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3rd International Conference on Mechanical, Electric and Industrial Engineering, MEIE 2020

作者： Hu, Xusheng Liu, Juan Key Laboratory of Unmanned Aerial Vehicle Development Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Ma'anshan243031 China Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Ma'anshan243031 China Ma'anshan Technical College Ma'anshan243030 China

In order to improve the effect of obstacle avoidance, a PID control algorithm combined with genetic algorithm is proposed. By establishing the mathematical model of UAV, the auxiliary control system of UAV is analyzed, and then the control system is optimized by genetic algorithm. Taking STM32 as the control platform, the obstacle avoidance experiment was carried out by building the experimental platform. The experimental results show that the algorithm can avoid obstacles effectively. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and Control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 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 University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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Design of Safety Monitoring System for Electrical laboratory in Colleges and Universities under the Background of Informatization

Design of Safety Monitoring System for Electrical Laboratory...

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Advances in Informatics, Electronics and Education (ISAIEE), International Symposium on

作者： Huahui Liu Juan Liu Xusheng Hu Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and Intellisense Ma'anshan China Key Laboratory of Unmanned Aerial Vehicle Development Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Ma'anshan China Ma'anshan Technical College Ma'anshan China

ISBN: (纸本)9781665478755

With the development of economic globalization, information technology has made great progress, and artificial intelligence technology has been widely used, such as car driving, robot automatic steering, rescue and disaster relief robot, underwater robot and so on. However, in the laboratory management of colleges and universities, the application of artificial intelligence is insufficient, and the traditional management mode is backward. Some universities still adopt manual management mode, which not only has high labor cost, but also has a large management gap, resulting in too many potential safety hazards, which is not conducive to the safety management of university laboratories. The market urgently needs to realize the combination of laboratory management and artificial intelligence. As a key laboratory of engineering, the electrical laboratory carries out and sets up multiple experimental courses according to the discipline training requirements. As an Engineering University, the course content spans multiple grades, faces more students and covers multiple majors in two colleges. It can be imagined that the experimental courses opened in the electrical laboratory basically take the power supply as the carrier, in this way, the requirements for students are very high. If the electrical experiment is misoperated or caused by emergencies, the risk is great and the accidents are very sudden. Based on the above situation, it fully shows that higher requirements are put forward for the safety management of the electrical laboratory and the management will be more difficult. In recent years, discipline construction has been paid more and more attention, but with the frequent occurrence of laboratory accidents, the fire safety related to the laboratory has attracted more and more attention. For the application-oriented laboratory in Colleges and universities, we must raise awareness, strengthen safety prevention, and strengthen the management of laboratory fire safet

关键词： Buildings Safety management Process control Control systems Software Safety Artificial intelligence

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Several classes of linear codes with few weights derived from Weil sums

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Finite Fields and Their applications 2025年 108卷

作者： Zhao Hu Mingxiu Qiu Nian Li Xiaohu Tang Liwei Wu Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education the Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan 430062 China State Key Laboratory of Integrated Service Networks Xi'an 710071 China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan 430062 China Information Coding & Transmission Key Lab of Sichuan Province CSNMT Int. Coop. Res. Centre (MoST) Southwest Jiaotong University Chengdu 610031 China Wuhan Maritime Communication Research Institute China

Linear codes with few weights have applications in secret sharing, authentication codes, association schemes and strongly regular graphs. In this paper, several classes of t -weight linear codes over F q are presented with the defining sets given by the intersection, difference and union of two certain sets, where t = 3 , 4 , 5 , 6 and q is an odd prime power. By using Weil sums and Gauss sums, the parameters and weight distributions of these codes are determined completely. Moreover, three classes of optimal codes meeting the Griesmer bound are obtained, and computer experiments show that many (almost) optimal codes can be derived from our constructions.

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Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

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2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Genetic algorithms

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CoReaAgents: A Collaboration and Reasoning Framework Based on LLM-Powered Agents for Complex Reasoning Tasks

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Applied Sciences 2025年第10期15卷 5663-5663页

作者： Zhonghe Han Jiaxin Wang Xiaolu Yan Zhiying Jiang Yuanben Zhang Siye Liu Qihang Gong Chenwei Song Key Laboratory of Target Cognition and Application Technology (TCAT) Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Network Information System Technology (NIST) Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 China Institute of Tracking and Telecommunications Technology Beijing 100094 China

As LLMs demonstrate remarkable reasoning capabilities, LLM-powered agents are seen as key to achieving AGI (Artificial General Intelligence) and are widely applied in various complex real-world scenarios. Nevertheless, existing studies still suffer from missing steps, deviated task execution and incorrect tool selection. This paper proposes CoReaAgents, a collaboration and reasoning framework based on LLM-powered agents, comprising the Plan Agent (as a precise task planner), the Tool Agent (as a proficient tool user) and the Reflect Agent (as an objective task evaluator). These agents simulate the social division of labor and synergistic cooperation to enable each agent to perform different specialized capabilities in order to solve complex tasks together. Through the above mechanism, the CoReaAgents framework has the skills of prospective thinking and flexible execution. To verify the capability of the CoReaAgents framework, this paper conducts extensive experiments on different complex tasks such as tool learning, math reasoning and multi-hop QA. The results show that the CoReaAgents framework outperforms various comparative methods in both quantity and quality.

关键词： LLM LLM-powered agent artificial intelligence multi-agent systems machine learning smart tools and applications

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Near-Field Hybrid Beamforming for Extremely Large-Scale (XL)-MIMO Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Yang, Songjie Elbir, Ahmet M. Chen, Hua Xiong, Youzhi Zhang, Zhongpei Yuen, Chau University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Istinye University Department of Electrical and Electronics Engineering Istanbul34396 Turkey at the University of Luxembourg L-1855 Luxembourg Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo315211 China Sichuan Normal University College of Physics and Electronic Engineering Chengdu610101 China Nanyang Technological University School of Electrical and Electronics Engineering China

As extremely large-scale (XL) arrays advance, near-field (NF) communications have gained significant *** this shift, traditional far-field techniques are being revised for compatibility with new XL NF communication paradigms. This work presents NF hybrid beamforming (NF-HBF) approaches for XL-MIMO, focusing on challenges like near-field effects and spatial non-stationarity. First, it redefines the sparse recovery-based NF-HBF problem, shifting from angular- to polar-domain code-books, leading to direct greedy hybrid beamforming (DG-HBF). However, challenges such as high computational complexity, phase shifter (PS) resolution, and spatial non-stationarities persist. To overcome these, this study proposes stepwise-individual and stepwise-joint greedy HBF methods, namely SIG-HBF and SJG-HBF. These methods simplify the process by approximating spherical-wave beams with planar-wave beams, promising lower PS resolution needs, reduced complexity, and the ability to tackle spatially non-stationary channels. Moreover, by exploring conjugate symmetric sequency-ordered Hadamard transforms, NF-HBF can be efficiently achieved using 2-bit PSs with values in {1,−1, j,−j}, facilitated by the SJG-HBF and SJG-HBF methods. Numerical simulations on the proposed methods demonstrate that DG-HBF can approach NF fully-digital beamforming, while SIG-HBF and SJG-HBF highlight the feasibility of utilizing angular-domain codebooks with low PS cost and low memory storage for NF-HBF. © 2002-2012 IEEE.

关键词： Hadamard transforms

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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