A. The timely computing base model and architecture

Introduction to Artificial Intelligence智慧树知到课后章节答案2023年下哈尔滨工程大学哈尔滨工程大学第一章测试1.All life has intelligence The following statements about intelligence arewrong（）A:All life has intelligence B:Bacteria do not have intelligence C:At present,human intelligence is the highest level of nature D:From the perspective of life, intelligence is the basic ability of life to adapt to the natural world答案:Bacteria do not have intelligence2.Which of the following techniques is unsupervised learning in artificialintelligence?（）A:Neural network B:Support vector machine C:Decision tree D:Clustering答案:Clustering3.To which period can the history of the development of artificial intelligencebe traced back?（）A:1970s B:Late 19th century C:Early 21st century D:1950s答案:Late 19th century4.Which of the following fields does not belong to the scope of artificialintelligence application?（）A:Aviation B:Medical C:Agriculture D:Finance答案:Aviation5.The first artificial neuron model in human history was the MP model,proposed by Hebb.（）A:对 B:错答案:错6.Big data will bring considerable value in government public services, medicalservices, retail, manufacturing, and personal location services. （）A:错 B:对答案:对第二章测试1.Which of the following options is not human reason:（）A:Value rationality B:Intellectual rationality C:Methodological rationalityD:Cognitive rationality答案:Intellectual rationality2.When did life begin? （）A:Between 10 billion and 4.5 billion years B:Between 13.8 billion years and10 billion years C:Between 4.5 billion and 3.5 billion years D:Before 13.8billion years答案:Between 4.5 billion and 3.5 billion years3.Which of the following statements is true regarding the philosophicalthinking about artificial intelligence?（）A:Philosophical thinking has hindered the progress of artificial intelligence.B:Philosophical thinking has contributed to the development of artificialintelligence. C:Philosophical thinking is only concerned with the ethicalimplications of artificial intelligence. D:Philosophical thinking has no impact on the development of artificial intelligence.答案:Philosophical thinking has contributed to the development ofartificial intelligence.4.What is the rational nature of artificial intelligence?（）A:The ability to communicate effectively with humans. B:The ability to feel emotions and express creativity. C:The ability to reason and make logicaldeductions. D:The ability to learn from experience and adapt to newsituations.答案:The ability to reason and make logical deductions.5.Which of the following statements is true regarding the rational nature ofartificial intelligence?（）A:The rational nature of artificial intelligence includes emotional intelligence.B:The rational nature of artificial intelligence is limited to logical reasoning.C:The rational nature of artificial intelligence is not important for itsdevelopment. D:The rational nature of artificial intelligence is only concerned with mathematical calculations.答案:The rational nature of artificial intelligence is limited to logicalreasoning.6.Connectionism believes that the basic element of human thinking is symbol,not neuron; Human's cognitive process is a self-organization process ofsymbol operation rather than weight. （）A:对 B:错答案:错第三章测试1.The brain of all organisms can be divided into three primitive parts:forebrain, midbrain and hindbrain. Specifically, the human brain is composed of brainstem, cerebellum and brain (forebrain). （）A:错 B:对答案:对2.The neural connections in the brain are chaotic. （）A:对 B:错答案:错3.The following statement about the left and right half of the brain and itsfunction is wrong （）.A:When dictating questions, the left brain is responsible for logical thinking,and the right brain is responsible for language description. B:The left brain is like a scientist, good at abstract thinking and complex calculation, but lacking rich emotion. C:The right brain is like an artist, creative in music, art andother artistic activities, and rich in emotion D:The left and right hemispheres of the brain have the same shape, but their functions are quite different. They are generally called the left brain and the right brain respectively.答案:When dictating questions, the left brain is responsible for logicalthinking, and the right brain is responsible for language description.4.What is the basic unit of the nervous system?（）A:Neuron B:Gene C:Atom D:Molecule答案:Neuron5.What is the role of the prefrontal cortex in cognitive functions?（）A:It is responsible for sensory processing. B:It is involved in emotionalprocessing. C:It is responsible for higher-level cognitive functions. D:It isinvolved in motor control.答案:It is responsible for higher-level cognitive functions.6.What is the definition of intelligence?（）A:The ability to communicate effectively. B:The ability to perform physicaltasks. C:The ability to acquire and apply knowledge and skills. D:The abilityto regulate emotions.答案:The ability to acquire and apply knowledge and skills.第四章测试1.The forward propagation neural network is based on the mathematicalmodel of neurons and is composed of neurons connected together by specific connection methods. Different artificial neural networks generally havedifferent structures, but the basis is still the mathematical model of neurons.（）A:对 B:错答案:对2.In the perceptron, the weights are adjusted by learning so that the networkcan get the desired output for any input. （）A:对 B:错答案:对3.Convolution neural network is a feedforward neural network, which hasmany advantages and has excellent performance for large image processing.Among the following options, the advantage of convolution neural network is（）.A:Implicit learning avoids explicit feature extraction B:Weight sharingC:Translation invariance D:Strong robustness答案:Implicit learning avoids explicit feature extraction;Weightsharing;Strong robustness4.In a feedforward neural network, information travels in which direction?（）A:Forward B:Both A and B C:None of the above D:Backward答案:Forward5.What is the main feature of a convolutional neural network?（）A:They are used for speech recognition. B:They are used for natural languageprocessing. C:They are used for reinforcement learning. D:They are used forimage recognition.答案:They are used for image recognition.6.Which of the following is a characteristic of deep neural networks?（）A:They require less training data than shallow neural networks. B:They havefewer hidden layers than shallow neural networks. C:They have loweraccuracy than shallow neural networks. D:They are more computationallyexpensive than shallow neural networks.答案:They are more computationally expensive than shallow neuralnetworks.第五章测试1.Machine learning refers to how the computer simulates or realizes humanlearning behavior to obtain new knowledge or skills, and reorganizes the existing knowledge structure to continuously improve its own performance.（）A:对 B:错答案:对2.The best decision sequence of Markov decision process is solved by Bellmanequation, and the value of each state is determined not only by the current state but also by the later state.（）A:对 B:错答案:对3.Alex Net's contributions to this work include: （）.A:Use GPUNVIDIAGTX580 to reduce the training time B:Use the modified linear unit (Re LU) as the nonlinear activation function C:Cover the larger pool to avoid the average effect of average pool D:Use the Dropouttechnology to selectively ignore the single neuron during training to avoid over-fitting the model答案:Use GPUNVIDIAGTX580 to reduce the training time;Use themodified linear unit (Re LU) as the nonlinear activation function;Cover the larger pool to avoid the average effect of average pool;Use theDropout technology to selectively ignore the single neuron duringtraining to avoid over-fitting the model4.In supervised learning, what is the role of the labeled data?（）A:To evaluate the model B:To train the model C:None of the above D:To test the model答案:To train the model5.In reinforcement learning, what is the goal of the agent?（）A:To identify patterns in input data B:To minimize the error between thepredicted and actual output C:To maximize the reward obtained from theenvironment D:To classify input data into different categories答案:To maximize the reward obtained from the environment6.Which of the following is a characteristic of transfer learning?（）A:It can only be used for supervised learning tasks B:It requires a largeamount of labeled data C:It involves transferring knowledge from onedomain to another D:It is only applicable to small-scale problems答案:It involves transferring knowledge from one domain to another第六章测试1.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is （）.A:Region growth method is to complete the segmentation by calculating the mean vector of the offset. B:Watershed algorithm, MeanShift segmentation,region growth and Ostu threshold segmentation can complete imagesegmentation. C:Watershed algorithm is often used to segment the objectsconnected in the image. D:Otsu threshold segmentation, also known as themaximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire image答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.2.Camera calibration is a key step when using machine vision to measureobjects. Its calibration accuracy will directly affect the measurementaccuracy. Among them, camera calibration generally involves the mutualconversion of object point coordinates in several coordinate systems. So,what coordinate systems do you mean by "several coordinate systems" here?（）A:Image coordinate system B:Image plane coordinate system C:Cameracoordinate system D:World coordinate system答案:Image coordinate system;Image plane coordinate system;Camera coordinate system;World coordinate systemmonly used digital image filtering methods:（）.A:bilateral filtering B:median filter C:mean filtering D:Gaussian filter答案:bilateral filtering;median filter;mean filtering;Gaussian filter4.Application areas of digital image processing include:（）A:Industrial inspection B:Biomedical Science C:Scenario simulation D:remote sensing答案:Industrial inspection;Biomedical Science5.Image segmentation is the technology and process of dividing an image intoseveral specific regions with unique properties and proposing objects ofinterest. In the following statement about image segmentation algorithm, the error is ( ).A:Otsu threshold segmentation, also known as the maximum between-class difference method, realizes the automatic selection of global threshold T by counting the histogram characteristics of the entire imageB: Watershed algorithm is often used to segment the objects connected in the image. C:Region growth method is to complete the segmentation bycalculating the mean vector of the offset. D:Watershed algorithm, MeanShift segmentation, region growth and Ostu threshold segmentation can complete image segmentation.答案:Region growth method is to complete the segmentation bycalculating the mean vector of the offset.第七章测试1.Blind search can be applied to many different search problems, but it has notbeen widely used due to its low efficiency.（）A:错 B:对答案:对2.Which of the following search methods uses a FIFO queue （）.A:width-first search B:random search C:depth-first search D:generation-test method答案:width-first search3.What causes the complexity of the semantic network （）.A:There is no recognized formal representation system B:The quantifiernetwork is inadequate C:The means of knowledge representation are diverse D:The relationship between nodes can be linear, nonlinear, or even recursive 答案:The means of knowledge representation are diverse;Therelationship between nodes can be linear, nonlinear, or even recursive4.In the knowledge graph taking Leonardo da Vinci as an example, the entity ofthe character represents a node, and the relationship between the artist and the character represents an edge. Search is the process of finding the actionsequence of an intelligent system.（）A:对 B:错答案:对5.Which of the following statements about common methods of path search iswrong（）A:When using the artificial potential field method, when there are someobstacles in any distance around the target point, it is easy to cause the path to be unreachable B:The A* algorithm occupies too much memory during the search, the search efficiency is reduced, and the optimal result cannot beguaranteed C:The artificial potential field method can quickly search for acollision-free path with strong flexibility D:A* algorithm can solve theshortest path of state space search答案:When using the artificial potential field method, when there aresome obstacles in any distance around the target point, it is easy tocause the path to be unreachable第八章测试1.The language, spoken language, written language, sign language and Pythonlanguage of human communication are all natural languages.（）A:对 B:错答案:错2.The following statement about machine translation is wrong （）.A:The analysis stage of machine translation is mainly lexical analysis andpragmatic analysis B:The essence of machine translation is the discovery and application of bilingual translation laws. C:The four stages of machinetranslation are retrieval, analysis, conversion and generation. D:At present,natural language machine translation generally takes sentences as thetranslation unit.答案:The analysis stage of machine translation is mainly lexical analysis and pragmatic analysis3.Which of the following fields does machine translation belong to? （）A:Expert system B:Machine learning C:Human sensory simulation D:Natural language system答案:Natural language system4.The following statements about language are wrong: （）。

