iJADE stock advisor: an intelligent agent based stock prediction system using hybrid RBF recurrent network

Financial predictions, such as stock forecasts, is always one of the hottest topics for research studies and commercial applications. With the rapid growth of Internet technology in recent years, e-finance has become a vital application of e-commerce. However, in this "sea" of information, made available through the Internet, an "intelligent" financial web-mining and stock prediction system can be a key to success. In this paper, the author introduces the iJADE Stock Advisor-an intelligent agent-based stock prediction system using our proposed hybrid radial basis-function recurrent network (HRBFN). By using ten-year stock pricing information (1990-1999), consisting of 33 major Hong Kong stocks for testing, the iJADE Stock Advisor has achieved promising results in terms of efficiency, accuracy, and mobility as compared with other contemporary stock prediction models. Also, various analyzes on this stock advisory system have been performed: including round trip time (RTT) analysis, window-size evaluation test (for both long-term trend and short-term prediction), and stock prediction performance test.     

Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce

Recently, as the Internet has become more widely used, Electronic Commerce (EC) has emerged and has developed a high-level business environment. The customer-centric EC model is important for the success of EC and this study presents a new customer-centric EC model in make-to-order (MTO) semiconductor manufacturing environment. In this study we proposed the EC model providing the process transparency of process sampling method that can provide online semiconductor customers with the performance information of available process sampling methods which can be used at all manufacturing process steps for their own products in MTO manufacturing environment, and then the capability to select a desirable one among them based on their purchase situations on EC web site. In the proposed EC model the customer can select a process sampling method that is most suitable to him/her according to the customer's purchase situation. In this model the use of intelligent decision support system called customized sampling decision support system (CSDSS) that can autonomously generate available customized sampling methods and provide the performance information of those methods to EC system is requisite. We implemented an Internet-based prototype of CSDSS which had an architecture based on intelligent agent technology and also the successful integration of data mining process for the generation of optimal sampling method into DSS framework by means of applying that technology.     

Modeling of agile intelligent manufacturing-oriented production scheduling system

Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.     

基于知识的加工过程合弄智能控制

加工过程是一个复杂多变的动态的系统，对其进行有效控制是制造系统自动化要解决 的基本问题之一．本文概述了合弄控制的产生及其特点和加工过程智能控制的发展，分析了 基于知识的加工过程智能控制系统的特性及其发展趋向，指出智能控制技术的深入研究和合 弄控制理论的发展将为加工过程智能控制向更高智能度的进化奠定坚实的基础．     

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.     

基于多智能体的人机协作智能信息系统

综合集成研讨厅理论体系是作为处理开放的复杂巨系统的有关问题而提出的,其实质 是一个人机协作的智能信息系统.本文主要探索研讨厅的实现途径,给出了基于网络的研讨厅的 层次模型与系统结构,提出了基于内嵌客户/Agent/服务器的请求器-中介器-供应器模型的多 智能体研讨厅的多层分布计算模型,并基于Java的智能信息Agent技术探讨了支持宏观经济决 策的多智能体研讨厅的构架及工作机制.研究表明,基于Internet有关技术与Agent计算技术有 可能实现研讨厅,并由于Agent技术的优势而可能得到较好的系统性能.     

基于ARM的塔机安全自动监控系统的设计

针对目前塔机运行过程中存在的安全隐患问题,提出了一种基于ARM的塔机自动安全监控系统。该系统以ARM为主控制器,实现了塔机运行时工作状态的监控、显示、记录和报警,通过无线通信技术,实现了相关塔机运行状态的信息传输及塔机之间的相互定位,依靠防碰撞算法,控制塔机输出,预防塔吊群交叉作业时的相互碰撞。该系统制造成本低、具有良好的实时性、可靠性以及广泛的应用价值,符合塔机监控系统小型化、智能化的发展方向。     

