18839021
Adaptive-Subcube Fault Tolerant Routing in Dual-Cube with Very Large Number of Faulty Nodes
The dual-cube is a newly proposed interconnection network for linking a large amount of nodes with low node degree. It uses low-dimensional hypercubes as building blocks and keeps the main desired properties of the hypercubes. In this paper, we give an efficient algorithm for fault tolerant routing in dual-cube networks with a large number of faulty nodes. Our algorithm uses the adaptive-subcube technique to select a suitable dimension to route a node. This technique not only increases the routing speed but also shortens the path and improves the successful routing rate. The experimental results show that, with high percentages of node failures, the algorithm can build routing paths with a very high probability. Our simulation results show that when a dual-cube with 32,768 nodes contains up to 20 percent faulty nodes, the success rate of constructing a faultfree path between any two nonfaulty nodes is 99.5 percent with a 4-subcube.
7971918
An Efficient Algorithm for Fault Tolerant Routing Based on Adaptive Binomial-Tree Technique in Hypercubes
We propose an efficient fault-tolerant routing algorithm for hypercube networks with a very large number of faulty nodes. The algorithm is distributed and local-information-based in the sense that each node in the network knows only its neighbors’ status and no global information of the network is required by the algorithm. For any two given nonfaulty nodes in a hypercube network that may contain a large fraction of faulty nodes, the algorithm can find a fault-free path of nearly optimal length with very high probability. The algorithm uses the adaptive binomial-tree to select a suitable dimension to route a node. We perform empirical analysis of our algorithm through simulations under a uniform node failure distribution and a clustered node failure distribution. The experimental results show that the algorithm successfully finds a fault-free path of nearly optimal length with the probability larger than 90 percent in a hypercube network that may contain up to 50 percent faulty nodes.
5886737
Using KCCA for Japanese–English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.
40993420
Heuristic Walkthroughs Evaluation of Pen-Based Chinese Word Edit System (PCWES) Usability
An evaluation experiment was conducted to compare four interface styles: Hanwang Fixed Window, Translucent Mobile Single Window, Translucent Mobile Double Window, and No Window. Subjects tested the four styles on a pen_based word edit system in free-form mode. The result shows that traditional interface style of the Hanwang Fix Window didn't satisfy the requirement of the nature interaction of the end user. And the TMDW mode are very suitable for user to perform the word processing task, in terms of the task performing time and mean accuracy rate. And the NW is the most nature and promising style, in terms of the subjective preference and the error rate of the performing time and accuracy rate of the experimental data. The experiment also shows that the moving window would influent the accuracy rate of the recognition. This experiment confirmed that a proper employment of interface style could improve the interactive efficiency of word editing systems. Also we give out some useful interface hints to improve the performance of the interface. Our tests for the first time give statistical support to the view that free pen_based interface is meaningful in the nature pen and gesture input of the word edit system.
29781034
Parameters identification and vibration control for modular manipulators
The joint parameters of redundant manipulators are prerequisite data for effective dynamics control. An identification method via fuzzy theory and Genetic Algorithm has been presented to study modular redundant robots. The Genetic Algorithm is used in the fuzzy optimization expecting to obtain global optimal solutions. Experimental modal analysis and Finite Element Method have been exploited in dynamics modeling. The joint parameters of a 9-DOF modular redundant robot have been identified. Active vibration control has been approached to a simplified 4-DOF modular manipulator by DOF reduction to the 9-DOF modular manipulator.
28387556
Perceiving Environments for Intelligent Agents
One of the important characteristics for intelligent agents is to be able to assess their environments in order to decide on the correct action to take. It is always difficult to do so because many factors including uncertain information, knowledge and bounded time will affect intelligent agent to perceive their environments. In this paper, we propose a procedure descriptive framework to perceive the environments for intelligent agents. The process of belief updating in this framework remains to be constantly changing, until the point of decision making is reached. During the dynamic change of beliefs, the intelligent agents will incorporate their knowledge about other agents, and the possible uncertain information they received from their local sensors and other agents.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.