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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

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A new quantity for statistical analysis: "Scaling invariable benford distance"

arXiv

引用

arXiv 2018年

作者： Luo, Peiyan Li, Yongqing College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China College of Physical Science and Technology Sichuan University Chengdu China

For the first time, we introduce "Scaling invariable Benford distance" and "Benford cyclic graph", which can be used to analyze any data set. Using the quantity and the graph, we analyze some date sets with common distributions, such as normal, exponent, etc., find that different data set has a much different value of "Scaling invariable Benford distance" and different figure feature of "Benford cyclic graph". We also explore the influence of data size on "Scaling invariable Benford distance", and find that it firstly reduces with data size increasing, then approximate to a fixed value when the size is large enough. Copyright © 2018, The Authors. All rights reserved.

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Constraints on Heavy Decaying Dark Matter from 570 Days of LHAASO Observations

引用

Physical Review Letters 2022年第26期129卷 261103-261103页

作者： Zhen Cao F. Aharonian Q. An Axikegu L. X. Bai Y. X. Bai Y. W. Bao D. Bastieri X. J. Bi Y. J. Bi J. T. Cai Zhe Cao J. Chang J. F. Chang E. S. Chen Liang Chen Long Chen M. J. Chen M. L. Chen Q. H. Chen S. H. Chen S. Z. Chen T. L. Chen Y. Chen H. L. Cheng N. Cheng Y. D. Cheng S. W. Cui X. H. Cui Y. D. Cui B. D’Ettorre Piazzoli B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe K. K. Duan J. H. Fan Y. Z. Fan Z. X. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng X. T. Feng Y. L. Feng B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao W. K. Gao M. M. Ge L. S. Geng G. H. Gong Q. B. Gou M. H. Gu F. L. Guo J. G. Guo X. L. Guo Y. Q. Guo Y. Y. Guo Y. A. Han H. H. He H. N. He S. L. He X. B. He Y. He M. Heller Y. K. Hor C. Hou X. Hou H. B. Hu Q. Hu S. Hu S. C. Hu X. J. Hu D. H. Huang W. H. Huang X. T. Huang X. Y. Huang Y. Huang Z. C. Huang X. L. Ji H. Y. Jia K. Jia K. Jiang Z. J. Jiang M. Jin M. M. Kang T. Ke D. Kuleshov K. Levochkin B. B. Li Cheng Li Cong Li F. Li H. B. Li H. C. Li H. Y. Li J. Li Jian Li Jie Li K. Li W. L. Li X. R. Li Xin Li Y. Z. Li Zhe Li Zhuo Li E. W. Liang Y. F. Liang S. J. Lin B. Liu C. Liu D. Liu H. Liu H. D. Liu J. Liu J. L. Liu J. S. Liu J. Y. Liu M. Y. Liu R. Y. Liu S. M. Liu W. Liu Y. Liu Y. N. Liu W. J. Long R. Lu Q. Luo H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao A. Masood Z. Min W. Mitthumsiri Y. C. Nan Z. W. Ou B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo A. Sáiz C. Y. Shao L. Shao O. Shchegolev X. D. Sheng J. Y. Shi H. C. Song Yu. V. Stenkin V. Stepanov Y. Su Q. N. Sun X. N. Sun Z. B. Sun P. H. T. Tam Z. B. Tang W. W. Tian B. D. Wang C. Wang H. Wang H. G. Wang J. C. Wang J. S. Wang L. P. Wang L. Y. Wang R. Wang R. N. Wang W. Wang X. G. Wang X. Y. Wang Y. Wang Y. D. Wang Y. J. Wang Y. P. Wang Z. H. Wang Z. X. Wang Zhen Wang Zheng Wang D. M. Wei J. J. Wei Y. J. Wei T. Wen C. Y. Wu H. R. Wu S. Wu X. F. Wu Y. S. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao G. G. Xin Y. L. Xin Y. Xing Z. Xiong D. L. Xu R. X. Xu L. Xue D. H. Yan J. Z. Yan Key Laboratory of Particle Astrophyics and Experimental Physics Division and Computing Center Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing China University of Chinese Academy of Sciences 100049 Beijing China TIANFU Cosmic Ray Research Center Chengdu Sichuan China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 69029 Heidelberg Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology and School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China College of Physics Sichuan University 610065 Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences 210023 Nanjing Jiangsu China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 200030 Shanghai China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education 850000 Lhasa Tibet China National Astronomical Observatories Chinese Academy of Sciences 100101 Beijing China Hebei Normal University 050024 Shijiazhuang Hebei China School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University 519000 Zhuhai & 510275 Guangzhou Guangdong China Dipartimento di Fisica dell’Università di Napoli “Federico II ” Complesso Universitario di Monte Sant’Angelo via Cinthia 80126 Napoli Italy School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de Sci

The kilometer square array (KM2A) of the large high altitude air shower observatory (LHAASO) aims at surveying the northern γ-ray sky at energies above 10 TeV with unprecedented sensitivity. γ-ray observations have long been one of the most powerful tools for dark matter searches, as, e.g., high-energy γ rays could be produced by the decays of heavy dark matter particles. In this Letter, we present the first dark matter analysis with LHAASO-KM2A, using the first 340 days of data from 1/2-KM2A and 230 days of data from 3/4-KM2A. Several regions of interest are used to search for a signal and account for the residual cosmic-ray background after γ/hadron separation. We find no excess of dark matter signals, and thus place some of the strongest γ-ray constraints on the lifetime of heavy dark matter particles with mass between 105 and 109 GeV. Our results with LHAASO are robust, and have important implications for dark matter interpretations of the diffuse astrophysical high-energy neutrino emission.

