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Chinese Automation Congress (CAC)

作者： Zhanjie Li Yajing Ma Institute of Advanced Technology for Carbon Neutrality Nanjing University of Posts and Telecommunications Nanjing China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology Sichuan Xihua University Chengdu China School of Internet of Things Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

In this paper, the consensus control against attacks for a type of nonlinear multi-agent switched systems under arbitrary switching is studied. The attacks corrupt the information data, which cannot be used for the feedback design. Meanwhile, the switching logic is not allowed to be designed. To tackle the complicated effects, some intermediate variables and adaptive laws are introduced to compensate the uncertainties. By constructing a common Lyapunov function, an adaptive protocol is proposed such that the outputs of the agents achieve consensus against attacks under arbitrary switching.

关键词： Switched systems Uncertainty Automation Adaptive systems Control design Switches Consensus control

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Sparsity-aware SSAF algorithm with individual weighting factors for acoustic echo cancellation

arXiv

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arXiv 2020年

作者： Yu, Yi Yang, Tao Chen, Hongyang de Lamare, Rodrigo C. Li, Yingsong School of Information Engineering Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang621010 China Research Center for Intelligent Networking Zhejiang Lab Hangzhou311121 China CETUC PUC-Rio Rio de Janeiro22451-900 Brazil Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom School of Information and Communication Engineering Harbin Engineering University Harbin150001 China

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Impulse noise

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A Positioning and Navigation System for Railway Technical Station Operators 4

A Positioning and Navigation System for Railway Technical St...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yang, Fan Wang, Zhixue Xiong, Gang Hu, Xiangyu Xu, Kejie Chen, Shichao Shandong Jiaotong University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Rail Transportation Jinan China Shandong Jiaotong University School of Rail Transportation Jinan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Beijing Technol. and Bus. Univ. State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Computer and Artificial Intelligence Beijing China China Mobile Communication Group Sichuan Co. Ltd Chengdu China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As an important node in the dismantling and grouping of freight trains, the operational tasks of the railway technical station are increasingly busy. Various business activities involve a large number of field personnel, and ensuring the personal safety and operational efficiency of these personnel is one of these issues that railway operations need to *** ensure the operational safety and standardization at the railway technical station, high-precision positioning system for the personnel is required. However, typical operational scenarios at railway technical stations often involve problems such as the carriage obstruction and the electromagnetic interference. Only rely on GNSS for positioning is often insufficient to meet practical requirements. Addressing the need of the high-precision positioning for the operational personnel at railway technical stations, this paper constructs a integrated navigation system based on RTK and IMU. It utilizes a Discrete Error State Kalman Filtering algorithm to fuse the solution data from RTK and IMU, thereby obtaining high-precision pedestrian positioning data. Compared to relying solely on GNSS positioning system, the integrated navigation system significantly improves its resistance to the interference and positioning accuracy. Experimental results demonstrate that this positioning system can achieve centimeter-level positioning in obstructed scenarios typical of railway technical stations, meeting the positioning requirements of the operational personnel at railway technical station work sites. © 2024 IEEE.

关键词： Kalman filters

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Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

arXiv

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arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Yu-Transnet: A Cnn-Transformer-Based Hybrid U-Shape Structure for Esophagus 3d Ct Image Gross Tumor Volume Segmentation

SSRN

引用

SSRN 2024年

作者： Yu, Songli Li, Yunxiang Jiao, Pengfei Liu, Yixiu Zhao, Jianxiang Yan, Chenggang Wang, Qifeng Wang, Shuai School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China Department of Radiation Oncology UT Southwestern Medical Center Dallas75390 United States School of Cyberspace Security Hangzhou Dianzi University Hangzhou310018 China Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou310018 China School of Communication Engineering Hangzhou Dianzi University Hangzhou310018 China Department of Radiation Oncology Sichuan Cancer Hospital and Institution Chengdu610000 China Sichuan Cancer Center Chengdu610000 China School of Medicine University of Electronic Science and Technology of China Chengdu610000 China Radiation Oncology Key Laboratory of Sichuan Province Chengdu610000 China Suzhou Research Institute of Shandong University Suzhou215123 China Lishui Institute of Hangzhou Dianzi University Lishui323010 China

