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Multi-Panel Extra-Large Scale MIMO Based Joint Activity Detection and Channel Estimation for Near-Field Massive IoT Access

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China Communications 2023年第5期20卷 232-243页

作者： Zhen Gao Hanlin Xiu Yikun Mei Anwen Liao Malong Ke Chun Hu Mohamed-Slim Alouini School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of TechnologyBeijing 100081China The Advanced Technology Research Institute of Beijing Institute of Technology Jinan 250307China Yangtze Delta Region Academy of Beijing Institute of Technology(Jiaxing) Jiaxing 314019China Electrical Engineering Program Division of Physical Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access ***,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication ***,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider *** combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)***,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV *** simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

关键词： extra-large scale MIMO massive IoT access active user detection channel estimation multipanel approximate message passing

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3D Object Detection Based on Spatially Adaptive Sparse Convolution

3D Object Detection Based on Spatially Adaptive Sparse Convo...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Hu, Jing Jing Fan, Yu Ling Zhang, Ping Duan, Chang Zhang, Chao Chen, Si Yu Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou China School of Computer Science And Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350391213

Autonomous driving has gradually gained attention in recent years due to its potential to reduce driver burden and improve safety, in which 3D object detection is the basis of the autonomous driving perception system, aiming at predicting the location, size, and class of 3D objects in the vicinity of an autonomous vehicle, which is an important guide for subsequent path planning, motion prediction, and so on. In particular, 3D object detection has difficulties in input point cloud data processing and multimodal fusion. Unlike the image distribution where pixels are regularly distributed in the image plane, the point cloud is a sparse and irregular 3D representation, so it is difficult to apply the traditional convolution directly on the point cloud for detection. In this paper, we carry out a method for 3D object detection for point clouds, using deformable convolution and sparse convolution as a tool to explore the feature extraction method of sparse convolution for irregular point clouds and propose a spatially adaptive sparse convolution-based method. © 2024 IEEE.

关键词： Motion planning

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Slip-draft embedded control system by adaptively adjusting the battery position for electric tractor

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International Journal of Agricultural and Biological engineering 2023年第5期16卷 155-164页

作者： Minghui Wang Pucai Ning Ke Su Gejima Yoshinori Wei Wang Yongjie Cui Gongpei Cui College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina SAIC Motor Technical Center Shanghai 201804China School of Arts and Design Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Faculty of Agriculture.University of Miyazaki Miyazaki 8892192Japan Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling 712100ShaanxiChina College of Mechanical&Electrical Engineering Henan Agricultural UniversityZhengzhou 450002China

A slip-draft embedded control system was designed and developed for an independent developed 2WD(two-wheel drive)electric tractor,to improve the traction efficiency,operation performance and ploughing depth stability of the electric *** this system,the battery of electric tractor was innovatively equivalent to the original counterweight of the fuel *** through dynamic analysis of electric tractor during ploughing,the mathematical model of adjusting the center of gravity about draft force and slip rate was *** the automatic adjustment of the center of gravity for electric tractor was realized through the adaptive adjustment of battery ***,the system was carried on electric tractor for performance evaluation under different ploughing conditions,the traction efficiency,slip rate and front wheel load of electric tractor were measured and controlled synchronously to make it close to the set *** the comparative experiments of ploughing operation were carried out under the two modes of adaptive adjustment of center of gravity and fixed center of *** test results showed that,based on the developed control system,the center of gravity of electric tractor can be adjusted in real time according to the complex changes of working *** ploughing operation with adjusting adaptively battery position,the average values of traction efficiency,slip rate,front wheel load and relative error of tillage depth of electric tractor were 64.5%,22.2%,2045.0 N and 2.0%*** were optimized by 15.0%,29.5%,19.6%and 80.0%respectively,compared with electric tractor with fixed battery *** slip-draft embedded control system can not only realize the adaptive adjustment of the center of gravity position in the ploughing process of electric tractor,but also improve the traction efficiency and the stability of ploughing depth,which can provide reference for the actual production operation of electric tractor.

关键词： electric tractor embedded control system adjusting adaptively center of gravity position performance test

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A novel active inverse memristor emulator with improved low frequency feature

A novel active inverse memristor emulator with improved low ...

