原文出处Australasian Transport Research Forum Adelaide
FUZZY LOGIC TRAFFIC SIGNAL
CONTROL
ZEESHAN RAZA ABDY
PREPARED FOR
DR NEDAL T. RATROUT
INTRODUCTION
Signal control is a necessary measure to maintain the quality and safety of traffic circulation. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The development of detection and computer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. Present adaptive control methods, like the British MOV A, Swedish SOS (isolated signals) and British SCOOT (area-wide control), use mathematical optimization and simulation techniques to adjust the signal timing to the observed fluctuations of traffic flow in real time. The optimization is done by changing the green time and cycle lengths of the signals. In area-wide control the offsets between intersections are also changed. Several methods have been developed for determining the optimal cycle length and the minimum delay at an intersection but, based on uncertainty and rigid nature of traffic signal control, the global optimum is not possible to find out.
As a result of growing public awareness of the environmental impact of road traffic many authorities are now pursuing policies to:
? manage demand and congestion;
? influence mode and route choice;
? improve priority for buses, trams and other public service vehicles;
?provide better and safer facilities for pedestrians, cyclists and other vulnerable road users;
? reduce vehicle emissions, noise and visual intrusion; and
? improve safety for all road user groups.
In adaptive traffic signal control the increase in flexibility increases the number of
overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult. For that reason, the adaptive signal control in most cases is not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal control in practice are based on tailor-made solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potentials of fuzzy control is a simulation of traffic signal control in an inter-section of two one-way streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in complex problems with multiobjective decisions. In traffic signal control several traffic flows compete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedestrian delays, maximum queue lengths and percentage of stopped vehicles. So, it is very likely that fuzzy control is very competitive in complicated real intersections where the use of traditional optimization methods is problematic. Benefits and disadvantages of fuzzy systems
Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies based on priori communication. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control process. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions.
At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classical mathematical method, they still contain black boxes, e.g. de fuzzification, which are very difficult to justify mathematically or logically. For example, fuzzy IF - THEN rules, which
are in the core of fuzzy inference systems, are often reported to be generalizations of classical Modus Ponens rule of inference, but literally this not the case; the relation between these rules and any known many-valued logic is complicated and artificial. Moreover, the performance of an expert system should be equivalent to that of human expert: it should give the same results that the expert gives, but warn when the control situation is so vague that an expert is not sure about the right action. The existing fuzzy expert systems very seldom fulfil this latter condition.
Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes 'Fuzzy membership...represents similarities of objects to imprecisely defined properties'. Taking this remark seriously, we study systematically many-valued equivalence, i.e. fuzzy similarity. It turns out that, starting from the Lukasiewicz well-defined many-valued logic, we are able to construct a method performing fuzzy reasoning such that the inference relies only on experts knowledge and on well-defined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the inference. Our basic observation is that any fuzzy set generates a fuzzy similarity, and that these similarities can be combined to a fuzzy relation which turns out to a fuzzy similarity, too. We call this induced fuzzy relation total fuzzy similarity. Fuzzy IF - THEN inference systems are, in fact, problems of choice: compare each IF-part of the rule base with an actual input value, find the most similar case and fire the corresponding THEN-part; if it is not unique, use a criteria given by an expert to proceed. Based on the Lukasiewicz welldefined many valued logic, we show how this method can be carried out formally.
Hypothesis and Principles of Fuzzy Traffic Signal Control Traffic signal control is used to maximize the efficiency of the existing traffic systems [6]. However, the efficiency of traffic system can even be fuzzy. By providing temporal separation of rights of way to approaching flows, traffic signals exert a profound influence on the efficiency of traffic flow. They can operate to the advantage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and efficient movement of traffic at intersections.
In traffic signal control, we can find some kind of uncertainties in many levels. The inputs of traffic signal control are inaccurate, and that means that we cannot handle the traffic of approaches exactly. The control possibilities are complicated, and handling these possibilities are an extremely complex task. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also not possible to explain in traffic signal control. These are typical features of fuzzy control.
