International Journal of Intelligent Engineering and Systems

8

Authors: Xin Xu, Jinguang Gu, Hongbing Zhu, Yimin Qiu

This paper discussed several classical algorithms for vector data compression firstly, and put forward an enhanced vector data compressing algorithm, basing on the analyses of the problems caused by applying these algorithms to the vector map. This algorithm integrates the merits of the classical algorithms, thus can tackle the problem of classical compressing algorithms, such as the distortion of the overall direction in James Algorithm, and the terrain distortion in Douglas - Peucker Algorithm and Light Column Algorithm. An evaluation was developed to test the reliability of this algorithm, and MapInfo data was adopted to show the vector map with the precision of 1:10000, from which the 3rd trunk road of Qingshan District in Wuhan was selected for experiment. Result shows that the proposed algorithm has the least difference in the comparison of the total length and average coordinate of the curve, with corresponding data before compression. And, it has the most similarity in its exterior compared to other classical compressing algorithms. Thus this algorithm could be effective in compressing the vector map. And the experimental research results will hopefully serve as useful feedback information for improvements for vector map compressing in personal navigation system.

8

Authors: Hongmei Kai, Hongbing Zhu, Xiaoli Lin, Kei Eguchi

Neural networks have good learning and associative memory abilities and have been widely applied to various fields. We employed the Backpropagation Neural Network (BPNN) to replace the fuzzy methods of the Intelligent Intrusion Detection, Decision, Response System (I2D2RS) [5] to decide the intrusion. Through this improvement the processing of the system was simplified and the performance of the system was enhanced in the intrusion decision. The effectivities of these improvements were confirmed with the experiments.

9

Authors: Li Li, Jian Huang, Xiaofei Huang, Long Chen

This paper presents a novel approach to detect traffic congestion on roads in a natural open world scene observed from TV cameras placed on poles or buildings. In this system, three algorithms are proposed to estimate the road status. They are based on background subtraction, frame differencing and time-spatial image respectively. The experimental results on real road traffic congestion estimation show that the time-spatial method is the most robust in complex lighting and traffic environment. The detailed algorithms and the comparison results are given in the paper.