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Search for Gravitational Waves Associated with Fast Radio Bursts Detected by CHIME/FRB during the LIGO-Virgo Observing Run O3a

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Astrophysical Journal 2023年第2期955卷 155-155页

作者： Abbott, R. Abbott, T.D. Acernese, F. Ackley, K. Adams, C. Adhikari, N. Adhikari, R.X. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Agatsuma, K. Aggarwal, N. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Allocca, A. Altin, P.A. Amato, A. Anand, C. Anand, S. Ananyeva, A. Anderson, S.B. Anderson, W.G. Ando, M. Andrade, T. Andres, N. Andrić, T. Angelova, S.V. Ansoldi, S. Antelis, J.M. Antier, S. Appert, S. Arai, Koya Arai, Y. Araki, S. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Aronson, S.M. Arun, K.G. Asada, H. Asali, Y. Ashton, G. Aso, Y. Assiduo, M. Aston, S.M. Astone, P. Aubin, F. Austin, C. Babak, S. Badaracco, F. Bader, M.K.M. Badger, C. Bae, S. Bae, Y. Baer, A.M. Bagnasco, S. Bai, Y. Baiotti, L. Baird, J. Bajpai, R. Ball, M. Ballardin, G. Ballmer, S.W. Balsamo, A. Baltus, G. Banagiri, S. Bankar, D. Barayoga, J.C. Barbieri, C. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Bartlett, J. Barton, M.A. Bartos, I. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Bejger, M. Belahcene, I. Benedetto, V. Beniwal, D. Bennett, T.F. Bentley, J.D. BenYaala, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bhandare, R. Bhardwaj, U. Bhattacharjee, D. Bhaumik, S. Bilenko, I.A. Billingsley, G. Bini, S. Birney, R. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boer, M. Bogaert, G. Boldrini, M. Bonavena, L.D. Bondu, F. Bonilla, E. Bonnand, R. Booker, P. Boom, B.A. Bork, R. Boschi, V. Bose, N. Bose, S. Bossilkov, V. Boudart, V. Bouffanais, Y. Boumerdassi, A. Bozzi, A. Bradaschia, C. Brady, P.R. Bramley, A. Branch, A. Branchesi, M. Brau, J.E. Breschi, M. Briant, T. Briggs, J.H. Brillet, A. Brinkmann, M. Brockill, P. Brooks, A.F. Brooks, J. Brown, D.D. Brunett, S. Bruno, G. Bruntz, R. Bryant, J. Buchanan, J. B LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Louisiana State University Baton Rouge 70803 LA United States Dipartimento di Farmacia Università di Salerno Fisciano Salerno I-84084 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia LIGO Livingston Observatory Livingston 70754 LA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena Jena D-07743 Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston 60208 IL United States Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino Torino I-10125 Italy Università di Napoli “Federico II Complesso Universitario di Monte S. Angelo Napoli I-80126 Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière Villeurbanne F-69622

We search for gravitational-wave (GW) transients associated with fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst Project, during the first part of the third observing run of Advanced LIGO and Advanced Virgo (2019 April 1 15:00 UTC-2019 October 1 15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets both binary neutron star (BNS) and neutron star-black hole (NSBH) mergers. A targeted search for generic GW transients was conducted on 40 FRBs. We find no significant evidence for a GW association in either search. Given the large uncertainties in the distances of our FRB sample, we are unable to exclude the possibility of a GW association. Assessing the volumetric event rates of both FRB and binary mergers, an association is limited to 15% of the FRB population for BNS mergers or 1% for NSBH mergers. We report 90% confidence lower bounds on the distance to each FRB for a range of GW progenitor models and set upper limits on the energy emitted through GWs for a range of emission scenarios. We find values of order 10⁵¹-10⁵⁷ erg for models with central GW frequencies in the range 70-3560 Hz. At the sensitivity of this search, we find these limits to be above the predicted GW emissions for the models considered. We also find no significant coincident detection of GWs with the repeater, FRB 20200120E, which is the closest known extragalactic FRB. © 2023. The Author(s). Published by the American Astronomical Society.

