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9th International Conference on Electromechanical control technology and Transportation, ICECTT 2024

作者： Wei, Shengjie Wang, Ranjun Han, Weiqiang Cheng, Tianji Liu, Zhiwen National Key Laboratory of Optical Field Manipulation Science and Technology Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Science Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement Chinese Academy of Sciences Chengdu610209 China

ISBN: (纸本)9781510682399

Aiming to solve the problems of slow convergence speed and poor positioning accuracy of the position servo system, we presents a novel second-order adaptive non-singular fast terminal sliding mode control method for the translation mechanism. First, We analyze a mathematical model of the translation mechanism that includes the uncertainty of other models. Then, we design a backstepping 2th-order adaptive non-singular terminal sliding mode controller(2ABNFTSMC) using the backstepping principle, which updates the control gain by establishing adaptive rules, resulting in high control accuracy. We theoretically demonstrate the stability of the method. By comparing with the traditional PI and sliding mode controllers, we verified the performance of the designed and developed controllers. The simulation results verify the effectiveness of the proposed 2ABFNTSMC in accelerating convergence and reducing steady-state error. © 2024 SPIE.

关键词： Backstepping

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Robust Radar Work Mode Recognition in Non-Ideal Situations with GRU-TCN Model

Robust Radar Work Mode Recognition in Non-Ideal Situations w...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Kanglong Ji Xianxiang Yu Yu Huang Kangan Feng Mou Wang Guolong Cui Siyuan Chen School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Dynamic beam-pointing capability of Multi-function radar presents significant challenges in work mode recognition. This paper considers the problem of airborne radar work mode recognition in non-ideal situations, such as measuring noise, lost pulse, and spurious pulse. Specifically, this paper commences with an analysis of the distinctive features exhibited by each work mode and investigates the transition rules between work modes, providing theoretical support for dataset generation. Then, a novel network model called GRU-TCN (Gated Recurrent Unit (GRU)-Temporal Convolutional Network (TCN)) is developed, which first employs a GRU network to extract temporal features among pulses and then leverages a TCN network to decode the extracted features. Simulation results show that the proposed model demonstrates superior performance compared to existing models in non-ideal situations.

关键词： Image recognition Pulse measurements Atmospheric modeling Simulation Noise Logic gates Feature extraction

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A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

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IEEE Transactions on Cognitive communications and Networking 2024年

作者： Lu, Yao Zhu, Yutao Li, Yuqi Xu, Dongwei Lin, Yun Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Binjiang Institute of Artificial Intelligence Hangzhou310056 China Chinese Academy of Sciences Institute of Computing Technology Beijing China Harbin Engineering University College of Information and Communication Engineering Harbin China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China Peng Cheng Laboratory PCL Research Center of Networks and Communications Shenzhen518000 China Hangzhou310056 China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for Automatic Modulation Recognition (AMR). Although these AMR models yield impressive results, they often come with high computational complexity and large model sizes, which hinders their practical deployment in communication systems. To address this challenge, we propose a novel layer pruning method, PSR. Specifically, we decompose the AMR model into several consecutive blocks, each containing consecutive layers with similar semantics. Then, we identify layers that need to be preserved within each block based on their contribution. Finally, we reassemble the pruned blocks and fine-tune the compact model. Extensive experiments on five datasets demonstrate the efficiency and effectiveness of PSR over a variety of state-of-the-art baselines, including layer pruning and channel pruning methods. © 2015 IEEE.

关键词： Signal modulation

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A Novel Integer Frequency Offset Estimation Method for OFDM Based on Preamble 7

A Novel Integer Frequency Offset Estimation Method for OFDM ...

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7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Liu, Xinxin Xu, Zhan Tian, Lu Yang, Xiaolong Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing100101 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9781665405829

Orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Although the existence of integer frequency offset in OFDM will not destroy the orthogonality between subcarriers, it will cause a cyclic shift of the frequency domain data after FFT transformation at the receiving end, which will affect the demodulation of data. In this paper, we propose a novel integer frequency offset estimation method based on the similarity property of adjacent subcarriers of the preamble. This method not only guarantees the original accuracy and performance of the conventional method but also achieves less resource consumption and lower computational complexity. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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RCDFNet: A 4-D Radar and Camera Dual-Level Fusion Network for 3D Object Detection

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IEEE Sensors Journal 2025年

作者： Cheng, Peifeng Yan, Hang Wang, Yukang Wang, Luping Sun Yat-sen University School of Electronics and Communication Engineering Shenzhen China Hangzhou Qianjiang Distinguished Expert Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

4-D radar, as an advanced vehicle sensor, provides denser point clouds and elevation information than millimeter-wave radar, establishing it as a valuable sensor for autonomous driving. Recently, the fusion of 4-D radar and camera has become a viable alternative to LiDAR for autonomous driving perception systems. However, existing fusion methods have not fully realized the potential of complementary advantages offered by these two modalities. To address this limitation, our study proposes RCDFNet, a dual-level fusion network for 3D object detection with 4-D radar and camera. Specifically, we first exploit the geometric information from the 4-D radar to extract semantic image features from the perspective view, yielding pseudo-camera features under the bird’s-eye view. In the first fusion level, the pseudo-camera features are integrated with the 4-D radar features using the spatial point fusion module to obtain the radar-guide fusion features. In the second fusion level, we employ the deformable attention mechanism to facilitate the interaction between camera-view-transform features and the radar-guide fusion features, to generate BEV-match fusion features. Furthermore, we propose the target-aware refinement module to learn of foreground targets’ occupancy adaptively in the bird’s-eye view, mitigating the impact of the background noise of two sensors. We validate the RCDFNet on the TJ4DRadSet and View-of-Delft (VoD) datasets. Experimental results demonstrate that RCDFNet significantly outperforms the baseline RCFusion, achieving state-of-the-art performance in 3D object detection. © 2001-2012 IEEE.

