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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Zhuo Wang Jie Dong Xinzhe Wang Jianchao Fan School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Floating raft aquaculture is an important part of mariculture, and the use of single source data is easy to inhibits the extraction of floating raft aquaculture, so the advantages of integrating multisource data are particularly important. However, multisource remote sensing fusion still has the problem of data information imbalance and feature redundancy. In this paper, a channel exchanging bottleneck attention network (CEBANet) is proposed to extract features from multisource remote sensing data, determine whether features are redundant by using the scaling factor of batch normalization (BN) layer, replace the current redundant features with another modal feature. Bottleneck attention module (BAM) and feature calibration module are added to improve the capability of feature extraction and receptive field processing. The CEBANet is optimized by the sparse constraint on channel exchanging condition and cross-entropy loss and consistency loss. Using GF-5 and GF-3 data from Jinzhou District of Dalian City, it is proved that the proposed model can realize the complementary advantages of multisource remote sensing data and improve the accuracy of extracting floating raft aquaculture areas.

关键词： Urban areas Redundancy Data integration Feature extraction information retrieval Data models Data mining

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Normalized Spatial Resolution Analysis Model for Different Radar Systems

Normalized Spatial Resolution Analysis Model for Different R...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jianyu Yang Fanyun Xu Deqing Mao Jifang Pei Yulin Huang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Several radar systems have been proposed in the past decades, including real aperture radar (RAR) and synthetic aperture radar (SAR). Spatial resolutions of different radar systems cannot be compared together because their work modes are different. In this paper, a normalized spatial resolution analysis model is proposed to deduce the spatial resolution of different systems. First, the normalized wavenumber spectra of different radar systems are deduced. Second, the relationship between spatial resolution and the wavenumber spectra distribution is analyzed. Finally, the point spread functions (PSFs) of different radar systems are simulated.

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Distributed Posterior Fusion for Vehicle Tracking with Gaussian Processes

Distributed Posterior Fusion for Vehicle Tracking with Gauss...

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International Conference on control, Automation and information Sciences (ICCAIS)

作者： Jiaye Yang Yunlian Tian Haiyi Mao Zhiyuan Zou Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

We focus on the distributed posterior fusion for vehicle tracking in this paper. With rectangular prior information of vehicles’ shapes, we build a non-zero mean Gaussian Processes (GP) model to initialize the extents. In order to improve the estimation accuracy of targets’ kinematic and extent states, we try to make progress in two aspects. For one thing, we propose the unscented Kalman extended target Probability Hypothesis Density (UK-ET-PHD) filter with a tailor-made GP model. It can track multiple targets simultaneously and solve the nonlinear problems. For another thing, we realize the posterior fusion using the Generalized Covariance Intersection (GCI) rule with the proposed UK-ET-PHD local trackers. Finally, we evaluate the performance of the proposed method in the view of both vehicles’ position and extent estimation.

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A Fast Super-Resolution Imaging Method via Subspace Detection and Near-Field Phase Compensation for mmW Automotive Radar

A Fast Super-Resolution Imaging Method via Subspace Detectio...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yanqin Xu Xiaoling Zhang Shunjun Wei Shi Jun Tianjiao Zeng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Millimeter-wave (mmW) automotive radar imaging technology has great potential in advanced driver assistance systems (ADAS). Existing super-resolution imaging methods can improve angular (azimuth) resolution for automotive radar with limited aperture. However, these super-resolution methods have high computational complexity meanwhile have poor imaging performance in single-snapshot. In this paper, combined with CS based on ℓ 1 -norm regularization, we proposed a fast super-resolution imaging method via subspace detection and near-field phase compensation. Frist, the range-pulse-compression (RPC) data in the near and far field is formed by using range FFT. Then, the subspace detection is presented to reduce the size of the measurement matrix by using angle FFT to obtain the potential angle subspace of the target with all RPC data. After, the near-field phase compensation is utilized to make the measurement matrix applicable to all RPC data. Finally, the iterative shrinkage-thresholding algorithm (ISTA) algorithm is utilized to form the super-resolution images based on the measurement matrices and all RPC data. The simulation results show the proposed method can significantly improve imaging resolution with lower computational complexity than other imaging methods.

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Multi-scale Graph Convolutional Neural Network for Marine Aquaculture Extraction Based on SAR Images

Multi-scale Graph Convolutional Neural Network for Marine Aq...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xibao Wang Xinzhe Wang Jianchao Fan Zixuan Zuo School of Information Science and Engineering Dalian Polytechnic University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Synthetic aperture radar (SAR) image segmentation is one of the hotspots in the field of remote sensing, and studying pixel level information and its relationships is of great significance. However, the coherent speckle noise and high sea state conditions caused by factors such as wind speed in SAR images pose challenges to feature extraction. Due to the fixed shape of convolutional kernels in convolutional neural networks (CNNs), it is difficult for the kernels to fully cover the marine aquaculture areas in SAR images. In this paper, we utilize the structural exploration and information propagation capabilities of graph convolutional networks (GCNs) between different pixels to complete this pixel-level task. In order to reduce the connection burden between different SAR pixels and the high computational cost of the entire SAR image, this the paper uses the superpixel segmentation method to segment SAR images, reducing the previously high pixel-level computational cost. However, modeling at a single scale can only extract a single feature. To break this limit and extract More representative of the characteristics, this paper proposes multi-level Superpixel structure diagram based on U-net combination GCN is used for SAR image semantic segmentation. In the meantime, The network constructs a new residual convolution attention Module (RCA). RCA modules include CNN and Beyond Self-attention. First, through CNN convolution, which provides a detailed initial space for the model Characteristic information; Then in Features to make it more accurate. This hybrid U-Net can be utilized The characteristics of SAR images are analyzed from multi-scale and hierarchical perspectives In perspective, its performance has been proven. Extensive experiments have been carried out on data sets of aquaculture methods such as floating rafts and cages in different areas. The effectiveness of the proposed method is verified, and the Accuracy and other precision of the segmentation results are

