Study on Robot Path Planning Based on an Improved Artificial Potential Field Method

The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure, small amount of calculation and relatively mature in theory. This paper puts forward the ＂Integral method＂ focusing on solving the problem of local minimization. The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole, By changing the parameters of the repulsive force field, robots can quickly get out of the minimum point and move to the target point. This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method, which projects a feasible path successfully and verifies this method.     

提高静态缺陷检测精度方法

误报率和漏报率是静态缺陷检测的关键技术指标,提高分析精度是降低误报和漏报的主要手段.文中介绍了缺陷模式及其有限状态机描述,提出基于传统数据流分析的缺陷检测方法.采用变量抽象取值来近似地表示程序动态执行信息,通过状态条件中的变量抽象取值范围为空来判断不可达路径,以实现路径敏感分析;使用缺陷相关的函数摘要来表示缺陷检测所需函数调用信息,其中缺陷相关的函数摘要包括前置约束信息、后置约束信息和函数特征信息三部分,实现了上下文敏感的跨函数分析.文中方法已在缺陷检测系统DTS中实现,在大型开源软件分析中的实验结果表明,该方法能减少误报和漏报.     

一种基于手指轨迹的密钥生成新方案

提出了一种利用摄像头捕获手指轨迹从而生成密钥的方法,这种方法能从源头上消除传统密码输入时产生的键盘消息,并将这一方法应用到了网站登录系统,能很大程度上提高登陆网站时个人信息的安全性.文中主要介绍了手指轨迹密钥生成的原理.具体实现方案以及将此方案与网站登录系统相结合的方法.     

分布式数据库复制问题的一种解决方案及应用

分布式数据库系统中的复制机制有许多优点,但同时也带来一些问题,解决这些问题的方法有很多。针对这些问题,从需求分析、数据分片和分布、异步复制、数据更新、复制模式、数据冲突、查询处理等多方面因素进行考虑,根据实际情况,选择或改进其中的方法,从而形成一种新的解决分布式数据库复制问题的方案,并将其应用到一个实际的出版物发行信息系统之中,较为完善地解决复制问题。     

Spectral clustering with eigenvector selection

The task of discovering natural groupings of input patterns, or clustering, is an important aspect of machine learning and pattern analysis. In this paper, we study the widely used spectral clustering algorithm which clusters data using eigenvectors of a similarity/affinity matrix derived from a data set. In particular, we aim to solve two critical issues in spectral clustering: (1) how to automatically determine the number of clusters, and (2) how to perform effective clustering given noisy and sparse data. An analysis of the characteristics of eigenspace is carried out which shows that (a) not every eigenvectors of a data affinity matrix is informative and relevant for clustering; (b) eigenvector selection is critical because using uninformative/irrelevant eigenvectors could lead to poor clustering results; and (c) the corresponding eigenvalues cannot be used for relevant eigenvector selection given a realistic data set. Motivated by the analysis, a novel spectral clustering algorithm is proposed which differs from previous approaches in that only informative/relevant eigenvectors are employed for determining the number of clusters and performing clustering. The key element of the proposed algorithm is a simple but effective relevance learning method which measures the relevance of an eigenvector according to how well it can separate the data set into different clusters. Our algorithm was evaluated using synthetic data sets as well as real-world data sets generated from two challenging visual learning problems. The results demonstrated that our algorithm is able to estimate the cluster number correctly and reveal natural grouping of the input data/patterns even given sparse and noisy data.     

A neural network-based scheme coupled with the RPV model inversion package

A hybrid scheme for the inversion of the Rahman-Pinty-Verstraete (RPV) model is presented. It combines the inversion technique described by Lavergne et al. (Lavergne, T., Kaminski, T., Pinty, B., Taberner, M., Gobron, N., Verstraete, M.M., Vossbeck, M., Widlowski, J.L., Giering, R. (2007). Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes. Remote Sensing of Environment, 107, 362-375.) and a multilayer backpropagation feedforward neural network. The RPV inversion package is applied to a sample set of pixels within the satellite scene. Subsequently the pairs of bidirectional reflectance factors (BRF) and model parameters estimated from the sample set of pixels are used to train the neural network. Since the mathematical formulation of the RPV model is embedded in these training data variables, the neural network can efficiently retrieve the model parameters for the whole satellite scene. This scheme has been tested for a MISR L2 BRF scene, MISR L1B2-derived BRF data corresponding to two different dates and a mosaic of MISR L2 BRF scenes acquired over Southern Africa covering a large extent of Miombo woodland. The results show this strategy retrieves the RPV model parameters and uncertainties with high accuracy and considerable speed over large areas.     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

服务接触中的不公平事件：从顾客的角度

This study was designed to answer several key questions regarding the service injustice customers perceived during the service delivery. Using the critical incident technique, the authors collected 195 unfair incidents from a variety of service organizations. The initial sorting of the incidents resulted in four major groups: information asymmetry, unfair procedure, inappropriate attitudes and manners of the service personnel, and unfair outcomes. Within the four major classifications of critical incidents, 13 secondary categories were identified. A further analysis was conducted to discover the underlying reason why customers feel unfair in the service encounters. Finally, some implications for service managers were discussed.     

在ASP.NET 2.0中自动化构建数据访问层

使用ASP.NET2.0所提供的BuildProvider类,将自动化构建的数据访问层源代码编译到程序集中,使得开发者利用Ob-jectDataSource控件,可以简单、快速地实现三层架构的Web应用。     

远程作业技术中公式编辑问题的解决方案

随着远程教育的不断发展,远程作业技术的研究与开发被提到越来越重要的位置。对于理工科专业的基础课来讲,学生在作业中需要输入大量的数学表达式,这些数学表达式最终必须由计算机自动进行识别和批改。针对这种情况,提出了适合于计算机自动识别的数据结构以及特殊结构的数据逻辑关系等,在此基础上对作业系统中的公式编辑器进行了设计和开发。