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Adaptive variable selection using non-negative garrote with application to soft sensor and fault detection

Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and technology 2012年第SUPPL.2期36卷 82-86页

作者： Wang, Jian-Guo Guo, Yong Ma, Shi-Wei Pan, Chang-Chun Jang, Shi-Shang Wong, David Shan-Hill School of Mechatronical Engineering and Automation Shanghai Key Lab. of Power Station Automation Technology Shanghai University Shanghai 200072 China Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China Department of Chemical Engineering National Tsing-Hua University Taiwan 30013 Taiwan

In order to improve the function of soft sensor to conduct variable selection, fault detection and model structure identification in the case of faulty state, a design method of new soft sensor is studied though the variable selection algorithm. A non-stationary time serial is introduced to describe the process output not being reflected by sensor variables and to detect whether the process enters the faulty state. A non-negative garrote method is adopted to identify the model structure and a modeling method for new soft sensors is presented. The obtained model can be used for both prediction, and detection of structural model change and the emergence of disturbance. Compared with the ordinary soft sensor based on partial least square algorithm, the advantages of the proposed method are demonstrated by a simulation example and an industrial application to temperature prediction of a blast furnace hearth.

关键词： Fault detection

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MRI-guided robot intervention—current state-of-the-art and new challenges

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Med-X 2023年第1期1卷 1-41页

作者： Huang, Shaoping Lou, Chuqian Zhou, Ying He, Zhao Jin, Xuejun Feng, Yuan Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT) Shanghai Jiao Tong University Shanghai China Materials and Technology Center of Robotics Empa Dübendorf Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong University Shanghai China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Magnetic Resonance Imaging (MRI) is now a widely used modality for providing multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing radiation. In addition to its diagnostic potential, its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping, as well as integrated instrument localization, target recognition, and in situ, in vivo monitoring of the therapeutic efficacy. Areas of current applications include neurosurgery, breast biopsy, cardiovascular intervention, prostate biopsy and radiotherapy. Emerging applications in targeted drug delivery and MRI-guided chemoembolization are also being pursued. Whilst promising progress has been made in recent years, there are still significant basic science research and engineering challenges. This paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation, sensing, new materials, interventional instruments, and interactive/real-time MRI. Potential future research directions and new clinical developments are also discussed.

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Motion vehicles tracking in intelligent transportation visual surveillance systems

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and technology 2012年第SUPPL.2期36卷 216-222页

作者： Wu, Lin-Feng Xiong, Gang Qian, Shou-Yi Xie, Li Zhejiang Provincial Key Lab. of Information Network Technology Department of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Cloud Computing Industrial Technology Innovation and Incubation Center Dongguan Research Institute of CASIA Chinese Academy of Sciences Dongguan 523808 China Wenzhou Research Institute of Electronics and Information Wenzhou 325003 China

In terms of the difficulty of vehicle tracking in complex environment of the visual surveillance system, an object tracking algorithm is proposed for the applications in practical visual surveillance systems for intelligent traffic. A block-based Gaussian mixture background modeling method for object detection is presented to reduce the computational complexity of moving vehicle object abstraction. An adaptive tracking algorithm fused with color features and texture features is described to better adapt the traffic scene variation. The experimental results show that the proposed algorithm can effectively deal with the complex urban traffic conditions and the tracking performance is better than the conventional particle filter method and single feature based non-adaptive object tracking method.

关键词： Tracking (position)

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Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

SSRN

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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School and Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225°C, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： Gallium nitride

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Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

SSRN

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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225℃, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： III-V semiconductors

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Investigating Correlation of Rough Surface Diffuse Scattering in Frequency Domain

Investigating Correlation of Rough Surface Diffuse Scatterin...

