比较与分析 (43)6CWB滚动调度算法与现有制造管理系统整合研究 (46)6.1 制造企业现行MRPⅡ运作基本流程 (46)6.2CWB滚动调度算法与现行MRPⅡ的不一致点 (47)6.3CWB滚动调度算法与MRPⅡ的整合方案 (48)7结论及展望 (50)参考文献 (51)附录 (56)本文涉及到的参数符号定义符号表示意义n 总的工件个数m 未调度的工件个数I 所有工件的集合S未调度工件的集合J i第i个工件（1≤i≤n）r i第i个工件的到达时间。

Polymer Interleaved Layered Double Hydroxide:A New Emerging Class of NanocompositesFabrice Leroux*and Jean-Pierre BesseLaboratoire des Mate´riaux Inorganiques,CNRS-UMR No.6002,Universite´Blaise Pascal,63177Aubie`re Cedex,FranceReceived February5,2001.Revised Manuscript Received June29,2001The present paper describes the synthesis and characterization of nanocomposite materials built from the assembly of organic polymers and two-dimensional host materials,particularly reviewing those composed of layered double hydroxide(LDH)inorganic frameworks.When the meaning commonly adopted for nanocomposites is narrowed,the system is constituted of sheets lying on top of each other in which covalent forces maintain the chemical integrity and define an interlamellar gap filled up with the polymer guest.The situation is different from an inorganic filler dispersed into a polymeric matrix.The incorporation of polymer between the galleries proceeds via different pathways such as coprecipitation,exchange,in situ polymerization,surfactant-mediated incorporation,hydrothermal treatment,reconstruc-tion,or restacking.The latter method,recently effective via the exfoliation of the LDH layers, appears to be more favorable,in terms of crystallinity,to capture monomer entities than the whole polymer.The nanocomposites are enlisted according to the preparation pathways. It is found that these multicomponent systems are thermally more stable than the pristine inorganic compounds,leading,for example,to potential applications in flame-retardant composites.A large variety of LDH/polymer systems may be tailored considering the highly tunable intralayer composition coupled to the choice of the organic moiety.The paper concludes with a brief discussion underlining the perspectives.Despite their appeal,the polymer/LDH class of nanocomposites has not yet been extensively studied for applications.1.IntroductionCurrently there is considerable interest for learning how to prepare,to shape,and to improve solids to match the ever-growing demand for multifunctional materials. The simple answer would be to mix together the compounds according to the desired properties,but it is known that an intimate mixture,at a nanometer scale,of the components is needed to reach a combina-tion of properties that are not available in any of the individual parts.The notion of nanocomposites defines organization presenting one of its component of nano-metric scale.Thus,this includes systems made of entrapment of species of nanometer size in one dimen-sion such as particles reinforcing the polymeric matrix. Largely developed for such an application,the idea of a reinforcing filler,from the polymer point of view,is not new and remains topical.1This is illustrated by silicate-layered materials dispersed as bundles or as small particles into the polymeric matrix,nylon,epoxy,or phenolic resin,poly(ethylene oxide)(PEO),poly(vinyl acetate),polyamide,etc.,and defined as organoclays. The layers of the two-dimensional(2D)host pristine materials are propped apart until exfoliation,giving rise to disordered materials.The properties of reinforcement, such as the tensile modulus and elongation at break, were found to be greater when the polymer was present between the sheets of the inorganic matrix rather than embedded in the inorganic framework,underlining the importance of the interface between the two compo-nents.2Because of their highly tunable properties,nanocom-posite materials are evaluated for applications in a large