Fuzzy logic based controllers are designed to capture the key factors for controlling a process without requiring many detailed mathematical formulas. Due to this fact, they have many advantages in real time applications. The controllers have a simple computational structure, since they do not require many numerical calculations. The IFTHEN logic of their inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets of control rules can be applied to them. If the system related knowledge is represented by simple fuzzy IFTHEN- rules, a fuzzy-based controller can control the system with efficiency and ease. The main goal of traffic signal control is to ensure safety at signalized intersections by keeping conflict traffic flows apart. The optimal performance of the signalized intersections is the combination of time value, environmental effects and traffic safety. Our goal is the optimal system, but we need to decide what attributes and weights will be used to judge optimality.
The entire knowledge of the system designer about the process, traffic signal control in this case, to be controlled is stored as rules in the knowledge base. Thus the rules have a basic influence on the closed-loop behaviour of the system and should therefore be acquired thoroughly. The development of rules is time consuming, and designers often have to translate process knowledge into appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:
1. operators experience
2. control engineer's knowledge
3. fuzzy modelling of the operator's control actions
4. fuzzy modelling of the process
Zimmermann added three sources more
5. crisp modeling of the process
6. heuristic design rules
7. on-line adaptation of the rules.
Usually a combination of some of these methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.
FUSICO PROJECT
The main goals of FUSICO-research project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of fuzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-actuated control strategies, like SOS, MOV A and LHOVRA, are the control algorithms of the first generation. The fuzzy control algorithm can be
one of the algorithms of the second generation, the generation of artificial intelligence (AI). The fuzzy control is capable of handling multi-objective, multi-dimensional and complicated traffic situations, like traffic signalling. The typical advantages of fuzzy control are simple process, effective control and better quality.
FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. Mr. Kari J. Sane, experienced traffic signal planner, was working at the Helsinki University of Technology at this time. Everyday discussions and working groups helped us to model his experience to our rules.
In particular pathological traffic jams or situations where there are very few vehicles in circulation; there first-in-first-out is the only reasonable control strategy. The Algorithm is looking for the most similar IF-part to the actual input value, and the corresponding THEN-part is then fired. Three realistic traffic signal control systems were constructed by means of the Algorithm and a simulation model tested their performance. Similar simulations were made to a non-fuzzy and classical Mamdani style fuzzy inference systems, too. The results with respect to average vehicle and pedestrian delay or average vehicle delay were in most cases better on fuzzy similarity based control than on the other control systems. Comparisons between fuzzy similarity based control and Mamdani style fuzzy control also strength an assumption that, in approximate reasoning, a fundamental concept is many-valued similarity between objects rather than a generalization of classical Modus Ponens rule of inference.
FUSICO PROJECT RESULTS
The results of this project have indicated that fuzzy signal control is the potential control method for isolated intersections. The comparison results of Pappis-Mamdani control, fuzzy isolated pedestrian crossing and fuzzy two-phase control are good. The results of isolated pedestrian crossing indicate that the fuzzy control provides the effective compromise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamdani control indicate that the application area of fuzzy control is very wide. The maximum delay improvement was more than 20 %, which means that the efficiency of fuzzy control can be better than the efficiency of traditional vehicle-actuated control.
According to these results, we can say that the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control nowadays. The biggest benefits can, probably, be achieved in more complicated intersections and environments. The FUSICO-project continues. The aim is to move step by step to more complicated traffic signals and to continue the theoretical work of fuzzy control. The first example will be the public transport priorities.