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European Conference on Antennas and Propagation, EuCAP

作者： Yang MIAO Mingming Gan Claude Oestges Katsuyuki Haneda Ke Guan Andrés Alayón Glazunov Wei Wang Yi Gong Dept. of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Universite catholique de Louvain Louvain-la-Neuve BE Institute of Information and Communication Technologies Electronics and Applied Mathematics (ICTM) Universit Catholique de Louvain Louvain-la-Neuve Belgium Dept. of Electronics and Nanoengineering Aalto University Espoo Finland State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Dept. of Electrical Engineering University of Twente Enschede Netherlands School of Information Engineering Chang’an University Xi’an China

This paper investigates numerically the correlation function in the frequency domain due to diffuse scattering originated from rough surfaces. The scattered fields are computed by means of physical optics methods. The irregularities of the rough surface are modeled as a correlated Gaussian process. The correlation of the channel transfer function comprising only diffuse scattering components is analyzed. In the analysis, radio propagation channels comprising orthogonally oriented dipoles at the link ends are considered while assuming different Gaussian roughness profiles. This study offers a useful characterization of the channel behavior in a multipath-rich environment due to diffuse scattering. The presented results are of particular interest to wireless systems based on orthogonal frequency-division multiplexing.

关键词： Rough surfaces Surface roughness Correlation Optical surface waves Scattering Surface impedance Frequency-domain analysis

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Large-Scale Spatiotemporal Deep Learning Predicting Residential Indoor Pm2.5 Concentration Using a Bayesian Neural Network Model

SSRN

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SSRN 2023年

作者： Dai, Hui Liu, Yumeng Wang, Jianghao Ren, Jun Gao, Yao Dong, Zhaomin Zhao, Bin Department of Building Science School of Architecture Tsinghua University Beijing100084 China Beijing Key Laboratory of Indoor Air Quality Evaluation and Control Tsinghua University Beijing100084 China School of Space and Environment Beihang University Beijing100191 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Sustainable Urbanization Lab Department of Urban Studies and Planning Massachusetts Institute of Technology CambridgeMA02139 United States Shenzhen Institute of Building Research Co. Ltd China

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of indoor daily average PM2.5 concentration based on multiple data sources availab.e from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95% prediction interval coverage of 85%. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. This model can be applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale, making it valuable for assessing and managing indoor air pollution-related health risks. © 2023, The Authors. All rights reserved.

关键词： Health risks

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Localization of chiral edge states by the non-Hermitian skin effect

arXiv

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arXiv 2023年

作者： Liu, Gui-Geng Mandal, Subhaskar Zhou, Peiheng Xi, Xiang Banerjee, Rimi Hu, Yuan-Hang Wei, Minggui Wang, Maoren Wang, Qiang Gao, Zhen Chen, Hongsheng Yang, Yihao Chong, Yidong Zhang, Baile Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Jiangsu Nanjing210093 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang ZJU-UIUC Institute Zhejiang University Hangzhou310027 China Centre for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

Quantum Hall systems host chiral edge states extending along the one-dimensional boundary of any two-dimensional sample. In solid state materials, the edge states serve as perfectly robust transport channels that produce a quantised Hall conductance;due to their chirality, and the topological protection by the Chern number of the bulk bandstructure, they cannot be spatially localized by defects or disorder. Here, we show experimentally that the chiral edge states of a lossy quantum Hall system can be localized. In a gyromagnetic photonic crystal exhibiting the quantum Hall topological phase, an appropriately structured loss configuration imparts the edge states’ complex energy spectrum with a feature known as point-gap winding. This intrinsically non-Hermitian topological invariant is distinct from the Chern number invariant of the bulk (which remains intact) and induces mode localization via the "non-Hermitian skin effect". The interplay of the two topological phenomena – the Chern number and point-gap winding – gives rise to a non-Hermitian generalisation of the paradigmatic Chern-type bulk-boundary correspondence principle. Compared to previous realisations of the non-Hermitian skin effect, the skin modes in this system have superior robustness against local defects and disorders. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling

arXiv

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well. © 2021, CC BY-NC-ND.

关键词： Machine learning

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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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