An effective coding approach for multiobjective integrated resource selection and operation sequences problem

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. Received: May 2005/Accepted: December 2005     

基于多Agent的模糊粗交流

提出了变异模糊粗集的概念及其相应的性质和定理;建立了模糊粗交流最优传递序列的数学模型,并给出了解决此模型的算法思想,算例证明了算法思想的有效性。     

马钢二烧结配料计算机控制系统

阐明了马钢二烧结配科计算机控制系统的目的、控制形式、控制原理；根据烧结配料控制对象的特点对信号采样和控制算法进行了探讨；提供了系统的主要硬件结构，软件功能和程序框图。     

A Least Squares Approach to Escalator Algorithms for Adaptive Filtering

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.     

USB数据传输中CRC校验码的并行算法实现

文章介绍了用于USB总线数据传输的CRC校验的原理和算法，并且采用并行电路实现USB2．0中的CRC产生和CRC校验，与传统的串行电路实现相比，并行电路实现方法虽然在芯片面积上大于串行电路实现，但由于降低了时钟频率，电路更容易综合实现，并且大大降低了功耗，有利于低功耗电路设计。     

基于进化算法的常减压装置模拟

提出了基于优选优生进化算法（Select—best and prepotency evolution algorithm，SPEA）的常减压装置模拟方法。以各塔的温度分布为目标，基于生产过程和分析数据，采用SPEA确定各塔板Murphree效率，从而精确描述各塔板平衡偏离程度。在实际应用中，获得具有良好精度的常减压装置模型。系统分析了SPEA算法的关键参数——优选领域大小与计算复杂性及寻优性能之间的关系，结果显示其取值为群体规模的10％最为合适。     

基于两层均匀媒质的GPEN SAR地下目标成像方法及其性能分析

地表穿透合成孔径雷达(Ground PENetration SAR,GPEN SAR)为了探测掩埋在地下的目标,通常工作在多层媒质的环境中。传统成像模型是建立在同一均匀媒质的假设上,不再适合于GPEN SAR的实际情况。本文首先建立了两层分区均匀媒质中的成像模型,然后利用后向投影(BP)算法定量分析了成像几何参数、土壤参数等对成像的影响,进而提出了一种修正的后向投影(MBP)算法。MBP算法不仅能够校正两层分区均匀媒质对成像定位的影响,还能估计目标的掩埋深度,提供目标三维位置坐标。仿真结果验证了MBP算法在不同信噪比环境下,对多目标的三维定位精度能满足实际的需要。     

Range Assignment for Biconnectivity and k-Edge Connectivity in Wireless Ad Hoc Networks

Depending on whether bidirectional links or unidirectional links are used for communications, the network topology under a given range assignment is either an undirected graph referred to as the bidirectional topology, or a directed graph referred to as the unidirectional topology. The Min-Power Bidirectional (resp., Unidirectional) k-Node Connectivity problem seeks a range assignment of minimum total power subject to the constraint that the produced bidirectional (resp. unidirectional) topology is k-vertex connected. Similarly, the Min-Power Bidirectional (resp., Unidirectional) k-Edge Connectivity problem seeks a range assignment of minimum total power subject to the constraint the produced bidirectional (resp., unidirectional) topology is k-edge connected. The Min-Power Bidirectional Biconnectivity problem and the Min-Power Bidirectional Edge-Biconnectivity problem have been studied by Lloyd et al. [23]. They show that range assignment based the approximation algorithm of Khuller and Raghavachari [18], which we refer to as Algorithm KR, has an approximation ratio of at most 2(2 – 2/n)(2 + 1/n) for Min-Power Bidirectional Biconnectivity, and range assignment based on the approximation algorithm of Khuller and Vishkin [19], which we refer to as Algorithm KV, has an approximation ratio of at most 8(1 – 1/n) for Min-Power Bidirectional Edge-Biconnectivity. In this paper, we first establish the NP-hardness of Min-Power Bidirectional (Edge-) Biconnectivity. Then we show that Algorithm KR has an approximation ratio of at most 4 for both Min-Power Bidirectional Biconnectivity and Min-Power Unidirectional Biconnectivity, and Algorithm KV has an approximation ratio of at most 2k for both Min-Power Bidirectional k-Edge Connectivity and Min-Power Unidirectional k-Edge Connectivity. We also propose a new simple constant-approximation algorithm for both Min-Power Bidirectional Biconnectivity and Min-Power Unidirectional Biconnectivity. This new algorithm applies only to Euclidean instances, but is best suited for distributed implementation. A preliminary version of this work appeared in the proceedings of the 2nd International Conference on AD-HOC Network and Wireless (Adhoc-Now 2003). Research performed in part while visiting the Max-Plank-Institut fur Informatik. Gruia Calinescu is an Assistant Professor of Computer Science at the Illinois Institute of Technology since 2000. He held postdoc or visiting researcher positions at DIMACS, University of Waterloo, and Max-Plank Institut fur Informatik. Gruia has a Diploma from University of Bucharest and a Ph.D. from Georgia Insitute of Technology. His research interests are in the area of algorithms. Peng-Jun Wan has joined the Computer Science Department at Illinois Institute of Technology in 1997 and has been an Associate Professor since 2004. He received his Ph.D. in Computer Science from University of Minnesota in 1997, M.S. in Operations Research and Control Theory from Chinese Academy of Science in 1993, and B.S. in Applied Mathematics from Tsinghua University in 1990. His research interests include optical networks and wireless networks.     

基于改进模糊综合评价法的水利工程社会评价研究

通过对水利工程社会评价及模糊综合评价法的介绍,讨论了模糊层次分析法中的一致性检验问题,并采用改进的模糊综合评价法对江垭水利枢纽工程进行社会评价,得到综合评价结果。     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.