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Adaptive Clustering SOFC Image Segmentation Based on Particle Swarm Optimization

Journal of Physics: Conference Series 2019年第1期1229卷

作者： Xuefei Yang Xiaowei Fu Xi Li College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China 430065 State Key Laboratory of Materials Processing and Die & Mould Technology Huazhong University of Science and Technology Wuhan China 430074 College of Automation Huazhong University of Science and Technology Wuhan China 430074

Microstructural parameters are important for analyzing the chemistry and performance of solid oxide fuel cells (SOFCs). Aiming at the YSZ / Ni anode optical microscopy (OM) image of SOFC, in this paper, particle swarm intelligent optimization algorithm is used to improve the fuzzy C-means clustering algorithm for image segmentation. Particle swarm optimization is used to adaptively search the initial clustering center, helping to avoid local optimization and preserve more image detail. The experimental results show that the proposed method can improve the segmentation accuracy of images. At the same time, it can accurately segment the SOFC three-phase and provide effective image segmentation results for the microstructure parameters.

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computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

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Journal of Healthcare Engineering 2020年第1期2020卷

作者： Yilin Xie Zhuoyue Wu Xin Han Hongyu Wang Yifan Wu Lei Cui Jun Feng Zhaohui Zhu Zhongyuanlong Chen Department of Information Science and Technology Northwest University Xi’an Shaanxi 710127 *** School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an 710127 Shaanxi *** Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC Urumqi Xinjiang Uygur Autonomous Region 830049 China

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Preventive Soot Blowing Strategy based on State of Health Prediction for Coal-fired Power Plant Boiler

Preventive Soot Blowing Strategy based on State of Health Pr...

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第37届中国控制会议

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering North University of China School of Computer Science and Technology North University of ChinaSchool of Computer Science and Technology Shanghai Jiao Tong University Department of Automationand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal-fired power plant boiler.A preventive soot blowing strategy based on state of health prediction for heat transfer surface was *** average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing *** to the theory of renewal process,an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was *** using Particle Swarm Optimization（PSO）,the optimal preventive cycle and soot-blowing threshold were obtained,and the long-run average cost rate was the *** model is validated with experiment data of a 300 MW coal-fired power plant boiler and the parameters of prediction model are *** results can verify the feasibility of proposed *** can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Coal-fired Boiler Heat Transfer Surface Soot-blowing State of Health（SOH） Prediction

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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A Resource for Inactivation of MicroRNAs Using Short Tandem Target Mimic technology in Model and Crop Plants

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Molecular Plant 2018年第11期11卷 1400-1417页

作者： Ting Peng Mengmeng Qiao Haiping Liu Sachin Teotia Zhanhui Zhang Yafan Zhao Bobo Wang Dongjie Zhao Lina Shi Cui Zhang Brandon Le Kestrel Rogers Chathura Gunasekara Haitang Duan Yiyou Gu Lei Tian Jinfu Nie Jian Qi Fanrong Meng Lan Huang Qinghui Chen Zhenlin Wang Jinshan Tang Xiaoqing Tang Ting Lan Xuemei Chen Hairong Wei Quanzhi Zhao Guiliang Tang Collaborative Innovation Center of Henan Grain Crops Henan Agricultural UniversityZhengzhou 450002China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Department of Kinesiology and Integrative Physiology Life Science and Technology institutedMichigan Technological UniversityHoughtonMI 49931USA School of Technology Michigan Technological UniversityHoughtonMI 49931USA Guangdong Provincial Key Laboratory for Plant Epigenetics College of Life Sciences and OceanographyShenzhen UniversityShenzhen 518060P.R.China Beijing Advanced Innovation Center for Tree Breeding by Molecular Design Beijing Forestry UniversityBeijing 100083P.R.China Key Laboratory of Plant Stress Biology State Key Laboratory of Cotton BiologySchool of Life SciencesHenan UniversityKaifeng 475004China Department of Biotechnology Sharda UniversityGreater Noida201306India Key Laboratory of Rice Biology in Henan Province Henan Agricultural UniversityZhengzhou 450002China Department of Biological Sciences Ufe Science and Technology InstituteMichigan Technological UniversityHoughtonMI 49931USA Department of Botany and Plant Sciences Institute of Integrative Genome BiologyUniversity of CaliforniaRiversideCA 92521USA School of Forest Resources and Environmental Science Life Science and Technology InstituteMichigan Technological UniversityHoughtonMI 49931USA Department of Computer Science Michigan Technological UniversityHoughtonMI 49931USA Anhui Province Key Laboratory of Medical Physics and Technology Center of Medical Physics and TechnologyHefei Institutes of Physical ScienceChinese Academy of SciencesHefeiAnhuiChina Hefei Cancer Hospital Chinese Academy of SciencesHefeiAnhuiChina Guangzhou Institute of Biomedicine and Health Chinese Academy of SciencesGuangzhouChina