PRIDE:A Data Abstraction Layer for Large-Scale2-tier Sensor NetworksWoochul Kang University of Virginia Email:wk5f@Sang H.SonUniversity of VirginiaEmail:son@John A.StankovicUniversity of VirginiaEmail:stankovic@Abstract—It is a challenging task to provide timely access to global data from sensors in large-scale sensor network applica-tions.Current data storage architectures for sensor networks have to make trade-offs between timeliness and scalability. PRIDE is a data abstraction layer for2-tier sensor networks, which enables timely access to global data from the sensor tier to all participating nodes in the upper storage tier.The design of PRIDE is heavily influenced by collaborative real-time ap-plications such as search-and-rescue tasks for high-rise building fires,in which multiple devices have to collect and manage data streams from massive sensors in cooperation.PRIDE achieves scalability,timeliness,andflexibility simultaneously for such applications by combining a model-driven full replication scheme and adaptive data quality control mechanism in the storage-tier. We show the viability of the proposed solution by implementing and evaluating it on a large-scale2-tier sensor network testbed. The experiment results show that the model-driven replication provides the benefit of full replication in a scalable and controlled manner.I.I NTRODUCTIONRecent advances in sensor technology and wireless connec-tivity have paved the way for next generation real-time appli-cations that are highly data-driven,where data represent real-world status.For many of these applications,data streams from sensors are managed and processed by application-specific devices such as PDAs,base stations,and micro servers.Fur-ther,as sensors are deployed in increasing numbers,a single device cannot handle all sensor streams due to their scale and geographic distribution.Often,a group of such devices need to collaborate to achieve a common goal.For instance,during a search-and-rescue task for a buildingfire,while PDAs carried byfirefighters collect data from nearby sensors to check the dynamic status of the building,a team of suchfirefighters have to collaborate by sharing their locally collected real-time data with peerfirefighters since each individualfirefighter has only limited information from nearby sensors[1].The building-wide situation assessment requires fusioning data from all(or most of)firefighters.As this scenario shows,lots of future real-time applications will interact with physical world via large numbers of un-derlying sensors.The data from the sensors will be managed by distributed devices in cooperation.These devices can be either stationary(e.g.,base stations)or mobile(e.g.,PDAs and smartphones).Sharing data,and allowing timely access to global data for each participating entity is mandatory for suc-cessful collaboration in such distributed real-time applications.Data replication[2]has been a key technique that enables each participating entity to share data and obtain an understanding of the global status without the need for a central server. In particular,for distributed real-time applications,the data replication is essential to avoid unpredictable communication delays[3][4].PRIDE(Predictive Replication In Distributed Embedded systems)is a data abstraction layer for devices performing collaborative real-time tasks.It is linked to an application(s) at each device,and provides transparent and timely access to global data from underlying sensors via a scalable and robust replication mechanism.Each participating device can transparently access the global data from all underlying sen-sors without noticing whether it is from local sensors,or from remote sensors,which are covered by peer devices. Since global data from all underlying sensors are available at each device,queries on global spatio-temporal data can be efficiently answered using local data access methods,e.g.,B+ tree indexing,without further communication.Further,since all participating devices share the same set of data,any of them can be a primary device that manages a sensor.For example,when entities(either sensor nodes or devices)are mobile,any device that is close to a sensor node can be a primary storage node of the sensor node.Thisflexibility via decoupling the data source tier(sensors)from the storage tier is very important if we consider the highly dynamic nature of wireless sensor network applications.Even with these advantages,the high overhead of repli-cation limits its applicability[2].Since potentially a vast number of sensor streams are involved,it is not generally possible to propagate every sensor measurement to all devices in the system.Moreover,the data arrival rate can be high and unpredictable.During critical situations,the data rates can significantly increase and exceed system capacity.If no corrective action is taken,queues will form and the laten-cies of queries will increase without bound.In the context of centralized systems,several intelligent resource allocation schemes have been proposed to dynamically control the high and unpredictable rate of sensor streams[5][6][7].However, no work has been done in the context of distributed and replicated systems.In this paper,we focus on providing a scalable and robust replication mechanism.The contributions of this paper are: 1)a model-driven scalable replication mechanism,which2significantly reduces the overall communication and computation overheads,2)a global snapshot management scheme for efficientsupport of spatial queries on global data,3)a control-theoretic quality-of-data management algo-rithm for robustness against unpredictable workload changes,and4)the implementation and evaluation of the proposed ap-proach on a real device with realistic workloads.To make the replication scalable,PRIDE provides a model-driven replication scheme,in which the models of sensor streams are replicated to peer storage nodes,instead of data themselves.Once a model for a sensor stream is replicated from a primary storage node of the sensor to peer nodes,the updates from the sensor are propagated to peer nodes only if the prediction from the current model is not accurate enough. Our evaluation in Section5shows that this model-driven approach makes PRIDE highly scalable by significantly re-ducing the communication/computation overheads.Moreover, the Kalmanfilter-based modeling technique in PRIDE is light-weight and highly adaptable because it dynamically adjusts its model parameters at run-time without training.Spatial queries on global data are efficiently supported by taking snapshots from the models periodically.The snapshot is an up-to-date reflection of the monitored situation.Given this fresh snapshot,PRIDE supports a rich set of local data orga-nization mechanisms such as B+tree indexing to efficiently process spatial queries.In PRIDE,the robustness against unpredictable workloads is achieved by dynamically adjusting the precision bounds at each node to maintain a proper level of system load,CPU utilization in particular.The coordination is made among the nodes such that relatively under-loaded nodes synchronize their precision bound with an relatively overloaded node. Using this coordination,we ensure that the congestion at the overloaded node is effectively resolved.To show the viability of the proposed approach,we imple-mented a prototype of PRIDE on a large-scale testbed com-posed of Nokia N810Internet tablets[8],a cluster computer, and a realistic sensor stream generator.We chose Nokia N810 since it represents emerging ubiquitous computing platforms such as PDAs,smartphones,and mobile computers,which will be expected to interact with ubiquitous sensors in the near future.Based on the prototype implementation,we in-vestigated system performance attributes such as communica-tion/computation loads,energy efficiency,and robustness.Our evaluation results demonstrate that PRIDE takes advantage of full replication in an efficient,highly robust and scalable manner.The rest of this paper is organized as follows.Section2 presents the overview of PRIDE.Section3presents the details of the model-driven replication.Section4discusses our pro-totype implemention,and Section5presents our experimental results.We present related work in Section6and conclusions in Section7.II.O VERVIEW OF PRIDEA.System ModelFig.1.A collaborative application on a2-tier sensor network. PRIDE envisions2-tier sensor network systems with a sensor tier and a storage tier as shown in Figure1.The sensor tier consists of a large number of cheap and simple sensors;S={s1,s2,...,s n},where s i is a sensor.Sensors are assumed to be highly constrained in resources,and per-form only primitive functions such as sensing and multi-hop communication without local storage.Sensors stream data or events to a nearest storage node.These sensors can be either stationary or mobile;e.g.,sensors attached to afirefighter are mobile.The storage tier consists of more powerful devices such as PDAs,smartphones,and base stations;D={d1,d2,...,d m}, where d i is a storage node.These devices are relatively resource-rich compared with sensor nodes.However,these devices also have limited resources in terms of processor cycles,memory,power,and bandwidth.Each storage node provides in-network storage for underlying sensors,and stores data from sensors in its vicinity.Each node supports multiple