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Multiple source localization by using improved single source bins detection

Journal of Information Hiding and Multimedia Signal Processing 2018年第1期9卷 145-152页

作者： Sun, Jundai Jia, Maoshen Bao, Changchun Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing China

In our previous work, the outline of a novel multiple source localization scheme based on single source bins(SSBs) detection is presented. By only retaining the Direction of Arrival (DOA) estimates among single source bins (SSBs). The detection of SSBs is completed based on the DOA convergence assumption by using a clustering algorithm. However, in the selection of clustering algorithm, the distribution characteristics of DOA estimates are ignored. This makes detection results may contain some pseudo single source points caused by the algorithm itself. To solve this issue, an improved SSBs detection method is proposed by applying k-means++ algorithm. For the post processing of DOA estimation, an intra-cluster distance weighting algorithm is proposed to improve the accuracy of the localization. Experimental results demonstrate the localization accuracy of the proposed method outperforms existing single source zone or SSBs detection based localization approaches. © 2018, Ubiquitous International. All rights reserved.

关键词： Direction of arrival

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RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation

arXiv

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arXiv 2019年

作者： Niu, Xuesong Shan, Shiguang Han, Hu Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Institute of Computing Technology CAS Beijing100190 China Peng Cheng Laboratory Shenzhen China Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Heart rate (HR) is an important physiological signal that reflects the physical and emotional status of a person. Traditional HR measurements usually rely on contact monitors, which may cause inconvenience and discomfort. Recently, some methods have been proposed for remote HR estimation from face videos;however, most of them focus on well-controlled scenarios, their generalization ability into less-constrained scenarios (e.g., with head movement, and bad illumination) are not known. At the same time, lacking large-scale HR databases has limited the use of deep models for remote HR estimation. In this paper, we propose an end-to-end RhythmNet for remote HR estimation from the face. In RyhthmNet, we use a spatial-temporal representation encoding the HR signals from multiple ROI volumes as its input. Then the spatial-temporal representations are fed into a convolutional network for HR estimation. We also take into account the relationship of adjacent HR measurements from a video sequence via Gated Recurrent Unit (GRU) and achieves efficient HR measurement. In addition, we build a large-scale multi-modal HR database (named as VIPL-HR1), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database contains various variations such as head movements, illumination variations, and acquisition device changes, replicating a less-constrained scenario for HR estimation. The proposed approach outperforms the state-of-the-art methods on both the public-domain and our VIPL-HR databases. Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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A reconstruction algorithm for bioluminescence tomography based on sparse and total variation regularizations

A reconstruction algorithm for bioluminescence tomography ba...

引用

Clinical and Translational Biophotonics, TRANSLATIONAL 2018

作者： Feng, Jinchao Li, Yanan Li, Zhe Sun, Zhonghua Jia, Kebin Faculty of Information and Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Beijing100124 China

A novel reconstruction algorithm for bioluminescence tomography was proposed by combining sparse (L1 norm) and total variation regularizations. Results of simulation image reconstructions showed that the proposed algo... 详细信息

ISBN: (纸本)9781943580415

关键词： Bioluminescence

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Photo-Based Monitoring of Particulate Matter in the Campus: A New Strategy for Student Health

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IOP Conference Series: Earth and Environmental Science 2020年第1期555卷

作者： Yonghui Zhang Ke Gu Zhifang Xia Junfei Qiao Faculty of Information Technology Beijing University of Technology 100124 Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education 100124 Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System 100124 Beijing China Beijing Artificial Intelligence Institute 100124 Beijing China State Information Center 100045 Beijing China

It is imperative for the students' future health to ensure the students in good physical levels. Recent years have witnessed the increasingly serious harm to student health caused by the continually growing concentration of Particulate Matters (PMs). Consequently, the task of preventing and controlling PM concentrations in the campus is eagerly required. A well-designed model for the monitoring of PM (as the basis for PM prevention and control) has posed a big challenge. Prior works have revealed that photo-based methods are available for the monitoring of PM. Towards validating the effectiveness of existing methods for PM monitoring in the campus, we construct a novel dataset that involves 1,500 photos collected in the beijing University of Technology. Results confirm that stated-of-the-art methods are far from ideal for the monitoring of PM in the campus. To solve the aforesaid issue, this paper further proposes a novel photo-based PM monitoring model by using the weighted average method solved by LASSO regression to fuse the above methods' outputs tested to infer the PM values. Results demonstrate the superiority of our proposed model as compared to state-of-the-art methods on the large-scale AQPDBJUT dataset.

关键词：

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An In-Motion Initial Alignment Algorithm for SINS Using Adaptive Lie Group Filter

An In-Motion Initial Alignment Algorithm for SINS Using Adap...

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第37届中国控制会议

作者： Hao Xu Fujun Pei Ning Jiang Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

In this paper, an in-motion initial alignment algorithm based on Lie group is proposed, which effectively solves the non-uniqueness of the unit-quaternion attitude description and the nonlinear problem when using unitquaternion for state estimation. The algorithm divides the attitude matrix into three successive special orthogonal matrices according to the property of the rotation on Lie group description and the basic principle of inertial navigation, and isolates the angular velocity and linear velocity of the carrier under dynamic conditions. Based on the mapping relation between Lie group and Lie algebra and the optimal estimation principle, and the uncertainty of measurement noise in modeling is analyzed, an adaptive Lie group filter method is proposed to estimate the initial inertial matrix directly. It can be seen from the experimental results that compare with the unit quaternion algorithm proposed algorithm has improved the alignment accuracy and stability. This method is an excellent situation to the SINS initial alignment in-motion.