number of fields such as those emphasizing mechanical enhancement,gas permeability,or polymer electro-lyte.1e,i,k,3In this contribution,we present an overview of systems defined as the assembly,strictly speaking, of two components,a polymer,and a2D host material, more specifically a layered double hydroxide(LDH),and in the case where the bidimensional inorganic arrange-ment is left unmodified.LDH host materials present the advantage of a large variety of compositions and a tunable layer charge density.4Moreover,the LDH sheets are constituted of one polyhedra-made layer, often corrugated,and therefore are more flexible than other bidimensional frameworks such as the2:1layered silicate.This has been verified by computing an ex-tended version of the discrete finite-layer rigidity model which includes both intra-and interlayer rigidity ef-fects.5The LDH structure is referred to as the natural hydrotalcite and described with the ideal formula [M II x M III1-x(OH)2]intra[A m-x/m‚n H2O]inter,where M II and M III are metal cations,A is the anion,and intra and inter denote the intralayer domain and the interlayer space,respectively.The structure consists of brucite-like layers constituted of edge-sharing M(OH)6octahe-dra.6Partial M II to M III substitution induces a positive*To whom correspondence should be addressed.E-mail: fleroux@chimtp-univ.bpclermont.fr.3507Chem.Mater.2001,13,3507-351510.1021/cm0110268CCC:$20.00©2001American Chemical SocietyPublished on Web09/20/2001charge for the layers,balanced with the presence of the interlayered anions.After characteristics of the LDH structure[i.e.,its anionic exchange capacity(AEC)and layer charge density]are pointed out,the strategy of its assembly with an organic polymer is referenced in comparison with the general formation of2D nanocom-posite materials.The state of the art in polymer/LDH systems will be presented.This paper concludes by presenting these emerging systems as potential candi-dates for a large variety of applications as is already exemplified with other nanocomposite parent materials.2.Synthesis and Description of LDHs2.1.Synthesis.To better picture the host material, we present its general characteristics induced by the chemical composition and by the nature of the intralayer cations.LDH materials are prepared via the coprecipi-tation method using M II and M III sources(chloride, nitrate,sulfate,carbonate,etc.)at constant pH.7Apart from a few compositions such as[Cu2Cr]and[Zn2Cr], most of the LDH materials are prepared under basic conditions.Because carbonate anions present a strong affinity and are difficult to exchange,synthesis is carried out under flowing inert gas.Nevertheless,contamina-tion by carbonate anions on the surface of the crystal-lites is difficult to avoid.Another method is employed for[Cu2Cr]which consists of leaving a solution of Cr III cations with finely dispersed CuO oxide slowly reacting.8 The resulting sample presents an intimate cation distribution different from the sample obtained by the coprecipitation method.The former method is largely preferred for exchange reaction,giving nicely open platelet-like morphology,whereas monolithic chunks are obtained with the coprecipitation method as shown in Figure1.The comparison to the sand-rose morphol-ogy of[Zn2AlCl]is also provided.Other synthetic pathways are adopted:for instance,[Ni2Fe]is prepared via a three-step method.The building of the slabs of Ni0.70Fe0.30O2composition is made via the preparation of NaNi0.70Fe0.30O2sodium nickelate by a high-temper-ature solid-state reaction;then an oxidizing hydrolysis leads to a layeredγ-oxyhydroxide,and a final reduction by adding hydrogen peroxide solution in the presence of the anions gives rise to a LDH