工程试验卫星7号的自动交会对接系统

日本航天局（ＮＡＳＤＡ）正在研究Ｈ－Ⅱ无人轨道飞机（ＨＯＰＥ）和Ｈ－Ⅱ转移飞行器（ＨＴＶ），经们将用于国际空间站（ＩＳＳ）进行后勤支持。交会对接（ＲＶＤ技术是ＨＯＰＥ、ＨＴＶ、轨道服务飞行器（ＯＳＶ）和月球探测器等航天顺完成各种空间活动的不可缺少的技术，目前正在研制的工程试验卫星－７（ＥＴＳ－Ⅱ）就是一琪无人自动ＲＶＤ系统的在轨演示计划，本文介绍ＲＶＤ系统的基本概念，ＥＴＳ－ⅦＲＶＤ在轨试验计划和     

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A decision support system for paper making based on simulation and optimization

In this paper a model-driven decision support system related to paper making is introduced. The intention is to emphasize the necessity of coupling different modeling techniques, multiobjective optimization, and software engineering in order to make the end user application realistic, practical and usable. Firstly the paper making process and selected aspects concerning its mathematical modeling, numerical simulation, and multiobjective optimization are introduced, then the related computerized system, called a virtual paper making line, is described. In addition, the associated decision support system, which provides a suitable level of automation to improve the quality of decision making and support the user’s expertise is discussed. Finally, an example presents different ways of using such a software tool.     

The effect of decision support system expertise on system use behavior and performance

We performed an empirical investigation into the effect of users’ decision support system (DSS) expertise on their problem-solving strategies. The results indicated that individuals who had only recently learned to use the DSS were confused or restricted by the set of functions provided by the system and did not plan well for their use of the DSS. Those who had previous knowledge of the system exhibited more focused and efficient problem-solving behavior. Our findings suggested that problem-solving strategies depended significantly on the user's level of system expertise.     

A multi-agent collaborative maintenance platform applying game theory negotiation strategies

Engineering asset management (EAM) is a broad discipline with distributed functions and services. When engineering assets are capital intensive, management requires specialized expertise for diagnosis, prognosis, maintenance and repairs. The current practice of EAM relies on self maintained experiential rules with coordinated collaboration and outsourcing for maintenance and repairs. In order to enhance the life long asset value and efficiency (from the stakeholder’s viewpoint) and after sales service quality (from the asset provider’s viewpoint), this research proposes a collaborative maintenance platform that integrates real time data collection with diagnostic and prognostic expertise. The collaborative system combines and delivers services among asset operation sites (the maintenance demanders), the service center (the intermediary coordinator), the system providers, the first tier maintenance collaborators, and the second and lower tier parts suppliers. Multi-agent system technology is used to integrate different systems and databases. Agents with autonomy and authority work to assist service providers and coordinate communications, negotiations, and maintenance decision support. Finally, game theory is used to design the decision models for strategic, tactical, and operational decision making during collaborative maintenance practices.     

Knowledge based evaluation of software systems: a case study

Solving software evaluation problems is a particularly difficult software engineering process and many contradictory criteria must be considered to reach a decision. Nowadays, the way that decision support techniques are applied suffers from a number of severe problems, such as naive interpretation of sophisticated methods and generation of counter-intuitive, and therefore most probably erroneous, results. In this paper we identify some common flaws in decision support for software evaluations. Subsequently, we discuss an integrated solution through which significant improvement may be achieved, based on the Multiple Criteria Decision Aid methodology and the exploitation of packaged software evaluation expertise in the form of an intelligent system. Both common mistakes and the way they are overcome are explained through a real world example.     

