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2021 OES China Ocean Acoustics, COA 2021

作者： Li, Yuelin Sun, Chao Liu, Yuechan Ai, Shiqin Harbin University of Science and Technology Harbin School of Measurement-Control Technology and Communication Engineering China Harbin City150080 China

ISBN: (纸本)9781728181905

Taking the heat exchange tube in the condenser as the research object, the three-dimensional finite element model of the heat exchange tube and the Internal and external fluid of pipe is established, and the bidirectional fluid-solid coupling calculation of the heat exchange tube is carried out. The time-domain and frequency-domain curves of lift, drag and displacement of the heat exchange tube and the time-domain and frequency-domain curves of the pressure pulsation in the tube are obtained. At the same time, the acoustic simulation calculation is carried out for the vibration noise of the heat exchange tube. The time domain and frequency domain curves of sound pressure near the heat exchange tube are obtained. The simulation results show that the frequency of pressure pulsation corresponds to the frequency of lift and drag. The flow velocity in the tube studied in this paper will affect the amplitude and waveform of the pressure pulsation but will not affect its frequency. The sound pressure frequency of the upper and lower sides of the heat exchange tube only corresponds to the lift frequency, and the sound pressure frequency on the left and right side corresponds to the lift and drag frequencies. © 2021 IEEE.

关键词： Flow velocity

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MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT images via a Deep Learning Architecture

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT ...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Tang, Xiaoyu Chai, Lin Song, Chengze School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China School of Sino-french Engineering Institute Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781837242689

Computer-aided diagnosis has emerged as a crucial trend in modern medicine. Recently, CT-based automatic detection algorithms for lung infectious diseases suffer from issues of low accuracy and complex architecture. In this paper, three MoB-NetV2 models (MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL) for the detection of COVID-19 in CTs are proposed. the MoB-NetV2 model is proposed by inserting batch normalization layers between bottlenecks in MobileNetV2 and substituting the fully connected layer with the Net-1 model (SNN, ELM, RVFL), i.e. MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL. After comparison, it is found that the proposed MoB-NetV2-RVFL achieves an average accuracy of 93.42% on a private dataset comprising of 2392 images, while the precision is reached at 97.33%. Furthermore, it is demonstrated that our MoB-NetV2-RVFL outperforms other existing algorithms in the literature. High classification accuracy and precision and low computing amount are achieved, thereby enhancing the potential application of the model in the field of medical image-aided diagnosis of COVID-19 virus. © The Institution of engineering & technology 2024.

关键词： COVID-19

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Polarization-Space-Time Processing in Mainlobe Blanket Jamming via CP Decomposition 8

Polarization-Space-Time Processing in Mainlobe Blanket Jammi...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Pan, Bunian Wang, Bing Dong, Luxin Yao, Xue Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Science and Technology on Electronic Information Control Laboratory Chengdu610036 China Southeast University School of Information Science and Engineering Nanjing211100 China

ISBN: (纸本)9798350397932

This paper deals with the problem of mainlobe jamming suppression for phased-array radar system via a joint polarization-space-time processing method resorting to tensor decomposition. Firstly, a dual-polarization receiving array model is established. Then, base on the polarization-space-time characteristics, CANDECOMP/PARAFAC (CP) decomposition is leveraged to recast the received signals as a third-order tensor consisting of three factor matrices. Further, the target echo and the jamming signal can be obtained via minimizing the Frobenius norm of the approximation error between the factor matrices and their iteration forms. Finally, the effectiveness of the proposed method by numerical simulation is verified, showing its capability to resist mainlobe jamming compared with the art competing method. © 2023 IEEE.

关键词： Numerical methods

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Research on frequency control strategy of combined wind- energy storage system 2

Research on frequency control strategy of combined wind- ene...

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2nd International Conference on Smart Grids and Energy Systems, SGES 2023

作者： Jiang, Zhihong Liu, Guiyuan Song, Kangnan Li, Han Yang, Hao Wu, Han Changchun Institute of Technology School of Electrical Engineering and Information Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun China International Joint Research Center for New Energy Power Generation and Network Control Technology of Jilin Province Changchun China

ISBN: (纸本)9798350371659

Under the high penetration rate of wind power, the power system puts forward technical requirements for the frequency regulation capability of wind farms. Considering that the frequency response capability of the double-fed induction generator (DFIG) is insufficient, it is proposed to combine energy storage (ES) with wind power for system frequency regulation. Based on the frequency characteristic models of the wind farm and the ES system, this paper establishes a prediction model for the over speed control of the wind farm and a prediction model for the electrochemical energy storage system, and optimizes the active power output of the wind turbine and the energy storage system. The strategy is based on the optimal control of the wind turbine output power and ES power variation, and on this basis, an optimal control model considering the ES state of charge (SOC) and power is designed. The simulation results show that the proposed control strategy can not only effectively suppress grid frequency deviation can also coordinate the ES state of charge and frequency control. © 2023 IEEE.

关键词： Model predictive control

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Intelligent Clustering Based Active Power Allocation Strategy for Wind Turbines 2

Intelligent Clustering Based Active Power Allocation Strateg...