material.9A particular case is LiAl2(OH)7‚2H2O[or LiAl2(OH)6Cl‚H2O]mate-rial,where Li cations diffuse to the open site(one-third of the total octahedra)available in the gibbsite Al-(OH)3.10A sol-gel route has also been recently employed using the hydrolysis of alkoxide and acetylacetonate precursors.11In this case,the average particle size is found to be smaller and the surface area slightly higher than that of samples prepared by coprecipitation.2.2.Structure.The structure of LDH material is presented in Figure2.The versatile intralayer composi-tion is such that many cations are accommodated in the layers of the HDL materials,such as most of the transition metal of the first row(as a divalent cation)combined with Fe,Al,and Ga as trivalent cations.12 Lately,it has been shown that tetravalent cations such as Zr4+and Sn4+could also be incorporated into the brucite-like LDH layer.13This is of importance because the presence of interlayer species is directly related to the net layer charge values,as exemplified with Figure 3where AEC is given versus the layer charge for some LDH compounds.The data for[Mg1-x Ga x]samples are taken from ref12a and represented by diamond in the figure.The vacancies of the intersheet domain are not great,taking into account the large packing of anions balancing the layer charge.For comparison,sodium montmorrillonite,acationicclayofcomposition(Na0.35K0.01-Ca0.02)(Si3.89Al0.11)(Al1.6Fe0.08Mg0.32)O10(OH)2,presents Figure1.SEM pictures of(a)[Zn2AlCl]and(b)[Cu2CrCl] prepared by coprecipitation or the salt and oxide method(c). The bar represents2µm.Pictures were recorded with a Cambridge stereoscan operating at15kV.3508Chem.Mater.,Vol.13,No.10,2001Reviewsan exchange capacity of 108mequiv/100g,14giving an area per charge of 70Å2/charge,whereas it is ranges between 25and 40Å2/charge for LDH materials.As will be discussed (vide infra),the smaller the exchange capacity (i.e.,the layer charge density),the easier the formation of the nanocomposite.A high AEC corre-sponds to the presence of layers tightly stacked via the attractive forces with the interlayer anions filling the gallery,and this is unfavorable for either an ion-exchange reaction or an exfoliation process.15This state-ment may explain the relatively small number of nano-composites reported in the literature,added to the fact that each LDH composition leads to a unique material whose properties (exchange,reconstruction,or exfolia-tion)are not easily transferred from one to another.2.3.Cation Order.The question of order within the LDH sheets has largely been studied.16Cation ordering is observed for [Zn 2AlSO 42-]16g and LiAl 2(OH)7‚2H 2O 10LDH materials.For the former,the order arises from the interlayer anion organization.Cation order is present at a local scale;16a,c,e a highly ordered 2D superlattice is,however,rarely observed.The lack of ordering may originate from the large difference in the divalent and trivalent cation radii.16fThis is important if one considers the matching host to guest,i.e.,the in -plane inorganic sheet organization with the size of the monomer unit.This is exemplified with a polyaniline (PANI)/FeOCl system,where the polymer orientation inside the solid is explained by the matching of two -NH -repetitions with a distance of chloride anions along the [101]direction.17a This is defined as an endotactic reaction.2.4.Particular Properties Suitable To Form Nanocomposites.Partial dehydroxylation occurs after thermal treatment,and LDH materials turn to an amorphous oxide,usually noted as layered double oxide (LDO).Some LDH materials present the property to be recovered,and the oxide reconstructs to the parent LDH on either cooling in air (uptake of carbonate anions)or soaking in