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Sparse belief propagation algorithm based on latent dirichlet allocation

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 2015年 30卷 506-511页

作者： Tu, Xionggang Chen, Jun Yang, Lu Yan, Jianfeng School of Design and Arts Zhejiang Industry Polytechnic College ShaoxingZhejiang312000 China School of Computer Science and Technology Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Latent Dirichlet allocation(LDA) is a popular and unsupervised tool for reducing dimension, has been applied in text mining and information retrieval. Belief propagation is competitive in both speed and accuracy compared with other inference algorithms of LDA. In this paper, we propose belief propagation algorithm with sparseness constraints and use L1-norm, L2-norm to measure the sparseness of vector. In iteration, we project the topic-word distribution to a sparse space for getting better semantic representation. Experimental results show that sparse belief propagation algorithm improves the accuracy of text clustering and classification than belief propagation, Gibbs sampling and nonnegative matrix factorization. ©, 2015, The editorial office of Transaction of China Electrotechnical Society. All right reserved.

关键词： Belief propagation

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The Novel Sensor Network Structure for Classification processing Based on the Machine Learning Method of the ACGAN

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Sensors (Switzerland) 2019年第14期19卷 3145.-3145.页

作者： Chen, Yuantao Tao, Jiajun Wang, Jin Chen, Xi Yang, Kai School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportatio Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Hunan Institute of Scientific and Technical Information Changsha410001 China Electronics & Information School Yangtze University Jingzhou434023 China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha410005 China

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more ekective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions. © Sensors (Switzerland) 2019. All Rights Reserved.

关键词： Generative adversarial networks

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The Effect of Mn Doping on the Electrical and Magnetic Properties of Cr2te3 Thin Films

SSRN

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SSRN 2024年

作者： Luo, Fu-Sheng Zhang, Jie-Rui Wang, Zhao-Cai Tang, Fang Fang, Yong Zhou, Wei-Ping School of Physics and Electronic Information Gannan Normal University Ganzhou341000 China School of Materials and Mechanic & Electrical Engineering Jiangxi Science and Technology Normal University Nanchang330038 China Jiangsu Laboratory of Advanced Functional Materials Department of Physics Changshu Institute of Technology Changshu215500 China School of Physics and Materials Science Nanchang University Nanchang330031 China Fujian Provincial Key Laboratory of Advanced Materials Processing and Application China

Mn doped Cr2-xMnxTe3 thin films were prepared by molecular beam epitaxy technology. The effect of Mn doping concentration on the structural, morphological, electrical and magnetic performance of Cr2Te3 thin films were detailed analyzed. By varying the beam current of the Mn source, it is found that Mn can be doped into the Cr2Te3 thin film when the Mn source temperature is lower than 700 ℃, while the Mn tends to bind with Te as MnTe crystals for Mn source temperature higher than 700 ℃. Cr2-xMnxTe3 films all exhibt strong ferromagnetism with a Curie temperature of around 175 K. The magnetization of the films decreases with increasing Mn doping concentration. A notable negative magnetoresistance effect is observed with the highest MR ratio about -60% in the vicinity of the Curie temperature. A clear topological Hall signal was observed in the sample with a Mn source temperature of 700 ℃ at low temperature (2 K). These results imply that the electrical and magnetic properties of Cr2Te3 materials can be effectively controlled by doping to meet the practical needs of different fields. © 2024, The Authors. All rights reserved.

关键词： Thin films

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A Photonics-aided MMW OFDM Joint Radar and Communication System with Velocity Accuracy Improvement

A Photonics-aided MMW OFDM Joint Radar and Communication Sys...

引用

Applied Sensing Conference (APSCON), IEEE

作者： Lanfeng Peng Dongxiang Luo Yaoqiang Xiao Fan Li School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou China Institute of Semiconductors South China Normal University Guangzhou China School of Chemistry and Chemical Engineering/Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

Photonics-aided millimeter wave (MMW) Orthogonal frequency division multiplexing (OFDM) system is a novel approach to realize the joint radar and communication system. The phase noise introduced by the laser will degrade the system performance. In this paper, we analyze the mechanism of phase noise acting on OFDM radar system. A method that combines chirp-Z transform (CZT) and compensation enabled by a reference signal is adopted to improve the velocity accuracy. RF pilot (RFP) and blind phase search (BPS) algorithm are adopted to improve communication performance. A simulation is demonstrated at 30 GHz with 2 GHz communication bandwidth. A twodimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 0.980 m/s and a communication capacity of 6.4 Gbps are obtained in the proposed joint radar and communication system.

