Differential evolution variants to schedule flexible assembly lines

Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness.     

拱坝裂缝开展有限元分析

本文提出一个数学模型,用于拱坝裂缝开展分析,所需机时省,内存要求较小,精度好。文末给出一算例,用以说明方法的特点及拱坝裂缝开展分析的意义。     

完全格上单调函数的不动点定理及其应用

给出一个完全格上单调函数的不动点定理，然后利用此定理给出关于超穷基数的Ｃａｎｔｏｔ－Ｓｃｔｒｏｄｅｒ－Ｂｅｒｎｓｔｅｉｎ定理的一种证明．     

高层建筑顺风响应的计算

本文计算了高层建筑在顺风荷载作用下的动力反应.根据基本参数、结构第一振型和质量模式的假定,编制了计算程序AWCTS.对国外三幢高楼进行计算,检验了本文提出风荷载模型的有效性.     

Finite-horizon scheduling of radar dwells with online template construction

Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism inherent in schedulers that assume a task will execute forever. The combination of feasible intervals and online dwell packing allows modular schedule updates whereby portions of a schedule can be altered without affecting the entire schedule, hence reducing the complexity of the scheduler. Through extensive simulations we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline. We also evaluate the impact of two parameters in our scheduling approach: the template length (or the extent of dwell nesting and interleaving) and the length of the finite horizon. Sathish Gopalakrishnan is a visting scholar in the Department of Computer Science, University of Illinois at Urbana-Champaign, where he defended his Ph.D. thesis in December 2005. He received an M.S. in Applied Mathematics from the University of Illinois in 2004 and a B.E. in Computer Science and Engineering from the University of Madras in 1999. Sathish’s research interests concern real-time and embedded systems, and the design of large-scale reliable systems. He received the best student paper award for his work on radar dwell scheduling at the Real-Time Systems Symposium 2004. Marco Caccamo graduated in computer engineering from the University of Pisa in 1997 and received the Ph.D. degree in computer engineering from the Scuola Superiore S. Anna in 2002. He is an Assistant Professor of the Department of Computer Science at the University of Illinois. His research interests include real-time operating systems, real-time scheduling and resource management, wireless sensor networks, and quality of service control in next generation digital infrastructures. He is recipient of the NSF CAREER Award (2003). He is a member of ACM and IEEE. Chi-Sheng Shih is currently an assistant professor at the Graduate Institute of Networking and Multimedia and Department of Computer Science and Information Engineering at National Taiwan University since February 2004. He received the B.S. in Engineering Science and M.S. in Computer Science from National Cheng Kung University in 1993 and 1995, respectively. In 2003, he received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. His main research interests are embedded systems, hardware/software codesign, real-time systems, and database systems. Specifically, his main research interests focus on real-time operating systems, real-time scheduling theory, embedded software, and software/hardware co-design for system-on-a-chip. Chang-Gun Lee received the B.S., M.S. and Ph.D. degrees in computer engineering from Seoul National University, Korea, in 1991, 1993 and 1998, respectively. He is currently an Assistant Professor in the Department of Electrical Engineering, Ohio State University, Columbus. Previously, he was a Research Scientist in the Department of Computer Science, University of Illinois at Urbana-Champaign from March 2000 to July 2002 and a Research Engineer in the Advanced Telecomm. Research Lab., LG Information & Communications, Ltd. from March 1998 to February 2000. His current research interests include real-time systems, complex embedded systems, QoS management, and wireless ad-hoc networks. Chang-Gun Lee is a member of the IEEE Computer Society. Lui Sha graduated with the Ph.D. degree from Carnegie-Mellon University in 1985. He was a Member and then a Senior Member of Technical Staff at Software Engineering Institute (SEI) from 1986 to 1998. Since Fall 1998, he has been a Professor of Computer Science at the University of Illinois at Urbana Champaign, and a Visiting Scientist of the SEI. He was the Chair of IEEE Real Time Systems Technical Committee from 1999 to 2000, and has served on its Executive Committee since 2001. He was a member of National Academy of Science’s study group on Software Dependability and Certification from 2004 to 2005, and is an IEEE Distinguished Visitor (2005 to 2007). Lui Sha is a Fellow of the IEEE and the ACM.     