关键词： Gravitational wave astronomy Radio transient sources

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High performance porous iron oxide-carbon nanotube nanocomposite as an anode material for lithium-ion batteries

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Electrochimica Acta 2016年 212卷 179-186页

作者： Qian Lin Jie Wang Yijun Zhong Jaka Sunarso Moses O. Tadé Li Li Zongping Shao Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM) State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical Engineering Nanjing Tech University No.5 Xin Mofan Road Nanjing 210009 PR China College of Chemical Engineering Nanjing Forestry University Nanjing 210037 PR China Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia Department of Chemical Engineering Curtin University Perth WA 6845 Australia Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM) State Key Laboratory of Materials-Oriented Chemical Engineering College of Energy Nanjing Tech University No.5 Xin Mofan Road Nanjing 210009 PR China

Here, we showed that relatively high content of Fe 3 O 4 nanoparticles (up to 83 wt. %) can be homogeneously dispersed into carbon nanotubes (CNTs) conductive networks using non-aqueous media by refluxing method. Three different Fe 3 O 4 -CNTs composites were prepared, i.e., Fe 3 O 4 -CNTs-50, Fe 3 O 4 -CNTs-80 and Fe 3 O 4 -CNTs-90 that contain increasing amount of Fe 3 O 4 from 50 wt. % to 83 wt. % and to 89 wt. %. These composites have higher surface area and higher pore volume than Fe 3 O 4 component due to CNTs content. The best composite, i.e., Fe 3 O 4 -CNTs-80 demonstrated negligible capacity loss up to 100 cycles and high discharge capacity of 930 mA h g −1 at 100th cycle and 100 mA g −1 current discharge rate. This composite also exhibited excellent rate capability where up to the 78.8% of original capacity can be retained at high current discharge rate of 1000 mA g −1 . These performances were enabled by a unique porous architecture based on homogenous dispersion of Fe 3 O 4 nanoparticles into CNTs networks that leads to short Li + diffusion path, high electric conductivity and buffering space to accommodate large volume change of Fe 3 O 4 component during the charge-discharge processes.

关键词： Anode Carbon nanotubes Fe 3 O 4 High performance Lithium-ion batteries

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Experimental Study of the Interaction between Building Clusters and Flash Floods

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Journal of Mountain Science 2015年第5期12卷 1334-1344页

作者： DU Jun HE Xiao-yan WANG Zhao-yin ZHANG Chen-di LI Wen-xin Changjiang River Scientific Research Institute China Institute of Water Resources and Hydropower Research Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources State Key Laboratory of Hydroscience and Engineering Tsinghua University Gansu Coal Geological Exploration Institute

In recent years, flash flood disasters have occurred frequently in southwest China due to the increased frequency of extreme climate events. To solve this problem, great efforts have been made in studying the process of flash flood. However, little attention was paid on bearing body of hazard, the clusters of buildings. Thus the real disaster mechanism of flash flood remains ***, based on the experiments of artificial flash floods in a conceptual solid model, this paper focuses on the flood-impacted inundation characteristics of the building clusters at different locations of the gully model, in order to obtain a better understanding of the disaster process and the interaction between the flash floods and building clusters. The results showed that, in a typical smallscale flash flood gully with hot and dry climate, 1)clusters of buildings on an alluvial fan could reduce about 35% of the flooding area by blocking the diffusion of the flood to the depression areas, and could also promote the deposition in lower reaches of the river channel by blocking the overbank flow from going back into the channel, making the width-depth ratio of the channel larger. 2) The flash flood rates of disaster and hazard on the alluvial fan are generally higher than that of the inner gully. For the inner gully,buildings located on the beaches along the lower river and the transitional areas of the straight channel and channel bends can easily be affected because of their lower elevations. For the alluvial fan, buildings nearby the meanders suffer the greatest impacts because of bank collapsing and flooding. 3) The safe vertical distance from a building to the river channel is 13 m for the buildings in the inner gully under extreme floods. Below this threshold, the smaller the vertical distance is, the greater the risk exposure is. For the buildings on the alluvial fan, especially for the buildings near the concave bank of the top rush point,the horizontal distance is more

关键词： Flash floods Building clusters Settlement＇s distribution Disaster process Fieldmodel experiment

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Madness: A multiresolution, adaptive numerical environment for scientific simulation

Madness: A multiresolution, adaptive numerical environment f...