REFERENCES
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中英文对照资料外文翻译文献 附件1：外文资料翻译译文 传感器新技术的发展 传感器是一种能将物理量、化学量、生物量等转换成电信号的器件。输出信号有不同形式，如电压、电流、频率、脉冲等，能满足信息传输、处理、记录、显示、控制要求，是自动检测系统和自动控制系统中不可缺少的元件。如果把计算机比作大脑，那么传感器则相当于五官，传感器能正确感受被测量并转换成相应输出量，对系统的质量起决定性作用。自动化程度越高，系统对传感器要求越高。在今天的信息时代里，信息产业包括信息采集、传输、处理三部分，即传感技术、通信技术、计算机技术。现代的计算机技术和通信技术由于超大规模集成电路的飞速发展，而已经充分发达后，不仅对传感器的精度、可靠性、响应速度、获取的信息量要求越来越高，还要求其成本低廉且使用方便。显然传统传感器因功能、特性、体积、成本等已难以满足而逐渐被淘汰。世界许多发达国家都在加快对传感器新技术的研究与开发，并且都已取得极大的突破。如今传感器新技术的发展，主要有以下几个方面： 利用物理现象、化学反应、生物效应作为传感器原理，所以研究发现新现象与新效应是传感器技术发展的重要工作，是研究开发新型传感器的基础。日本夏普公司利用超导技术研制成功高温超导磁性传感器，是传感器技术的重大突破，其灵敏度高，仅次于超导量子干涉器件。它的制造工艺远比超导量子干涉器件简单。可用于磁成像技术，有广泛推广价值。 利用抗体和抗原在电极表面上相遇复合时，会引起电极电位的变化，利用这一现象可制出免疫传感器。用这种抗体制成的免疫传感器可对某生物体内是否有这种抗原作检查。如用肝炎病毒抗体可检查某人是否患有肝炎，起到快速、准确作用。美国加州大学巳研制出这类传感器。 传感器材料是传感器技术的重要基础，由于材料科学进步，人们可制造出各种新型传感器。例如用高分子聚合物薄膜制成温度传感器；光导纤维能制成压力、流量、温度、位移等多种传感器；用陶瓷制成压力传感器。
forced concrete structure reinforced with an overviewRein Since the reform and opening up, with the national economy's rapid and sustained development of a reinforced concrete structure built, reinforced with the development of technology has been great. Therefore, to promote the use of advanced technology reinforced connecting to improve project quality and speed up the pace of construction, improve labor productivity, reduce costs, and is of great significance. Reinforced steel bars connecting technologies can be divided into two broad categories linking welding machinery and steel. There are six types of welding steel welding methods, and some apply to the prefabricated plant, and some apply to the construction site, some of both apply. There are three types of machinery commonly used reinforcement linking method primarily applicable to the construction site. Ways has its own characteristics and different application, and in the continuous development and improvement. In actual production, should be based on specific conditions of work, working environment and technical requirements, the choice of suitable methods to achieve the best overall efficiency. 1、steel mechanical link 1.1 radial squeeze link Will be a steel sleeve in two sets to the highly-reinforced Department with superhigh pressure hydraulic equipment (squeeze tongs) along steel sleeve radial squeeze steel casing, in squeezing out tongs squeeze pressure role of a steel sleeve plasticity deformation closely integrated with reinforced through reinforced steel sleeve and Wang Liang's Position will be two solid steel bars linked Characteristic: Connect intensity to be high, performance reliable, can bear high stress draw and pigeonhole the load and tired load repeatedly.
当今时代是一个自动化时代，交通灯控制等很多行业的设备都与计算机密切相关。因此，一个好的交通灯控制系统，将给道路拥挤，违章控制等方面给予技术革新。随着大规模集成电路及计算机技术的迅速发展，以及人工智能在控制技术方面的广泛运用，智能设备有了很大的发展，是现代科技发展的主流方向。本文介绍了一个智能交通的系统的设计。该智能交通灯控制系统可以实现的功能有：对某市区的四个主要交通路口进行控制：个路口有固定的工作周期，并且在道路拥挤时中控制中心能改变其周期：对路口违章的机动车能够即时拍照，并提取车牌号。在世界范围内，一个以微电子技术，计算机和通信技术为先导的，一信息技术和信息产业为中心的信息革命方兴未艾。而计算机技术怎样与实际应用更有效的结合并有效的发挥其作用是科学界最热门的话题，也是当今计算机应用中空前活跃的领域。本文主要从单片机的应用上来实现十字路口交通灯智能化的管理，用以控制过往车辆的正常运作。 研究交通的目的是为了优化运输，人流以及货流。由于道路使用者的不断增加，现有资源和基础设施有限，智能交通控制将成为一个非常重要的课题。但是，智能交通控制的应用还存在局限性。例如避免交通拥堵被认为是对环境和经济都有利的，但改善交通流也可能导致需求增加。交通仿真有几个不同的模型。在研究中，我们着重于微观模型，该模型能模仿单独车辆的行为，从而模仿动态的车辆组。 由于低效率的交通控制，汽车在城市交通中都经历过长时间的行进。采用先进的传感器和智能优化算法来优化交通灯控制系统，将会是非常有益的。优化交通灯开关，增加道路容量和流量，可以防止交通堵塞，交通信号灯控制是一个复杂的优化问题和几种智能算法的融合，如模糊逻辑，进化算法，和聚类算法已经在使用，试图解决这一问题，本文提出一种基于多代理聚类算法控制交通信号灯。 在我们的方法中，聚类算法与道路使用者的价值函数是用来确定每个交通灯的最优决策的，这项决定是基于所有道路使用者站在交通路口累积投票，通过估计每辆车的好处(或收益)来确定绿灯时间增益值与总时间是有差异的，它希望在它往返的时候等待，如果灯是红色，或者灯是绿色。等待，直到车辆到达目的地，通过有聚类算法的基础设施，最后经过监测车的监测。 我们对自己的聚类算法模型和其它使用绿灯模拟器的系统做了比较。绿灯模拟器是一个交通模拟器，监控交通流量统计，如平均等待时间，并测试不同的交通灯控制器。结果表明，在拥挤的交通条件下，聚类控制器性能优于其它所有测试的非自适应控制器，我们也测试理论上的平均等待时间，用以选择车辆通过市区的道路，并表明，道路使用者采用合作学习的方法可避免交通瓶颈。 本文安排如下：第2部分叙述如何建立交通模型，预测交通情况和控制交通。第3部分是就相关问题得出结论。第4部分说明了现在正在进一步研究的事实，并介绍了我们的新思想。
