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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Tao Sun Chaopeng Luo Mengna Liu Liqing Zhou Xianxiu Tang Riheng Meng Xinjian Tang Weifeng Hao Electronic Information School of Wuhan University Wuhan China Science and Technology on Near-Surface Detection Laboratory Wuxi China Hubei Water Resources Technical College Wuhan China State Key Laboratory of Geomechanics and Geotechnical Engineering Institutude of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan China Antarctic Center of Sureying and Mapping of Wuhan Univeristy Wuhan China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Ground penetrating radar is an efficient close-range remote sensing and non-destructive-testing technique, which probes ultra-wide-band electromagnetic pulses to detect underground targets. Then theirs geometric features and material property characters will been figured out. A major clutter interference coupling with the response of the subsurface-buried-landmine is direct air wave and direct ground reflected waves, which hinder shallow buried landmine precise detection and discrimination. As for shallow buried targets, in this paper, a clutter and target response echo decoupling method with Linear-Gain-Compensation-Robust-PCA algorithm is proposed based on low-rank prior of clutters and target distribution spatial sparse prior. Compared with Principal Component Analysis, mean subtract algorithm, the performance of LGC-RPCA method is tested with simulations and indoor sand-box experiments. These demonstrate that the LGC-RPCA method with inexact augmented Lagrangian function is an efficient algorithm for shallow buried landmine detection and discrimination. Then, it will reduce the false alarm rate for identification of shallow buried anti-personnel Mine.

关键词： Sparse representation Ground Penetrating Radar Low-Rank Linear-Gain-Compensation Robust-PCA sparse representation ground penetrating radar clutter underground Antipersonnel mines electromagnetic pulses false alarm probability Lagrangian efficient algorithm

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Research on Propagation Characteristics of Seismic Signal for Moving Armoured Targets

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IOP Conference Series: Earth and Environmental science 2021年第1期660卷

作者： Kai Ding Shoujun Zheng Xinghua Li Yingjiao Rong Science and Technology on Near–Surface Detection Laboratory Wuxi China 214035 Army Engineering University of PLA Nanjing China 210007

This paper is focused on the Seismic Signal characteristics of wheel and track vehicles. The effect of vehicle speed of these two vehicles on the Seismic Signal was studied through experimental and theorical methods. The Seismic Signal s of these two vehicles with two different speeds were tested firstly. Then the measured signals were analyzed by means of the power spectrum method and the wavelet transform method, respectively. And the time- and frequency domain features of the signals were obtained subsequently. Finally, the influence of the speed and type of vehicle on the time- and frequency domain features of the signal and its propagation characteristic were investigated. The results show that, with the propagation of vehicle speed and distance, there is a similar varying law of the Seismic Signal for different vehicles. However, the difference of the time- and frequency domain feature between the two vehicles are striking.

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Search for invisible decays

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Journal of High Energy Physics 2025年第5期2025卷 1-19页