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2021 China Automation Congress, CAC 2021

作者： Song, Xingwen Dai, Guangzhen Zhao, Zhenyu Han, Mingjun High-end Equipment Advanced Perception and Intelligent Control Ministry of Education Heavy Laboratory School of Electrical Engineering Anhui Polytechnic University WuHu241000 China

ISBN: (纸本)9781665426473

Based on several active elements, a modified inverse memristor emulator is proposed, which presents better performance than the previous emulators. The published inverse memristor emulators with active blocks show that lobes' size of pinched hysteresis loops (PHLs) shrinks early with decreasing the applied frequency, while the new one can show good cyclic hysteresis phenomenon when low frequency signal is applied. Obviously, the lobes' area of the improved inverse memristor emulator widens faster than the existed active models with increasing frequency in the low frequency range. And the circuit model just only includes two second generation current conveyor (CCII), one analog voltage multiplier and six passive elements. The improved emulator is demonstrated by implementing LTspice simulations and verified by experiments. A comparison between the proposed model and the representative current-controlled one highlights the superiority of the former in the lower frequency range. © 2021 IEEE

关键词： Memristors

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Bit Bounce Detection for Drilling Process Based on Multi-Feature Graph and Graph Convolutional Network 49

Bit Bounce Detection for Drilling Process Based on Multi-Fea...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Zhang, Peng Hu, Wenkai Cao, Weihua Wu, Min Wang, Jiandong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Province Qingdao China

ISBN: (纸本)9798350331820

Prompt detection of bit bounce can prevent serious incidents and is of great importance for safe and efficient deep geological drilling. In the early stage of bit bounce, signal changes are relatively weak. In addition, there are differences in the topological relationships of samples at different time instances in normal state and bit bounce. These factors present a challenge to timely and accurate bit bounce detection. Therefore, this paper proposes a bit bounce detection method based on multi-feature graph and graph convolution networks. A multi-feature graph construction method using process variables, mean value, Mahalanobis distance, and Euclidean distance is proposed, and a two-layer graph convolutional network is designed to realize deep feature extraction and incident detection. The effectiveness and superiority of the proposed method are demonstrated by a real drilling industrial case. © 2023 IEEE.

关键词： Infill drilling

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Blind Source Separation based on Refracted and Elite Opposition-based Learning Firefly Algorithm

Blind Source Separation based on Refracted and Elite Opposit...

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Chinese Automation Congress (CAC)

作者： Jing Wang Wei Li Xinrui Hong Shijie Wang Key Laboratory of Advanced Perception and Intelligent Control for High-end Equipment Ministry of Education School of Electrical Engineering Anhui Polytechnic University Wuhu China

To overcome the shortcomings of many parameters, slow convergence speed, and poor separation accuracy in applying traditional swarm intelligence algorithm in blind source separation, a Firefly Algorithm integrating Refracted and Elite Opposition-based learning is proposed. The proposed algorithm enhances the global search ability of the original algorithm, improves the population diversity, and accelerates the convergence speed. The simulation results show that in the Blind Source Separation, the similarity coefficients of the separated signals obtained by the proposed algorithm are all above 0.99.

关键词： Automation Simulation Sociology Blind source separation Statistics Particle swarm optimization Convergence

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An improved memristor emulator with a new shaping function

An improved memristor emulator with a new shaping function

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2021 China Automation Congress, CAC 2021

作者： Zhao, Zhenyu Dai, Guangzhen Song, Xingwen Han, Mingjun High-end Equipment Advanced Perception and Intelligent Control Ministry of Education Heavy Laboratory School of Electrical Engineering Anhui Polytechnic University WuHu241000 China

ISBN: (纸本)9781665426473

Recently, an emerging memristor has aroused people's research enthusiasm in many fields due to its unique properties and potential applications. However, because of its inaccessibility, the memristor emulator has been used as a good substitute to study the features and applications of it. A new shaping function circuit is designed here and used in the current-controlled memristor model. Mathematical analysis and simulation results of the constructed model show that the new emulator has a larger hysteresis loop than the prior, and moreover it has a distinct pinched hysteresis loop until 15kHz. Comparison with the existed emulators highlights the advantages of the new one. © 2021 IEEE

关键词： Memristors

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Development of completion tool pressure test control system 10

Development of completion tool pressure test control system

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2023 10th International Conference on advanced Manufacturing Technology and Materials engineering, AMTME 2023

作者： Sun, Zhiyu Zhao, Lianyu Wang, Chenglin Zhao, Xiaolei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Offshore Oil Engineering Co. Ltd. Tianjin300461 China

In oil exploitation, the completion tool is the key point to ensure the effective development of oil wells, and it must undergo strict pressure testing. Due to the high test pressure, there are potential safety hazards in the manual operation process. To improve safety and test efficiency, the automatic precision test system is developed. The detection system must realize the pressure detection process, provide feedback on the key data, give the corresponding curve reference, and generate the detection report. Through the PID algorithm, the precise control of the motor is realized. The system safety is guaranteed, and the detection accuracy is improved. The test results show that the completion tool pressure test control system can greatly improve detection efficiency. It can also fully reflect the entire detection process, accurately calculate some key parameters such as pressure drop, and obtain accurate results. The detection error is kept within 0.5%, ensuring the reliability of the detection process. © Published under licence by IOP Publishing Ltd.

关键词： Control systems

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