关键词： 3D object detection 4-D radar-camera fusion Autonomous driving camera deep learning radar

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Parallel Power Distribution control Strategy of Wind-Solar Storage Multi-micro-grid Virtual Synchronous Machines 1

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18th Chinese Intelligent Systems Conference, CISC 2022

作者： Yang, Chengdong Lv, Shuaishuai Wang, Hui Meng, Xiangping Li, Zijie School of Electrical and information Engineering Changchun Institute of Technology NO. 395 Kuanping Road Chaoyang District Changchun130012 China National and Local Joint Engineering Research Center for Measurement Control and Safe Operation of Intelligent Distribution Networks Changchun130012 China

ISBN: (数字)9789811962264

ISBN: (纸本)9789811962257

Aiming at the difficulty in decision-making of coordinated power allocation of multiple wind-solar storage micro-grids, a power allocation control strategy for virtual synchronous machines in parallel is proposed considering the wind-solar capacity of each micro-grid. First, lithium batteries and Super capacitor are introduced to stabilize fluctuations to achieve DC bus voltage regulation in the micro-grid, and then according to the different wind and solar capacities in each micro-grid, the inverters are controlled by virtual synchronous machine multi-machine parallel adaptive power distribution to achieve real-time Finally, the validity of the proposed control strategy is verified through the analysis of computing power. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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Fault Classification Method of Information System Based on Cascaded Convolutional Neural Network Model 5

Fault Classification Method of Information System Based on C...

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5th IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Ou, Ruiting Chen, Shuo Qiao, Lin Liu, Ziang Cheng, Xu Qiao, Yongfeng State Grid Liaoning Information and Communication Company Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China College of Economics and Management Shenyang Agricultural University Shenyang China Dandong Dongfang Measurement & Control Technology Co. Ltd Dandong China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

Artificial intelligence plays an important role in data anomaly detection, which can significantly improve the speed and accuracy of fault diagnosis and detection in power information system. With its powerful fitting ability, deep learning begins to emerge in various fields, especially in the field of da-ta anomaly detection, which can also use deep learning methods. Due to various types and similar features of power data sets, fault classification cannot be carried out accurately. At present, frequent detection of text information needs to be performed manually. To solve this problem, this paper proposes a fault classification method for information system based on cascaded convolutional neural network model. BERT deep learning network is used for model training, and a cascaded classification network is constructed. Multiple BERT models are called hierarchically to achieve the fault detection effect of hierarchical classification. The method presented in this paper improves the accuracy of fault prediction and classification, which also provides fast, accurate and reliable decision information basis for improving fault diagnosis. © 2022 IEEE.

关键词： Failure analysis

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Lorentz inertial stability platform control based on KF-LESO-PID

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2022年第6期48卷 1072-1081页

作者： Xiong, Ying Liu, Qiang Ren, Yuan Fan, Yahong Sun, Jinji Institute of Precision Electromagnetic Equipment and Advanced Measurement Technology Beijing Institute of Petrochemical Technology Beijing102617 China Department of Areospace Science and Technology Space Engineering University Beijing101416 China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100190 China Key Laboratory of Inertial Technology Beihang University Beijing100083 China

To overcome the disadvantages of the existing inertial stabilization platform such as large interference for using mechanical bearings, high difficulty for using air/liquid bearings and poor linearity for using magnetic resistance magnetic bearings, a new Lorentz inertial stability platform (LISP) based on Lorentz force deflection magnetic bearing is proposed. To suppress the influence of coupling effect and load-bearing friction resonance interference on the high-frequency attitude compensation control of the platform deflection channel, a digital control scheme based on LESO-PID combined with Kalman filter (KF) feedback is proposed. According to the structural characteristics of rotor tilt supported by Lorentz force magnetic bearing (LFMB), a dynamics model for LISP deflection is established;the tilting relationship of two radial channels is analyzed with the established model, and the linear extended state observer (LESO) and Kalman filter feedback control is introduced into PID controller to suppress friction resonance interference and coupling effects;a digital control system based on DSP and FPGA is construed, and the control method is digitalized in a discrete form. The stability of the proposed control method is analyzed by logarithmic frequency characteristic criterion and Nichols curve, and the stabilities of the rotor deflection channel before and after importing LESO-Kalman are compared through simulation. Experimental results show that with traditional PID, the rotor system causes serious distortion at high frequency, while the system reduces noise and interference greatly after importing LESO-Kalman control. Meanwhile, the internal state parameters of the system can be monitored in real time. Experimental results verify the effectiveness of proposed control method to suppress the frictional resonance interference and coupling effects. © 2022, Editorial Board of JBUAA. All right reserved.

关键词： Friction

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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A High frequency millimeter wave circular patch antenna for Radar Applications and satellite communications

A High frequency millimeter wave circular patch antenna for ...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Manish Varun Yadav Abhi Chaurasia Salla Sai Charith Sudheendra Prabhu K Tanweer Ali Swati Varun Yadav Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

This article proposes a millimeter wave circular patch microstrip antenna. The design illustrates a circular patch antenna which bears a range of 19.4-110GHz having a bandwidth of 90.6 GHz and having high resonance at frequencies of 51 GHz, 61.5 GHz, 71.8 GHz and 101 GHz. It depicts a good impedance matching at these frequencies, a decent gain and a directional radiation pattern. The material FR4 is utilized for designing the substrate of the antenna. The antenna shows a simulated gain of 8.8 dB, 8.9 dB, 7.7 dB, and 8.1 dB at the above specified operating frequencies.

关键词： Slot antennas Patch antennas Resonant frequency Microstrip antennas Bandwidth Millimeter wave radar Microstrip

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