The implementation of neural network for semiconductor PECVD process

In semiconductor manufacturing, the monitoring system has been developed very excellently and can be used for comprehensively collecting the historical data of process information and quality characteristics of equipment. However, due to the high turnover rate of personnel and the great variance in manufacturing process, the previous control technique by using intuition and experience of engineers for manufacturing process parameter settings to achieve good product quality is no longer appropriate. Therefore, this research establishes a quality predictor for analyzing the relationship between manufacturing process parameter setting and final product quality in the plasma-enhanced chemical vapor deposition (PECVD) of semiconductor manufacturing by applying the back-propagation neural network (BPNN) algorithm and Taguchi method. The experimental data are categorized into 500 pieces of training data and 150 pieces of verifying data. The proposed analysis method for using in the PECVD process of semiconductor manufacturing is verified by comparing the predicted film thickness of SiO₂ and the predicted refractive index of silicon dioxide films with the measured data. According to the comparison result, the proposed model has an excellent prediction capability of final product quality and can be applied in process control for related manufacturing fields.     

基于灰色马尔可夫过程的化学铣切厚度预测

化学铣切是现代民用飞机薄壁结构加工制造的主要方式之一,化学铣切加工区域的厚度则是主要控制的工艺参数,并需要在加工过程中进行测量,以监控化学铣切过程。通常测量获得化学铣切加工区域的厚度信息为一组有限的离散数据,传统的数据预测方法通常需要基于大量数据,当样本数量少、信息不足时,预测精度将难以保证。应用灰色马尔可夫过程方法,基于灰色系统小样本数据理论,建立了化学铣切厚度预测模型,用来预测化学铣切厚度信息,并以一组化学铣切加工的实测厚度值为实例,进行了实际预测。结果表明,当测量数据样本较少时,使用灰色马尔可夫过程预测的化学铣切加工厚度具有良好的精度,可以用来表征化学铣切加工厚度趋势。     

MetaMorph II: an agent-based architecture for distributed intelligent design and manufacturing

Agent technology derived from Distributed Artificial Intelligence is increasingly being considered for next generation computer-integrated manufacturing systems, to satisfy new requirements for increased integrability, configurability, adaptability, extendibility, agility, and reliability. This paper reviews our previous research on the application of the agent-based technology to intelligent design and manufacturing and describes the current research project MetaMorph II (an agent-based architecture for distributed intelligent design and manufacturing).     

A new approach to detecting the process changes for multistage systems

The study aims to develop a new control chart model suitable for monitoring the process quality of multistage manufacturing systems.Considering both the auto-correlated process outputs and the correlation occurring between neighboring stages in a multistage manufacturing system, we first propose a new multiple linear regression model to describe their relationship. Then, the multistage residual EWMA and CUSUM control charts are used to monitor the overall process quality of multistage systems. Moreover, an overall run length (ORL) concept is adopted to compare the detecting performance for various multistage residual control charts. Finally, a numerical example with oxide thickness measurements of a three-stage silicon wafer manufacturing process is given to demonstrate the usefulness of our proposed multistage residual control charts in the Phase II monitoring. A computerized algorithm can also be written based on our proposed scheme for the multistage residual EWMA/CUSUM control charts and it may be further converted to an expert and intelligent system. Hopefully, the results of this study can provide a better alternative for detecting process change and serve as a useful guideline for quality practitioners when monitoring and controlling the process quality of multistage systems with auto-correlated data.     

An intelligent agent-based architecture for strategic information system applications

Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.     

Nonlinear prediction of manufacturing systems through explicit and implicit data mining

Many processes in the industrial realm exhibit stochastic and nonlinear behavior. Consequently, an intelligent system must be able to ndapt to nonlinear production processes as well as probabilistic phenomena. To this end, an intelligent manufacturing system may draw on techniques from disparate fields, involving knowledge in both explicit and implicit form.In order for a knowledge based system to control a manufacturing process, an important capability is that of prediction: forecasting the future trajectory of a process as well as the consequences of the control action. This paper presents a comparative study of explicitaand implicit methods to predict nonlinear chaotic behavior. The evaluated models include statistica; procedures as well as neural networks and case based reasoning. The concepts are crystallized through a case study in the prediction of chaotic processes adulterated by various patterns of noise.     

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.     