关键词： Dark matter Gamma ray astronomy

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Design and Dynamic Performance Analysis of an Electro-Hydraulic Robot Joint

Design and Dynamic Performance Analysis of an Electro-Hydrau...

引用

International Conference on Mechanical, Control and Computer engineering (ICMCCE)

作者： Yuanfang Zhao Guangzhu Chen Changwei Miao Changyou Zhang College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu P.R. China

An electro-hydraulic joint is the key component of a hydraulic joint robot, and its structure and performance importantly affect the working performance of the hydraulic robot. The electro-hydraulic joint mainly consists of pressure sensors, an electro-hydraulic proportional reversing valve, an electro-hydraulic proportional relief valve and the hydraulic motor, the electro-hydraulic proportional reversing valve controls the rotation direction and speed of the electro-hydraulic joint, while the electro-hydraulic proportional relief valve controls the working load. After introducing the mechanical and hydraulic system of the electro-hydraulic joint, simulation and experiments are built, and the output torque and speed change rule of the electro-hydraulic joint are analyzed. Structural characteristic and performance analysis of the electro-hydraulic joint lays the foundation of the control principle.

关键词： Handheld computers Valves Mathematical model Conferences Robots Performance analysis

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Research of Backscattering Properties of Vegetation Fire Based On Ground-Based Scatterometer Measurement

Research of Backscattering Properties of Vegetation Fire Bas...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Longfei Tan Wanruo Zhang Zejiang Zhang Hang Yin Xun Yang Ling Tong Sichuan Fire Research Institute of Ministry of Emergency Management Chengdu Sichuan China Glasgow College University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China School of Automation Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China

This paper investigates the backscattering properties of vegetation fire based on ground-based scatterometer measurement in the combustion period. According to the different states of combustion during and after vegetation fire, respectively, X-band and C-band scattering coefficient of full polarization (HH, HV, VH, and VV ) are measured to exploiting the spatial and temporal responses of fire effect on vegetation. Meanwhile, the relevant influencing factors, which include the parameters of vegetation, ground and environment, are obtained in observation area. Besides the process before and after fire, it is discussed in detail for analysis of microwave scattering coefficients changing with different burning degrees in the fire development process. Based on the experimental result, the distinction through combustion is shown by the microwave method effectively, which proves its potential in information extraction from vegetation fire.

关键词： Vegetation mapping Scattering Combustion Backscatter Microwave measurement Microwave imaging Microwave theory and techniques

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Part-Based Feature Aggregation Method for Dynamic Scene Recognition

Part-Based Feature Aggregation Method for Dynamic Scene Reco...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xiaoming Peng Abdesselam Bouzerdoum School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

Existing methods for dynamic scene recognition mostly use global features extracted from the entire video frame or a video segment. In this paper, a part-based method is proposed for aggregating local features from multiple video frames. A pre-trained Fast R-CNN model is used to extract local convolutional layer features from the regions of interest (ROIs) of training images. These features are then clustered to locate representative parts. A set cover problem is formulated to select the discriminative parts, which are further refined by fine-tuning the Fast R-CNN. Local convolutional layer features and fully-connected layer features are extracted using the fine-tuned Fast R-CNN model, and then aggregated separately from a video segment to form two feature representations. They are concatenated into a global feature representation. Experimental results show that the proposed method outperforms several state-of-the-art features on two dynamic scene datasets.

关键词： Feature extraction Image segmentation Training Three-dimensional displays Optical imaging Aggregates Optimization

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An improved SNIP denoising algorithm applied in EDXRF

引用

Journal of Physics: Conference Series 2019年第1期1423卷

作者： Liangquan Ge Hui Li Chuanfeng Tang Fei Li Kai Zhu Jin Yang Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Based on the phenomenon of peak height reduction in existing denoising algorithms, this paper modifies the traditional SNIP algorithm, proposes an improved SNIP algorithm and applies it to EDXRF denoising. Two evaluation parameters RMSE and PCC are used to compare the improved SNIP algorithm with the traditional SNIP algorithm. The experimental results show that the improved SNIP algorithm has great advantages both in similarity and relevance, and the results of the improved SNIP algorithm are more consistent with the characteristics of the original data peak than the original SNIP algorithm, which is beneficial to the qualitative and quantitative analysis of the energy dispersive X-ray fluorescence spectrum.

