Concurrent consideration of evacuation safety and productivity in manufacturing facility planning using multi-paradigm simulations

Manufacturing facilities are expected to maintain a high level of production and at the same time, employ strict safety standards to ensure the safe evacuation of the people in the event of emergencies (fire is considered in this paper). These two goals are often conflicting. This paper presents a methodology to evaluate evacuation safety versus productivity concurrently for various, widely known manufacturing layouts. While the safety performance indicators such as evacuation times are inferred from the crowd (agent based) simulation, the productivity performance indicators (e.g. throughput) are analyzed using the discrete event simulation. To this end, this research focuses on creating innovative techniques for developing accurate crowd simulations, where Belief-Desire-Intention (BDI) agent framework is employed to build each person’s individual actions and the interactions between them. The data model and rule based action algorithms for each agent are reverse-engineered from the human-in-the-loop experiments in the immersive virtual reality environments. Finally, experiments are conducted using the constructed simulations to compare safety and productivity for different layouts. To demonstrate the proposed methodology, an automotive power-train (engine and transmission) manufacturing plant was used. Initial results look quite promising.     

A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems

In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that our algorithm is efficient and robust.     

Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line

Product recovery involves the recovery of materials and components from returned or end-of-life products. Disassembly, an element of product recovery, is the systematic separation of an assembly into its components, subassemblies or other groupings. Stricter environmental regulations together with dramatic decrease in natural resources and landfills have increased the importance of disassembly as all product recovery options require some level of disassembly. Due to changes made during the lifetime of a product by customers or service personnel, the number and the version of components prior to disassembly is unknown. Customers may also discriminate between and demand different versions of components. The existence of non-functional components further adds to the uncertainty associated with disassembly yield. Sensors implanted into products during their production can address this uncertainty by providing information on the number, condition and version of components prior to disassembly. In this study, we evaluate the impact of sensor embedded products (SEPs) on the various performance measures of a washing machine (WM) disassembly line controlled by a multi-kanban system, which takes into consideration the highly stochastic behavior of the line while managing material and kanban flows. First, separate design of experiments studies based on orthogonal arrays are performed for conventional products and SEPs. In order to observe the response of each experiment, detailed discrete event simulation (DES) models for both types of products are developed considering the precedence relationships among the components of a WM. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. According to the results, SEPs provide significant reductions in all costs (viz., backorder, holding, disassembly, disposal, testing and transportation) while increasing revenue and profit.     

Finite difference discretization of the extended Fisher-Kolmogorov equation in two dimensions

Approximate solutions are considered for the extended Fisher-Kolmogorov (EFK) equation in two space dimension with Dirichlet boundary conditions by a Crank-Nicolson type finite difference scheme. A priori bounds are proved using Lyapunov functional. Further, existence, uniqueness and convergence of difference solutions with order O(h²+k²) in the L^∞-norm are proved. Numerical results are also given in order to check the properties of analytical solutions.     

Exponential stability analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with mixed delays

In this paper, we study a new class of stochastic Cohen-Grossberg neural networks with reaction-diffusion and mixed delays. Without the aid of nonnegative semimartingale convergence theorem, the method of variation parameter and linear matrix inequalities technique, a set of novel sufficient conditions on the exponential stability for the considered system is obtained by utilizing a new Lyapunov-Krasovskii functional, the Poincaré inequality and stochastic analysis theory. The obtained results show that the reaction-diffusion term does contribute to the exponentially stabilization of the considered system. Therefore, our results generalize and improve some earlier publications. Moreover, two numerical examples are given to show the effectiveness of the theoretical results and demonstrate that the stability criteria existed in the earlier literature fail.     

Mean square exponential stability in high-order stochastic impulsive BAM neural networks with time-varying delays

In this paper, a class of stochastic impulsive high-order BAM neural networks with time-varying delays is considered. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order BAM neural networks.     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

基于Xenomai的实时嵌入式Linux操作系统的构建

为了便于在ARM平台构建嵌入式实时Linux操作系统,设计了一种简单高效的实时化Linux方案.采用ARMXScale系列中的PXA270作为硬件平台,利用Xenomai实时化方案改造标准Linux内核,构建基于Xenomai的实时嵌入式操作系统,并设计实验测试系统用户态程序与内核态程序的实时性指标.实验结果表明,基于...     

煤矿安全监控网络中嵌入式网关的研究

设计了一个嵌入式异构网络互联网关,实现计算机网络与现场总线异种网络的互联。讨论了网关的硬件构架以及以太网接口、CAN总线接口的具体设计,介绍分析了嵌入式Linux操作系统下的设备驱动程序、嵌入式TCP/IP的实现过程。在实验室条件下模拟了网关的应用环境,对网关的接口和性能的可靠性和正确性进行了测试,实验结果表明,网关较好地实现了数据通信和协议转换功能。     

基于数字图像处理的虚拟输入方法

针对当前嵌入式设备输入方法存在的高成本低体验问题,提出了一种运行于嵌入式系统中基于数字图像处理的虚拟输入方法.该方法利用嵌入式设备附加的摄像设备,对用户在虚拟键盘上的按键过程进行图像采集,通过角点识别和图像分割进行按键识别,以便通过采集到的图像定位出用户点击的目标按键,从而实现信息输入过程.通过实例验证了该方法的可行性...