Programmable‐logical‐controllers Synthesis for Automated‐guided‐vehicle Systems Using Ordinary Petri Nets

An ordinary Petri‐net (PN) based approach is proposed to design the programmable logical controller (PLC) for preventing collisions between vehicles in an automated guided vehicles (AGV) system. First, method is proposed to model an AGV system as an ordinary PN, called the plant PN. Second, for collision prevention in an AGV system, module control methods are proposed to design the PN supervisor (the closed‐loop PN) by augmenting the plant PN. In detail, three modules are defined the line, divide, and merge modules, in the plant PN, and the control module methods are presented for each of these three modules. As a result, the closed‐loop PN of an AGV system is obtained and can be analyzed using the PN theory. Finally, method is proposed to translate a closed‐loop PN into a ladder diagram. Consequently, the PN supervisor is implemented by a PLC using these proposed methods. A laboratory AGV system is used to illustrate this approach.     

AUTOSIM: Automated Urban Traffic Operation Simulation via Meta-Learning

Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management. It has been a challenging problem due to three aspects: 1) The diversity of traffic patterns due to heterogeneous layouts of urban intersections; 2) The nature of complex spatiotemporal correlations; 3) The requirement of dynamically adjusting the parameters of traffic models in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning (AUTOSIM). In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes. Through a meta-learning technique, AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations. Besides, AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner. Through computational experiments, we demonstrate the effectiveness of the meta-learning-based framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.     

Process vs resource‐oriented Petri net modeling of automated manufacturing systems

Since the 1980s, Petri nets (PN) have been widely used to model automated manufacturing systems (AMS) for analysis, performance evaluation, simulation, and control. They are mostly based on process‐oriented modeling methods and thus termed as process‐oriented PN (POPN) in this paper. The recent study of deadlock avoidance problems in AMS led to another type of PN called resource‐oriented PN (ROPN). This paper, for the first time, compares these two modeling methods and resultant models in terms of modeling power, model complexity for analysis and control, and some critical properties. POPN models the part production processes straightforwardly, while ROPN is more compact and effective for deadlock resolution. The relations between these two models are investigated. Several examples are used to illustrate them. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于ESN的污水处理过程优化控制

针对污水处理过程能耗过高的问题,提出了一种基于状态回声网络(ESN)的在线优化控制方法。建立了污水处理过程预测模型,实现性能指标的预测;根据系统的状态以及预测的性能指标,采用ESN实时优化控制变量的设定值;将优化后的设定值传送给底层控制器进行跟踪控制。将ESN优化控制方法在污水处理过程基准仿真模型(BSM1)上进行了验证,实验结果表明,该方法不但能够满足出水水质的要求,而且降低了污水处理过程运行成本。     

Software reengineering for complex systems

Reengineering complex software is a difficult task that requires the expenditure of significant resources. To reduce associated costs and improve resultant systems, automated techniques have been developed that can be used throughout all phases of the reengineering process. Three systems are described that have been used successfully for reengineering at The Johns Hopkins University Applied Physics Laboratory. The first system automatically transforms software written in CMS-2 to software written in Ada. The second system supports database design and automated query formulation, and employs conceptual-level intermediate representations for reverse and forward engineering as well as reengineering. The final system utilizes automated techniques to reengineer critical functions for performance enhancement. The systems described are evolving, and will provide a basis for future reengineering efforts.     

数据和知识驱动的城市污水处理过程多目标优化控制

城市污水处理过程优化控制是降低能耗的有效手段, 然而, 如何提高出水水质的同时降低能耗依然是当前城市污水处理过程面临的挑战. 围绕上述挑战, 文中提出了一种数据和知识驱动的多目标优化控制(Data-knowledge driven multiobjective optimal control, DK-MOC)方法. 首先, 建立了出水水质、能耗以及系统运行状态的表达关系, 获得了运行过程优化目标模型. 其次, 提出了一种基于知识迁徙学习的动态多目标粒子群优化算法, 实现了控制变量优化设定值的自适应求解. 最后, 将提出的DK-MOC应用于城市污水处理过程基准仿真模型1 (Benchmark simulation model No. 1, BSM1). 结果表明该方法能够实时获取控制变量的优化设定值, 提高了出水水质, 并且有效降低了运行能耗.     