附录 About Modenr Industrial Manipulayor Robot is a type of mechantronics equipment which synthesizes the last research achievement of engine and precision engine, micro-electronics and computer, automation control and drive, sensor and message dispose and artificial intelligence and so on. With the development of economic and the demand for automation control, robot technology is developed quickly and all types of the robots products are come into being. The practicality use of robot not only solves the problems which are difficult to operate for human being, but also advances the industrial automation program. Modern industrial robots are true marvels of engineering. A robot the size of a person can easily carry a load over one hundred pounds and move it very quickly with a repeatability of 0.006inches. Furthermore these robots can do that 24hours a day for years on end with no failures whatsoever. Though they are reprogrammable, in many applications they are programmed once and then repeat that exact same task for years. At present, the research and development of robot involves several kinds of technology and the robot system configuration is so complex that the cost at large is high which to a certain extent limit the robot abroad use. To development economic practicality and high reliability robot system will be value to robot social application and economy development. With he rapid progress with the control economy and expanding of the modern cities, the let of sewage is increasing quickly; with the development of modern technology and the enhancement of consciousness about environment reserve, more and more people realized
中英文资料对照外文翻译 基于网络共享的无线传感网络设计 摘要：无线传感器网络是近年来的一种新兴发展技术，它在环境监测、农业和公众健康等方面有着广泛的应用。在发展中国家，无线传感器网络技术是一种常用的技术模型。由于无线传感网络的在线监测和高效率的网络传送，使其具有很大的发展前景，然而无线传感网络的发展仍然面临着很大的挑战。其主要挑战包括传感器的可携性、快速性。我们首先讨论了传感器网络的可行性然后描述在解决各种技术性挑战时传感器应产生的便携性。我们还讨论了关于孟加拉国和加利 尼亚州基于无线传感网络的水质的开发和监测。 关键词：无线传感网络、在线监测 1.简介 无线传感器网络，是计算机设备和传感器之间的桥梁，在公共卫生、环境和农业等领域发挥着巨大的作用。一个单一的设备应该有一个处理器，一个无线电和多个传感器。当这些设备在一个领域部署时，传感装置测量这一领域的特殊环境。然后将监测到的数据通过无线电进行传输，再由计算机进行数据分析。这样，无线传感器网络可以对环境中各种变化进行详细的观察。无线传感器网络是能够测量各种现象如在水中的污染物含量，水灌溉流量。比如，最近发生的污染涌流进中国松花江，而松花江又是饮用水的主要来源。通过测定水流量和速度，通过传感器对江水进行实时监测，就能够确定污染桶的数量和流动方向。 不幸的是，人们只是在资源相对丰富这个条件下做文章，无线传感器网络的潜力在很大程度上仍未开发，费用对无线传感器网络是几个主要障碍之一，阻止了其更广阔的发展前景。许多无线传感器网络组件正在趋于便宜化（例如有关计算能力的组件），而传感器本身仍是最昂贵的。正如在在文献[5]中所指出的，成功的技术依赖于
Study on Project Cost Control of Construction Enterprises By: R. Max Wideman Abstract With the increasing maturity of construction market, the competition between construction enterprises is becoming fierce. The project profit is gradually decreasing. It demands that all construction enterprises enhance their cost control, lower costs, improve management efficiency and gain maximal profits. This paper analyses the existing problems on project cost control of Chinese construction enterprises, and proposes some suggestions to improve project cost control system. Key Words ：Construction enterprises, Project management, Cost control After joining the WTO, with Chinese construction market becoming integrated, the competition among architectural enterprises is turning more intense. Construction enterprises must continually enhance the overall competitiveness if they want to develop further at home and abroad construction market. Construction Enterprises basically adopt the "project management-centered" model, therefore, it is particularly important to strengthen project cost control. 