作者： Ablikim, M. Achasov, M. N. Adlarson, P. Afedulidis, O. Ai, X. C. Aliberti, R. Amoroso, A. An, Q. Bai, Y. Bakina, O. Balossino, I. Ban, Y. Bao, H.-R. Batozskaya, V. Begzsuren, K. Berger, N. Berlowski, M. Bertani, M. Bettoni, D. Bianchi, F. Bianco, E. Bortone, A. Boyko, I. Briere, R. A. Brueggemann, A. Cai, H. Cai, X. Calcaterra, A. Cao, G. F. Cao, N. Cetin, S. A. Chai, X. Y. Chang, J. F. Che, G. R. Che, Y. Z. Chelkov, G. Chen, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Choi, S. K. Cibinetto, G. Cossio, F. Cui, J. J. Dai, H. L. Dai, J. P. Dbeyssi, A. de Boer, R. E. Dedovich, D. Deng, C. Q. Deng, Z. Y. Denig, A. Denysenko, I. Destefanis, M. De Mori, F. Ding, B. Ding, X. X. Ding, Y. Dong, J. Dong, L. Y. Dong, M. Y. Dong, X. Du, M. C. Du, S. X. Duan, Y. Y. Duan, Z. H. Egorov, P. Fan, G. F. Fan, J. J. Fan, Y. H. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Farinelli, R. Fava, L. Feldbauer, F. Felici, G. Feng, C. Q. Feng, J. H. Feng, Y. T. Fritsch, M. Fu, C. D. Fu, J. L. Fu, Y. W. Gao, H. Gao, X. B. Gao, Y. N. Gao, Yang Garbolino, S. Garzia, I. Ge, P. T. Ge, Z. W. Geng, C. Gersabeck, E. M. Gilman, A. Goetzen, K. Gong, L. Gong, W. X. Gradl, W. Gramigna, S. Greco, M. Gu, M. H. Gu, Y. T. Guan, C. Y. Guo, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guskov, A. Gutierrez, J. Han, K. L. Han, T. T. Hanisch, F. Hao, X. Q. Harris, F. A. He, K. K. He, K. L. Heinsius, F. H. Heinz, C. H. Heng, Y. K. Herold, C. Holtmann, T. Hong, P. C. Hou, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hu, B. Y. Hu, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Huang, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Hussain, T. Hölzken, F. Hüsken, N. in der Wiesche, N. Jackson, J. Janchiv, S. Ji, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Jia, X. Q. Jia, Z. K. Jiang, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiao, J. B. Jiao, J. K. Jiao, Z. Ji Institute of High Energy Physics Beijing People’s Republic of China Budker Institute of Nuclear Physics SB RAS (BINP) Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Uppsala University Uppsala Sweden Bochum Ruhr-University Bochum Germany Zhengzhou University Zhengzhou People’s Republic of China Johannes Gutenberg University of Mainz Mainz Germany University of Turin Turin Italy INFN Turin Italy University of Science and Technology of China Hefei People’s Republic of China State Key Laboratory of Particle Detection and Electronics Beijing People’s Republic of China Southeast University Nanjing People’s Republic of China Joint Institute for Nuclear Research Dubna Russia INFN Sezione di Ferrara Ferrara Italy Peking University Beijing People’s Republic of China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing People’s Republic of China University of Chinese Academy of Sciences Beijing People’s Republic of China National Centre for Nuclear Research Warsaw Poland Institute of Physics and Technology Ulaanbaatar Mongolia INFN Laboratori Nazionali di Frascati Frascati Italy Carnegie Mellon University Pittsburgh USA University of Muenster Muenster Germany Wuhan University Wuhan People’s Republic of China Turkish Accelerator Center Particle Factory Group Istinye University Istanbul Turkey Nankai University Tianjin People’s Republic of China Moscow Institute of Physics and Technology Moscow Russia China University of Geosciences Wuhan People’s Republic of China Soochow University Suzhou People’s Republic of China Henan University Kaifeng People’s Republic of China Nanjing University Nanjing People’s Republic of China North China Electric Power University Beijing People’s Republic of China Tsinghua University Beijing People’s Republic of China Institute of Modern Physics Lanzhou People’s Republic of China Jilin University Changchun People’s Republic of China Hunan University Changsha People’s Republic of

Based on (1.0087 ± 0.0044) × 1010 J/ψ events collected with the BESIII detector at the BEPCII e+e− storage ring, we search for $$ {K}_S^0 $$ invisible decays via the J/ψ → $$ \phi {K}_S^0{K}_S^0 $$ process. No significant signal is observed, and the upper limit of the branching fraction of these invisible decays is set at 8.4 × 10−4 at the 90% confidence level. This is the first experimental search for $$ {K}_S^0 $$ invisible decays.

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Research on the Energy Characteristics of Battlefield Blasting Noise Based on Wavelet Packet

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IOP Conference Series: Materials science and Engineering 2017年第1期274卷

作者： Kai Ding Shoucheng Yan Yichao Zhu Ming Zhao Bi Mei Science and Technology on Near-Surface Detection Laboratory Wuxi China. School of Mechanics & Civil Engineering China University of Mining and Technology Beijing China.