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作者： Harrison, Robert J. Beylkin, Gregory Bischoff, Florian A. Calvin, Justus A. Fann, George I. Fosso-Tande, Jacob Galindo, Diego Hammond, Jeff R. Hartman-Baker, Rebecca Hill, Judith C. Jia, Jun Kottmann, Jakob S. Ou, M-J. Yvonne Pei, Junchen Ratcliff, Laura E. Reuter, Matthew G. Richie-Halford, Adam C. Romero, Nichols A. Sekino, Hideo Shelton, William A. Sundahl, Bryan E. Thornton, W. Scott Valeev, Edward F. Vázquez-Mayagoitia, Álvaro Vence, Nicholas Yanai, Takeshi Yokoi, Yukina Stony Brook University Stony BrookNY11794 United States University of Colorado at Boulder BoulderCO80309 United States Institut für Chemie Humboldt-Universität Zu Berlin Unter den Linden 6 Berlin10099 Germany Department of Chemistry Virginia Tech. BlacksburgVA24061 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemistry and Biochemistry Florida State University TallahasseeFL32306 United States Parallel Computing Lab Intel Corporation PortlandOR97219 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States LinkedIn Mountain ViewCA94043 United States Department of Mathematical Sciences University of Delaware NewarkDE19716 United States State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Department of Physics University of Washington SeattleWA98195 United States Computer Science and Engineering Toyohashi University of Technology ToyohashiAichi441-8580 Japan Louisiana State University Baton RougeLA70803 United States Department of Physics LaSierra University RiversideCA92505 United States Theoretical and Computational Molecular Science Institute for Molecular Science OkazakiAichi444-8585 Japan

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision that are based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics. © 2016 Society for Industrial and Applied Mathematics.

关键词： Multiresolution analysis

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Junk code elimination based on idle object set

Junk code elimination based on idle object set

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International Conference on Information Science and Technology (ICIST)

作者： Wei Lin Jin-long Fei Rui-jie Cai State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Junk code increase manual analysis difficulty in reverse engineering, and seriously disturb the automatic analysis process of ant code obfuscating, so find a junk code removing method has a great significance in the field of reverse engineering. Based on this, aiming at the problem that the executable context-dependent junk code is difficult to remove automatically, this paper proposes a junk code removing method based on idle register slicing, through analyzing data dependencies between instructions, get all the idle register of all instruction in code block, slice the code block by idle registers respectively, remove all the junk code in the slice instruction. Experiments show that, this method can remove embedded executable junk code rapidly and accurately, improve the efficiency of reverse engineering.

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Fault-Diagnosis for Reciprocating Compressors Using Big Data

Fault-Diagnosis for Reciprocating Compressors Using Big Data

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IEEE International Conference on Big Data computing Service and Applications (BigDataService)

作者： Keerqinhu Guanqiu Qi Wei-Tek Tsai Yi Hong Wenxiang Wang Guangxin Hou Zhiqin Zhu Research Center of China National Offshore Oil Corporation Beijing China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA State Key Laboratory of Software Development Environment Beihang University Beijing China College of Automation Chongqing University Chongqing China

Reciprocating compressors are widely used in the petroleum industry, and a small fault in reciprocating compressors may cause serious issues in operation. Monitoring and detecting potential faults help compressors to continue normal operation. This paper proposes a fault-diagnosis system for compressors using machine-learning techniques to detect potential faults. The system has been evaluated using 100TB operation data collected from China National Offshore Oil Corporation, and the data are first de-noised, coded, and then SVM classification is applied, with 50% of data used for training, the remaining for testing. The results demonstrated that the system can efficiently diagnose potential faults in compressors with 80% accuracy.