radios;an802.11radio to connect to a wireless mesh network and a802.15.4to communicate with underlying sensors.Each node in this tier can be either stationary(e.g.,base stations), or mobile(e.g.,smartphones and PDAs).The sensor tier and the storage tier have loose coupling; the storage node,which a sensor belongs to,can be changed dynamically without coordination between the two tiers.This loose coupling is required in many sensor network applications if we consider the highly dynamic nature of such systems.For example,the mobility of sensors and storage nodes makes the system design very complex and inflexible if two tiers are tightly coupled;a complex group management and hand-off procedure is required to handle the mobility of entities[9]. Applications at each storage node are linked to the PRIDE layer.Applications issue queries to underlying PRIDE layer either autonomously,or by simply forwarding queries from external users.In the search-and-rescue task example,each storage node serves as both an in-network data storage for nearby sensors and a device to run autonomous real-time applications for the mission;the applications collect data by issuing queries and analyzing the situation to report results to thefirefighter.Afterwards,a node refers to a storage node if it is not explicitly stated.3Fig.2.The architecture of PRIDE(Gray boxes).age ModelIn PRIDE,all nodes in the storage tier are homogeneous in terms of their roles;no asymmetrical function is placed on a sub-group of the nodes.All or part of the nodes in the storage tier form a replication group R to share the data from underlying sensors,where R⊂D.Once a node joins the replication group,updates from its local sensors are propagated to peer nodes;conversely,the node can receive updates from remote sensors via peer nodes.Any storage node,which is receiving updates directly from a sensor,becomes a primary node for the sensor,and it broadcasts the updates from the sensor to peer nodes.However,it should be noted that,as will be shown in Section3,the PRIDE layer at each node performs model-driven replication,instead of replicating sensor data,to make the replication efficient and scalable.PRIDE is characterized by the queries that it supports. PRIDE supports both temporal queries on each individual sensor stream and spatial queries on current global data.Tem-poral queries on sensor s i’s historical data can be answered using the model for s i.An example of temporal query is “What is the value of sensor s i5minutes ago?”For spatial queries,each storage node provides a snapshot on the entire set of underlying sensors(both local and remote sensors.)The snapshot is similar to a view in database ing the snapshot,PRIDE provides traditional data organization and access methods for efficient spatial query processing.The access methods can be applied to any attributes,e.g.,sensor value,sensor ID,and location;therefore,value-based queries can be efficiently supported.Basic operations on the access methods such as insertion,deletion,retrieval,and the iterating cursors are supported.Special operations such as join cursors for join operations are also supported by making indexes to multiple attributes,e.g.,temperature and location attributes. This join operation is required to efficiently support complex spatial queries such as“Return the current temperatures of sensors located at room#4.”III.PRIDE D ATA A BSTRACTION L AYERThe architecture of PRIDE is shown in Figure2.PRIDE consists of three key components:(i)filter&prediction engine,which is responsible for sensor streamfiltering,model update,and broadcasting of updates to peer nodes,(ii)query processor,which handles queries on spatial and temporal data by using a snapshot and temporal models,respectively,and (iii)feedback controller,which determines proper precision bounds of data for scalability and overload protection.A.Filter&Prediction EngineThe goals offilter&prediction engine are tofilter out updates from local sensors using models,and to synchronize models at each storage node.The premise of using models is that the physical phenomena observed by sensors can be captured by models and a large amount of sensor data can be filtered out using the models.In PRIDE,when a sensor Input:update v from sensor s iˆv=prediction from model for s i;1if|ˆv−v|≥δthen2broadcast to peer storage nodes;3update data for s i in the snapshot;4update model m i for s i;5store to cache for later temporal query processing;6else7discard v(or store for logging);8end9Algorithm2:OnUpdateFromPeer.stream s i is covered by PRIDE replication group R,each storage node in R maintains a model m i for s i.Therefore, all storage nodes in R maintain a same set of synchronized models,M={m1,m2,...,m n},for all sensor streams in underlying sensor tier.Each model m i for sensor s i are synchronized at run-time by s i’s current primary storage node (note that s i’s primary node can change during run-time because of the network topology changes either at sensor tier or storage tier).Algorithms1and2show the basic framework for model synchronization at a primary node and peer nodes,respec-tively.In Algorithm1,when an update v is received from sensor s i to its primary storage node d j,the model m i is looked up,and a prediction is made using m i.If the gap between the predicted value from the model,ˆv,and the sensor update v is less than the precision boundδ(line2),then the new data is discarded(or saved locally for logging.)This implies that the current models(both at the primary node and the peer nodes)are precise enough to predict the sensor output with the given precision bound.However,if the gap is bigger than the precision bound,this implies that the model cannot capture the current behavior of the sensor output.In this case, m i at the primary node is updated and v is broadcasted to all peer nodes(line3).In Algorithm2,as a reaction to the broadcast from d j,each peer node receives a new update v and updates its own model m i with v.The value v is stored in local caches at all nodes for later temporal query processing.4As shown in the Algorithms,the communication among nodes happens only when the model is not precise enough. Models,Filtering,and Prediction So far,we have not discussed a specific modeling technique in PRIDE.Several distinctive requirements guide the choice of modeling tech-nique in PRIDE.First,the computation and communication costs for model maintenance should be low since PRIDE han-dles a large number of sensors(and corresponding models for each sensor)with collaboration of multiple nodes.The cost of model maintenance linearly increases to the number of sensors. Second,the parameters of models should be obtained without an extensive learning process,because many collaborative real-time applications,e.g.,a search-and-rescue task in a building fire,are short-term and deployed without previous monitoring history.A statistical model that needs extensive historical data for model training is less applicable even with their highly efficientfiltering and prediction performance.Finally, the modeling should be general enough to be applied to a broad range of applications.Ad-hoc modeling techniques for a particular application cannot be generally used for other applications.Since PRIDE is a data abstraction layer for wide range of collaborative applications,the generality of modeling is important.To this end,we choose to use Kalmanfilter [10][6],which provides a systematic mechanism to estimate past,current,and future state of a system from noisy measure-ments.A short summary on Kalmanfilter follows.Kalman Filter:The Kalmanfilter model assumes the true state at time k is evolved from the state at(k−1)according tox k=F k x k−1+w k;(1) whereF k is the state transition matrix relating x k−1to x k;w k is the process noise,which follows N(0,Q k);At time k an observation z k of the true state x k is made according toz k=H k x k+v k(2) whereH k is the observation model;v k is the measurement noise,which follows N(0,R k); The Kalmanfilter is a recursive minimum mean-square error estimator.This means that only the estimated state from the previous time step and the current measurement are needed to compute the estimate for the current and future state. In contrast to batch estimation techniques,no history of observations is required.In what follows,the notationˆx n|m represents the estimate of x at time n given observations up to,and including time m.The state of afilter is defined by two variables:ˆx k|k:the estimate of the state at time k givenobservations up to time k.P k|k:the error covariance matrix(a measure of theestimated accuracy of the state estimate). Kalmanfilter has two distinct phases:Predict and Update. The predict phase uses the state estimate from the previous timestep k−1to produce an estimate of the state at the next timestep k.In the update phase,measurement information at the current timestep k is used to refine this prediction to arrive at a new more accurate state estimate,again for the current timestep k.When a new measurement z k is available from a sensor,the true state of the sensor is estimates using the previous predictionˆx k|k−1,and the weighted prediction error. The weight is called Kalman gain K k,and it is updated on each prediction/update cycle.The true state of the sensor is estimated as follows,ˆx k|k=ˆx k|k−1+K k(z k−H kˆx k|k−1).