When the acoustic fuse of smart landmines tries to detect and recognize a ground vehicle target, it is usually affected by gun shooting, explosive blasting or other similar noises on the actual battlefield. To improve the target recognition of smart landmines, it would be necessary to study the characteristics of these acoustic signals. Using sample data of the shooting noise of a certain type of rifle, the blasting noise of TNT, and the acoustic signals of a certain type of WAV, the energy characteristics of these noise signals are compared and analyzed. The result shows that the wavelet-packet energy method is effective in describing the characteristics of these acoustic signals with distinct intertype variations, and the frequency at the peak energy value can serve as a signature parameter for recognizing battlefield blasting noise signals from vehicle target signals.

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Application of update lifting morphological wavelet and non-negative matrix factorization for wheeled and tracked vehicles classification

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IOP Conference Series: Earth and Environmental science 2021年第1期660卷

作者： Kai Ding Kai Du Xiaogang Qi Yuelin Xu Junqi Cui Science and Technology on Near –Surface Detection Laboratory Wuxi China 214035 31104 troops Nanjing China 210007 Xidian University Xi'an China 710071

This paper presents an available scheme based on update lifting morphological wavelet (ULMW) and Non-negative matrix factorization (NMF) for wheeled and tracked vehicles classification. The ULWM algorithm which utilizes the update operator, means the morphological filter to replace the linear filter can preserve the impulsive shape details in seismic signal. Meanwhile the NMF method can reduce the computation cost. The traditional linear wavelet analysis and statistical analysis are compared with the presented scheme. Experimental results demonstrate that the presented scheme achieves a promising performance on extracting impulsive features of seismic signal and recognizing ground moving target.

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A Closed-Form Localization Algorithm in Scan-Based Sonar

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Journal of Physics: Conference Series 2021年第1期1848卷

作者： Ping Sun Yingjiao Rong Ocean Exploration Technology Institute Co. Ltd. Wuxi Jiangsu 214035 China Science and Technology on Near-Surface Detection Laboratory Wuxi Jiangsu 214135 China

Multi-station TDOA positioning is a more accurate positioning method, which can locate the acoustic emitter by processing the arrival time of signals collected by three or more measuring stations. This paper presents a closed-form localization algorithm using the angle-of-arrival and difference time of scan time measurements from the scan-based sonar (SBS).The basic principle of multi station TDOA passive location is proposed. Location scheme based on the combination of different platforms is used. The basic principle of TDOA passive location is discussed, as well as the realization scheme of the main problems such as the synchronization of observation station and acoustic target, the selection of ground observation station, error correction and so on.

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Knowledge-Driven Techniques for Geological Map Compilation: Case Study from the Tibetan Plateau

Knowledge-Driven Techniques for Geological Map Compilation: ...

引用

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Wenhua Zhang Lirong Hao Yanggang Wang Li Li Xuezhou He Center for Geophysical Survey China Geological Survey Langfang China Technology Innovation Center for Earth Near Surface Detection China Geological Survey Langfang China Applied Geology Data Research Center Laboratory for Big Data and Decision Beijing China Center of Development Research China Geology Survey Beijing China Ecological Environment Geo-Service Center of Henan

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

This study presents a knowledge-driven approach to geological map compilation, focusing on improving the efficiency and accuracy of geological map-making in the Tibetan Plateau. The Geological Compiling & Map-making (GCM) toolkit, developed to integrate data- and knowledge-driven methodologies, automates data processing and geological feature representation. By leveraging expert geological knowledge, this approach minimizes manual labor and reduces time while maintaining high precision in geological interpretation. The results demonstrate significant advancements in GCM efficiency and accuracy, highlighting the potential of knowledge-driven techniques to make geological map. The knowledge-driven technique offers geologists an improved solution for diverse geological maps making.

