Jiang Yunliang

The research on video supervision technology based on mathematical morphology

This paper studies some aspects needing to be improved in current video supervision technology. It puts forward video supervision background self-adaptive algorithm in complex environment, by using mathematical morphology, genetic algorithm, rough set theory, etc. We construct morphological structure element according with traffic moving target, and propose mathematical morphology analysis model for traffic video images. At the same time, we study feature extraction based on mathematical morphology and tracking detection methods, and establish typical violation pattern base by utilizing domain expert knowledge.

Approximate Reduction Algorithm Based on Rough Set Theory

The attribute reduction of information system can enhance accuracy and efficiency of knowledge discovery, machine learning, etc. After studying reduction strategy in rough set theory, the concept of approximate reduction and an approximate reduction algorithm are proposed in this paper. This algorithm can retain minimal attributes in the basic style of information system, i.e. reduce as many attributes as possible. That can save much time for the systems later disposal. The algorithms time complexity hasnpsilat been improved, but attributes after reduction are reduced greatly. The original information system has a certain loss, but this can be accepted under a certain significance level. Lastly, the reduction strategy is compared with approximate reduction strategy by nine attributes which belong to the information system.

The Study of Fusing the Data Facing Environment Monitoring

Although the data fusion and data mining belong to the data processing technology, former researchers combine these technologies quite a few. In fact these technologies are nearly correlative, and both serve the goal of the knowledge detects. The target of data fusion is forming the data foundation of data mining, and the target of data mining is extracting the useful knowledge in the foundation of above data, to complete the knowledge detection. Therefore, our researchs aim is combining the two technologies, designs the valid algorithm to carry on the data mining in the foundation of data fusion, mainly on the artificial intelligence theory and the Bayes method, excavates the valid information in the data of environmental monitoring as far as possible.