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2nd International Conference on Smart Grids and Energy Systems, SGES 2023

作者： Jiang, Zhihong Song, Kangnan Liu, Guiyuan Wu, Han Li, Han Yang, Hao Changchun Institute of Technology School of Electrical Engineering and Information Technology Changchun China National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control With Safety Operation Technology Changchun China International Joint Research Center for New Energy Power Generation and Network Control Technology of Jilin Province Changchun China

ISBN: (纸本)9798350371659

Aiming at the problem of uneven distribution of active power of each unit in a wind farm, an artificial intelligence clustering method based active power distribution strategy of wind turbine is proposed. Firstly, according to the actual operation data of the wind farm, the Pearson correlation coefficient is used to select the clustering data. Then, the Gaussian mixture model is used for clustering experiments. Finally, according to the clustering results, the distribution method of weight proportional coefficient is adopted to evenly distribute power to all units. The simulation results show that the proposed clustering method has a good evaluation score, and the power allocation strategy based on this can fully meet the scheduling criterias. All units can realize the smooth and orderly power rise and fall in the safe operation state, and avoid the problem of frequent start-stop and overload operation of some units caused by uneven power distribution. © 2023 IEEE.

关键词： Cluster analysis

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JOINT PULSE SIGNAL DETECTION AND DOA ESTIMATION ALGORITHM BASED ON EIGENVALUE DISTRIBUTION of SAMPLE AUTOCORRELATION MATRIX

JOINT PULSE SIGNAL DETECTION AND DOA ESTIMATION ALGORITHM BA...

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IET International Radar Conference 2023, IRC 2023

作者： Peng, Xiang Yu Liu, Rui Dong Liu, Ning Chang, Kai Wan, Qun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Northern Institute of Electronic Equipment Beijing China Precision Measurement Radar System Technology Key Laboratory of Sichuan Province China

ISBN: (纸本)9781839539954

Under the condition of Nyquist sampling, direction finding algorithm generally uses all time domain or frequency domain snapshots for direction finding, which is difficult to apply to passive applications where the time-frequency domain of the signal is unknown or the signal-to-noise ratio is very low. Aiming at the problem that the direction finding of pulse signal is without guidance in time domain, this paper proposes a direction estimation algorithm combined with pulse detection. The algorithm first determines the position of the pulse signal in time domain by using the largest eigenvalue distribution of the sample autocorrelation matrix, and then guides the direction finding in time domain. The joint estimated Cramer-Rao Bound is also derived. Simulation results show that the estimation performance of the proposed algorithm is significantly better than that of the direction finding algorithm without pulse detection. © The Institution of engineering & technology 2023.

关键词： Autocorrelation

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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Data-Knowledge-Driven for UAV Swarm communication Interference Recognition

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IEEE Internet of Things Journal 2025年

作者： Wang, Bin Li, Aiping Wang, Anyi Sun, Yanjing Li, Song Xi'an University of Science and Technology School of Communication and Information Engineering Xi'an710054 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China

Achieving high-precision interference recognition for unmanned aerial vehicle (UAV) swarm communications in complex electromagnetic environments is of great significance for developing efficient anti-interference schemes and improving the security of UAV swarm communication. Although deep learning-based interference recognition methods for UAV swarm communication can achieve good recognition performance, they usually rely on a large number of high-quality labelled samples and only consider a single representation of the interference signal as input. This leads to low accuracy and poor robustness of interference recognition in scenarios with changing electromagnetic environments or insufficient samples. To address these issues, this paper proposes a data-knowledge-driven method for UAV swarm communication interference recognition. A dual-input interference recognition network (DIRNet) with a few model parameters is designed, incorporating deep features extracted based on the data-driven approach and manual features designed based on expert knowledge. Simulation experiments are conducted under sufficient-sample, cross-environment, and insufficient-sample scenarios. The results demonstrate that the proposed UAV swarm communication interference recognition method not only improves the recognition accuracy of interference signals under these conditions. Moreover, it also shows good robustness in complex dynamic environments. © 2014 IEEE.

关键词： Aircraft communication

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Improved Semantic Segmentation of Urban Road Scenes with Deep Dual-Resolution Network 25

Improved Semantic Segmentation of Urban Road Scenes with Dee...

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Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security

作者： Penggang Guo Yunfan Liu Longfei Li Changyuan Liu School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin Heilongjiang China School of Modern Post Nanjing University of Posts and Telecommunications Nanjing Jiangsu China School of Information Engineering Minzu University of China Beijing China

ISBN: (纸本)9798400713453

In response to the requirements for efficiency and accuracy in segmentation models in autonomous driving scenarios, an improved urban road semantic segmentation algorithm based on the Deep Dual-Resolution Network (DDRNet) has been proposed. Firstly, a Channel Residual Attention Module (CRAM) based on standard attention mechanisms is introduced at the end of two parallel branches of the DDRNet to focus on important channel features. Secondly, the backbone network incorporates Depthwise Over-parameterized Convolution (DO-Conv) to boost feature extraction performance. Finally, a Deep Aggregation Pyramid Pooling Module (DAPPM) with Hybrid Dilated Convolution (HDC) is designed to capture multi-scale feature information, and Group Normalization (GN) is utilized to mitigate the impact of reduced batch size. The experimental results demonstrate that the proposed method achieves a Mean Intersection over Union (MIoU) of 77.0% on the Cityscapes dataset, representing an improvement of 1.1% over the baseline model. Additionally, the segmentation speed reaches 106 Frames Per Second (FPS), achieving an optimal balance between segmentation accuracy and computational efficiency.

关键词： Channel attention mechanism

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor 42

A Nonsmooth Observer Based on Friction Model Compensation fo...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Zaishan Zhu, Wenwu Yu, Ke Wang, Xiangyu Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9789887581543

In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main factors affecting the tracking accuracy of the motor at low speed, especially in the back and forth motion of the motor. Firstly, based on the Stribeck friction model, a nonlinear friction model of the motor is established. Secondly, a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor, and the lumped disturbance of the system at the same time. Further, the stability analysis of this nonsmooth observer is given. Finally, the effectiveness of the proposed method is verified by the simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Sensorless control

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