关键词： Accuracy Automation Geology Knowledge based systems Refining Focusing Manuals Transforms Data processing Intelligent systems

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Search for KS0 invisible decays

arXiv

引用

arXiv 2025年

作者： Ablikim, M. Achasov, M.N. Adlarson, P. Afedulidis, O. Ai, X.C. Aliberti, R. Amoroso, A. An, Q. Bai, Y. Bakina, O. Balossino, I. Ban, Y. Bao, H.-R. Batozskaya, V. Begzsuren, K. Berger, N. Berlowski, M. Bertani, M. Bettoni, D. Bianchi, F. Bianco, E. Bortone, A. Boyko, I. Briere, R.A. Brueggemann, A. Cai, H. Cai, X. Calcaterra, A. Cao, G.F. Cao, N. Cetin, S.A. Chai, X.Y. Chang, J.F. Che, G.R. Che, Y.Z. Chelkov, G. Chen, C. Chen, C.H. Chen, Chao Chen, G. Chen, H.S. Chen, H.Y. Chen, M.L. Chen, S.J. Chen, S.L. Chen, S.M. Chen, T. Chen, X.R. Chen, X.T. Chen, Y.B. Chen, Y.Q. Chen, Z.J. Choi, S.K. Cibinetto, G. Cossio, F. Cui, J.J. Dai, H.L. Dai, J.P. Dbeyssi, A. de Boer, R.E. Dedovich, D. Deng, C.Q. Deng, Z.Y. Denig, A. Denysenko, I. Destefanis, M. De Mori, F. Ding, B. Ding, X.X. Ding, Y. Ding, Y. Dong, J. Dong, L.Y. Dong, M.Y. Dong, X. Du, M.C. Du, S.X. Duan, Y.Y. Duan, Z.H. Egorov, P. Fan, G.F. Fan, J.J. Fan, Y.H. Fang, J. Fang, J. Fang, S.S. Fang, W.X. Fang, Y.Q. Farinelli, R. Fava, L. Feldbauer, F. Felici, G. Feng, C.Q. Feng, J.H. Feng, Y.T. Fritsch, M. Fu, C.D. Fu, J.L. Fu, Y.W. Gao, H. Gao, X.B. Gao, Y.N. Gao, Y.N. Gao, Yang Garbolino, S. Garzia, I. Ge, P.T. Ge, Z.W. Geng, C. Gersabeck, E.M. Gilman, A. Goetzen, K. Gong, L. Gong, W.X. Gradl, W. Gramigna, S. Greco, M. Gu, M.H. Gu, Y.T. Guan, C.Y. Guo, A.Q. Guo, L.B. Guo, M.J. Guo, R.P. Guo, Y.P. Guskov, A. Gutierrez, J. Han, K.L. Han, T.T. Hanisch, F. Hao, X.Q. Harris, F.A. He, K.K. He, K.L. Heinsius, F.H. Heinz, C.H. Heng, Y.K. Herold, C. Holtmann, T. Hong, P.C. Hou, G.Y. Hou, X.T. Hou, Y.R. Hou, Z.L. Hu, B.Y. Hu, H.M. Hu, J.F. Hu, Q.P. Hu, S.L. Hu, T. Hu, Y. Huang, G.S. Huang, K.X. Huang, L.Q. Huang, P. Huang, X.T. Huang, Y.P. Huang, Y.S. Hussain, T. Hölzken, F. Hüsken, N. in der Wiesche, N. Jackson, J. Janchiv, S. Institute of High Energy Physics Beijing100049 China Beihang University Beijing100191 China Bochum Ruhr-University BochumD-44780 Germany Novosibirsk630090 Russia Carnegie Mellon University PittsburghPA15213 United States Central China Normal University Wuhan430079 China Central South University Changsha410083 China China Center of Advanced Science and Technology Beijing100190 China China University of Geosciences Wuhan430074 China Chung-Ang University Seoul06974 Korea Republic of COMSATS University Islamabad Lahore Campus Defence Road Off Raiwind Road Lahore54000 Pakistan Fudan University Shanghai200433 China GSI Helmholtzcentre for Heavy Ion Research GmbH DarmstadtD-64291 Germany Guangxi Normal University Guilin541004 China Guangxi University Nanning530004 China Hangzhou Normal University Hangzhou310036 China Hebei University Baoding071002 China Helmholtz Institute Mainz Staudinger Weg 18 MainzD-55099 Germany Henan Normal University Xinxiang453007 China Henan University Kaifeng475004 China Henan University of Science and Technology Luoyang471003 China Henan University of Technology Zhengzhou450001 China Huangshan College Huangshan245000 China Hunan Normal University Changsha410081 China Hunan University Changsha410082 China Indian Institute of Technology Madras Chennai600036 India Indiana University BloomingtonIN47405 United States INFN Laboratori Nazionali di Frascati FrascatiI-00044 Italy INFN Sezione di Perugia PerugiaI-06100 Italy University of Perugia PerugiaI-06100 Italy INFN Sezione di Ferrara FerraraI-44122 Italy University of Ferrara FerraraI-44122 Italy Inner Mongolia University Hohhot010021 China Institute of Modern Physics Lanzhou730000 China Institute of Physics and Technology Peace Avenue 54B Ulaanbaatar13330 Mongolia Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica1000000 Chile Jilin University Changchun130012 China Johannes Gutenberg University of Mainz

