The evidence for neural information processing with precise spike-times: A survey

This paper surveys recent findings in neuroscience regarding the behavioral relevancy of the precise timing with which real spiking neurons emit spikes. The literature suggests that in almost any system where the processing-speed of a neural (sub)-system is required to be high, the timing of single spikes can be very precise and reliable. Additionally, new, more refined methods are finding precisely timed spikes where previously none where found. This line of evidence thus provides additional motivation for researching the computational properties of networks of artificial spiking neurons that compute with more precisely timed spikes. This revised version was published online in June 2006 with corrections to the Cover Date.     

Neural networks for estimating the tool path length in concurrent engineering applications

This paper deals with the development of a neural computing system that can predict the cutting tool path length for milling an arbitrary pocket defined within the domain of a product design, in a computer numerically controlled (CNC) setting. Existing computer aided design and manufacturing systems (CAD/CAM) consume significant amounts of time in terms of data entry pertaining to the geometries and subsequent modifications to them. In the concurrent engineering environment, where even the designer needs information from the CAD/CAM systems, such time-consuming processes can be expensive. To alleviate this problem, a neural network system can be used to estimate machining time by predicting cost-dependent variables such as tool path length for the pocket milling operation. Pockets are characterized and classified into various groups. A randomized design is described so that the training samples that have been chosen represent the domain evenly. An appropriate network was built and trained with the sample pocket geometries. The analysis of the performance of the system in terms of tool path length prediction for new pocket geometries is presented.     

The super-connected property of recursive circulant graphs

In a graph G, a k-container C_k(u,v) is a set of k disjoint paths joining u and v. A k-container C_k(u,v) is k∗-container if every vertex of G is passed by some path in C_k(u,v). A graph G is k∗-connected if there exists a k∗-container between any two vertices. An m-regular graph G is super-connected if G is k∗-connected for any k with 1?k?m. In this paper, we prove that the recursive circulant graphs G(2^m,4), proposed by Park and Chwa [Theoret. Comput. Sci. 244 (2000) 35-62], are super-connected if and only if m≠2.     

A novel polarizer‐free dye‐doped polymer‐dispersed liquid crystal for reflective TFT displays

Abstract— A novel preparation method for dichroic dye‐doped polymer‐dispersed liquid crystals has been developed. This was achieved by creating a porous polymer matrix first by washing out the liquid crystal from a polymer‐dispersed liquid crystal (PDLC), which is then refilled with dye‐doped liquid crystal. Optimizing the liquid crystal used in the refilling results in decreased turn‐on voltage and faster response time. Poster‐standard reflectivity and newspaper‐standard contrast was demonstrated with a 3.8‐in. QVGA reflective TFT display with a drive voltage of 10 V.     

Modeling of microstrip lines using neural networks: Applications to the design and analysis of distributed microstrip circuits

In this article, two models for solving microstrip lines are presented. The models utilize radial-basis-function neural networks. Using the first model, one estimates the effective dielectric constant and the width of the microstrip line, knowing its characteristic impedance and the frequency. The second model provides the effective dielectric constant and the characteristic impedance of the line based on knowledge of its width and the frequency. Besides their remarkably fast responses, the proposed models are capable of estimating the required quantities with very high accuracy. The potential of the proposed models is demonstrated in the design and analysis of two distributed microstrip circuits. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14, 166–173, 2004.     

拱坝近似优化的人工神经网络方法

以国内已建拱坝体形参数为基础,以ＢＰ神经网络方法为手段,建立了拱坝体形参数近似优化的人工神经网络模型．借助该模型不仅可以为拱坝体形优化快速提供高质量的初始可行解,而且可为中、小拱坝体形设计直接提供近似最优解．     

影响聚合物PEDT导电性能的因素分析

本文用化学聚合方法制备了新一代导电聚合物材料PEDT（3，4-聚乙烯二氧噻吩），分析了聚合物PEDT的导电机理，研究了聚合温度、聚合改良剂、单体氧化剂配比、溶剂含量、烘干温度等工艺条件对PEDT导电性能的影响。     

神经元网络——它的背景、进展和问题

近年来神经元网络研究的进展,在国际学术界引起了强烈的轰动,成了各界学人的热门话题.本文分析了神经元网络研究的来龙去脉,简要介绍了若干代表性的进展,指出了这一研究的意义、问题和进一步研究的方向,希望引起各界同仁的关注.     

神经网络在结构损伤模拟中的应用

识别一个结构在震动状态下的变化，在结构监测中是十分重要的，神经网络就非常适用于这种目的。本文研究了使用可分析的学习样本来训练神经网络的可行性问题。神经网络从损伤状态中训练产生，然后用于诊断一个五层钢框架在一系列震动模拟中的状态。结果表明，使用神经网络可使在线结构诊断更加可行。     

聚醚固体电解质锂电池放电性能研究

利用氧亚甲基连接的聚氧乙烯-聚氧丙烯多嵌段共聚物或氧亚甲基连接的聚氧乙烯多嵌段共聚物与高氯酸锂的络合物作电池的电解质,锂片作负极,几种含钒的化合物作正极,组装了全固态二次锂电池.研究了不同正极材料、不同电解质和不同温度对电池短路放电性能的影响以及电池的重复短路放电性能.研究表明,正极材料V_6O_(13)的性能优于Na_(1+x)V_3O_8;电解质(PEO)_n·LiClO_4的性能优于(PEO-PPO)_n·LiClO_4,这与它们的电导率分别为3.2×10~(-3)S·cm~(-1)和2.2×10~(-3)S·cm~(-1)是一致的.