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Corrigendum to “Mobile Target Tracking Based on Hybrid Open-Loop Monocular Vision Motion control Strategy”

Discrete Dynamics in Nature and Society 2016年第1期2016卷

作者： Cao Yuan Ma Lianchuan Weigang Ma National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing 100044 *** School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 *** State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 *** Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 ***

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Research on Image Matching Technology for the Spherical Stereo Vision

Research on Image Matching Technology for the Spherical Ster...

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IEEE International Conference on Mechatronics and Automation

作者： Baofeng Zhang Na Liu Yingkui Jiao Yongchen Li Junchao Zhu Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology 391 Binshui Xidao Xiqing District Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education of China School of Computer and Communication Engineering Tianjin University of Technology 391 Binshui Xidao Xiqing District Tianjin 300384 China

ISBN: (纸本)9781479970995

The technique of stereo matching is an important research goal of spherical stereo vision system, which is widely used in visual navigation, motion analysis and panoramic surveillance areas. Fisheye lens can get view images of large field, this paper uses two fisheye lens whose optical axis are perpendicular to construct spherical stereo vision system and does research in stereo matching of spherical stereo vision system. As the image that fish eye lens collected has lager distortion, this paper does stereo matching with the images the system collected without distortion correction using matching algorithm based on maximally stable extremal regions (MSER) and affine scale invariant feature transform (ASIFT) to reduce the correction error generated in the process. The experiment results show that the algorithm can be applied in stereo matching of fish eye image without distortion, and the results meets the need of 3D reconstruction.

关键词： Matching Spherical stereo vision Maximally Stable Extremal Regions (MSER) Affine Scale Invariant Feature Transform (ASIFT)

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The Application of Artificial Immune controllers in Intelligent Vehicle controlling

The Application of Artificial Immune Controllers in Intellig...

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2015 International Conference on control, Automation and Artificial Intelligence(CAAI 2015)

作者： Yan-ting LAN Jin-ying HUANG Xiao-dong CHEN North University of China Shool of Computer and Control Engineering North University of China School of Mechanical and Power Engineering Key Laboratory of Farming System Ministry of Agriculture of the People’s Republic of ChinaCollege of Agronomy and BiotechnologyChina Agricultural University

Relying on the two different regulatory effects of T cells of promoting or suppressing in varied immune phases,it ensures that the biological immune system responds rapidly and keeps its sound stability when different types of antigens invade,and thereby remains the balance within the biology *** for reference of the collaborative immune mechanism of the T cells and B cells in the biological specific immunity process,this paper presents an artificial immune approach and have it applied into the intelligent vehicle *** the model of the intelligent vehicle steering system is established,and then biological immune mechanism is interpreted with feedback control theory to achieve the intelligent control by artificial immune *** the end system simulation is conducted through MATLAB software and comparisons are made as well to the control effect of normal PID *** show that the artificial controllers work much better than the normal PID controllers in both response rate and dynamic *** approach provides a new feasible way for intelligent vehicle controlling.

关键词： Intelligent vehicle Steering system model Immune mechanism Artificial immune controller

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A statistical method for no-reference image quality assessment

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Journal of Computational Information systems 2014年第16期10卷 7203-7212页

作者： Lu, Fangfang Zhao, Qunfei Liu, Huanxi College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

This paper proposes a statistical method for no-reference image quality assessment using steerable pyramid decomposition without any prior knowledge about the distortions of the original image. Because the means of (log2 of) subband coefficient amplitudes (MLSCAs) of the natural images have certain statistical properties independent of their contents, a predicted MLSCAs based on these properties can be considered as a reference index for describing the distorted images. A subband distortion is then defined as the difference between the predicted and real MLSCA of the distorted image. A set of weights for the subbands is used to combine the subband distortions into a quality score. Experimental results show that the proposed method outperforms six no-reference algorithms using natural scene statistics. 1553-9105/Copyright © 2014 Binary Information Press

关键词： Statistics

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A new automatic mass detection method for breast cancer with false positive reduction

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Neurocomputing 2015年 152卷 388-402页

作者： Xiaoming Liu Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 China

Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristi

关键词： Mass detection Breast cancer Level set Local binary pattern Support vector machine

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A new approach of conditions on δ2s(Φ) for s-sparse recovery

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Science China(Information Sciences) 2014年第4期57卷 107-113页

作者： CEN YiGang ZHAO RuiZhen MIAO ZhenJiang CEN LiHui CUI LiHong School of Computer & Information Technology Beijing Jiaotong University School of Information Science and Engineering Central South University Key Laboratory of System Control and Information Processing Ministry of Education Department of Mathematics Beijing University of Chemical Technology

In this paper,we provide a unified expression to obtain the conditions on the restricted isometry constantδ2s(Φ).These conditions cover the important results proposed by Candes et *** each of them is a sufficient condition for sparse signal *** the noiseless case,whenδ2s(Φ)satisfies any one of these conditions,the s-sparse signal can be exactly recovered via(l1)constrained minimization.

关键词： concentration of mutilated vector in kerΦ s-largest mutilated vector restricted isometry constant δ2s(Φ) exact recovery of s-sparse signals via(l1) maximum value of single variable function

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RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria

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Natural Hazards 2015年第2期77卷 1055-1068页

作者： Olatomiwa, Lanre Mekhilef, Saad Shamshirband, Shahaboddin Petkovic, Dalibor Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Department of Electrical and Electronic Engineering Federal University of Technology Minna Nigeria Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department for Mechatronics and Control Faculty of Mechanical Engineering University of Niš Nis Serbia

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration ( $$ \bar{n} $$ ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.

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Mixed H2/H− pitch control of wind turbine generator system based on exact linearization and regional pole placement

Mixed H2/H− pitch control of wind turbine generator system ...

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Chinese control Conference (CCC)

作者： Mu Zhu Jizhen Liu Zhongwei Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing Beijing CN

When the wind power electricity system working above the rated wind speed, the disturbed wind speed can lead to the output power ripple easily, which causes a significant negative influence on the stability of the power grid. In order to overcome this disadvantage, this paper discusses the mixed H 2 /H ∞ pitch angle control design problem for the nonlinear wind turbine generator system (WTGS), where the turbulence is regarded as the disturbance input. Especially, the traditional exact linearization and the pole placement techniques are also applied to guarantee the desired control performance and expected dynamic characteristics in the situation of a large-scale variety of the system operation points. The simulation results show the effectiveness of the proposed method.

关键词： Wind speed Wind turbines Generators Rotors Torque Wind power generation

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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