microRNAs (miRNAs)are endogenous small non-coding RNAs that bind to mRNAs and target them for cleavage and/or translational repression,leading to gene *** previously developed short tandem target mimic (STTM)technology to deactivate endogenous miRNAs in ***,we created hundreds of STTMs that target both conserved and species-specific miRNAs in Arabidopsis,tomato,rice,and maize,providing a resource for the functional interrogation of *** not only revealed the functions of several miRNAs in plant development,but also demonstrated that tissue-specific inactivation of a few miRNAs in rice leads to an increase in grain size without adversely affecting overall plant growth and ***-seq and small RNAseq analyses of STTM156/157 and STTM165/166 transgenic plants revealed the roles of these miRNAs in plant hormone biosynthesis and activation,secondary metabolism,and ion-channel activity-associated electrophysiology,demonstrating that STTM technology is an effective approach for studying miRNA *** facilitate the study and application of STTM transgenic plants and to provide a useful platform for storing and sharing of information about miRNA-regulated gene networks,we have established an online Genome Browser (https://***/***) to display the transcriptomic and miRNAomic changes in STTMinduced miRNA knockdown plants.

关键词： short tandem target mimic (STTM) miRNA RNA-seq Arabidopsis crop

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Corrigendum: Ethanol-controlled peroxidation in liquid-anode discharges (2019 J. Phys. D: Appl. Phys. 52 425205)

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Journal of Physics D: Applied Physics 2021年第22期54卷

作者： Qiang Chen Jiao Lin Xinyi He Hailong Hu Junshuai Li Qing Xiong Bingyan Chen Zhengyong Song Hai Liu Qing Huo Liu Kostya (Ken) Ostrikov Institute of Electromagnetics and Acoustics Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Department of Electronic Science Xiamen University Xiamen 361005 People's Republic of China College of Physics and Information Engineering Fuzhou University Fuzhou 350116 People's Republic of China Key Laboratory of Special Function Materials and Structure Design of the Ministry of Education and School of Physical Science and Technology Lanzhou University Lanzhou 730000 People's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing 400044 People's Republic of China Department of Mathematics and Physics Hohai University Changzhou 213022 People's Republic of China School of Civil Engineering Guangzhou University Guangzhou 510006 People's Republic of China Department of Electrical and Computer Engineering Duke University Durham NC 27708 United States of America Institute for Future Environments and School of Chemistry Physics and Mechanical Engineering Queensland University of Technology Brisbane QLD 4000 Australia CSIRO-QUT Joint Sustainable Processes and Devices Laboratory Commonwealth Scientific and Industrial Research Organization PO Box 218 Lindfield NSW 2070 Australia

