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Identification of Sources of Potential Fields with Directional Wavelet Transform:Application to Mineral Exploration
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作者 Dianhua Cao Anjian Wang 《地学前缘》 EI CAS CSCD 北大核心 2009年第S1期146-146,共1页
For the sustainable supply of mineral resources,blind deposits are becoming the emphasis of exploration after long-period exploitation of exposed deposits.The collection and analysis of gravity or magnetic data repres... For the sustainable supply of mineral resources,blind deposits are becoming the emphasis of exploration after long-period exploitation of exposed deposits.The collection and analysis of gravity or magnetic data represents one of the cheapest forms of large-scale geophysical exploration.With the identification of potential fields,we can get the map of worms or skeletonizations showing the three-dimension structure of shallow crust. 展开更多
关键词 potential field directional wavelet transform multidirectionaland multiscale edges detection mineral exploration
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Identification of Potential Mineral Fields with Directional Wavelet Transform:Application to Mineral Exploration
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作者 Dianhua Cao Anjian Wang 《地学前缘》 EI CAS CSCD 北大核心 2009年第S1期171-172,共2页
For the sustainable supply of mineral resources,blind deposits are becoming the emphasis of exploration after long-period exploitation of exposed deposits.The collection and analysis of gravity or magnetic data repres... For the sustainable supply of mineral resources,blind deposits are becoming the emphasis of exploration after long-period exploitation of exposed deposits.The collection and analysis of gravity or magnetic data represents one of the cheapest ways of large-scale geophysical exploration. 展开更多
关键词 potential field directional wavelet transform multidirectional and multiscale edges detection mineral exploration
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Direction of Arrivals Estimation for Correlated Broadband Radio Signals by MVDR Algorithm Using Wavelet 被引量:3
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作者 Ying Liu Chi Xie Yourun Zhang 《China Communications》 SCIE CSCD 2017年第3期190-197,共8页
A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This l... A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This leads naturally to the derivation of minimum variance distortionless response(MVDR) algorithm, which combines the benefits of subspace methods with those of wavelet, and spatially smoothed versions are utilized which exhibits good performance against correlated signals. We test the method's performance by simulating and comparing the performance of proposed algorithm, FFT MVDR and MVDR with correlated signals, and an improved performance is obtained. 展开更多
关键词 array antenna broadband radio signal direction of arrival MVDR algorithm wavelet
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DIRECT DISPLACEMENT OF PARALLEL MECHANISM WITH WAVELET NETWORK 被引量:1
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作者 CHEN Weishan CHEN Hua LIU Junkao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第2期69-72,共4页
A new method solution for the direct displacement of parallel mechanism, wavelet network method, is proposed. Comparing with the classical analytical and numerical methods, this method can be extended to any parallel ... A new method solution for the direct displacement of parallel mechanism, wavelet network method, is proposed. Comparing with the classical analytical and numerical methods, this method can be extended to any parallel mechanism with any selected degree of freedom and configuration. A wavelet network suiting to approach multi-input and multi-output system is constructed. The network is optimized by analyzing the sparseness of input data and selecting the fitting wavelets by orthogonalization method according to the output data. Then it is applied to solve the direct displace- ment of a general six-degree-of-freedom parallel mechanism as a numerical example. For comparison purposes, a BP neural network is also used for this problem. Simulation results show that the wavelet network performs better than BP neural network. In addition, the wavelet network learns much faster than BP network. 展开更多
关键词 Direct displacement Parallel mechanism wavelet network
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