Wastewater treatment: New insight provided by interactive multiobjective optimization

In this paper, we describe a new interactive tool developed for wastewater treatment plant design. The tool is aimed at supporting the designer in designing new wastewater treatment plants as well as optimizing the performance of already available plants. The idea is to utilize interactive multiobjective optimization which enables the designer to consider the design with respect to several conflicting evaluation criteria simultaneously. This is more important than ever because the requirements for wastewater treatment plants are getting tighter and tighter from both environmental and economical reasons. By combining a process simulator to simulate wastewater treatment and an interactive multiobjective optimization software to aid the designer during the design process, we obtain a practically useful tool for decision support. The applicability of our tool is illustrated with a case study related to municipal wastewater treatment where three conflicting evaluation criteria are considered.     

基于USB2831数据采集卡的污水处理在线检测仪设计

采用USB2831数据采集卡与外部其它器件构成污水处理过程监控系统,控制污水处理过程参数和采集过程关健产物的荧光光谱数据,实现污水处理过程在线检测.该仪器不仅应用于污水处理,在啤酒酿造以及发酵领域有很好的应用前景.     

Balanced scorecard: evening the odds of successful BPR

To realize the benefits of costly business process reengineering, companies need a control mechanism that links performance evaluation to strategic objectives. Researchers have long addressed how to control, measure, and evaluate this business process reengineering (BRP), but many so-called solutions continue to focus almost exclusively on the technical aspects of change. In this article, we present a control and evaluation framework, BSC-IS (balanced scoreboard-information system) that integrates BSC and BPR in the context of an IS or ERP (enterprise resource planning) project. BSC-IS is viewed as the roots of a solid evaluation tool for companies struggling to make costly BPR pay off.     

工业自动化结合办公自动化的应用研究

通过一个城市污水处理厂自动控制系统的设计开发过程作为实例,说明了在采取PLC自动控制方案对上位机编程组态时,可充分利用当前流行的办公自动化软件,微软的OFFICE套件来扩展对在线数据的处理能力,同时利用办公自动化软件实现了局域网内共享部分在线数据,从而将基于PLC的生产过程自动化与办公自动化OA进行了有机结合。     

基于移动Agent技术的自动拍卖模拟研究

自动拍卖的基本思想是用某种代理机制——Agent代替人监控整个拍卖过程。提出分阶段第二价格封标拍卖模式,设计了多Agent网上自动拍卖协议,和多Agent拍卖系统体系结构。提出三种拍卖策略模型：平稳加价型策略、跳跃型策略和保密型策略。利用移动Agent开发平台Aglet实现了一个模拟系统,并进行了多次仿真模拟实验,对实验数据进行了详细分析。实验结果表明该模拟系统基本达到设计目的,并为进一步研究自动拍卖相关策略、协议提供了一个实验平台。     

污水处理单元操作工具箱的开发

采用面向对象的基本思想,在MATLAB／SIMULINK环境下,开发出污水处理操作工具箱并以此工具箱为基础进行了污水过程模拟的尝试。与Bsm1稳态仿真结果对比表明,污水处理工具箱能成功应用于污水处理过程模拟。污水处理工具箱为污水处理过程可视化,面向对象建模提供了一个迅速、高效的软件开发环境。     

Heuristic Dynamic Programming Using Echo State Network For Multivariable Tracking Control Of Wastewater Treatment Process

This paper proposes a heuristic dynamic programming (HDP) scheme to simultaneously control the dissolved oxygen concentration and the nitrate level in wastewater treatment processes (WWTP). Unlike traditional HDP schemes, the optimal control values are calculated in an analytical way by the proposed HDP controller. It can reduce the learning burden of the HDP controller to a great extent. The system model and the evaluation index J are approximated by two echo state networks (ESNs). Gradient‐based learning algorithms are employed to train both ESNs online, and the convergence of the training algorithm is investigated based on Lyapunov theory. The performance of the proposed ESN‐based HDP (E‐HDP) controller is tested and evaluated on a WWTP benchmark. Experimental results demonstrate that the proposed approach can achieve effective performance.     

基于单片机的油田污水水质测控系统设计

以油田污水处理为题,分析了在污水处理过程中,如何通过设计一个可行的在线检测水质的系统来控制污水改性的生产过程,并给出了系统性能的一般分析.最主要的是对水的浊度和钙离子的测控方法与研究,讲述了油田污水的水质分析原理.详细给出了系统的设计思想、硬件电路及应用程序和应注意的问题.     