(3)P k|k=(I−K k H k)P k|k−1.(4) The Kalman gain K k is updated as follows,K k|k=P k|k−1H T k(H k P k|k−1H T k+R k).(5) At each prediction step,the next state of the sensor is predicted by,ˆx k|k−1=F kˆx k−1|k−1.(6) Example:For instance,a temperature sensor can be described by the linear state space,x k= x dxdtis the derivative of the temperature with respect to time.As a new(noisy)measurement z k arrives from the sensor1,the true state and model parameters are estimated by Equations3-5.The future state of the sensor at(k+1)th time step after∆t can be predicted using the Equation6, where the state transition matrix isF= 1∆t01 .(7) It should be noted that the parameters for Kalmanfilter,e.g., K and P,do not have to be accurate in the beginning;they can be estimated at run-time and their accuracy improves gradually by having more sensor measurements.We do not need massive past data for modeling at deployment time.In addition,the update cycle of Kalmanfilter(Equations3-5) is performed at all storage nodes when a new measurement is broadcasted as shown in Algorithm1(line5)and Algorithm2 (line2).No further communication is required to synchronize the parameters of the models.Finally,as will be shown in Section5,the prediction/update cycle of Kalmanfilter incurs insignificant overhead to the system.1Note that the temperature component of zk is directly acquired from the sensor,and dx5B.Query ProcessorThe query processor of PRIDE supports both temporal queries and spatial queries with planned extension to support spatio-temporal queries.Temporal Queries:Historical data for each sensor stream can be processed in any storage node by exploiting data at the local cache and linear smoother[10].Unlike the estimation of current and future states using one Kalmanfilter,the optimized estimation of historical data(sometimes called smoothing) requires two Kalmanfilters,a forwardfilterˆx and a backward filterˆx b.Smoothing is a non-real-time data processing scheme that uses all measurements between0and T to estimate the state of a system at a certain time t,where0≤t≤T(see Figure3.)The smoothed estimateˆx(t|T)can be obtained as a linear combination of the twofilters as follows.ˆx(t|T)=Aˆx(t)+A′ˆx(t)b,(8) where A and A′are weighting matrices.For detailed discus-sion on smoothing techniques using Kalmanfilters,the reader is referred to[10].Fig.3.Smoothing for temporal query processing.Spatial Queries:Each storage node maintains a snapshot for all underlying local and remote sensors to handle queries on global spatial data.Each element(or data object)of the snapshot is an up-to-date value from the corresponding sensor.The snapshot is dynamically updated either by new measurements from sensors or by models2.The Algorithm1 (line4)and Algorithm2(line1)show the snapshot updates when a new observation is pushed from a local sensor and a peer node,respectively.As explained in the previous section, there is no communication among storage nodes when models well represent the current observations from sensors.When there is no update from peer nodes,the freshness of values in the snapshot deteriorate over time.To maintain the freshness of the snapshot even when there is no updates from peer nodes,each value in the snapshot is periodically updated by its local model.Each storage node can estimate the current state of sensor s i using Equation6without communication to the primary storage node of s i.For example,a temperature after30seconds can be predicted by setting∆t of transition matrix in Equation7to30seconds.The period of update of data object i for sensor s i is determined,such that the precision boundδis observed. Intuitively,when a sensor value changes rapidly,the data object should be updated more frequently to make the data object in the snapshot valid.In the example of Section3.1.1, 2Note that the data structures for the snapshot such as indexes are also updated when each value of the snapshot is updated.the period can be dynamically estimated as follows:p[i]=δ/dxdtis the absolute validity interval(avi)before the data object in the snapshot violates the precision bound,which is±δ.The update period should be as short as the half of the avi to make the data object fresh[11].Since each storage node has an up-to-date snapshot,spatial queries on global data from sensors can be efficiently han-dled using local data access methods(e.g.,B+tree)without incurring further communication delays.(a)δ=5C(b)δ=10CFig.4.Varying data precision.Figure4shows how the value of one data object in the snapshot changes over time when we apply different precision bounds.As the precision bound is getting bigger,the gap be-tween the real state of the sensor(dashed lines)and the current value at the snapshot(solid lines)increases.In the solid lines, the discontinued points are where the model prediction and the real measurement from the sensor are bigger than the precision bound,and subsequent communication is made among storage nodes for model synchronization.For applications and users, maintaining the smaller precision bound implies having a more accurate view on the monitored situation.However, the overhead also increases as we have the smaller precision bound.Given the unpredictable data arrival rates and resource constraints,compromising the data quality for system sur-vivability is unavoidable in many situations.In PRIDE,we consider processor cycles as the primary limited resource,and the resource allocation is performed to maintain the desired CPU utilization.The utilization control is used to enforce appropriate schedulable utilization bounds of applications can be guaranteed despite significant uncertainties in system work-loads[12][5].In utilization control,it is assumed that any cycles that are recovered as a result of control in PRIDE layer are used sensibly by the scheduler in the application layer to relieve the congestion,or to save power[12][5].It can also enhance system survivability by providing overload protection against workloadfluctuation.Specification:At each node,the system specification U,δmax consists of a utilization specification U and the precision specificationδmax.The desired utilization U∈[0..1]gives the required CPU utilization not to overload the system while satisfying the target system performance6 such as latency,and energy consumption.The precisionspecificationδmax denotes the maximum tolerable precision bound.Note there is no lower bound on the precision as in general users require a precision bound as short as possible (if the system is not overloaded.)Local Feedback Control to Guarantee the System Spec-ification:Using feedback control has shown to be very effec-tive for a large class of computing systems that exhibit unpre-dictable workloads and model inaccuracies[13].Therefore,to guarantee the system specification without a priori knowledge of the workload or accurate system model we apply feedbackcontrol.Fig.5.The feedback control loop.The overall feedback control loop at each storage node is shown in Figure5.Let T is the sampling period.The utilization u(k)is measured at each sampling instant0T,1T,2T,...and the difference between the target utilization and u(k)is fed into the ing the difference,the controller computes a local precision boundδ(k)such that u(k)converges to U. Thefirst step for local controller design is modeling the target system(storage node)by relatingδ(k)to u(k).We model the the relationship betwenδ(k)and u(k)by using profiling and statistical methods[13].Sinceδ(k)has higher impact on u(k)as the size of the replication group increases, we need different models for different sizes of the group. We change the number of members of the replication group exponentially from2to64and have tuned a set offirst order models G n(z),where n∈{2,4,8,16,32,64}.G n(z)is the z-transform transfer function of thefirst-order models,in which n is the size of the replication group.After the modeling, we design a controller for the model.We have found that a proportional integral(PI)controller[13]is sufficient in terms of providing a zero steady-state error,i.e.,a zero difference between u(k)and the target utilization bound.Further,a gain scheduling technique[13]have been used to apply different controller gains for different size of replication groups.For instance,the gain for G32(z)is applied if the size of a replication group is bigger than24and less than or equal to48. Due to space limitation we do not provide a full description of the design and tuning methods.Coordination among Replication Group Members:If each node independently sets its own precision bound,the net precision bound of data becomes unpredictable.For example, at node d j,the precision bounds for local sensor streams are determined by d j itself while the precision bounds for remote sensor streams are determined by their own primary storage nodes.PRIDE takes a conservative approach in coordinating stor-age nodes in the group.As Algorithm3shows,the global precision bound for the k th period is determined by taking the maximum from the precision bounds of all nodes in theInput:myid:my storage id number/*Get localδ.*/1measure u(k)from monitor;2calculateδmyid(k)from local controller;3foreach peer node d in R−˘d myid¯do4/*Exchange localδs.*/5/*Use piggyback to save communication cost.*/ 6sendδmyid(k)to d;7receiveδi(k)from d;8end9/*Get thefinal globalδ.*/10δglobal(k)=max(δi(k)),where i∈R;11。

