Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface...
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ISBN:
(纸本)9781424415304;1424415306
Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. Wavelet neural network (WNN) is used widely in tool wear detection, but the curse of dimensionality and shortage in the responding speed and learning ability is brought about by the traditional models. An improved WNN algorithm which combines with modified particle swarm optimization (MPSO) is presented to overcome the problems. Based on the cutting power signal, the method is used to estimate the tool wear. The Daubechies-wavelet is used to decompose the signals into approximation and details. The energy and square-error of the signals in the detail levels is used as characters which indicating tool wear, the characters are input to the trained WNN to estimate the tool wear. Compared with conventional BP neutral network, conventional WNN and genetic algorithm-based WNN, a simpler structure and faster converge WNN is obtained by the new algorithm, and the accuracy for estimate tool wear is tested by simulation and experiments.
In ultra dense heterogeneous networks (HetNets), the interference becomes more serious since multiple small cells coexist in the coverage area of the macrocells and share the same spectrum. An efficient interference c...
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ISBN:
(纸本)9781479935130
In ultra dense heterogeneous networks (HetNets), the interference becomes more serious since multiple small cells coexist in the coverage area of the macrocells and share the same spectrum. An efficient interference coordination method is adjusting the transmit powers of all cells in a cooperative way. However, under practical serving cell selection rules in which serving cells of users alter with the variation of cell powers, finding optimal cell powers becomes a difficult problem. In this paper, an improved modified particle swarm optimization (MPSO) is proposed to tackle this difficult problem caused by the altering of serving cell. Local search and multi-restart process are introduced to guarantee the local and then global optimality, and the convergence conditions and global optimality are proved by mathematical deduction to guide the selection of the parameters. Simulations show that the proposed algorithm can significantly improve system throughput compared with existing algorithms which do not consider the alteration of serving cells. By improving MPSO, the proposed algorithm can spend less iteration time to achieve higher system throughput, and exhibit similar performance as exhaustive search in both system throughput and the signal to interference plus noise ratio (SINR) of users.
This paper presents an improved Elman neural network(IENN) based on algorithm for optimal wind energy control with maximum-power-point-tracking(MPPT). An on-line training IENN controller using back-propagation learnin...
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This paper presents an improved Elman neural network(IENN) based on algorithm for optimal wind energy control with maximum-power-point-tracking(MPPT). An on-line training IENN controller using back-propagation learning algorithm with modified particle swarm optimization(MPSO) is designed to allow the pitch adjustment for power regulation. The node connecting weights of the IENN are trained online by back-propagation(BP) methodology. MPSO is adopted to adjust the learning rates in the BP process to improve the learning capability. Performance of the proposed ENN with MPSO is verified by many experimental results.
Global electricity market deregulation makes compatible changes and new challenges in power system operation planning problem. Maintenance is required for the generating unit to reduce the risk of capacity outage and ...
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ISBN:
(纸本)9781467359085
Global electricity market deregulation makes compatible changes and new challenges in power system operation planning problem. Maintenance is required for the generating unit to reduce the risk of capacity outage and to improve availability of units and thereby extending equipment lifetime. modified particle swarm optimization (MPSO) for the generator maintenance scheduling (MS) generates optimal, feasible solution and overcomes the limitation of the conventional methods such as extensive computational effort which increases exponentially as the size of the problem increases. The objective of this paper is to reduce the loss of load probability (LOLP) and maximize the profit of generating units using levelized risk method (LRM). Market participants submit the MS proposal based on market clearing price (MCP) and they request permission and receive approval for planned maintenance outages from the independent system operator (ISO) in competitive electricity markets. Mainly, we are concerned with a primary framework for ISO's maintenance coordination in order to determine LOLP values in the maintenance time intervals using LRM that uses LOLP convolution algorithm. The ISO will put forward its best endeavor to adjust individual generator maintenance schedules according to the estimated LOLP values. The proposed method is tested on five generating companies model of IEEE reliability test system (RTS) to demonstrate the effectiveness of the proposed method and the applicability of the elucidation scheme for large-scale MS coordination problems.
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