基于ASP的SVG交互技术在电子地图中的应用

基于SVG技术主要面向网络应用,满足Web对动态、可缩放和与平台无关地展现复杂内容并实现灵活交互的需求的优点,它正逐渐的被广泛应用于网络图形开发。针对传统电子地图在网络交互中表现出的不足,结合ASP技术,开发C/S模式的SVG电子地图查询系统,以JavaScript作为脚本工具,实现与SVG地图在网页中的动态交互以及使用ASP访问数据库查询详细的地图信息。当实际地图信息发生改变时,可以根据客户端需要对地图具体位置增加或删除相应的元素,或修改地图元素的属性,快速并轻松的完成对地图的更改。     

共轴圆线圈和圆板的互感系数计算

本文给出了同轴圆线圈和圆板间互感系数的近似函数表达式。并用Matlab绘制了该互感系数随线板间距离和圆板大小的变化曲线。此外,根据磁感应强度的解析表达式,计算了载流线圈的磁力线分布图,分析了其磁感应强度的分布,并给出了它的径向分布曲线。     

基于遗传算法的高校智能排课系统

新形势高校的发展,高校教务管理中的排课问题显得更为复杂。排课问题是一个典型的组合优化和不确定性调度问题,也是一个完全问题。该文根据高校实际情况,从遗传算法的基本理论着手,研究如何利用遗传算法解决高校排课中的资源冲突、课表优化等问题,并引用了哈希表和时间粒度的概念,对传统遗传算法染色体的编码模式进行了有益修正,增强了模式的灵活性。实验证明．遗传算法能够简化程序的复杂度和减少生成最佳课表的时间。达到了较高的师生满意度。     

地图构建中动态障碍干扰滤除的一种方法

在移动机器人环境建图中,动态障碍物的存在直接影响传感器的读数,导致产生不一致的环境地图,因此,移动机器人构建地图必须滤除动态障碍物干扰。采用直线插补的方法在先前的局部图上搜寻机器人与目标点之间是否存在障碍物,若存在障碍,则可判定该障碍物已移走(即为动态障碍),应该予以滤除。实验结果证明,该方法能在建图过程中有效地滤除动态障碍,并能有效减少静态障碍物探测的误差累积,算法复杂度小。     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.     

A Multi-parameter Approach to Automated Building Grouping and Generalization

This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,’ ‘average’ or ‘weak.’ The ‘weak’ groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in blocks.

The memetic self-organizing map approach to the vehicle routing problem

The paper presents an extension of the self- organizing map (SOM) by embedding it into an evolutionary algorithm to solve the Vehicle Routing Problem (VRP). We call it the memetic SOM. The approach is based on the standard SOM algorithm used as a main operator in a population based search. This operator is combined with other derived operators specifically dedicated for greedy insertion moves, a fitness evaluation and a selection operator. The main operators have a similar structure based on the closest point findings and local moves performed in the plane. They can be interpreted as performing parallels and massive insertions, simulating the behavior of agents which interact continuously, having localized and limited abilities. This self-organizing process is intended to allow adaptation to noisy data as well as to confer robustness according to demand fluctuation. Selection is intended to guide the population based search toward useful solution compromises. We show that the approach performs better, with respect to solution quality and/or computation time, than other neural network applications to the VRP presented in the literature. As well, it substantially reduces the gap to classical Operations Research heuristics, specifically on the large VRP instances with time duration constraint.     

Effective indices to characterize short sequences of human random generations

Brain impediments such as dementia are a serious problem today. It would be very useful if software for private diagnosis were available. In this paper, we show the effectiveness of the human random generation test (HRG) for such software, and propose a set of four indices to be used for classifying the HRG data. Human-generated random numbers have strong characteristics compared to computer-generated random numbers, and these are known to be correlated to the individual characters of the subjects. However, analysis using the correlation dimension or HMM requires a long data sequence, and thus is not suitable for diagnoses. We therefore focus on short sequences of HRG and search for effective indices to detect signs of brain disability hidden in the HRG data. We studied data from subjects of different age groups, and successfully differentiated the data from the different groups. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

A corner differentiation algorithm by a single sonar sensor for mobile robots

Ultrasonic sensors have been widely applied in mobile robots to obtain environmental information and avoid obstacles. In general, a typical domestic environment consists of planes, edges and corners. It is usually difficult to distinguish a plane from a corner directly with a single ultrasonic sensor. To overcome this difficulty, a corner differentiation algorithm for a single ultrasonic sensor is proposed in this paper. The algorithm is based on the features of all of the actual reflection points from the environment obtained by a reflection search algorithm from which the corners are realized by a corner differentiation algorithm. The experimental results show that the developed algorithm can successfully detect all planes and corners. Furthermore, an environmental map can be built based on the information obtained on planes and corners. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Solving the generalized Sylvester matrix equation AV +BW = VF via Kronecker map

This note considers the solution to the generalized Sylvester matrix equation AV ＋ BW = VF with F being an arbitrary matrix, where V and W are the matrices to be determined. With the help of the Kronecker map, an explicit parametric solution to this matrix equation is established. The proposed solution possesses a very simple and neat form, and allows the matrix F to be undetermined.