绝密★启用前冲刺2023年高考英语真题重组卷02北京地区专用（解析版）注意事项：1．答卷前，考生务必将自己的姓名、考生号等填写在答题卡和试卷指定位置上。

2．回答选择题时，选出每小题答案后，用铅笔把答题卡对应题目的答案标号涂黑。

如需改动，用橡皮擦干净后，再选涂其他答案标号。

回答非选择题时，将答案写在答题卡上。

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3．考试结束后，将本试卷和答题卡一并交回。

第一部分知识运用（共两节，30分）第一节（共10小题；每小题1.5分，共15分）阅读下面短文，掌握其大意，从每题所给的A、B、C、D四个选项中，选出最佳选项，并在答题卡上将该项涂黑。

（2021·北京·高考真题）Recently，I took a trip home by train．I got a seat opposite a middle-aged man with sharp eyes，who kept____1____a young woman in a window seat with a little boy on her lap．The woman's eye fell on the man's face，and she immediately looked down and____2____her scarf．As the night wore on，people began to close their eyes，but the seats were so uncomfortable that only a very heavy sleeper could manage to get any____3____．The woman looked over at the man．He was still staring at her．She looked back at him with fire in her eyes．I was beginning to get____4____，too．The train moved on．The little boy was ____5____ awake on his mother's lap，but the woman dropped off to sleep．A moment later，he began to____6____the half-open window—one leg went over it．The man jumped up and ____7____the child just before he fell out．The____8____ woke up the woman．She seemed to be in____9____，and then reality dawned （显现）．“Your child has been looking for an opportunity to climb out of the window，”the man said as he gave the child back to her. ．“So I have been watching the whole time．”The woman was _____10_____，and so was I．1．A．guiding B．bothering C．watching D．monitoring 2．A．adjusted B．changed C．packed D．waved3．A．air B．joy C．space D．rest 4．A．nervous B．embarrassed C．angry D．disappointed5．A．almost B．still C．hardly D．even6．A．drag B．climb C．knock D．push 7．A．grabbed B．rocked C．touched D．picked 8．A．alarm B．quarrel C．risk D．noise 9．A．sorrow B．relief C．panic D．pain 10．A．astonished B．confused C．concerned D．inspired【语篇解读】这是一篇记叙文。