关键词： Phase noise Laser radar Image resolution Communication systems OFDM Bandwidth Radar imaging

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FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

arXiv

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arXiv 2023年

作者： Lin, Qinghua Li, Zuoyong Zeng, Kun Fan, Haoyi Li, Wei Zhou, Xiaoguang School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Automation Beijing University of Post and Telecommunications Beijing100876 China School of Economics and Management Minjiang University Fuzhou350121 China

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Fires

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An ontology-supported inquiry learning technique

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 16-28页

作者： Xue, Xingsi Zhang, Jie Yang, Chaofan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou Fujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin City Guanxi Province537000 China School of Computer Science and Mathematics Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guilin Guangxi541004 China

Although Inquiry-based Learning (IBL) offers compelling opportunities for the engineering education, one of the challenges to the implementation of it is that the learner always lacks of the background knowledge. To provide him with the necessary knowledge when making the inquiry, this paper proposes an Ontology Supported Inquiry Learning technique (OSIL), which works on the basis of two ontologies, i.e. curriculum ontology and the learner’s ontology. The experimental results show that the constructed curriculum ontology and learner ontology is of high-quality, and the answers recommended by OSIL almost perfectly match with the true answers. From the experimental results, we can see that it is a feasible way of using the software engineering and prototype evolution method to construct the curriculum and learner ontology, and the individual inquiry based on two ontologies is able to provide the learners with high-quality personalized services. © 2021, Taiwan Ubiquitous information. All rights reserved.

关键词： Curricula

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2

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Protein & Cell 2021年第9期12卷 734-740页

作者： Dan Zhao Weifan Xu Xiaofan Zhang Xiaoting Wang Yiyue Ge Enming Yuan Yuanpeng Xiong Shenyang Wu Shuya Li Nian Wu Tingzhong Tian Xiaolong Feng Hantao Shu Peng Lang Jingxin Li Fengcai Zhu Xiaokun Shen Haitao Li Pilong Li Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China Beijing Advanced Innovation Center for Structural Biology Frontier Research Center for Biological Structure School of Life SciencesTsinghua UniversityBeijing 100084China Winghua-Peking Center for Life Sciences Tsinghua UniversityBeijing 100084China Silexon Al Technology Co. LtdNanjing 210033China NHC Key Laboratory of Enteric Pathogenic Microbiology Jiangsu Provincial Center for Diseases Control and PreventionNanjing 210009China Bioinformatics Division BNRIST/Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Protein Preparation and Identification Facility Technology Center for Protein ScienceTsinghua UniversityBeijing 100084China Institute of Pathology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430074China Center for Global Health Nanjing Medical UniversityNanjing 210009China Convalife(Shanghai)Co. LtdShanghai 201203China Ministry of Education Key Laboratory of Protein Sciences Beijing Advaneed Innovation Center for Structural BiologyBeijing Frontier Research Center for Biological StructureDepartment of Basic Medical SciencesSchool of MedicineTsinghua UniversityBeijing 100084China

Dear Editor To date,tens of millions of people have been infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),causing the outbreak of the respiratory disease named the coronavirus disease 2019(COVID-19).As a newly emerged member of the coronavirus family,SARS-CoV-2 is an enveloped positive-strand RNA virus,which has probably the largest genome(approximately 30 kb)among all RNA *** nucleocapsid(N)protein of SARS-CoV-2 is mainly responsible for recognizing and wrapping viral RNA into helically symmetric structures(Malik,2020).

关键词： respiratory acute therapeutic

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A novel image tag completion method based on convolutional neural transformation

arXiv

引用

arXiv 2017年

作者： Geng, Yanyan Zhang, Guohui Li, Weizhi Gu, Yi Liang, Ru-Ze Liang, Gaoyuan Wang, Jingbin Wu, Yanbin Patil, Nitin Wang, Jing-Yan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Huawei Technologies Co. Ltd. Shanghai China Suning Commerce R&D Center USA Inc. Palo AltoCA94304 United States Analytics & Research Travelers HartfordCT06183 United States King Abdullah University of Science and Technology Saudi Arabia Jiangsu University of Technology Jiangsu213001 China Information Technology Service Center Intermediate People’s Court of Linyi City Linyi China Hebei University of Economics and Business Shijiazhuang050061 China Savitribai Phule Pune University Pune Maharashtra411007 India

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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A Dual-Energy Metal Artifact Redcution Method for DECT Image Reconstruction

A Dual-Energy Metal Artifact Redcution Method for DECT Image...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tianling Lyu Wei Zhao Wei Gao Jian Zhu Yan Xi Yang Chen Wentao Zhu Zhejiang Lab Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Hangzhou China Department of Computer Science and Technology Laboratory of Imaging Science and Technology Southeast University Nanjing China Department of Radiation Physics and Technology Cancer Hospital Affiliated to Shandong First Medical University Jinan China Shanghai First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing China

Metal implants are one of the culprits for image quality degradation in CT imaging, introducing so-called metal artifacts. With the help of the virtual-monochromatic imaging technique, dual-energy CT has been proven to be effective in metal artifact reduction. However, the virtual monochromatic images with suppressed metal artifacts show reduced CNR compared to polychromatic images. To remove metal artifacts on polychromatic images, we propose a dual-energy NMAR (deNMAR) algorithm in this paper that adds material decomposition to the widely used NMAR framework. The dual energy sinograms are first decomposed into water and bone sinograms, and metal regions are replaced with water on the reconstructed material maps. Prior sinograms are constructed by polyenergetically forward projecting the material maps with corresponding spectra, and they are used to guide metal trace interpolation in the same way as in the NMAR algorithm. We performed experiments on authentic human body phantoms, and the results show that the proposed deNMAR algorithm achieves better performance in tissue restoration compared to other compelling methods. Tissue boundaries become clear around metal implants, and CNR rises to 2.58 from ~1.70 on 80 kV images compared to other dual-energy-based algorithms.

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