An agent-based service-oriented integration architecture for collaborative intelligent manufacturing

The rapidly changing needs and opportunities of today's global market require unprecedented levels of interoperability to integrate diverse information systems to share knowledge and collaborate among organizations. The combination of Web services and software agents provides a promising computing paradigm for efficient service selection and integration of inter-organizational business processes. This paper proposes an agent-based service-oriented integration architecture to leverage manufacturing scheduling services on a network of virtual enterprises. A unique property of this approach is that the scheduling process of an order is orchestrated on the Internet through the negotiation among agent-based Web services. A software prototype system has been implemented for inter-enterprise manufacturing resource sharing. It demonstrates how the proposed service-oriented integration architecture can be used to establish a collaborative environment that provides dynamic resource scheduling services.     

基于文档相关度计算的网页预测模型

基于网络日志挖掘的网页预测系统可以有效地分析用户未来的网络访问请求,从而达到智能推荐、改善网络性能等目的。针对目前的预测模型缺乏有效的语义处理的问题,将词语语义信息和统计语言模型相结合,提出一种基于文档相关度计算的网页统计预测模型。通过词频信息和知网(HowNet)中词的概念计算模型计算网页文档间的主题相关度,再将该语义信息与统计模型计算的条件概率值相结合,以此作为预测的依据。实验表明,该技术使预测模型的性能获得了较大的提高。     

Agent-Based Supply Chain Integration

Supply chain management represents a critical competency in today's fast-paced, global business environment. However, in the current transition from EDI to Web-based supply chain technologies, much of the capability for process integration is being lost. And the integration of buyer and seller supply chain processes is critical for speed and responsiveness in today's hypercompetitive product and service markets. Intelligent agent technology offers the potential to overcome many limitations of current supply chain technologies. This paper presents intelligent supply chain agents that represent and autonomously conduct business on behalf of product users, buyers and vendors. We classify and present numerous extant agent applications and extend a technological framework to compare and contrast intelligent agents with other classes of information technology. We then describe an agent-based supply chain process design, along with its developmental techniques, and the structure and behavior of an agent federation used for integration in a major enterprise. We present results of this exploratory research in terms of technical feasibility and process performance in the enterprise context. This work is novel in that it integrates process-level knowledge from operational enterprises with distributed agent technologies. And it makes a contribution by demonstrating how agent-based supply chain integration can be effected along a large-scale, operational, inter-organizational process.     

A new intelligent system for senior executives to maintain remote control of their company

The mobile-enterprise concept has emerged as an innovation with the increasing use of personal digital assistant (PDA) on wireless data networks. To realize the concept, simply use the PDA to search for information is not enough, where an intelligent paradigm is required to deal with the request for information as well as the command execution in a distributed information system. This paper proposes a mobile-enterprise multi-agent paradigm (MMAP) which is an agent-based intelligent solution provider developed for small to medium enterprises (SMEs) in order to increase their flexibility and competitiveness in the market. The system is driven by intelligent software agents. Their design and prototype implementation are demonstrated in this paper.     

MamMoeT: An intelligent agent-based communication support platform for multimodal transport

In this paper, an intelligent agent-based communication support platform for multimodal transport is developed. The rationale for doing so is found in the potential of such a system to increase cost efficiency, service and safety for different transport-related actors. Although, at present several comparable systems exist, their current implementation is far from successful because of technological and economic obstacles. The new expert communication platform put forward here (called MamMoeT) addresses these two issues by using a software agent-based approach. Software agents are pieces of software representing a single user. They are autonomous, communicative and intelligent. The MamMoeT system developed can be described as a real-time decision support system in which intelligent software agents handle communicative tasks, exchange desired amounts of information among different users using common exchange protocols which act as translators between different systems.     

复杂工业过程智能优化决策系统的现状与展望

流程工业是制造业的重要组成部分,是我国国民经济和社会发展的重要支柱产业.新一代信息技术和人工智能技术为流程工业的发展带来新的挑战和机遇.只有与流程工业的特点与目标密切结合,充分利用大数据,将人工智能、移动互联网、云计算、建模、控制与优化等信息技术与工业生产过程的物理资源紧密融合与协同,实现流程工业智能优化制造,才可能实现流程工业的跨越式发展.本文聚焦流程工业的复杂生产过程,从其智能优化决策系统的角度,描述了复杂工业过程优化决策系统的问题、回顾总结了复杂工业过程全流程优化决策系统的现状,分析了智能优化决策系统的必要性,提出了智能优化决策系统的发展目标及愿景,并对智能优化决策系统的下一步重点研究方向进行了展望.