Chuanzhi Zang

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Damage detection in structures is one of the research topics that have received growing interest in research communities. While a number of damage detection and localization methods have been proposed , very few attempts have been made to explore the structure damage classification problem. This paper presents an Artificial Immune Pattern Recognition (AIPR)(More)
This paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR). The fuzzy clustering technique is used to initialize the pattern representative (memory cell) for each data pattern and cluster training data into a specified number of patterns. To improve(More)
This paper presents an unsupervised structure damage classification algorithm based on the data clustering technique and the artificial immune pattern recognition. The presented method uses time series measurement of a structure's dynamic response to extract damage-sensitive features for the structure damage classification. The Data Clustering (DC)(More)
The distinctive characteristic of target tracking sensor networks is that the delay of data transmission is constrained, which poses a difficult problem for predicting the application lifetime for such sensor networks. In this paper, we first map the delay constraints to the hop bound in routing. By analysing the energy consumption in recurring bounded hop(More)
Microgrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power(More)
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