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Deeply supervised depth map super-resolution as novel view synthesis

arXiv

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arXiv 2018年

作者： Song, Xibin Dai, Yuchao Qin, Xueying School of Computer Science and Technology Shandong University China Baidu Research Beijing China National Engineering Laboratory of Deep Learning Technology and Application China Shaanxi Key Lab of Information Acquisition and Processing School of Electronics and Information Northwestern Polytechnical University School of Software Shandong University China

Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used;and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as ×8;×16. In this paper, we propose a new framework to tackle the above problems. First, we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks. The novel view synthesis sub-task aims at generating (synthesizing) a depth map from different camera pose, which could be learned in parallel. Second, to handle large up-sampling factors, we present a deeply supervised network structure to enforce strong supervision in each stage of the network. Third, a multi-scale fusion strategy is proposed to effectively exploit the feature maps at different scales and handle the blocking effect. In this way, our proposed framework could deal with challenging depth map super-resolution efficiently under large up-sampling factors (e.g.×8;×16). Our method only uses the low-resolution depth map as input, and the support of color image is not needed, which greatly reduces the restriction of our method. Extensive experiments on various benchmarking datasets demonstrate the superiority of our method over current state-of-the-art depth map super-resolution methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical resolving power

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Enhanced comprehensive learning particle swarm optimization with exemplar evolution 11th

Enhanced comprehensive learning particle swarm optimization ...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Yu, Xiang Liu, Yunan Feng, Xiangsheng Chen, Genhua Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology 289 Tianxiang Road NanchangJiangxi330099 China School of Information Engineering Nanchang Institute of Technology 289 Tianxiang Road NanchangJiangxi330099 China

ISBN: (纸本)9783319687582

Enhanced comprehensive learning particle swarm optimization (ECLPSO) is a metaheuristic recently proposed by us for global optimization. ECLPSO is balanced in exploration and exploitation;however, it still cannot satisfactorily address some complex multimodal problems. In this paper, we investigate further improving the exploration performance of ECLPSO through exemplar evolution (EE). EE encourages information exchange among different dimensions of the search space and performs mutation and selection on personal best positions that are exemplars guiding the flight of particles. EE is able to prevent the dimensions from getting stuck in stagnancy. Experimental results on various benchmark functions demonstrate that the EE strategy significantly improves the exploration performance of ECLPSO and helps ECLPSO to locate the global optimum region on all of the functions. © Springer International Publishing AG 2017.

关键词： Global optimization

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Frontispiece: Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

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Chemistry – A European Journal 2020年第46期26卷

作者： Dicheng Chen Zi Wang Prof. Di Guo Prof. Vladislav Orekhov Prof. Xiaobo Qu Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University P.O. Box 979 Xiamen 361005 P.R. China School of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 P.R. China Department of Chemistry and Molecular Biology University of Gothenburg Box 465 Gothenburg 40530 Sweden

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Deep supervised qantization by self-organizing map 25

Deep supervised qantization by self-organizing map

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25th ACM International Conference on Multimedia, MM 2017

作者： Wang, Min Zhou, Wengang Tian, Qi Pu, Junfu Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Computer Science Department University of Texas San Antonio United States

ISBN: (纸本)9781450349062

Approximate Nearest Neighbour (ANN) search is an important research topic in multimedia and computer vision fields. In this paper, we propose a new deep supervised quantization method by Self-Organizing Map (SOM) to address this problem. Our method integrates the Convolutional Neural Networks (CNN) and Self-Organizing Map into a unified deep architecture. The overall training objective includes supervised quantization loss and classification loss. With the supervised quantization loss, we minimize the differences on the maps between similar image pairs, and maximize the differences on the maps between dissimilar image pairs. By optimization, the deep architecture can simultaneously extract deep features and quantize the features into the suitable nodes in the Self-Organizing Map. The experiments on several public standard datasets prove the superiority of our approach over the existing ANN search methods. Besides, as a byproduct, our deep architecture can be directly applied to classification task and visualization with little modification, and promising performances are demonstrated on these tasks in the experiments. © 2017 ACM.

关键词： Self organizing maps

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