Accurate and reliable segmentation of esophageal gross tumor volume (GTV) in computed tomography (CT) is beneficial for diagnosing and treating. However, this remains a challenging task because the esophagus has a variable shape and extensive vertical range, resulting in tumors potentially appearing at any position within it. In this paper, we propose a novel CNN-Transformer-based U-shape structure to improve the segmentation accuracy of esophageal GTV. Specifically, we propose a long-range relay mechanism to converge all layer feature information by progressively passing adjacent layer feature maps in the pixel and semantic pathways. Moreover, we introduce two ready-to-use blocks to implement this mechanism concretely. The Dual FastViT block interacts with feature maps from two paths to enhance feature representation capabilities. The Dual AxialViT block acts as a secondary auxiliary bottleneck to acquire global information for more precise feature map reconstruction. We build a new esophageal tumor dataset with 1665 real-world patient CT samples annotated by five expert radiologists and employ multiple evaluation metrics to validate our model. The results demonstrate that our model outperforms the state-of-the-art models. It is of great significance to improve the accuracy and clinical application of esophageal tumor segmentation. © 2024, The Authors. All rights reserved.

关键词： Tumors

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A Positioning and Navigation System for Railway Technical Station Operators

A Positioning and Navigation System for Railway Technical St...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Fan Yang Zhixue Wang Gang Xiong Xiangyu Hu Kejie Xu Shichao Chen School of Rail Transportation Shandong Jiaotong University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Jinan China School of Rail Transportation Shandong Jiaotong University Jinan China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Artificial Intelligence Beijing Technology and Business University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China China Mobile Communication Group Sichuan Co. Ltd Chengdu China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

As an important node in the dismantling and grouping of freight trains, the operational tasks of the railway technical station are increasingly busy. Various business activities involve a large number of field personnel, and ensuring the personal safety and operational efficiency of these personnel is one of these issues that railway operations need to *** ensure the operational safety and standardization at the railway technical station, high-precision positioning system for the personnel is required. However, typical operational scenarios at railway technical stations often involve problems such as the carriage obstruction and the electromagnetic interference. Only rely on GNSS for positioning is often insufficient to meet practical requirements. Addressing the need of the high-precision positioning for the operational personnel at railway technical stations, this paper constructs a integrated navigation system based on RTK and IMU . It utilizes a Discrete Error State Kalman Filtering algorithm to fuse the solution data from RTK and IMU, thereby obtaining high-precision pedestrian positioning data. Compared to relying solely on GNSS positioning system, the integrated navigation system significantly improves its resistance to the interference and positioning accuracy. Experimental results demonstrate that this positioning system can achieve centimeter-level positioning in obstructed scenarios typical of railway technical stations, meeting the positioning requirements of the operational personnel at railway technical station work sites.

关键词： Global navigation satellite system Pedestrians Satellites Accuracy Standardization Rail transportation Safety Trajectory Personnel Kalman filters

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Near surface camera informed agricultural land monitoring for climate smart agriculture

Climate Smart Agriculture

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Climate Smart Agriculture 2024年第1期1卷

作者： Le Yu Zhenrong Du Xiyu Li Qiang Zhao Hui Wu Duoji weise Xinqun Yuan Yuanzheng Yang Wenhua Cai Weimin Song Pei Wang Zhicong Zhao Ying Long Yongguang Zhang Jinbang Peng Xiaoping Xin Fei Xu Miaogen Shen Hui Wang Yuanmei Jiao Yong Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing China Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing China School of Information and Communication Engineering Dalian University of Technology Dalian China Geothermal Geological Brigade Tibet Autonomous Region Geological and Mineral Exploration and Development Bureau Lhasa China International Department Beijing No. 35 High School Beijing China Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education Nanning Normal University Nanning China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning China Key Laboratory of Coastal Environmental Processes and Ecological Restoration Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China Yellow River Delta Field Observation and Research Station of Coastal Wetland Ecosystem Chinese Academy of Sciences Dongying China Department of Landscape Architecture School of Architecture Tsinghua University Beijing China Institute for National Parks Tsinghua University Beijing China School of Architecture and Hang Lung Center for Real Estate Key Laboratory of Eco Planning & Green Building Ministry of Education Tsinghua University Beijing China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Nanjing China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing Univer

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

关键词： Near surface camera (NSCam) Climate-smart agriculture (CSA) Agricultural monitoring Data fusion Sentinel-2

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