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Construction of Ship Target Image Library Based on 3DS MAX and ap algorithm

Modern Electronic Technology 2023年第2期7卷 20-25页

作者： Chao Ji Weixing Xia Zhengping Tang Department of Navigation and Communication Navy Submarine AcademyQingdaoShandong266041China

To achieve accurate classification and recognition of ship target types,it is necessary to establish a sample library of ship targets to be *** the basis of exploring the principles of building a ship target image library,the paper determines the sample *** 3DS MAX software as the platform,combined with the accurate 3D model of the ship in an offline state,the software fully utilizes its own rendering and animation functions to achieve the automatic generation of multi-view and multi-scale views of ship *** reduce the storage capacity of the image database,a construction method of the ship target image database based on the ap algorithm is *** algorithm can obtain the optimal cluster number,reduce the data storage capacity of the image database,and save the calculation amount for the subsequent matching calculation.

关键词： ap algorithm Ship target image library 3DS MAX Image recognition

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THE PARALLEL RECURSIVE ap ADapTIVE algorithm BASED ON VOLTERRA SERIES

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Journal of Pharmaceutical Analysis 2005年第2期17卷 3-6页

作者：孔祥玉魏瑞轩韩崇昭 School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 China

Aiming at the nonlinear system identification problem, a parallel recursive affine projection (ap) adaptive algorithm for the nonlinear system based on Volterra series is presented in this paper. The algorithm identifies in parallel the Volterra kernel of each order, recursively estimate the inverse of the autocorrelation matrix for the Volterra input of each order, and remarkably improve the convergence speed of the identification process compared with the NLMS and conventional ap adaptive algorithm based on Volterra series. Simulation results indicate that the proposed method in this paper is efficient.

关键词： nonlinear system Volterra series adaptive identification ap algorithm

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Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation

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Neuroscience Bulletin 2013年第3期29卷 333-347页

作者： Xiaoguang Tian Cirong Liu Tianzi Jiang Joshua Rizak Yuanye Ma Xintian Hu Kunming Institute of Zoology Chinese Academy of Sciences The University of Queensland Queensland Brain Institute LIAMA Center for Computational Medicine National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Yunnan Key Lab of Primate Biomedical Research State Key Laboratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences Kunming Bio-International

Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose featurereduction approaches to reduce the redundancy and to develop semisimulated data with defined ground truth to evaluate these approaches. We proposed a featurereduction approach based on the Affinity Propagation algorithm （apA） and compared it with the classic feature reduction approach based on Principal Component Analysis （PCA）. We tested the two approaches to the parcellation of both semisimulated and real seed regions using the Kmeans algorithm and designed two experiments to evaluate their noise resistance. We found that all functional connectivitymaps （with/without feature reduction） provided correct information for the parcellation of the semi simulated seed region and the computational efficiency was greatly improved by both feature reduction approaches. Meanwhile, the apAbased featurereduction approach outperformed the PCA based approach in noiseresistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and featurereduction does not significantly change the information. Considering the improvement in computational efficiency and the noiseresistance, featurereduction of functional connectivity maps before cortical parcellation is both feasible and necessary.

关键词： cortical parcellation resting-state fMRI functional connectivity feature reduction stimulateddata ap algorithm

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