water.18This technique was used to incor-porate large anions such as polyoxometalates.19Nanolayer exfoliation is obviously an advantage to preparing nanocomposites.Some 2D solids present the property (vide supra),such as MoS 2after reaction of LiMoS 2with water,20of layered protonic titanate after reaction with alkylammonium cations,21zirconium hy-drogen phosphate,22oxovanadium phosphate,23etc.Low layer charge density,i.e.,greater area per charge,is more appropriate for the exfoliation process.Neverthe-less,a method was recently reported to exfoliate LDH layers by a two-step approach.24An organic spacer molecule is incorporated between the sheets to weaken the attractive forces between layers.Surfactant sodium dodecyl sulfate (Na-DS)was used,propping apart the layers of the LDH compounds.The DS-LDH exchanged phase is then refluxed in a butanol solution.Restacking of the highly pronounced lamellar phase is observed in contrast to the sand-rose morphology of the pristine material (Figure 4).3.Basic Principles To Obtain a PolymerIntercalated Inorganic Host 2D systems are composed of sheets lying on top of each other in which covalent forces are maintaining the chemical integrity,whereas weak interlayer interactions are present between lamellae.To be completely sand-wiched,the organic moiety has to diffuse between the inorganic layers.There are several possible strategies to incorporate the polymer at the core of the host material as under-lined by Scho ¨llhorn in a review paper in 1996,25in which the classification of the synthesis was listed as three principal options:(a)intercalation of the monomer molecules;(b)direct intercalation of extended polymer chains in the host lattice;(c)transformation of the host material into a colloid system and precipitation in the presence of the polymer.Figure 2.Pictorial scheme of the LDH structure.Figure 3.AEC vs layer charge for some LDHcompositions.Figure 4.SEM pictures of [Zn 2AlCO 32-]obtained by restack-ing of the layers.The bar represents 2µm.Reviews Chem.Mater.,Vol.13,No.10,20013509Figure 5illustrates the pathways.For the first pathway (a),the polymerization is occurring between the layers with subsequent thermal,photoinduced,or redox treatment.For a question of size or affinity (hydrophobicity),the incorporation of the guest may be unfavorable.An alternative method (a2)consists of the pre-intercalation of a molecule acting as a spacer,a modifying layer agent,or an in situ reagent.This was illustrated by the incorporation of molecules such as terephthalate in LDH,26,27alkylammonium between silicate layers,28or hexacyanoferrate for [Cu 2Al]LDH material,29respectively.The second pathway deals with the incorporation of the polymer as a whole between the 2D host material either directly (b1)or after the expansion and chemical change of the layers (b2).Nanocomposite materials are also obtained via the restacking of exfoliated layers.The layer by layer deposition allows one,theoretically,to overcome the diffusion problem when dealing with polymer.Unfortunately,the method is only applicableto the 2D host whose layers can be delaminated.As for pathway a,an alternative (c2)may be employed:restack-ing of the layers on the monomer,thus avoiding the problem of compatibility of size and/or of charge density between the polymer chain and the single layer.The in situ polymerization (a)method is used for the incorpo-ration of various monomers such as aniline,pyrrole,nylon from the polycondensation of -aminocaproic acid,methyl methacrylate,vinylbenzene sulfonate,vinylpyr-rolidone,vinyl acetate,etc.In situ polymerization is limited by two factors:(i)the distance from monomer to monomer when it is strongly anchored (or grafted)to the host matrix,i.e.,its