Abstract: Scheduling is the main difficulty in heterogeneous computing (HC) systems in achieving the high performance. In this paper, a meta-heuristic approach based on Particle Swarm Optimization (PSO) is adopted for solving task scheduling problem. PSO is a population-based algorithm to find the optimal solutions, but its performance is decreased when considering multi-optimization problem. In this paper, an Adaptive Weighted Particle Swarm Optimization is proposed for multi-objective optimization. AWPSO is an efficient and simple tool for multi-objective and multi-dimensional problem. AWPSO enhance the global search ability and to overcome the local optimum by introducing an acceleration factor. The goal is to minimize the makespan and flowtime. The experimental results showed that the performance of the proposed method is effective compared with other heuristic optimization technique namely PSO in finding the optimal solutions.

• Publication Date: 12-Mar-2012
• DOI: 10.15224/978-981-07-1847-3-773
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Abstract: Ball bearings are one of the most important components in the machine involving the rotary motion. Ball bearing failure can cause a significant amount of maintenance cost and serious safety problems. Hence detection of the fault at its early stage is important. In this paper, a novel approach for fault detection of ball bearing by combining time, frequency and time-frequency domains are used. Two layer multiclasssvmis used for the classification of fault into an outer race fault, inner race fault, ball fault and healthy bearing. A comparison between Envelope Wavelet Packet Transform, Wavelet Packet Transform and proposed method is carried out. The experimental observation shows that the proposed method is able to detect the faulty condition withbrijes high accuracy.

• Publication Date: 12-Mar-2012
• DOI: 10.15224/978-981-07-1847-3-779
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