An efficient partitioning algorithm for distributed virtualenvironment systems

Distributed virtual environment (DVE) systems model and simulate the activities of thousands of entities interacting in a virtual world over a wide area network. Possible applications for DVE systems are multiplayer video games, military and industrial trainings, and collaborative engineering. In general, a DVE system is composed of many servers and each server is responsible to manage multiple clients who want to participate in the virtual world. Each server receives updates from different clients (such as the current position and orientation of each client) and then delivers this information to other clients in the virtual world. The server also needs to perform other tasks, such as object collision detection and synchronization control. A large scale DVE system needs to support many clients and this imposes a heavy requirement on networking resources and computational resources. Therefore, how to meet the growing requirement of bandwidth and computational resources is one of the major challenges in designing a scalable and cost-effective DVE system. In this paper, we propose an efficient partitioning algorithm that addresses the scalability issue of designing a large scale DVE system. The main idea is to dynamically divide the virtual world into different partitions and then efficiently assign these partitions to different servers. This way, each server will process approximately the same amount of workload. Another objective of the partitioning algorithm is to reduce the server-to-server communication overhead. The theoretical foundation of our dynamic partitioning algorithm is based on the linear optimization principle. We also illustrate how one can parallelize the proposed partitioning algorithm so that it can efficiently partition a very large scale DVE system. Lastly, experiments are carried out to illustrate the effectiveness of the proposed partitioning algorithm under various settings of the virtual world     

High-gain W band pseudomorphic InGaAs power HEMTs

The authors have fabricated 0.1-μm T-gate pseudomorphic (PM) InGaAs power high-electron-mobility transistors (HEMTs) with record power and gain performance at 94 GHz. Devices with 40-μm gate peripheries achieved 10.6-mW output power with 7.3-dB gain and 14.3% power-added efficiency (PAE). Devices with 160-μm gate peripheries achieved 62.7-mW output power with 4.0-dB gain and 13.2% PAE. The authors believe the superior performance of these devices is due to the combination of a short 0.1-μm T-gate, high-quality material, optimized device profile, and the reduction in source inductance due to source vias     

Untersuchungen über das Strangpressen kubisch-flächenzentrierter Metalle

Investigation of the extrudability of face-centered cubic metals Part I represents investigations of the behaviour of the interface during the extrusion of lead and lead alloys. The alloying tendency (welding) and the coefficient of friction μ do not show any relationship to the power requirements during the process of extrusion. Due to the high pressure inside the extrusion presses an interfacial movement (friction) can take place only if the coefficient of friction is in the range of μ ≦ 0,001. By knowing the normal pressure and the shear strength of the material the coefficient of friction for slip-stick conditions can be determined from a chart. The dynamic friction is presented as the shearing off of the interfacial contact points and the stationary friction as the creep of these contact points. Attempts to calculate the power requirements of industrial presses on the basis of the shear strength of the material and shear area were successful.     

GPS信号发生器射频模块的一种实现方案

为了测试GPS（Global Positioning System）接收机的性能，需要用GPS信号发生器来模拟各种条件下的真实GPS信号。国外高性能GPS信号发生器的功能已经相当完善，但其核心技术对我国高度保密，而且市场上的GPS信号模拟器售价比较高。因此，自主研制GPS信号模拟器对我国的国防建设具有重大意义。本文介绍了GPS信号发生器射频模块的一种较优的实现方案，该方案采用移相法实现频率调制，具有电路实现简单、灵活性较大、性能指标较高等特点。     

A novel weighted fuzzy LDA for face recognition using the genetic algorithm

Fuzzy linear discriminate analysis (FLDA), the principle of which is the remedy of class means via fuzzy optimization, is proven to be an effective feature extraction approach for face recognition. However, some of the between-class distances in the projected space after FLDA may be too small, which can render some classes inseparable. In this paper we propose a weighted FLDA approach that aims to increase the smallest of the between-class distances. This is accomplished by introducing some weighting coefficients to the between-class distances in FLDA. Since the optimal selection of these weighting coefficients is not tractable via standard optimization techniques, the genetic algorithm is adopted as an alternative solution in this paper. The experimental results on some benchmark data sets reveal that the proposed weighted fuzzy LDA can improve the worst recognition rate effectively and also exceed LDA and FLDA’s average performance index.