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2021 International Conference on Computer technology and Media Convergence Design, CTMCD 2021

作者： Sun, Ming Li, Wenbo Zhou, Qingpeng School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Physics Zhengzhou University Zhengzhou China School of Process Equipment and Control Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665448567

Noise estimation methods are widely utilized in many areas of image processing and detection, such as image deblurring, mosaic detection. Recently, many researchers tried to design the corresponding estimation algorithm from the three directions of image spatial domain, transform domain and image statistical properties. Based on the scale invariance of kurtosis and piecewise stationarity of natural image in band-pass domain, this paper proposes a noise estimation algorithm based on Super-pixel segmentation and K-means clustering considering the influence of dominant edge and texture on the statistical characteristics in the image space domain. In this algorithm, we use PCA filters to transform the noisy image to transform domain. Furthermore, we can transform the noise estimation problem into a nonconvex optimization problem by using scale invariance of kurtosis. Finally, a large number of experiments verify the reliability of the algorithm, and the results show that the effect of the algorithm acts as a counterweight to the recent state-of-the-art algorithms. © 2021 IEEE

关键词： K-means clustering

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Multi-objective Group Decision and Cooperative Planning Based on Preference and Fuzzy measure 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Ma, Xiwen Xie, Wei Yang, Jingsong Zhang, Weidong Lu, Jinhui School of Oceanography Shanghai Jiao Tong University Shanghai China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China Zhuhai China School of Information and Communication Engineering Hainan University Hainan Haikou China National Engineering Research Center of Ship and Shipping Control System Shanghai China Crossocean of Suzhou technology No. 218 East Oingdao Road Suzhou China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

To pursue interest balance and improve collaboration efficiency, this paper proposes a multi-agent cooperation method integrating decision-making and planning. Based on preference and fuzzy measures, this method can balance individual and group benefits under fuzzy uncertain environment. The equilibrium solution of a multi-agent multi-objective game, namely group decision-making (GDM) solution, can be obtained by the Gaussian oscillation particle swarm optimization (GOPSO) algorithm, which overcomes the dilemma of a multi-objective group game and improves the computational efficiency. Considering the smoothness and continuity of the trajectory, a heuristic motion planning (MP) generation model that satisfies the kinematic constraints and minimizes differential thrust is proposed, which improves the cooperation efficiency. To verify the performance of this proposed method, simulation results are presented and some comparative statements are given. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Polarized Modulation for RIS-Aided Vehicular communication Networks

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IEEE Internet of Things Journal 2025年第12期12卷 18616-18627页

作者： Shang, Pingping Xie, Longhui Li, Jun Feng, Li Hai, Han Xu, Binbin Li, Jie Yu, Ling Hubei University of Technology School of Electrical and Electronic Engineering Wuhan430068 China Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430068 China Guangzhou University Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou510006 China Key Laboratory of On-Chip Communication and Sensor Chip of Guangdong Higher Education Institutes Guangzhou510006 China Donghua University School of Information Science and Technology Shanghai201620 China Maritime Communication Research Institute Wuhan430068 China Guangdong Provincial Hospital of Chinese Medicine Guangzhou510120 China State Key Laboratory of Traditional Chinese Medicine Syndrome Guangzhou510120 China

The reconfigurable intelligent surface (RIS) stands as an emergent technique poised to revolutionize future vehicular communication. In order to facilitate the application of RIS in vehicular communication, this paper proposes two effective RIS-aided schemes tailored for polarization modulation (PM) aiming to achieve remarkable spectral efficiency. Specifically, Scheme I proposes a novel index modulation (IM) aided PM-RIS framework that innovatively encodes information across three distinct dimensions, the indices of dual-polarized antennas (DP-AEs), PM symbols and the reflecting surface indices of the RIS. Scheme II employs a deep neural network method to analyze the channel state information (CSI) for transmit antenna selection, that enhances the reliability and interference immunity in dynamic conditions while reducing computational complexity. Furthermore, a stepwise estimation (SE) method is introduced at the detection stage, which significantly reduces the complexity associated with information decoding. The simulation results highlight the superiority of the proposed system. Compared with traditional PM systems, it demonstrates enhanced performance in terms of spectral efficiency, channel stability, and bit error rate (BER). © 2014 IEEE.

关键词： Signal modulation

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Two Enhanced-Rate Power Allocation Strategies for Active IRS-Assisted Wireless Network

Two Enhanced-Rate Power Allocation Strategies for Active IRS...

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Computer communication and Artificial Intelligence (CCAI), International Conference on

作者： Qiankun Cheng Rongen Dong Wenlong Cai Ruiqi Liu Feng Shu Jiangzhou Wang Hainan University School of Information and Communication Engineering Haikou China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Wireless and Computing Research Institute ZTE Corporation Beijing China University of Kent School of Engineering Canterbury U.K.

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Newton's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and approaches exhaustive search as the number of IRS reflecting elements goes to large-scale.

关键词： Array signal processing Wireless networks Simulation Polynomials Vectors Resource management Newton method

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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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A Baseline Modeling and Anomaly Detection Method for Inertial Navigation System Based on Integrated 1DCNN

A Baseline Modeling and Anomaly Detection Method for Inertia...