An Intelligent Decision Support System for Investment Analysis

We present an intelligent decision support system that combines decision analysis and traditional investment evaluation and analysis. The system brings to the ordinary user expertise in both decision analysis and investment evaluation techniques, as well as domain knowledge about the market. The system supports the entire decision analysis cycle and provides facilities and tools for decision modeling, probability assessment, model evaluation, and sensitivity analysis. An example based on the Shanghai Stock Market is presented. We demonstrate how an investment decision model for the stock market is constructed by the system and how the optimal decision is obtained. Sensitivity and value of information analysis were also carried out by the system. Received 12 April 1999 / Revised 8 February 2000 / Accepted 2 March 2000     

基于赛场态势的群体智能决策技术

复杂环境下态势的正确评估是开发高水平决策系统所必需解决的关键问题。为了对机器人足球赛场态势进行精确的评估,结合专家经验和对实际比赛的分析,提炼影响赛场态势的几个关键因素,在此基础上提出一个全新的态势决策模型。该模型中机器人集合构成一个决策群体,智能群体决策的结果使得足球机器人系统具有很强的战斗力,其有效性已在实验及比赛中得到验证。     

A hybrid AI/OR decision support tool for backbone communications network design

Much of existing DSS literature views the role of human expertise as primarily that of selecting appropriate formal models for solving a problem or synthesizing sequences thereof. Once a model (or model sequence) is determined, values of decision variables are determined by the model(s) alone. Hence, automated methods for facilitating model selection and synthesis have received considerable attention. However, a single model is often not an accurate abstraction of reality. Also, results from multiple formal models often have to be combined heuristically to obtain practical solutions. Thus, in this paper we explore the premise that human expertise needs to interact with formal models during the process of searching for solution values. Specifically, we describe a hybrid decision support tool for the design of backbone communication networks, a problem recognized as being of considerable complexity. An internal representation of the design process that employs a blackboard, a truth maintainence system and dependency directed backtracking, allows human expertise and formal models to jointly determine decision variable values in a uniform manner. The design tool has been implemented using a combination of Lisp and Fortran. Computational experiments indicate that incorporating human expertise during the search process results in superior complete solutions and added flexibility in satisfying ad hoc requirements. We conjecture that this hybrid search approach is not limited to the telecommunication network design problem and can be extended to other applications.     

An expert system for real-time fault diagnosis of complex chemical processes

This paper presents the development and implementation of an expert system for real-time fault diagnosis of chemical processes. The expert system is applied as a real-time computer aided decision support system, providing operation suggestions to help field operators when abnormal situations occur. The knowledge base structure, representation of knowledge, and access to expertise are technically considered. Industrial applications to the fluid catalystic cracking process in refinery indicate that the expert system diagnoses abnormal events efficiently and promptly.     

A decision support system for strategic planning in large-scale marketing channels: An example in the tile industry

This paper presents a decision support system for strategic planning in marketing channels. A dynamic model of the marketing channel is employed which comprises manufacturer and retailer levels. Decision making is achieved through a game theoretical inference mechanism in which each player (manufacturer/retailer) optimises for a long-term profit maximisation objective. Both historical data and managerial expertise are used for the parameterisation of the system's knowledge base. The decision support system provides a forecast of profit and sales and computes pricing and shelf space allocation strategies that maximise long-term profit. It offers facilities such as the study of coalitions, long-term decision-making in all phases of a product's life cycle, the impact of pricing, allocation strategies, production expansion, cost regulation, and others. The operationality of the system is illustrated in decision-making situations in the tile industry.     

Automating the Diagnosis and Rectification of Deflection Yoke Production Using Hybrid Knowledge Acquisition and Case-Based Reasoning

Deflection yoke (DY) is one of the main components of the color display tube (CDT) that determines the image quality of a computer monitor. Once a DY anomaly is found during production, the remedy process is performed in two steps: identifying the type of anomaly from the observed problem pattern and adjusting manufacturing process parameters to rectify it. To support this process, we introduce a knowledge-based system using a hybrid knowledge acquisition technique and case-based reasoning. The initial phase of the knowledge acquisition employs a systematic and quantitative data processing including stepwise regression and an inductive learning algorithm. This automated expertise elicitation produces strategies, which are represented by decision trees or if-then rules, to specify DY anomalies from display patterns. The strategies are then refined by introducing human expertise. The knowledge acquisition process was designed to support for this cognitive cooperation. For coordinating the process parameters to remedy the specified anomalies, a case-based reasoning is utilized. The laboratory and field test proved that the developed knowledge-based system could produce highly effective decisions for the process control in DY production.