degree of freedom;(ii)the condition that the polymerization (temperature,pH,or redox reaction)must leave the layered structure intact.The so-called “reductive intercalative polymerization”developed by Kanatzidis et al .consists of the incorpora-tion of conductive polymers into a host material.The latter may be an oxidizing bidimensional matrix such as vanadium pentoxide xerogel,leading to nanocom-posites of different nature,polymer/V 2O 5[polymer )polythiophene (PTH),PPY,PANI].30During the process,a concomitant polymerization is occurring via the redox reaction provided by V 5+cations.For a no-oxidizing matrix,an external agent such as FeCl 3or (NH 4)2S 2O 8is used,as illustrated,for instance,with the polymer-ization of aniline or pyrrole into HNbMoO 6,31FeOCl,32R -RuCl 3,33MoO 3,34and graphitic oxide.35Solventless oxidative polymerization was recently reported for hec-torite clay exchanged with Cu 2+and Fe 3+cations.36Thermal postsynthesis treatment is employed to induce the monomer linkage,as exemplified with the insertion of the precursor p -xylene -R -dimethylsulfonium chloride into MoO 3hydrated bronze 37or with the incorporation of -aminocaproic acid into R -ZrP,leading to a nylon nanocomposite after treatment at 200°C under nitro-gen.38Direct intercalation of polymers can be achieved via dispersion of clay mineral in dissolved polymers such as PEO/montmorillonite 14or PEO/MoO 339systems.The former presents 2D ionic conductivity due to the motion of the intracrystalline cations confined in PEO.It was found that,even for dispersed phases such as an epoxy/clay system,the key was to load the clay gallery with a hydrophobic tail surfactant.1i To overcome the incom-patibility between the polymer and the host,the nature of the interlayer space is often modified;the lipophiliza-tion of the clays by alkylammonium cations reduces the surface polarity of the silicate layers,enhancing the affinity between the silicate and the matrix.The so-called “refined guest displacement”and “surfactant-mediated pathway”are employed for the poly(vinylpyr-rolidone)(PVP)/kaolinite system via kaolinite/ammonium acetate precursor 40and for the incorporation of poly(p -phenylene)into molybdenum bronze,41respectively.Finally,polymer inclusion is reported for nanocom-posites PANI/MoS 2,42poly(ethylene glycol)(PEG),PVP,and PEO/NbSe 2.43The formation proceeds with the restacking of the layers in an appropriate solvent.Intercalation of polymers into the interlayer space of smectites has been extensively studied.It is,however,difficult to avoid reaggregation of the layers when the polymer interacts with the fully delaminated smectiteFigure 5.Pathway of nanocomposite preparation by (a)monomer exchange and in situ polymerization,(b)direct polymer exchange,and (c)restacking of the exfoliated layers over the polymer.3510Chem.Mater.,Vol.13,No.10,2001Reviewscolloid.44Some examples illustrating the nanocomposite formation are enlisted in Table1.4.Polymer LDH Nanocomposites Polymer/LDH nanocomposites can be referred to as organoceramics.A large variety of anionic polymers have been introduced between the layers of hydrotalcite-or hydrocalumite-type materials.FTIR and CPMAS solid-state13C NMR spectroscopies are commonly used to confirm the presence of the organic moiety in the nanocomposites.The preparation is carried out via several different pathways.4.1.In Situ Polymerization.The incorporation ofa polymer between the LDH sheets may be achieved by the in situ polymerization.For instance,an acrylate/ [Mg2Al]LDH hybrid material,obtained via exchange with the interlayered anions(Cl-or NO32-),is further polymerized after thermal treatment at80°C.The basal spacing was found to slightly decrease from13.8Åto 13.4Å.45The IR