1.The Current Domestic Project Cost Classification and Control Methods Cost refers to the consumption from producing and selling of certain products, with the performance of various monetary standing for materialized labor and labor-consuming. Direct and indirect costs constitute the total cost, also known as production cost or manufacturing cost. Enterprise product cost is the comprehensive indicator to measure enterprise quality of all aspects. It is not only the fund compensation scale, but also the basis to examine the implementation of cost plan. Besides, it can provide reference for product pricing According to the above-mentioned definition and current domestic cost classification, construction project cost can be divided into direct costs and indirect costs. Direct costs include material cost, personnel cost, construction machinery cost, material transportation cost, temporarily facility cost, engineering cost and other direct cost. Indirect costs mainly result from project management and company's cost-sharing, covering project operating costs (covering the commission of foreign projects), project's management costs (including exchange losses of
译文资料： 机器人 首先我介绍一下机器人产生的背景，机器人技术的发展，它应该说是一个科学技术发展共同的一个综合性的结果，同时，为社会经济发展产生了一个重大影响的一门科学技术，它的发展归功于在第二次世界大战中各国加强了经济的投入，就加强了本国的经济的发展。另一方面它也是生产力发展的需求的必然结果，也是人类自身发展的必然结果，那么随着人类的发展，人们在不断探讨自然过程中，在认识和改造自然过程中，需要能够解放人的一种奴隶。那么这种奴隶就是代替人们去能够从事复杂和繁重的体力劳动，实现人们对不可达世界的认识和改造，这也是人们在科技发展过程中的一个客观需要。 机器人有三个发展阶段，那么也就是说，我们习惯于把机器人分成三类，一种是第一代机器人，那么也叫示教再现型机器人，它是通过一个计算机，来控制一个多自由度的一个机械，通过示教存储程序和信息，工作时把信息读取出来，然后发出指令，这样的话机器人可以重复的根据人当时示教的结果，再现出这种动作，比方说汽车的点焊机器人，它只要把这个点焊的过程示教完以后，它总是重复这样一种工作，它对于外界的环境没有感知，这个力操作力的大小，这个工件存在不存在，焊的好与坏，它并不知道，那么实际上这种从第一代机器人，也就存在它这种缺陷，因此，在20世纪70年代后期，人们开始研究第二代机器人，叫带感觉的机器人，这种带感觉的机器人是类似人在某种功能的感觉，比如说力觉、触觉、滑觉、视觉、听觉和人进行相类比，有了各种各样的感觉，比方说在机器人抓一个物体的时候，它实际上力的大小能感觉出来，它能够通过视觉，能够去感受和识别它的形状、大小、颜色。抓一个鸡蛋，它能通过一个触觉，知道它的力的大小和滑动的情况。第三代机器人，也是我们机器人学中一个理想的所追求的最高级的阶段，叫智能机器人，那么只要告诉它做什么，不用告诉它怎么去做，它就能完成运动，感知思维和人机通讯的这种功能和机能，那么这个目前的发展还是相对的只是在局部有这种智能的概念和含义，但真正完整意义的这种智能机器人实际上并没有存在，而只是随着我们不断的科学技术的发展，智能的概念越来越丰富，它内涵越来越宽。 下面我简单介绍一下我国机器人发展的基本概况。由于我们国家存在很多其
压力传感器 合理进行压力传感器的误差补偿是其应用的关键。压力传感器主要有偏移量误差、灵敏度误差、线性误差和滞后误差，本文将介绍这四种误差产生的机理和对测试结果的影响，同时将介绍为提高测量精度的压力标定方法以及应用实例。 目前市场上传感器种类丰富多样，这使得设计工程师可以选择系统所需的压力传感器。这些传感器既包括最基本的变换器，也包括更为复杂的带有片上电路的高集成度传感器。由于存在这些差异，设计工程师必须尽可能够补偿压力传感器的测量误差，这是保证传感器满足设计和应用要求的重要步骤。在某些情况下，补偿还能提高传感器在应用中的整体性能。 本文以摩托罗拉公司的压力传感器为例，所涉及的概念适用于各种压力传感器的设计应用。 摩托罗拉公司生产的主流压力传感器是一种单片压阻器件，该器件具有 3 类： 1.基本的或未加补偿标定； 2.有标定并进行温度补偿； 3.有标定、补偿和放大。 偏移量、范围标定以及温度补偿均可以通过薄膜电阻网络实现，这种薄膜电阻网络在封装过程中采用激光修正。 该传感器通常与微控制器结合使用，而微控制器的嵌入软件本身建立了传感器数学模型。微控制器读取了输出电压后，通过模数转换器的变换，该模型可以将电压量转换为压力测量值。传感器最简单的数学模型即为传递函数。该模型可在整个标定过程中进行优化，并且模型的成熟度将随标定点的增加而增加。 从计量学的角度看，测量误差具有相当严格的定义：它表征了测量压力与实际压力之间的差异。而通常无法直接得到实际压力，但可以通过采用适当的压力标准加以估计，计量人员通常采用那些精度比被测设备高出至少 10 倍的仪器作为测量标准。 由于未经标定的系统只能使用典型的灵敏度和偏移值将输出电压转换为压 力，测得的压力将产生如图 1 所示的误差。 这种未经标定的初始误差由以下几个部分组成： a.偏移量误差。由于在整个压力范围内垂直偏移保持恒定，因此变换器扩散和激光调节修正的变化将产生偏移量误差。 b.灵敏度误差，产生误差大小与压力成正比。如果设备的灵敏度高于典型值，灵敏度误差将是压力的递增函数（见图 1）。如果灵敏度低于典型值，那么灵敏度误差将是压力的递减函数。该误差的产生原因在于扩散过程的变化。
（ 二 〇 一 二 年 六 月 外文文献及翻译 题 目： About Buiding on the Structure Design 学生姓名： 学 院：土木工程学院 系 别：建筑工程系 专 业：土木工程(建筑工程方向) 班 级：土木08-4班 指导教师：