关键词： strapdown inertial navigation initial alignment Lie group filter innovation adaptive

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Numerical Simulation Model of Dioxin Production in Grate Furnace-based Municipal Solid Waste Incineration Process

Numerical Simulation Model of Dioxin Production in Grate Fur...

引用

第37届中国控制会议

作者： Zihao Guo Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Municipal solid waste incineration（MSWI） is the most widely used solid waste treatment techniques. Dioxin（DXN） is a highly toxic and persistent pollutant discharged from MSWI process. The mechanism model of DXN is difficult to establish because of the complex physical and chemical characteristics. In the practical industrial process, DXN emission concentration is off-line detected for monthly or seasonal periods, which cannot be used to operational optimization and feedback control of the MSWI process. In this paper, a numerical simulation model of DXN production based on commercial process simulation soft ware is constructed for a practical MSWI plant. At first, the production mechanism of DXN and a practical MSWI heat and mass balance process are given out. Then, the sensitive analysis of DXN production with several key variables is made based on the constructed numerical model. It shows the constructed numerical simulation model is effective. More detailed works should be done in the future.

关键词： Municipal solid waste incineration （MSWI） Dioxins （DXN） emission concentration Mechanism analysis Numerical simulation Heat and mass balance

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Dioxin Emission Concentration Soft Measuring Method Based on Selective Ensemble Least Square Support Vector Machine Algorithm

Dioxin Emission Concentration Soft Measuring Method Based on...

引用

第37届中国控制会议

作者： Jian Tang Junfei Qiao Zihao Guo Aijun Yan Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Dioxin(DXN) is a highly toxic and persistent pollutant discharged from municipal solid waste incineration(MSWI).The first principal model of DXN is difficult to establish due to the complex physical and chemical characteristics of the incineration process. In the practical process, DXN emission concentration is off-line detected with monthly or seasonal *** at such small sample modeling problem, a soft measuring method based on selective ensemble(SEN) least square support vector machine(LSSVM) for modeling DXN emission concentration is proposed. At first, candidate training sub-samples are produced from original training samples. Then, different candidate sub-sub-models based on the same kernel parameter and regularization parameter are constructed by using LSSVM. Thirdly, ensemble sub-models are selected by using the genetic algorithm optimization tool box and prior knowledge. Finally, these ensemble sub-models are combined by using partial least squares algorithm in terms of reduction con-linearity among different prediction outputs. Simulation results show effectiveness of the proposed approach by using dataset in reference [18].

关键词： Municipal solid waste incineration (MSWI) dioxin (DXN) selective ensemble least square support vector machine soft measuring model

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Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

Fuzzy PID Control for a Cubic Robot Balancing on Its Corner

引用

2018 International Conference on Computer,Communication and Network Technology(CCNT 2018)

作者： Xiao-gang RUAN Yuan LI Zhi-gang CHEN Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

In this paper, a cubic self-balancing robot is designed. Taking a prototype of a cubic robot that has been designed as a specific research object. For the attitude control of this institution. Using Lagrangian Method to establish a mathematical model of cubic robot. For its attitude control method. Fuzzy PID control method was proposed. Establishing simulation models in Matlab/Simulink environment. The simulation experiments using conventional PID control and fuzzy PID control were performed for comparison. The results show that the adoption of fuzzy PID control has faster response speed and smaller overshoot than traditional PID control and has better control effect.

关键词： Cubic robot Lagrangian method Simulink Fuzzy PID control

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Author Correction: Maximizing carbon sequestration potential in Chinese forests through optimal management

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Nature communications 2024年第1期15卷 4020页

作者： Zhen Yu Shirong Liu Haikui Li Jingjing Liang Weiguo Liu Shilong Piao Hanqin Tian Guoyi Zhou Chaoqun Lu Weibin You Pengsen Sun Yanli Dong Stephen Sitch Evgenios Agathokleous Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. zyu@***. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 100091 Beijing China. liusr@***. Key Laboratory of Forest Management and Growth Modelling China's National Forestry and Grassland Administration Research Institute of Forest Resource Information Techniques Chinese Academy of Forestry 100091 Beijing China. Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI) Department of Forestry and Natural Resources Purdue University West Lafayette IN 47907 USA. College of Forestry Northwest agriculture and Forestry University Yangling 712100 China. Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University 100871 Beijing China. Schiller Institute for Integrated Science and Society Department of Earth and Environmental Sciences Boston College Chestnut Hill Massachusetts MA 02467 USA. Key Laboratory of Ecosystem Carbon Source and Sink China Meteorological Administration (ECSS-CMA) School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China. Department of Ecology Evolution and Organismal Biology Iowa State University Ames IA 50011 USA. College of Forestry Fujian Agriculture and Forestry University Fuzhou 350002 China. Key Laboratory of Forest Ecology and Environment China's National Forestry and Grassland Administration Ecology and Nature Conservation Institute Chinese Academy of Forestry 10

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