spectroscopy provides information on the polymerization with the disappearance of the C d C vibration band.It is noteworthy that the carbonate LDH phase does not react with acrylate.Acrylic acid was also intercalated in the lamellar structure of an iron-substituted nickel LDH material.46In this study,potas-sium persulfate is used as an initiator for the polym-erization process.The resulting phase was although partially exchanged by SO42-.Insertion of the polymer leads to a phase presenting a basal spacing of12.6Åthinner than that of an acrylate-intercalated monomer phase(13.6Å).This was explained by the absence of electrostatic repulsion between the C d C double bonds. Insertion of conjugated polymers into the LDH frame-work was first reported by Challier and Slade.29Tereph-thalate-and hexacyanoferrate-exchanged[Cu2Cr]LDH phases are used as host matrixes for the oxidative polymerization of aniline.The reaction performed under reflux conditions gives rise to a rather poorly defined material with a basal spacing of≈13.5Å.From IR diagnostics,the authors conclude that PANI polymeris present as short chains of oligomers under its emer-aldine base form.The nanocomposite material was found to exhibit a poor temperature stability.An alternative consists of incorporation of a soluble anionic monomer such as aniline-2-sulfonate or meta-nilic acid(3-aminobenzenesulfonic acid,H2NC6H4SO3H). Polymerization of the monomer requires conditions less drastic than those for aniline,giving rise to a relatively well-ordered system.47In situ polymerization performed at200°C in air induces a reorientation of the organic part as evidenced by a decrease of the basal spacing. The structure of the nanocomposite PANI/Cu2Cr sus-tains much a higher temperature than the pristine material,which transforms into Cu2OCl2at200°C. The contraction of the interlayer distance is also noted for the in situ polymerization of vinylbenzene sulfonate between[Zn2Al]LDH sheets(Figure6a).The methods listed in Figure5are evaluated for the poly(styrene sulfonate)(PSS)/Zn2Al nanocomposite system(vide in-fra).48Following a preswelling method described by Drezdon,26a terephthalate form of[Mg2Al]LDH is used as a starting material to capture the negatively charged polystyrene(PS)oligomers.49The authors found that the dianions are rapidly ion-exchanged for the oligomers.4.2.Direct Intercalation.Poly(R, -aspartate)was inserted in the[Mg3Al]LDH phase by in situ thermal polycondensation or by direct intercalation as a cosolute in the basic reaction solution.50The condensation pro-cess proceeds from the aminosuccinic acid via a polysuc-cinimide intermediate which rearranges to give polyas-partate at220°C.It was found that the basal spacing decreases during the condensation process from11.1to 9.0Å,giving an available space of only4.2Åfor the accommodation of the polymer.A larger space(10.3Å) is observed in the case of the direct synthesis,but this is associated with a change of the trivalent to divalent cation ratio from3to1.33.Incorporation of PSS was extensively studied.The presence of polymer between a[Zn2Al]LDH is evidenced on the X-ray diffraction(XRD)diagrams,showing an increase of the interlayer distance from7.74to≈21Å. This is an indication of a substantial uptake of polymer between the sheets and corresponds to an increase of the available gallery height(up to15.9Å),consistent with the presence of a bilayer of PSS paving either side of the LDH sheets.48PSS/LDH nanocomposite materials are also prepared by a templated reaction;51i.e.,polymer Figure6.XRD patterns of Zn2Al/PSS nanocomposites ob-tained via the following:(a)In situ polymerization(1).The diagrams of the monomer-exchanged phase(2)and of the pristine material at room temperature(3)and at150°C(4) are also reported for comparison.