关键词： Compressors Fault diagnosis Data analysis Support vector machines Servers Testing Sparse matrices

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Laplacian Scale-Space Behavior of Planar Curve Corners

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2015年第11期37卷 2207-2217页

作者： Zhang, Xiaohong Qu, Ying Yang, Dan Wang, Hongxing Kymer, Jeff Key Laboratory of Dependable Service Computing Cyber Physical Society Ministry of Education Chongqing400044 China School of Software Engineering Chongqing University Chongqing401331 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China

Scale-space behavior of corners is important for developing an efficient corner detection algorithm. In this paper, we analyze the scale-space behavior with the Laplacian of Gaussian (LoG) operator on a planar curve which constructs Laplacian Scale Space (LSS). The analytical expression of a Laplacian Scale-Space map (LSS map) is obtained, demonstrating the Laplacian Scale-Space behavior of the planar curve corners, based on a newly defined unified corner model. With this formula, some Laplacian Scale-Space behavior is summarized. Although LSS demonstrates some similarities to Curvature Scale Space (CSS), there are still some differences. First, no new extreme points are generated in the LSS. Second, the behavior of different cases of a corner model is consistent and simple. This makes it easy to trace the corner in a scale space. At last, the behavior of LSS is verified in an experiment on a digital curve. © 2015 IEEE.

关键词： Laplace transforms

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Dress image retrieval based on improvement of deep learning

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ICIC Express Letters, Part B: Applications 2016年第6期7卷 1371-1376页

作者： Zhang, Yang Fu, Jinhua College of Mathematics and Information Science Zhengzhou University of Light Industry No. 5 Dongfeng Road Zhengzhou450002 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou450000 China School of Computer and Communication Engineering Zhengzhou University of Light Industry No. 5 Dongfeng Road Zhengzhou450002 China

Online dress shopping is becoming an increasingly popular way of shopping and nowadays image-based dress retrieval plays an important role in identifying their favorite items for customers. In this paper, we present an efficient retrieval of dress image via improvement of deep learning. The deep learning is usually realized by convolutional neural networks (CNNs). We design the CNN by four sublayers followed by an output, which gives a retrieval of dress image. Further, we improve CNN by activation function to induce the sparsity in the hidden units. The deep network can be efficiently trained using the improvement. The results show that the matching results can be efficiently acquired, which proves the effectiveness of our method such as accuracy and computation complexity. © 2016 ICIC International.

关键词： Complex networks

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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

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Classical and Quantum Gravity 2023年第8期40卷 085015-085015页