高二英语机器学习单选题50题1.Machine learning is a field of study that focuses on the development of algorithms that can learn from ___.A.dataB.experienceC.intuitionD.opinion答案：A。

本题主要考查机器学习的基本概念。

机器学习是通过数据进行学习的，选项A“data”符合题意。

选项B“experience”通常指人的经验，机器学习主要依据数据而非人的经验。

选项C“intuition”是直觉，机器学习是基于数据和算法的，不是直觉。

选项D“opinion”是观点，机器学习不是基于观点进行学习。

2.The main goal of machine learning is to ___.A.predict future eventsB.create new algorithmsC.solve complex equationsD.store large amounts of data答案：A。

机器学习的主要目标是根据已有数据预测未来事件，选项 A 正确。

选项B“create new algorithms”不是机器学习的主要目标，虽然在研究中可能会产生新算法，但不是主要目的。

选项C“solve complex equations”是数学等领域的任务，不是机器学习的主要目标。

选项D“store large amounts of data”只是存储数据，不是机器学习的目标。

3.Machine learning algorithms can be used in ___.A.image recognitionB.math calculationsC.physical experimentsD.literary analysis答案：A。

机器学习算法可以用于图像识别，选项A 正确。

The development and tendency of Big DataAbstract: "Big Data" is the most popular IT word after the "Internet of things" and "Cloud computing". From the source, development, status quo and tendency of big data, we can understand every aspect of it. Big data is one of the most important technologies around the world and every country has their own way to develop the technology.Key words: big data; IT; technology1 The source of big dataDespite the famous futurist Toffler propose the conception of “Big Data” in 1980, for a long time, because the primary stage is still in the development of IT industry and uses of information sources, “Big Data” is not get enough attention by the people in that age[1].2 The development of big dataUntil the financial crisis in 2008 force the IBM ( multi-national corporation of IT industry) proposing conception of “Smart City”and vigorously promote Internet of Things and Cloud computing so that information data has been in a massive growth meanwhile the need for the technology is very urgent. Under this condition, some American data processing companies have focused on developing large-scale concurrent processing system, then the “Big Data”technology become available sooner and Hadoop mass data concurrent processing system has received wide attention. Since 2010, IT giants have proposed their products in big data area. Big companies such as EMC、HP、IBM、Microsoft all purchase other manufacturer relating to big data in order to achieve technical integration[1]. Based on this, we can learn how important the big data strategy is. Development of big data thanks to some big IT companies such as Google、Amazon、China mobile、Alibaba and so on, because they need a optimization way to store and analysis data. Besides, there are also demands of health systems、geographic space remote sensing and digital media[2].3 The status quo of big dataNowadays America is in the lead of big data technology and market application. USA federal government announced a “Big Data’s research and development” plan in March,2012, which involved six federal government department the National Science Foundation, Health Research Institute, Department of Energy, Department of Defense, Advanced Research Projects Agency and Geological Survey in order to improve the ability to extract information and viewpoint of big data[1]. Thus, it can speed science and engineering discovery up, and it is a major move to push some research institutions making innovations.The federal government put big data development into a strategy place, which hasa big impact on every country. At present, many big European institutions is still at the primary stage to use big data and seriously lack technology about big data. Most improvements and technology of big data are come from America. Therefore, there are kind of challenges of Europe to keep in step with the development of big data. But, in the financial service industry especially investment banking in London is one of the earliest industries in Europe. The experiment and technology of big data is as good as the giant institution of America. And, the investment of big data has been maintained promising efforts. January 2013, British government announced 1.89 million pound will be invested in big data and calculation of energy saving technology in earth observation and health care[3].Japanese government timely takes the challenge of big data strategy. July 2013, Japan’s communications ministry proposed a synthesize strategy called “Energy ICT of Japan” which focused on big data application. June 2013, the abe cabinet formally announced the new IT strategy----“The announcement of creating the most advanced IT country”. This announcement comprehensively expounded that Japanese new IT national strategy is with the core of developing opening public data and big data in 2013 to 2020[4].Big data has also drawn attention of China government.《Guiding opinions of the State Council on promoting the healthy and orderly development of the Internet of things》promote to quicken the core technology including sensor network、intelligent terminal、big data processing、intelligent analysis and service integration. December 2012, the national development and reform commission add data analysis software into special guide, in the beginning of 2013 ministry of science and technology announced that big data research is one of the most important content of “973 program”[1]. This program requests that we need to research the expression, measure and semantic understanding of multi-source heterogeneous data, research modeling theory and computational model, promote hardware and software system architecture by energy optimal distributed storage and processing, analysis the relationship of complexity、calculability and treatment efficiency[1]. Above all, we can provide theory evidence for setting up scientific system of big data.4 The tendency of big data4.1 See the future by big dataIn the beginning of 2008, Alibaba found that the whole number of sellers were on a slippery slope by mining analyzing user-behavior data meanwhile the procurement to Europe and America was also glide. They accurately predicting the trend of world economic trade unfold half year earlier so they avoid the financial crisis[2]. Document [3] cite an example which turned out can predict a cholera one year earlier by mining and analysis the data of storm, drought and other natural disaster[3].4.2 Great changes and business opportunitiesWith the approval of big data values, giants of every industry all spend more money in big data industry. Then great changes and business opportunity comes[4].In hardware industry, big data are facing the challenges of manage, storage and real-time analysis. Big data will have an important impact of chip and storage industry,besides, some new industry will be created because of big data[4].In software and service area, the urgent demand of fast data processing will bring great boom to data mining and business intelligence industry.The hidden value of big data can create a lot of new companies, new products, new technology and new projects[2].4.3 Development direction of big dataThe storage technology of big data is relational database at primary. But due to the canonical design, friendly query language, efficient ability dealing with online affair, Big data dominate the market a long term. However, its strict design pattern, it ensures consistency to give up function, its poor expansibility these problems are exposed in big data analysis. Then, NoSQL data storage model and Bigtable propsed by Google start to be in fashion[5].Big data analysis technology which uses MapReduce technological frame proposed by Google is used to deal with large scale concurrent batch transaction. Using file system to store unstructured data is not lost function but also win the expansilility. Later, there are big data analysis platform like HA VEn proposed by HP and Fusion Insight proposed by Huawei . Beyond doubt, this situation will be continued, new technology and measures will come out such as next generation data warehouse, Hadoop distribute and so on[6].ConclusionThis paper we analysis the development and tendency of big data. Based on this, we know that the big data is still at a primary stage, there are too many problems need to deal with. But the commercial value and market value of big data are the direction of development to information age.忽略此处..[1] Li Chunwei, Development report of China’s E-Commerce enterprises, Beijing , 2013,pp.268-270[2] Li Fen, Zhu Zhixiang, Liu Shenghui, The development status and the problems of large data, Journal of Xi’an University of Posts and Telecommunications, 18 volume, pp. 102-103,sep.2013 [3] Kira Radinsky, Eric Horivtz, Mining the Web to Predict Future Events[C]//Proceedings of the 6th ACM International Conference on Web Search and Data Mining, WSDM 2013: New York: Association for Computing Machinery,2013,pp.255-264[4] Chapman A, Allen M D, Blaustein B. It’s About the Data: Provenance as a Toll for Assessing Data Fitness[C]//Proc of the 4th USENIX Workshop on the Theory and Practice of Provenance, Berkely, CA: USENIX Association, 2012:8[5] Li Ruiqin, Zheng Janguo, Big data Research: Status quo, Problems and Tendency[J],Network Application,Shanghai,1994,pp.107-108[6] Meng Xiaofeng, Wang Huiju, Du Xiaoyong, Big Daya Analysis: Competition and Survival of RDBMS and ManReduce[J], Journal of software, 2012,23(1): 32-45。