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International Conference on Electronic measurement and Instruments, ICEMI

作者： Wenjing Mo Chuanjin Han Yuan Wang Chengdu Aircraft Industrial (Group) Co. Ltd. Chengdu China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

Flight test is an essential step before aircraft delivery. Safe flight test can not only reduce the cost of aircraft research, but also has great significance for the study of aircraft performance. In order to ensure the safety of flight test, it is necessary to implement anomaly detection for aircraft systems. Inertial navigation system, as a complex system can provide accurate position, velocity, and attitude information for aircraft during flight test. It is very important to monitor the state of inertial navigation system for ensuring the safety of flight test. However, traditional single anomaly detection models perform poorly on different test data and have poor generalization capabilities. In order to improve the generalization ability of the anomaly detection model with inertial navigation data, this paper proposes a prediction-based anomaly detection method based on an integrated one-dimensional convolutional neural network (1DCNN). Firstly, multiple training sets are divided based on the bootstrap method, and then integrated 1DCNN prediction models are built for corresponding training sets. Finally, a static threshold judgment method based on the PauTa criterion is used to detect whether any abnormality occurs. This paper verifies the effectiveness of the proposed method through experiments with real inertial navigation data. The results show that compared with the traditional 1DCNN method, the proposed method significantly improves the performance of anomaly detection and has good generalization ability on different test conditions. The TPR of anomaly data detection reaches more than 97%, which proves that the method has effective anomaly data detection for inertial data.

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A Signal Demodulation Algorithm Based on Generative Adversarial Networks 3

A Signal Demodulation Algorithm Based on Generative Adversar...

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3rd IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2021

作者： Lijun, Wang Minghong, Zhang Yu, Han Lei, Shen Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665425186

In recent years, extensive research has been done to obtain better demodulation performance by combining signal demodulation with deep learning algorithms, such as convolutional neural networks (CNN). However, the demodulation performance of CNN is not much improved compared with traditional coherent demodulation algorithms. This paper proposes a signal demodulation algorithm based on generative adversarial networks (GAN). The proposed GAN-based demodulation network uses the confrontation characteristics between the generator and the discriminator in GAN to greatly improve the demodulation ability of the signal. Numerical results show that the demodulation performance is better than the coherent demodulation algorithm and the CNN-based demodulation algorithm. © 2021 IEEE

关键词： Demodulation

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Unsupervised Feature Enrichment and Fidelity Preservation Learning Framework for Skeleton-based Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Chuankun Li, Shuai Gao, Yanbo Gao, Xingyu Chen, Ping Li, Jian Li, Wanqing North University of China State Key Laboratory of Dynamic Testing Technology School of Information and Communication Engineering Taiyuan030051 China Shandong University School of Control Science and Engineering School of Software Jinan250100 China Chinese Academy of Sciences Institute of Microelectronics Beijing100029 China University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

Unsupervised skeleton-based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton-based action recognition is first investigated. It is observed that skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method tends to produce features that discriminate the different samples rather than action classes, resulting in the overfitting problem. To address this problem, this paper proposes an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation (U-FEFP) learning framework to generate rich distributed features that contain all the information of a skeleton sample. A spatial-temporal feature transformation subnetwork is developed using channel-wise topology refinement graph convolutional block and graph convolutional gated recurrent unit block as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent-based learning is utilized to generate rich distributed features, and the unsupervised pretext task-based learning is employed to preserve the information contained in the skeleton. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on four widely used benchmarks, namely NTU-RGB+D-60, PKU-MMD, NTU-RGB+D-120 and UAV-Human dataset, demonstrate that the proposed U-FEFP obtains the best result compared with the state-of-the-art unsupervised learning methods. © 1991-2012 IEEE.

关键词： Convolution

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Strip Weld Inspection Based on Improved VGG16

Strip Weld Inspection Based on Improved VGG16

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Sisi Fu Yuan Chen Dexin Xian Shiyuan Kong Hao Wang Jinsong Wang School of Optoelectronic Engineering Changchun University of Science and Technology Zhongshan Guangdong China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin Heilongjiang China School of Optoelectronic Engineering Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China School of Optoelectronic Engineering Changchun University of Science and Technology Zhongshan Institute of Changchun University of Science and Technology Zhongshan Guangdong China

As one of the indispensable materials in modern industry, high precision and high efficiency defect detection of strip weld has become an important part of production and quality control. In response to the shortcomings of the existing strip weld detection technology with low detection accuracy, this paper proposes a deep learning based strip weld defect detection and classification technique. First, the CBAM attention mechanism is introduced into the network structure to improve the attention to important features; second, the traditional convolution layer is replaced by using the dilated convolution to reduce the computation while increasing the perceptual field; finally, the original ReLU activation function is replaced by the PReLU activation function to effectively overcome the problem of neuron death in the Relu activation function. The improved model finally achieves a recognition rate of 98.1% in the test set, which is 22.1% higher than that of the unimproved model, greatly improving the accuracy and robustness of the algorithm.

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Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism 5

Small Object Detection Algorithm Based on RPANet and Positio...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tang, Zongbing Yang, Dan Qu, Junsuo School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an Robertic Intelligent Systems International Science and Technology Cooperation Base Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

With the development of deep learning, small object detection has a significant role in application fields such as smart factories and remote sensing images. In order to address the problem of difficult and low accuracy detection of small objects due to small pixel scale and little feature information. In this paper, we present a path aggregation network with residual characteristic RPANet on YOLOv3 algorithm, which can twice use the feature information of the backbone network to enhance the small object feature information, and also offer a positional convolution attention mechanism module PCAM to thoroughly learn and extract the small object feature information as well as reduce the unnecessary feature information in the background, so as to further enhance the detection capability of the model for small objects. The experimental results demonstrate that the improved YOLOv3 algorithm is more effective for small object detection. © 2022 ACM.

关键词： Convolution

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