(b)Polymer direct exchange (1),reconstruction(2),or restacking of the layers over the polymer(3)or the monomer(4).A diffracted beam monochro-mator Cu K R source and steps of0.04°with a counting time of4s are used.Patterns are offset for clarity.Reviews Chem.Mater.,Vol.13,No.10,20013511is introduced during the coprecipitation using a [Mg 2-Al]or [Zn 3Al]LDH material.The insertion of polymer affects not only the crystal-linity but also the dimension and morphology of the pristine host material.Scanning electron micrographs (SEM)of PSS/LDH nanocomposites (Figure 7)are compared to the pristine material (Figure 1).The sand-rose morphology of the pristine materials disappears to the profit of a lamellar arrangement.The sheets are more or less crumpled depending on the pathway.The layers are on the order of a few nanometers in size,much smaller than those of the nanocomposites pre-pared with the templating method.51Other systems are prepared by the templated reaction such as those composed of poly(acrylic acid)(PA)and/or poly(vinyl sulfonate)(PVS)with hydrotalcite 52,53or hydrocalu-mite 54type compounds.Recently,we have reported the direct intercalation of long-chain polymers such as PEG/alkenylsulfonic acid between [Cu 2Cr]layers.554.3.Restacking Process.Illustrated with the PSS/Zn 2Al system,the restacking of layers over the polymer gives rise to poorly defined material.A few harmonics are only present after the encapsulation of the monomer (Figure 6b).48Although,it is well suited for the uptake of monomer,which can be further polymerized.When an amorphous LDO is contacted with a solution containing PSS,it is possible to obtain a nanocomposite,which presents similarities in terms of crystallinity and morphology with the phase prepared by a direct ex-change (Figure 6b).48For the first time,the reconstruc-tion method was found to be suitable for the incorpo-ration of molecular guests as cumbersome as polymer chains.From the information reported in the literature and our recent studies,it is clear that the organic toinorganic framework assembly is highly sensitive to the preparation conditions as illustrated by the difference in stacking regularity in the nanocomposite phases.X-ray absorption spectroscopy experiments performed on the nanocomposites show that the intralayer cation order is mostly unchanged with the incorporation of polymer,except in the case of the exfoliation -restacking of the layers.24b The mentioned nanocomposites are listed in Table 2.The influence of preparation for the PSS/Zn 2Al system is evidenced with the SEM pictures (Figure 7).The [Mg 2AlCO 32-]LDH phase exhibits a positive electrophoretic mobility,whereas the PSS/Mg 2Al nano-composite presents a negative one,indicating that the surface properties are dominated by the anionic sul-fonate end group.52This is in agreement with PSS adsorption measurements,48where PSS adsorbed on the surface of the particles was found to represent 10%of the total exchange capacity of the pristine material.The poly(vinyl acetate)(PVA)/Ca 2Al layered structure was found to be stable up to a temperature of 400°C.54The authors speculated that the nature of the interface between the organic and inorganic components may be the reason of the high thermal stability.The organo-ceramic transforms at high temperature into an inor-ganic solid of composition different from that for the pristine host material.SEM pictures show how the nanocomposite is degraded in temperature (Figure 8);the organic residue encompasses the inorganic crystal-lites,thus preventing the Ca(OH)2crystallization.5.Potential Applications and Perspectives The constrained environment provided by the host material