高中英语科技前沿词汇单选题50题1. In the field of computer science, when we talk about data storage, "cloud computing" provides a ______ solution.A. revolutionaryB. traditionalC. limitedD. temporary答案：A。

本题考查词汇含义。

“revolutionary”意为“革命性的”，“cloud computing”（云计算）在数据存储方面提供的是一种革命性的解决方案。

“traditional”表示“传统的”，不符合云计算的特点。

“limited”指“有限的”，与云计算的强大存储能力不符。

“temporary”意思是“临时的”，也不符合云计算作为长期数据存储方式的特性。

2. The development of artificial intelligence requires advanced algorithms and powerful ______.A. processorsB. memoriesC. screensD. keyboards答案：A。

“processors”是“处理器”，人工智能的发展需要先进算法和强大的处理器。

“memories”是“内存”，内存并非发展人工智能的关键硬件。

“screens”是“屏幕”，对人工智能发展并非核心硬件。

“keyboards”是“键盘”，与人工智能发展所需的硬件无关。

3. In the era of big data, ______ plays a crucial role in extracting valuable information.A. data miningB. data hidingC. data deletingD. data adding答案：A。

“data mining”是“数据挖掘”，在大数据时代，数据挖掘在提取有价值信息方面起着关键作用。

正确答案：A、B 你选对了Quizzes for Chapter 11 单选(1 分)图灵测试旨在给予哪一种令人满意的操作定义得分/ 5 多选(1 分)选择下列计算机系统中属于人工智能的实例得分/总分总分A. Web搜索引擎A. 人类思考B.超市条形码扫描器B. 人工智能C.声控电话菜单该题无法得分/1.00C.机器智能 1.00/1.00D.智能个人助理该题无法得分/1.00正确答案：A、D 你错选为C、DD.机器动作正确答案： C 你选对了6 多选(1 分)选择下列哪些是人工智能的研究领域得分/总分2 多选(1 分)选择以下关于人工智能概念的正确表述得分/总分A.人脸识别0.33/1.00A. 人工智能旨在创造智能机器该题无法得分/1.00B.专家系统0.33/1.00B. 人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/1.00C.图像理解C.人工智能将其定义为人类智能体的研究该题无法D.分布式计算得分/1.00正确答案：A、B、C 你错选为A、BD.人工智能是为了开发一类计算机使之能够完成通7 多选(1 分)考察人工智能(AI) 的一些应用，去发现目前下列哪些任务可以通过AI 来解决得分/总分常由人类所能做的事该题无法得分/1.00正确答案：A、B、D 你错选为A、B、C、DA.以竞技水平玩德州扑克游戏0.33/1.003 多选(1 分)如下学科哪些是人工智能的基础？得分/总分B.打一场像样的乒乓球比赛A. 经济学0.25/1.00C.在Web 上购买一周的食品杂货0.33/1.00B. 哲学0.25/1.00D.在市场上购买一周的食品杂货C.心理学0.25/1.00正确答案：A、B、C 你错选为A、CD.数学0.25/1.008 填空(1 分)理性指的是一个系统的属性，即在_________的环境下正确答案：A、B、C、D 你选对了做正确的事。

得分/总分正确答案：已知4 多选(1 分)下列陈述中哪些是描述强AI (通用AI )的正确答案？得1 单选(1 分)图灵测试旨在给予哪一种令人满意的操作定义得分/ 分/总分总分A. 指的是一种机器，具有将智能应用于任何问题的A.人类思考能力0.50/1.00B.人工智能B. 是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑0.50/1.00C.机器智能 1.00/1.00C.指的是一种机器，仅针对一个具体问题D.机器动作正确答案： C 你选对了D.其定义为无知觉的计算机智能，或专注于一个狭2 多选(1 分)选择以下关于人工智能概念的正确表述得分/总分窄任务的AIA. 人工智能旨在创造智能机器该题无法得分/1.00B.专家系统0.33/1.00B. 人工智能是研究和构建在给定环境下表现良好的C.图像理解智能体程序该题无法得分/1.00D.分布式计算C.人工智能将其定义为人类智能体的研究该题无法正确答案：A、B、C 你错选为A、B得分/1.00 7 多选(1 分)考察人工智能(AI) 的一些应用，去发现目前下列哪些任务可以通过AI 来解决得分/总分D.人工智能是为了开发一类计算机使之能够完成通A.以竞技水平玩德州扑克游戏0.33/1.00常由人类所能做的事该题无法得分/1.00正确答案：A、B、D 你错选为A、B、C、DB.打一场像样的乒乓球比赛3 多选(1 分)如下学科哪些是人工智能的基础？得分/总分C.在Web 上购买一周的食品杂货0.33/1.00A. 经济学0.25/1.00D.在市场上购买一周的食品杂货B. 哲学0.25/1.00正确答案：A、B、C 你错选为A、CC.心理学0.25/1.008 填空(1 分)理性指的是一个系统的属性，即在_________的环境下D.数学0.25/1.00 做正确的事。