Based on (1.0087±0.0044)×1010 J/ψ events collected with the BESIII detector at the BEPCII e+e− storage ring, we search for KS0 invisible decays via the J/ψ → KS0 KS0 process. No significant signal is observed, and the upper limit of the branching fraction of these invisible decays is set at 8.4 × 10−4 at the 90% confidence level. This is the first experimental search for KS0 invisible decays. Copyright © 2025, The Authors. All rights reserved.

关键词： Storage rings

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Sonar image reconstruction method based on adaptive compensation of very shallow water acoustic channel features

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Journal of Physics: Conference Series 2021年第1期1861卷

作者： Menglei Li Yingjiao Rong Shirui Wang Yuchen Wang Ping Sun CSSC Ocean Exploration Technology Institute Co. Ltd. Wuxi Jiangsu 214035 China Science and Technology on Near-Surface Detection Laboratory Wuxi Jiangsu 214135 China

Aiming at the problem of eliminating the influence of channel interference on the detection results in the process of using sonar to detect very shallow water targets, a sonar image reconstruction method based on adaptive compensation of very shallow water acoustic channel features is proposed. Standard acoustic source and remote server are used to complete the adaptive calibration process of channel characteristic parameters before detection. The calibrated channel characteristic parameters are used in the subsequent beamforming algorithm to compensate the channel interference. The time-of-flight parameters calculated by the compensation algorithm are used in the subsequent point cloud generation and image reconstruction. Simulation and field test show that the proposed compensation method can improve sonar target detection ability by 25% in very shallow water and complex acoustic channel environment, and the quality of sonar image reconstruction is significantly improved.

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Research on dynamic ocean surface simulation based on 3D sonar image

引用

Journal of Physics: Conference Series 2021年第1期1961卷

作者： Menglei Li Hejun Jiang Yingjiao Rong Shirui Wang Ping Sun CSSC Ocean Exploration Technology Institute Co. Ltd. Wuxi Jiangsu 214135 China Science and Technology on Near-Surface Detection Laboratory Wuxi Jiangsu 214035 China

The motion state of the ocean surface is changeable and has special irregularity. It is impossible to describe these characteristics in detail, which leads to the low accuracy of simulation for the sea surface. A simulation method of realistic dynamic ocean surface based on 3D sonar image algorithm is proposed. The number of adjacent points iteration of two adjacent images is calculated, and all the dynamic shallow surface sonar images are matched and combined. According to the edge coordinate information between the nearest adjacent images, the overlapping part between the two sonar image frames is analyzed. The two frames of sonar images beyond the threshold range of overlap are registered by ICP algorithm, The simulation of the dynamic ocean surface with the statistical model and FFT based wave generation method is carried out. The simulation results show that the research method of 3D sonar image algorithm is more accurate and can achieve more real simulation results.

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