活性污泥脱氮过程的建模与控制

有效处理废水的各种方法包括过程的最佳设计、安装更多的仪表和自动控制系统等日益受到关注.综述了污水生化处理过程,主要是脱氮工艺的建模与控制方面的最新研究成果.讨论了活性污泥、生物膜和厌氧消化过程的模型化以及最新的Benchmark仿真问题.在控制策略方面,强调了曝气、污泥停留时间和外碳源补加等有效的控制手段.最后指出了一些值得关注的研究问题:过程与控制并行设计、集成化的城市污水处理系统以及现代微生物分析方法的利用.     

MIMO最小二乘支持向量机污水处理在线软测量研究

污水处理系统是一个包含海量信息的非线性复杂系统。针对污水处理出水水质BOD（生物化学需氧量）、COD（化学需氧量）、TN（总含氮量）等难以在线实时检测等问题,建立了基于在线MIMO-LSSVM（多输入多输出最小二乘支持向量机）和PSO（微粒子群算法）的污水处理软测量模型。仿真结果表明,建立的软测量模型精度高、速度快,能很好地实现污水处理出水指标COD、BOD、TN等参数的实时测量和估计,为污水处理的实时在线控制创造必要的前提条件。     

The use of domain knowledge in program understanding

Program understanding is an essential part of all software maintenance and enhancement activities. As currently practiced, program understanding consists mainly of code reading. The few automated understanding tools that are actually used in industry provide helpful but relatively shallow information, such as the line numbers on which variable names occur or the calling structure possible among system components. These tools rely on analyses driven by the nature of the programming language used. As such, they are adequate to answer questions concerning implementation details, so called what questions. They are severely limited, however, when trying to relate a system to its purpose or requirements, the why questions. Application programs solve real‐world problems. The part of the world with which a particular application is concerned is that application's domain. A model of an application's domain can serve as a supplement to programming‐language‐based analysis methods and tools. A domain model carries knowledge of domain boundaries, terminology, and possible architectures. This knowledge can help an analyst set expectations for program content. Moreover, a domain model can provide information on how domain concepts are related. This article discusses the role of domain knowledge in program understanding. It presents a method by which domain models, together with the results of programming‐language‐based analyses, can be used to answers both what and why questions. Representing the results of domain‐based program understanding is also important, and a variety of representation techniques are discussed. Although domain‐based understanding can be performed manually, automated tool support can guide discovery, reduce effort, improve consistency, and provide a repository of knowledge useful for downstream activities such as documentation, reengineering, and reuse. A tools framework for domain‐based program understanding, a dowser, is presented in which a variety of tools work together to make use of domain information to facilitate understanding. Experience with domain‐based program understanding methods and tools is presented in the form of a collection of case studies. After the case studies are described, our work on domain‐based program understanding is compared with that of other researchers working in this area. The paper concludes with a discussion of the issues raised by domain‐based understanding and directions for future work.     

Model predictive control for in situ bioremediation system

This study describes development of a dynamic predictive control system for the in situ bioremediation process. This automated control system not only balances the complex physical, chemical, and biological processes involved in the remediation process, it also minimizes overall cost of the entire remediation process. The control system is on-line, dynamic, and built based on a predictive model. It includes an optimization tool that consists of a simulation model and an optimization function. The numerical simulation model describes the fate and transport of the subsurface contaminants, while the optimization function is a constrained nonlinear function that has been implemented using genetic algorithms. The system was applied to a lab experiment, and tested with data from a real world site. The results indicated that the dynamic, simulation model-based control system can generate an appropriate control strategy and adjust control actions dynamically. This helps to improve efficiencies of the in situ bioremediation process at petroleum-contaminated groundwater systems.     

An online expert system for fault diagnosis in hydraulic systems

Fault diagnosis has become increasingly important for industrial automation and many approaches have been investigated for the online diagnostic task. This work demonstrates a new online expert system for dynamic industrial automated processes. The emphasis of this diagnostic system lies in the functions provided for fault detection, prediction, compensation and diagnosis. The system uses experiential knowledge cooperatively with scientific knowledge in a new interactive formation. It is developed in two parts using the DASYLab software for the numerical computations and the KPWin development tool for the symbolic representations. The system was developed in collaboration with the company Automation Systems S.A., which specializes in hydraulic systems, and is used for fault detection in production machines.     

Advanced monitoring platform for industrial wastewater treatment: Multivariable approach using the self-organizing map

Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant.