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Traditionally, economic dispatch and demand response (DR) are considered separately, or implemented sequentially, which may degrade the energy efficiency of the power grids. One important goal of optimal energy management (OEM) is to maximize the social welfare through the coordination of the suppliers' generations and customers' demands. Thus, it is(More)
Wireless sensor networks have recently gained a lot of attention. Target tracking is a canonical application of wireless sensor networks and task allocation is an unavoidable problem for target tracking. To improve the tracking quality and save more energy, an auction-based dynamic coalition scheme of task allocation for single target tracking in wireless(More)
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)
Due to the variety of applications and their importance, wireless sensor networks (WSNs) would need to be connected to the Internet. Some approaches have been proposed to connect wireless sensor networks to the existing TCP/IP networks, such as the application-level gateways or overlay networks. However, most existing approaches have to consume network(More)
For the energy management problems for demand response in electricity grid, a Metropolis Criterion based fuzzy Q-learning consumer energy management controller (CEMC) is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for the consumer behavior in electricity grid. In this case, the(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)
When finding the moving target in the acoustic sensor network, a dynamic cluster is formed to triangulate the target's position. While the target is moving, the old cluster is revoked and a new cluster is formed to follow the target. An election strategy based on the probability is used to select the cluster head and thus improve the cluster formation(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)