A real-time planning algorithm for obstacle avoidance of redundant robots

A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D _p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D _p>d _min (d _min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.     

掺镉纳米SnO2的热稳定性,电导及气敏性能

本文研究了ＣｄＯ掺杂对纳米ＳｎＯ２粉料的热稳定性，电导及气敏特性的影响。结果表明，以非晶状态均匀分散在ＳｎＯ２颗粒表面的ＣｄＯ能阻止ＳｎＯ２之间的相互扩散，提高了纳米ＳｎＯ２的热稳定性；固溶于ＳｎＯ２昌粒中的ＣｄＯ量（Ｃｄ／Ｓｎ〈０．０５）很小，但对元件的电导影响显著；纳米级的晶粒尺寸（〈６ｎｍ）及ＣｄＯ的掺杂大大改善了ＳｎＯ２的气敏特性。     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

Deterministic learning of completely resonant nonlinear wave systems with Dirichlet boundary conditions

In this paper, we investigate the approximation of completely resonant nonlinear wave systems via deterministic learning. The plants are distributed parameter systems (DPS) describing homogeneous and isotropic elastic vibrating strings with fixed endpoints. The purpose of the paper is to approximate the infinite-dimensional dynamics, rather than the parameters of the wave systems. To solve the problem, the wave systems are first transformed into finite-dimensional dynamical systems described by ordinary differential equation (ODE). The properties of the finite-dimensional systems, including the convergence of the solution, as well as the dominance of partial system dynamics according to point-wise measurements, are analyzed. Based on the properties, second, by using the deterministic learning algorithm, an approximately accurate neural network (NN) approximation of the the finite-dimensional system dynamics is achieved in a local region along the recurrent trajectories. Simulation studies are included to demonstrate the effectiveness of the proposed approach.     

Negative effects of sufficiently small initial weights on back-propagation neural networks

In the training of feedforward neural networks,it is usually suggested that the initial weights should be small in magnitude in order to prevent premature saturation.The aim of this paper is to point out the other side of the story:In some cases,the gradient of the error functions is zero not only for infinitely large weights but also for zero weights.Slow convergence in the beginning of the training procedure is often the result of sufficiently small initial weights.Therefore,we suggest that,in these cases,the initial values of the weights should be neither too large,nor too small.For instance,a typical range of choices of the initial weights might be something like(0.4,0.1) ∪(0.1,0.4),rather than(0.1,0.1) as suggested by the usual strategy.Our theory that medium size weights should be used has also been extended to a few commonly used transfer functions and error functions.Numerical experiments are carried out to support our theoretical findings.     

无线传感器网络（WSN）在花卉大棚环境监测中的应用研究

针对传统花卉大棚环境参数监测系统的某些缺点,本文对基于Zigbee技术的无线传感器网络在花卉大棚中的应用可行性进行分析和研究,并建立了花卉大棚无线环境监测系统,使得该监测系统的各节点位置、监测范围、节点扩展性、节点间距离都得以改善和提高。     

基于MSP430的低功耗近距无线图像传输系统设计与实现

近距无线图像传输系统主要用于无线传感器网络中的图像采集、传输、处理和显示,本文设计并实现了一种具有较强实用性的近距无线图像传输系统。采用OV7670图像传感器作为图像数据采集器,高速低功耗的nRF24L01射频模块作为无线收发器,采用MSP430F14916位超低功耗单片机作为微处理器,所构成的系统具有功耗低、图像传输速度快等优点。作者详细阐述了该系统的硬件结构和软件设计。     

工业计量点无线监测系统

工业企业是我国能源消耗大户。为了加强工业企业的能源计量管理,保障工业企业节能降耗的基础工作,文章在分析了现有工业计量体系所存在的技术难点的基础上,提出将无线传感器网络技术应用于能源计量管理的实现方法,同时介绍了工业计量点无线监测系统的构成和优点。     

三网融合的技术现状和未来发展

三网融合是网络发展的主要方向,无线网络是接入有线网络的一个重要途径。本文主要概述了因特网、电信网络、有线电视网络和无线网络的技术现状;分析在三网融合的形势下,它们各自都可以为发展未来包含现在由三种网络提供的所有服务的综合业务网提供核心技术的能力;指出没有单个网络技术能够成为无争议的赢家:融合的网络将具有异构网络集合的特征,提供多种多样的服务;然后在更广阔的视野上阐明了在三网融合之后的未来全局多媒体网络的特征以及建立具有这样的特征的网络所面临的挑战和所需要的变革和创新。