英文原文： Building construction concrete crack of prevention and processing Abstract The crack problem of concrete is a widespread existence but again difficult in solve of engineering actual problem, this text carried on a study analysis to a little bit familiar crack problem in the concrete engineering, and aim at concrete the circumstance put forward some prevention, processing measure. Keyword:Concrete crack prevention processing Foreword Concrete's ising 1 kind is anticipate by the freestone bone, cement, water and other mixture but formation of the in addition material of quality brittleness not and all material.Because the concrete construction transform with oneself, control etc. a series problem, harden model of in the concrete existence numerous tiny hole, spirit cave and tiny crack, is exactly because these beginning start blemish of existence just make the concrete present one some not and all the characteristic of quality.The tiny crack is a kind of harmless crack and accept concrete heavy, defend Shen and a little bit other use function not a creation to endanger.But after the concrete be subjected to lotus carry, difference in temperature etc. function, tiny crack would continuously of expand with connect, end formation we can see without the
英文 Because of the rapid development of our economy resulting in the car number of large and medium-sized cities surged and the urban traffic, is facing serious test, leading to the traffic problem increasingly serious, its basically are behaved as follows: traffic accident frequency, to the human life safety enormous threat, Traffic congestion, resulting in serious travel time increases, energy consumption increase; Air pollution and noise pollution degree of deepening, etc. Daily traffic jams become people commonplace and had to endure. In this context, in combination with the actual situation of urban road traffic, develop truly suitable for our own characteristics of intelligent signal control system has become the main task. Preface In practical application at home and abroad, according to the actual traffic signal control application inspection, planar independent intersection signal control basic using set cycle, much time set cycle, half induction, whole sensor etc in several ways. The former two control mode is completely based on planar intersection always traffic flow data of statistical investigation, due to traffic flow the existence of variable sex and randomicity, the two methods have traffic efficiency is low, the scheme, the defects of aging and half inductive and all the inductive the two methods are in the former two ways based on increased vehicle detector and according to the information provided to adjust cycle is long and green letter of vehicle, it than random arrived adaptability bigger, can make vehicles in the parking cord before as few parking, achieve traffic flowing effect In modern industrial production,current,voltage,temperature, pressure, and flow rate, velocity, and switch quantity are common mainly controlled parameter. For example: in metallurgical industry, chemical production, power engineering, the papermaking industry, machinery and food processing and so on many domains, people need to transport the orderly control. By single chip microcomputer to control of traffic, not only has the convenient control, configuration simple and flexible wait for an advantage, but also can greatly improve the technical index by control quantity, thus greatly improve product quality and quantity. Therefore, the monolithic integrated circuit to the traffic light control problem is an industrial production we often encounter problems. In the course of industrial production, there are many industries have lots of traffic equipment, in the current system, most of the traffic control signal is accomplished by relays, but relays response time is long, sensitivity low, long-term after use, fault opportunity increases greatly, and adopts single-chip microcomputer control, the accuracy of far greater than relays, short response time, software reliability, not because