Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

Security concepts for the dynamics of autonomous vehicle networks

The secure operation of autonomous vehicle networks in the presence of adversarial observation is examined, in the context of a canonical double-integrator-network (DIN) model. Specifically, we study the ability of a sentient adversary to estimate the full network’s state, from noisy local measurements of vehicle motions. Algebraic, spectral, and graphical characterizations are provided, which indicate the critical role of the inter-vehicle communication topology and control scheme in achieving security.     

基于TCN的地铁列车控制方案的研究

本文针对地铁车厢内控制设备分散的问题,设计了基于TCNRTP的地铁列车控制网络系统。采用NS2网络模拟软件构建的虚拟网络环境,编写消息数据传输层协议,通过测试列控网络的通信效率,得出了影响通信效率的因素;通过配置列控通信设备的基本功能和设定装置运行参数达到配置网络化通信的目的。     

大学英语课堂教学中合作学习有效性探讨

合作学习已经越来越多的被大学教师引入到英语教学中。文章探讨了合作学习中遇到的问题,分析合作学习受阻的原因,提出解决问题的对策。     

一种可靠的服务器冗余控制算法

本文分析了目前工业计算机控制系统中服务器冗余控制存在的问题,提出一种高可靠的服务器冗余控制算法模型,并给出了该算法的详细及其实现。     

基于SSH的住宅小区电子商务平台方案设计

为了促进住宅小区电子商务平台建设,提出一种基于J2EE和SSH架构的解决方案,引入了JQuery、AJAX等前台开发技术,以提高开发效率和提升客户体验,同时采用基于角色的访问控制、日志器等安全技术,充分保证系统安全性。     

几种主流编程语言的特点与比较

计算机编程语言种类很多,但仅有少数几种语言得到了比较广泛的应用。本文选择了C语言、VB、VB．NET和Java作为讨论对象,首先总结比较了它们的数据类型、控制语句的语法,然后对编码规则的细节进行了阐述,最后讨论了这几门语言的技术性能特点。     

Multi-Step H∞ Generalized Predictive Control

An H generalized control law is derived which has properties in common with both the GH and GPC control laws. The stability and robustness properties are the same as for a GH optimal controller but a cost on future predictive error and control action is dealt with in the same manner as for the GPC or model based predictive control laws. This type of long range predictive control algorithm has stability and robustness problems which are much improved through the H formalism. The general two-block problem solution is considered first and this is then reduced to a novel one-block multi-step GH control law which is very attractive computationally. This latter algorithm has all the benefits of one-block H controllers and yet the equations can be written in a form where GPC type constrained solutions can be obtained.     

Analogue VLSI primitives for perceptual tasks in machine vision

A variety of computational tasks in early vision can be formulated through lattice networks. The cooperative action of these networks depends upon the topology of interconnections, both feedforward and recurrent ones. The Gabor-like impulse response of a 2nd-order lattice network (i.e. with nearest and next-to-nearest interconnections) is analysed in detail, pointing out how a near-optimal filtering behaviour in space and frequency domains can be achieved through excitatory/inhibitory interactions without impairing the stability of the system. These architectures can be mapped, very efficiently at transistor level, on VLSI structures operating as analogue perceptual engines. The hardware implementation of early vision tasks can, indeed, be tackled by combining these perceptual agents through suitable weighted sums. Various implementation strategies have been pursued with reference to: (i) the algorithm-circuit mapping (current-mode and transconductor approaches); (ii) the degree of programmability (fixed, selectable and tunable); and (iii) the implementation technology (2 and 0.8 gate lengths). Applications of the perceptual engine to machine vision algorithms are discussed.