作者： H Abe T Akutsu M Ando A Araya N Aritomi H Asada Y Aso S Bae Y Bae R Bajpai K Cannon Z Cao E Capocasa M Chan C Chen D Chen K Chen Y Chen C-Y Chiang Y-K Chu S Eguchi M Eisenmann Y Enomoto R Flaminio H K Fong Y Fujii Y Fujikawa Y Fujimoto I Fukunaga D Gao G-G Ge S Ha I P W Hadiputrawan S Haino W-B Han K Hasegawa K Hattori H Hayakawa K Hayama Y Himemoto N Hirata C Hirose T-C Ho B-H Hsieh H-F Hsieh C Hsiung H-Y Huang P Huang Y-C Huang Y-J Huang D C Y Hui S Ide K Inayoshi Y Inoue K Ito Y Itoh C Jeon H-B Jin K Jung P Jung K Kaihotsu T Kajita M Kakizaki M Kamiizumi N Kanda T Kato K Kawaguchi C Kim J Kim J C Kim Y-M Kim N Kimura T Kiyota Y Kobayashi K Kohri K Kokeyama A K H Kong N Koyama C Kozakai J Kume Y Kuromiya S Kuroyanagi K Kwak E Lee H W Lee R Lee M Leonardi K L Li P Li L C -C Lin C-Y Lin E T Lin F-K Lin F-L Lin H L Lin G C Liu L-W Luo M Ma’arif E Majorana Y Michimura N Mio O Miyakawa K Miyo S Miyoki Y Mori S Morisaki N Morisue Y Moriwaki K Nagano K Nakamura H Nakano M Nakano Y Nakayama T Narikawa L Naticchioni L Nguyen Quynh W-T Ni T Nishimoto A Nishizawa S Nozaki Y Obayashi W Ogaki J J Oh K Oh M Ohashi T Ohashi M Ohkawa H Ohta Y Okutani K Oohara S Oshino S Otabe K-C Pan A Parisi J Park F E Pe na Arellano S Saha Y Saito K Sakai T Sawada Y Sekiguchi L Shao Y Shikano H Shimizu K Shimode H Shinkai T Shishido A Shoda K Somiya I Song R Sugimoto J Suresh T Suzuki H Tagoshi H Takahashi R Takahashi S Takano H Takeda M Takeda K Tanaka T Tanaka S Tanioka A Taruya T Tomaru T Tomura L Trozzo T Tsang J-S Tsao S Tsuchida T Tsutsui D Tuyenbayev N Uchikata T Uchiyama A Ueda T Uehara K Ueno G Ueshima T Ushiba M H P M van Putten J Wang T Washimi C Wu H Wu T Yamada K Yamamoto T Yamamoto K Yamashita R Yamazaki Y Yang S Yeh J Yokoyama T Yokozawa T Yoshioka H Yuzurihara S Zeidler M Zhan H Zhang Y Zhao Z-H Zhu The KAGRA Collaboration Graduate School of Science Tokyo Institute of Technology Meguro-ku Tokyo 152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Gravitational Wave Science Project Hirosaki University Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences Yuseong-gu Daejeon 34047 Republic of Korea School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Beijing 100875 People’s Republic of China Department of Applied Physics Fukuoka University Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy National Tsing Hua University Hsinchu 30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City 32001 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan Institute of Physics Academia Sinica Nankang Taipei 11529 Taiwan Univ. Grenoble Alpes Laboratoire d’Annecy de Physique des Particules (LAPP) Université Savoie M

During April 7–21 2020, KAGRA conducted its first scientific observation in conjunction with the GEO600 detector. The dominant noise sources during this run were found to be suspension control noise in the low-frequency range and acoustic noise in the mid-frequency range. In this study, we show that their contributions in the observational data can be reduced by a signal processing method called independent component analysis (ICA). The model of ICA is extended from that studied in the initial KAGRA data analysis to account for frequency dependence, while the linearity and stationarity of the coupling between the interferometer and the noise sources are still assumed. We identify optimal witness sensors in the application of ICA, leading to successful mitigation of these two dominant contributions. We also analyze the stability of the transfer functions for the entire two weeks of data to investigate the applicability of the proposed subtraction method in gravitational wave searches.

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Message extraction method for plaintext LSB steganography in spatial domain

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) 2015年第5期43卷 73-78页

作者： Liu, Jiufen Wang, Junchao Han, Tao State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science Beijing100093 China

Under the condition of plaintext, by using the bit's statistical property of both English and Chinese words, a message extraction method for the sequential LSB (least significant bit) steganography in spatial domain was promoted and the extraction message accuracy was improved. Next a message extraction method for the random LSB steganography in spatial domain was put forward. Firstly, the unicity distance was obtained to recover the stego key;secondly, by stego key exhaustive searching, each stego key's steganographic path was determined and the LSB was extracted with the length of T;thirdly, a new subsequence was gained with the first bit of the LSB as the beginning and 7 sampling interval, and the true stego key could be determined if the subsequence was the sequence of all zeros or all non-zeros. Besides, the leak detection was also studied. The experimental results have proved the feasibility and effectiveness of the method. ©, 2015, Huazhong University of Science and Technology. All right reserved.

关键词： Extraction

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