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Application of small sample BP neural network in quantitative analysis of EDXRF

引用

Journal of Physics: Conference Series 2019年第1期1423卷

作者： Fei Li Zhuoyao Tang Liangquan Ge Nanxing Wu Feng Cheng Kun Sun Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Quantitative analysis of EDXRF is affected by matrix effect, randomness of nuclear radiation, interaction of elements, statistical fluctuation of radiation detection process, etc. Its algorithm needs to consider many factors, and the established functional relationship is often a complex non-linear function. Therefore, effective quantitative analysis method has always been the key research direction of spectral resolution technology. In this paper, we use MATLAB software to study the effect of BP neural network in quantitative analysis of EDXRF by establishing the non-linear relationship between the counting rate of each element and the content of a single element in a small sample. The experimental results show that the small sample neural network can establish a stable structure and be applied to quantitative analysis of EDXRF, but the number of samples restricts the accuracy of prediction results, most of which can only guarantee 20% to 30% relative error.

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Research on gamma spectrum semi-quantitative analysis based on convolutional neural network

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Journal of Physics: Conference Series 2019年第1期1423卷

作者： Fei Li Jing Wang Liangquan Ge Fangyan Hu Feng Cheng Kun Sun Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

In the field of practical application of nuclear technology, one of the vital steps is the interpretation of gamma energy spectrum, which can obtain the type and content of radionuclides and achieve further analyzation. Convolutional neural network is one the most basic and effective algorithm structures in deep learning because it has local receptive field, weight sharing and down sampling structure, it solves the problem of parameter expansion caused by full connection and reduces the number of weights. That can be used to study the resolution of gamma-ray spectra of convolutional neural networks. This paper introduces the spectral forming principle of gamma spectra and the convolutional neural network, and USES the convolutional neural network to study the spectral decomposition of gamma spectra. In this paper, a multi-layer convolution neural network model is built based on C# software. The convolution neural network is applied to gamma-ray spectrum decomposition, and the U, Th, K nuclides are identified and semi-quantitatively calculated. By identifying and analysing different energy spectrum data, it is shown that the modified model structure can be applied to the spectral decomposition of gamma spectrum.

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First experimental constraints on WIMP couplings in the effective field theory framework from CDEX

arXiv

引用

arXiv 2020年

作者： Wang, Y. Zeng, Z. Yue, Qian Yang, L.T. Kang, K.J. Li, Y.J. Agartioglu, M. An, H.P. Chang, J.P. Chen, J.H. Chen, Y.H. Cheng, J.P. Chiang, C.Y. Dai, W.H. Deng, Z. Geng, X.P. Gong, H. Guo, Q.J. Guo, X.Y. He, H.J. He, L. He, S.M. Hu, J.W. Huang, T.S. Huang, H.X. Jia, H.T. Jia, L.P. Li, H.B. Li, J.M. Li, J. Li, M.X. Li, R.M.J. Li, X. Li, Y.L. Liao, B. Lin, F.K. Lin, S.T. Liu, S.K. Liu, Y.D. Liu, Y.Y. Liu, Z.Z. Ma, H. Mao, Y.C. Nie, Q.Y. Ning, J.H. Pan, H. Qi, N.C. Qiao, C.K. Ren, J. Ruan, X.C. Shang, C.S. Sharma, V. She, Z. Singh, L. Singh, M.K. Sun, T.X. Tang, C.J. Tang, W.Y. Tian, Y. Wang, G.F. Wang, L. Wang, Q. Wang, Y.C. Wang, Y.X. Wang, Z. Wong, H.T. Wu, S.Y. Wu, Y.C. Xing, H.Y. Xu, Y. Xue, T. Yan, Y.L. Yi, N. Yu, C.X. Yu, H.J. Yue, J.F. Zeng, M. Zhang, B.T. Zhang, L. Zhang, F.S. Zhao, M.G. Zhou, J.F. Zhou, Z.Y. Zhu, J.J. Key Laboratory of Particle and Radiation Imaging Ministry of Education Department of Engineering Physics Tsinghua University Beijing100084 China Department of Physics Tsinghua University Beijing100084 China Institute of Physics Academia Sinica Taipei11529 Taiwan NUCTECH Company Beijing100084 China YaLong River Hydropower Development Company Chengdu610051 China College of Nuclear Science and Technology Beijing Normal University Beijing100875 China School of Physics Peking University Beijing100871 China Sino-French Institute of Nuclear and Technology Sun Yet-sen University Zhuhai519082 China Department of Nuclear Physics China Institute of Atomic Energy Beijing102413 China College of Physics Sichuan University Chengdu610065 China Department of Physics Banaras Hindu University Varanasi221005 India Department of Physics Beijing Normal University Beijing100875 China School of Physics Nankai University Tianjin300071 China

We present weakly interacting massive particles (WIMPs) search results performed using two approaches of effective field theory from the China Dark Matter Experiment (CDEX), based on the data from both CDEX-1B and CDEX-10 stages. In the nonrelativistic effective field theory approach, both time-integrated and annual modulation analyses were used to set new limits for the coupling of WIMP-nucleon effective operators at 90% confidence level (C.L.) and improve over the current bounds in the low mχ region. In the chiral effective field theory approach, data from CDEX-10 were used to set an upper limit on WIMP-pion coupling at 90% C.L. We for the first time extended the limit to the mχ 2 region. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

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