is interesting for many applications such asFigure 7.SEM pictures of Zn 2Al/PSS nanocomposites prepared via (a)in situ polymerization,(b)reconstruction,(c)direct exchange,and (d)restacking.The bar represents 2µm.3512Chem.Mater.,Vol.13,No.10,2001Reviewsthose regarding the protection of the polymer from the UV degradation or as flame retardant.For instance,polymer-layered silicate nanocomposites were found to present a unique combination of reduced flammability and improved physical properties.This has been exem-plified with poly(propylene-graft -maleic anhydride)and PS-layered silicate nanocomposites using montmorillo-nite and fluorohectorite.56The authors found that theTable 1.Examples of Polymer/2D Host Systems Illustrating the Pathways of Nanocomposite Formation a LDH 2D matrixpolymerpathwayd (∆d )(Å)ref V 2O 5‚n H 2O PANI a 13.830FeOCl PPY a 13.2(5.2)32R -ZrPnylonb 12.238montmorillonite poly(vinylpyridine)(1,2form)a 15.22MoS 2PANIc 10.3742a NbSe 2PVP,PEO,PEG c 24.0,19.6,18.843[(Li/Na)H 2O]0.25MoO 3bronze poly(p -phenylene)b 11.96(5.0)41graphitic oxidePANI,PVAa11.52(4.9)35a,baThe intersheet distance is noted when available.PANI )polyaniline,PPY )polypyrrole,PTH )polythiophene,PVP )poly(vinylpyrrolidone),PEO )poly(ethylene oxide),PEG )poly(ethylene glycol),and PVA )poly(vinyl acetate).Table 2.Polymer/LDH Nanocomposite Classification (See Text)aLDH 2D matrixpolymerpathway bd (Å)ref Cu 0.66Cr 0.33(OH)2(terephthalate)0.17‚n H 2O PANI a 13.329Cu 0.66Al 0.33(OH)2(hexacyanoferrate)0.17‚n H 2O PANIa 13.529Ca 0.66Al 0.33(OH)2(OH)-0.33‚H 2Opoly(vinyl alcohol)a 1854Mg 0.74Al 0.26(OH)2(CO 3)2-0.13(NO 3)-0.006‚0.32H 2O poly(R , -aspartate)a,b 9.050Mg 0.66Al 0.33(OH)2(CO 3)2-0.17‚n H 2O PSS cop 20.851Zn 0.75Al 0.25(OH)2(CO 3)2-0.13‚n H 2OPSS cop 21.651Mg 0.66Al 0.33(OH)2(terephthalate)2-0.17‚n H 2O PSa 23.249Mg 0.66Al 0.33(OH)2(CO 3)2-0.17‚n H 2O PA,PVS cop 12.0,13.152Zn 0.75Al 0.25(OH)2(CO 3)2-0.13‚n H 2O PA,PVS cop 12.4,13.353Co 0.75Al 0.25(OH)2(OH)-0.13‚n H 2O PVScop 13.353Ca 0.66Al 0.33(OH)2(CO 3)2-0.17‚n H 2O PA,PVS,PSS cop 12.4,13.1,19.653Mg 0.66Al 0.33(OH)2(NO 3)-0.33‚n H 2O polyacrylate a 13.445Ni 0.7Fe 0.3(OH)2(SO 4)2-0.17‚n H 2O polyacrylate b 12.646Zn 0.66Al 0.33(OH)2(Cl)-0.33‚0.63H 2O PSS a -c 15.6,21.2,19.848Cu 0.66Cr 0.33(OH)2(Cl)-0.33‚1.14H 2O PEGb 30.155CU 0.66Cr 0.33(OH)2(Cl)-0.33‚1.14H 2OPEG/alkenylsulfonic acid b 37.455Cu 0.66Cr 0.33(OH)2(dodecylsulfate)-0.33‚n H 2OPANI sulfonatea14.247aVinylic polymers:poly(acrylic acid)(PA),poly(vinyl sulfonate)(PVS),poly(styrene sulfonate)(PSS).b cop )templating reaction.Figure 8.SEM pictures of PVA/hydrocalumite at (a)room temperature,(b)300°C,(c)500°C,and (d)1000°C.43The magnification bars in the micrographs correspond to 2µm.Reviews Chem.Mater.,Vol.13,No.10,20013513。

最低松弛度优先调度算法最低松弛度优先（LLF）算法是根据任务紧急（或松弛）的程度，来确定任务的优先级。

任务的紧急程度愈⾼，为该任务所赋予的优先级就愈⾼，使之优先执⾏。

在实现该算法时要求系统中有⼀个按松弛度排序的实时任务就绪队列，松弛度最低的任务排在队列最前⾯，被优先调度。

松弛度的计算⽅法如下：任务的松弛度=必须完成的时间-其本⾝的运⾏时间-当前时间其中其本⾝运⾏的时间指任务运⾏结束还需多少时间，如果任务已经运⾏了⼀部分，则：任务松弛度=任务的处理时间-任务已经运⾏的时间 – 当前时间⼏个注意点：1. 该算法主要⽤于可抢占调度⽅式中，当⼀任务的最低松弛度减为0时，它必须⽴即抢占CPU，以保证按截⽌时间的要求完成任务。

2. 计算关键时间点的各进程周期的松弛度，当进程在当前周期截⽌时间前完成了任务，则在该进程进⼊下个周期前，⽆需计算它的松弛度。

3. 当出现多个进程松弛度相同且为最⼩时，按照“最近最久未调度”的原则进⾏进程调度。

1、结构体描述进程定义及其意义如下：typedef struct process //进程{char pname[5]; //进程名int deadtime; //周期int servetime; //执⾏时间//周期进程某⼀次执⾏到停⽌的剩余需执⾏时间（考虑到抢占），初始为deadtimeint lefttime;int cycle; //执⾏到的周期数//进程最近⼀次的最迟开始执⾏时间，- currenttime 即为松弛度int latestarttime;//进程下⼀次最早开始时间int arivetime;intk; //k=1，表⽰进程正在运⾏，否则为0，表⽰进程不在执⾏期间/*若存在最⼩松弛度进程个数多于1个，则采⽤最近最久未使⽤算法采⽤⼀计数器LRU_t*/intLRU_t;}process;2、循环队列存储进程定义及其意义如下：typedef struct sqqueue //循环队列{process *data[queuesize];int front,rear;} sqqueue;重难点分析1、实时系统可调度条件当实时系统中有M个硬实时任务，它们的处理时间可表⽰为Ci ，周期时间表⽰为Pi，则在采⽤N个处理机的系统中，必须满⾜限制条件：Σ<=N系统才是可调度的。