Matlab is a numerical computing, symbolic computation and graphics processing power in one of the scientific computing language. As a powerful scientific computing platform, it almost to meet all your computing needs. In the United States and other developed countries in the universities of science and technology, Matlab can become a compulsory course, in research institutes, engineering departments of large companies or enterprises, Matlab is one of the most common calculation tools.Matlab has the following advantages and features:Friendly platform and into the environment: with the commercialization of Matlab and the continuous upgrading of the software itself, Matlab user interface is also increasingly delicate, closer to Windows standards interface, human-computer interaction more, operation simple. And new versions of Matlab provides complete online queries, help systems, greatly facilitate the user's use. A simple programming environment provides a more complete debugging system, the program can be run directly without compiled, and able to report errors that occur in a timely manner and the cause of the error analysis.Easy to use programming language: Matlab language is based on the most popular on the basis of the c language, grammatical features and very similar to the c language, but more simple, more in line with the scientific and technical personnel on digital writing format. Make it more conducive to non-computer professional technical personnel to use. And this language very good portability, scalability, and this is a Matlab can go to scientific research and engineering calculation of various areas of important reasons.Powerful scientific computing and data processing capacity: Matlab has more than 600 more engineering used in mathematical operations function, it to easily implement various calculation functions required to support. Function of acid is used in the latest research results in scientific and engineering calculations, and by a variety of optimization and fault-tolerant processing, so use very high robustness and reliability. Typically, you can use it to replace the underlying programming language such as c and C++ languages. In the case of computing requirements are the same as, using Matlab programming effort is greatly reduced. Matlab function to solve the problems included matrix calculations and solution of linear equations, solution of differential equations and partial differential equations, symbols, operations, statistical analysis, Fourier transform and data engineering optimization problems, sparse matrix operations, complex operations, trigonometric functions and other elementary mathematical operations, multidimensional array operations such as modeling and dynamic simulation.Excellent of graphics processing function: Matlab since produced of day up on has convenient of data Visual of function, new version of Matlab on entire graphics processing function do has is large of improved and improve, makes it not only in general data Visual of software are has of function (for example second dimension curve and three dimensional surface of draws and processing,) aspects more perfect, and for some other software no of function (for example graphics of light processing, and chroma processing and four data of performance,), Matlab also performance has excellent of processing ability. Also for some special visual needs, such as graphics, animation, Matlab has a corresponding function, ensuring the user different requirements. In addition, new versions of Matlab also focus on the graphical user interface (GUI) on the making of a great deal of improvement, this support can also have special requirements are met.Widely used set of modules and Toolkit: Matlab for many specialized areas have developed apowerful set of modules or the Toolbox. In General, they are all developed by experts in a particular field, users can simply use the Toolbox to learn, apply and evaluate different methods without the need to write the code yourself. Currently, Matlab has to Toolbox extends to has scientific research and engineering application of many area, such as data acquisition, and database interface, and probability statistics, and sample section intended close, and optimization algorithm, and partial differential equation solution, and neural network, and Wavelet analysis, and signal processing, and figure as processing, and system identification, and control system design, and LMI control, and Lu bar control, and model forecast, and fuzzy logic, and financial analysis, and map tools, and nonlinear control design, and real-time fast prototype and the half in simulation, embedded system development, and fixed-point simulation, and DSP and communications, and power system simulation,, are in Toolbox (Toolbox) family in the has has own of place.Practical program interface and publishing platform: Matlab using the Matlab compiler and C/C++ mathematics and graphics libraries, your own Matlab program automatically converted to Matlab-independent c and C++ code that is running. Matlab mesh services programs also allow in Web applications using their own Matlab mathematics and graphics.Modular design and system-level simulation: Simulink is a branch of the Matlab product, mainly used to achieve the modeling and dynamic simulation of engineering problems. In the context of world-wide wave of modeling, Simulink precisely reflects the modular design and system-level simulation of concrete thinking, making built to imitate is really as easy as taking the plot. Implementation of Simulink simulation can be applied to dynamic systems, design of signal control, communications, financial accounting and in bio-medical and other fields of study.Because Matlab has incomparable advantages over other computer languages, at present it has as an industry standard of engineering and science education. As it became increasingly popularity worldwide, also started to learn Matlab boom in China.Simulink simulation environment is a United States Math Works specifically for Matlab software company in 1990 provides charts of programming language design and simulation of special-purpose software tool, under Matlab6.X as Simulink3.1 above. User program and looks in the simulation environment is the control structure diagram of the system, its operation is based on structure diagram for system simulation. Using Simulink provides of entered signal (signal source module) on structure figure by description of system imposed incentive, using Simulink provides of output device (output interface module) get system of output response data or time response curve, became graphics of, and module of way of control system simulation, makes dynamic system of simulation and built die more simple convenient, this cannot but said is control system simulation tools of a large breakthrough progress.Simulink simulation environment supporting system simulation and modeling of various types, such as linear, nonlinear systems, continuous-time systems, sampling systems, as well as the continuous-discrete hybrid system. In addition, the simulation of sampling system also supports mixing sample rates.Simulink provides graphical user interface (GUI), dragging with the mouse, you can build charts control system in the form of models. Simulink chart module provides a variety of standard library, which mainly include: signal source units, the output device unit, linear unit, linear units and modules, connection unit. Also, open design offers a variety of file s-function design methods, allows users to design their own chart module.Simulink model using graphical system inherited from the bottom up and top down technology. Users can double-click access lower-level modules, to help users understand the internal structure of module Design.Map to Simulink system after the model of system simulation. Implementation of emulator can type models in Matlab platform on which the command file file name to start, or directly by the menu command to start under the Simulink. Simulation operation menu is entirely user interaction, such as simulation algorithm, change the parameter setting, use analog oscilloscope, observation system output or responses within the curve. In addition, the simulation results can be variables, returned later in the Matlab command platform to facilitate the simulation data processing.Simulink simulation performance and simulation accuracy affected by multiple factors, including the selection of design and simulation of model parameters, and so on. With the default parameters for the solution of basically meet the requirements of most of the performance of system simulation and emulation accuracy. However, for some simulation problems, if you try a different solution, or adjust the simulation parameters, simulation results can be better. Further, if to take into account the simulation object information, if it in the solution, then the simulation results will be greatly improved.If simulation is slow for a variety of reasons, ①Matlab in the simulation model of function modules. In each time step simulation requires calling Matlab interpreter, thus slowing down simulation speed. When a simulation in the structure, to make use of internal function modules or using arithmetic module. ②simulation model contains m s-function for the file. Same as repeatedly calling Matlab interpreter while slowing down simulation speed. S functions into subsystems or can be converted to s-function of the C-mex file. ③have a memory module in the simulation model. Use memory module makes changing solution in order to order reset to 1 for each simulation, thus slowing down simulation speed. ④When multi-sampling rate system simulation, between different sample rates does not satisfy the multiple relationship between each other, resulting in simulation solution when you need to adopt steps small enough to fit different sampling rate requirements, thus slowing down the speed of simulation. ⑤algebraic loop in the simulation model. Solution of algebraic loops are calculated at each time step of the iteration, so greatly impact simulation performance. In the same type of reasons there are many, is not going to elaborate here.Improvement of simulation accuracy, first check the simulation of the reasonable time period, reduce the relative or absolute accuracy after setting the simulation again, comparing the differences. If the result makes little difference, can verify that the basic simulation methods and results are correct. If simulation just start from the basic motion behaviors, can reduce the simulation step size to ensure that simulation is not out of the basic movement. If the simulation on the effective period of instability, probe into the possible causes and treatment methods are as follows: ①the object itself is not a stable system. ②can swap calculation. If the simulation result is not accurate, may for the following reasons: ①for those close to zero value of the model State, possibly due to set absolute accuracy is too large, resulting in a State of zero values near the neighborhood of simulation step is too small. Can reduce the absolute accuracy of the set value, or a single integrator in the dialog box to adjust the status. ②If this effect is not significant, consider reducing the relative accuracy of parameters setting, so that errors reduce to an acceptable error, reduce the simulation step size and use more simulation steps to resolve.。