An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

基于Internet的远程教育系统模型研究

随着计算机网络技术的发展，远程教学系统的结构发生了重大的变化，逐步由原来的客户机／服务器(C／S)结构转变为基于Internet的浏览器／服务器(B／S)结构。本文提出了基于CORBA技术的B／S结构系统模型。分析了其运行过程，并与传统的结构进行了比较，指明了其优越性。在本文的结尾。指出了使用这种技术的一个具体例子。     

汽车计算平台工程国外相关标准概述

结合我国汽车计算平台工程,分析了国外相关的标准现状和基本框架,并为今后工作的开展提出了建议.     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

An Effective Software Pipelining Algorithm for Clustered Embedded VLIW Processors

This paper proposes a software pipelining framework, CALiBeR (ClusterAware Load Balancing Retiming Algorithm), suitable for compilers targetingclustered embedded VLIW processors. CALiBeR can be used by embedded systemdesigners to explore different code optimization alternatives, that is, high-qualitycustomized retiming solutions for desired throughput and program memory sizerequirements, while minimizing register pressure. An extensive set of experimentalresults is presented, demonstrating that our algorithm compares favorablywith one of the best state-of-the-art algorithms, achieving up to 50% improvementin performance and up to 47% improvement in register requirements. In orderto empirically assess the effectiveness of clustering for high ILP applications,additional experiments are presented contrasting the performance achievedby software pipelined kernels executing on clustered and on centralized machines.     

用于实时数据传输的滑动帧方法及其实现

根据某雷达系统对测量信号的快速传输要求,提出采用滑动帧的处理方法并在FPGA中实现,克服了传统帧处理难以满足快速传输要求的不足。阐述了滑动帧结构的构造思想,详述了滑动帧结构的特点及具体功能模块的实现。根据实际应用需求构造了滑动帧结构处理器,试验和应用结果表明设计可行有效,能充分满足实时数据传输的要求。     

计算机远程控制系统的设计与实现

介绍了一个计算机远程控制系统的设计与实现，该系统采用客户机／服务器模式，实现远程屏幕的实时传输，采用LZW压缩算法对图像进行压缩传输，在接受方进行解码并且实现了远程文件创建和删除、显示被控制端的主机信息、发送实时消息等功能。采用鼠标，键盘的模拟方式对计算机进行实时控制。     

令牌多环网性能—可靠性综合评价

网络的性能与可靠性已分别得到广泛的研究,然而,怎样分析具有容错机制的环网的性能仍属有待开拓的研究领域,本文提出了一种分析具有自修复机制的多环网络系统的方法,并给出了报文传输时间分布的拉氏变换式,平均等待时间及平均队列长度公式,分析结果可用于评估故障与修复过程对实时令牌环网的实时响应特性的影响,对基于LAN的分布式实时系统设计具有指导意义。     

海洋石油开发工业污染物产生系数和排放系数研究

污染物产生系数和排放系数是工程环境影响评价的重要指标,但对于海洋石油开发工程环境影响评价来说,迄今尚无具权威性的系数可资使用。在我国近海石油开发工程污染源系统调查的基础上,对海洋石油开发行业污染物产生系数和排放系数的定义、计算方法、应用范围及应用方式进行了探讨,研究结果可为海洋石油开发工程项目环境影响评价及环境保护管理,以及行业或企业有关标准的制定提供参考。