working time reduced its performance sake, compared with, this solution has the high feasibility. About AT89C51 (1)function characteristics description: AT89C51 is a low power consumption, high performance CMOS8 bit micro-controller, has the 8K in system programmable Flash memory. Use high-density Atmel company the beltpassword nonvolatile storage technology and manufacturing, and industrial 80S51 product instructions and pin fully compatible. Chip Flash allow program memory in system programmable, also suitable for conventional programmer. In a single chip, have dexterous 8 bits CPU and in system programmable Flash, make AT89C51 for many embedded control application system provides the high flexible, super efficient solution. AT89C51 has the following standard function: 8k bytes Flash, 256 bytes RAM, 32-bit I/O mouth line, the watchdog timer, two data pointer, three 16 timer/counter, a 6 vector level 2 interrupt structure, full-duplex serial port, piece inside crystals timely clock circuit. In addition, AT89C51 can drop to 0Hz static logic operation, support two software can choose power saving mode. Idle mode, the CPU to stop working, allowing the RAM, timer/counter, serial ports, interruption continue to work. Power lost protection mode, RAM content being saved, has been frozen, microcontroller all work stop, until the next interruption or hardware reset so far. As shown in
Basic knowledge of transducers A transducer is a device which converts the quantity being measured into an optical, mechanical, or-more commonly-electrical signal. The energy-conversion process that takes place is referred to as transduction. Transducers are classified according to the transduction principle involved and the form of the measured. Thus a resistance transducer for measuring displacement is classified as a resistance displacement transducer. Other classification examples are pressure bellows, force diaphragm, pressure flapper-nozzle, and so on. 1、Transducer Elements Although there are exception ,most transducers consist of a sensing element and a conversion or control element. For example, diaphragms,bellows,strain tubes and rings, bourdon tubes, and cantilevers are sensing elements which respond to changes in pressure or force and convert these physical quantities into a displacement. This displacement may then be used to change an electrical parameter such as voltage, resistance, capacitance, or inductance. Such combination of mechanical and electrical elements form electromechanical transducing devices or transducers. Similar combination can be made for other energy input such as thermal. Photo, magnetic and chemical,giving thermoelectric, photoelectric,electromaanetic, and electrochemical transducers respectively. 2、Transducer Sensitivity The relationship between the measured and the transducer output signal is usually obtained by calibration tests and is referred to as the transducer sensitivity K1= output-signal increment / measured increment . In practice, the transducer sensitivity is usually known, and, by measuring the output signal, the input quantity is determined from input= output-signal increment / K1. 3、Characteristics of an Ideal Transducer The high transducer should exhibit the following characteristics a) high fidelity-the transducer output waveform shape be a faithful reproduction of the measured; there should be minimum distortion. b) There should be minimum interference with the quantity being measured; the presence of the transducer should not alter the measured in any way. c) Size. The transducer must be capable of being placed exactly where it is needed.