关键词： Convolution Shape Message passing Neural networks Feature extraction Radar polarimetry Task analysis

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Real-Time nonlinear predictive controller design for drive-by-wire vehicle lateral stability with dynamic boundary conditions

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Fundamental Research 2022年第1期2卷 131-143页

作者： Xiyue Zhang Ping Wang Jiamei Lin Hong Chen Jinlong Hong Lin Zhang State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130025China The Department of Control Science and Engineering School of Communication EngineeringJilin UniversityChangchun 130025China College of Electronics and Information Engineering Tongji UniversityShanghai201804China The Postdoctoral Station of Mechanical Engineering School of Automotive StudiesTongji UniversityShanghai201804China

Due to flexible drive-by-wire technology,vehicle stability control can improve handling and lateral stability under extreme ***,this technology can also increase the probability of random transmission *** paper proposes a nonlinear model predictive control(NMPC)strategy to improve vehicle stability and compensate for the random time ***,by combining the nonlinear dynamic characteristics and driver behavior,we obtain a stable region of the yaw rate and the sideslip angle under complex driving ***,an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral ***,the actuator receives the optimized control sequence and compensates for the random time delay of the transmission ***/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.

关键词： Nonlinear model predictive control Stable region Random time delay Delay compensator Vehicle stability control

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Cylindrical Distributed Coprime Conformal Array for 2-D DOA and Polarization Estimation

Cylindrical Distributed Coprime Conformal Array for 2-D DOA ...

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IEEE National Conference on Radar

作者： Mingcheng Fu Zhi Zheng Yizhen Jia Bang Huang Wen-Qin Wang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In this paper, we devise a novel cylindrical conformal array, termed cylindrical distributed coprime conformal array (CDCCA), for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation. The proposed CDCCA avoids the lag redundancies between two adjacent linear subarrays of cylindrical conformal array, and increases the unique lags number in its difference coarray. Moreover, it provides a larger array aperture than the exiting cylindrical conformal arrays under the same number of sensors. Therefore, the CDCCA configuration can resolve a larger number of sources and provide a higher estimation accuracy. Numerical results demonstrate its superiority in comparison to several existing conformal arrays.

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QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

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IEEE Internet of Things Journal 2025年第12期12卷 19215-19230页

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China School of Information and Control Engineering China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China University of Science and Technology of China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive Deep Neural Network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this paper, we propose an edge-enabled multi-video analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov Decision Process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion deep reinforcement learning based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2025 IEEE.

关键词： Markov processes

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Detection of Insulator Defects in High-Voltage Transmission Lines Based on Improved YOLOv8

Detection of Insulator Defects in High-Voltage Transmission ...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Xiaoyong Bo Weidong Zhang Yi Zhang Meng Yuan School of Electrical Engineering Northeast Electric Power University Jilin China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Insulators serve as a key component in maintaining the safety and stability of power systems in high-voltage transmission lines. However, when insulators are damaged, they can cause line failures and even lead to extensive power outages. Meanwhile, the complexity of external environments, diverse backgrounds, and limited data samples pose significant challenges to achieving high-precision and efficient detection of insulator defects. To overcome these challenges, this study introduces an improved YOLOv8-based insulator defects detection model designed to enhance performance in real-world scenarios. First, actual drone-captured images and synthetic data are combined to enrich the diversity of training samples and improve the model's generalization capability. Then, an Multi-Scale Convolutional Attention (MSCA) mechanism is incorporated into the model, enabling more precise focus on key regions. The final experimental findings reveal that, compared with the original YOLOv8, the proposed method improves AP@0.5 and AP@0.5:0.95 by 1.3 % and 1.5 %, respectively. These findings underscore the feasibility and practical value of the proposed approach for detection of insulator defects.

关键词： Training YOLO Power transmission lines High-voltage techniques Power system stability Insulators Data models Thermal stability Defect detection Synthetic data

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Vital Sign Detection System Based On Multi-Vital Box Fitting Approximation

Vital Sign Detection System Based On Multi-Vital Box Fitting...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mingxu He Jizhen Ma Chengyu Wan Zongjie Cao Zongyong Cui School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Recently, people have become more concerned about life and health, and are paying more attention to the detection of their vital signs indicators. Non-contact detection of respiration rate(RR) and heart rate (HR) by using millimeter-wave (mmWave) radar is common. In this article, we propose a detection system based on multi-vital box fitting approximation using millimeter wave radar. On the basis of using different vital boxes signals with a certain period correlation, a new vital signal can be obtained by joint multi-boxes fitting approximation to a new vital signal, and taking measurements of respiration rate and heart rate. The experimental results show that the accuracy of the system using the multi-vital box vital signal fitting approximation improves about 3% for the detection of RR and about 2% for the detection of HR than the traditional single vital box system.

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