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2024 China Automation Congress, CAC 2024

作者： Wang, Wei Meng, Hao Liu, Guozhong School of Instrument Science and Opto-Electronic Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798350368604

EEG signals are often used to classify motor imagery in the field of brain-computer interface research. Deep learning is used in EEG signal classification to focus on extracting features, selecting features, and classifying them. Taking the frequency, spatial and temporal features of the signal into account at the same time, in the 4D-CRNN model, motor imagery can be classified from EEG signals based on its characteristics. The model achieved good results in this area. As a part of the experiment, attention module has been introduced into the model, resulting in a partially improved model effect. © 2024 IEEE.

关键词： Deep learning

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An Improved YOLOv8n Algorithm for Object Detection with CARAFE, MultiSEAMHead, and TripleAttention Mechanisms 7

An Improved YOLOv8n Algorithm for Object Detection with CARA...

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7th International Conference on Computer information science and Application technology, CISAT 2024

作者： Tang, Hao Jiang, Yuepeng Beijing Information Science and Technology University School of Instrument Science and Opto-electronic Engineering Beijing China

ISBN: (纸本)9798350375107

To improve the accuracy of the YOLOv8n algorithm for object detection, this paper proposes an enhanced YOLOv8n algorithm. This algorithm introduces the CARAFE module, TripleAttention mechanism, and MultiSEAMHead module into the YOLOv8n backbone network to enhance feature extraction capabilities. Extensive experiments were conducted on the NWPU dataset. Experimental results show that compared with the original YOLOv8n algorithm, the enhanced algorithm improves precision, recall, and mean average precision (mAP) by 2.4%, 3.0%, and 4.0%, respectively. These results validate the effectiveness and robustness of the proposed method. © 2024 IEEE.

关键词： Object recognition

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Enhanced intelligent water drops with genetic algorithm for multi-objective mixed time window vehicle routing

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Neural Computing and Applications 2024年 1-15页

作者： Guo, Zhibao Karimi, Hamid Reza Jiang, Baoping Wu, Zhengtian Cheng, Yukun School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou100190 China Mechanical Engineering Politecnico di Milano Milan10587 Italy School of Business Jiangnan University Wuxi214122 China

In the context of addressing the multi-objective vehicle routing problem, a hybrid time window multi-objective vehicle model was established using integer programming and the intelligent water drop algorithm. To overcome the limitation of the intelligent water drop algorithm potentially converging to local optimal solutions, enhancements were proposed through genetic algorithms, particularly by introducing genetic crossover and single-point recombination operators. Subsequently, the intelligent water drop algorithm was refined, and its effectiveness was evaluated through a real-world case study. Comparative analyses were conducted among three algorithms: IWD, GA, and SA. The results demonstrate that the improved algorithm effectively alleviates the common issue of traditional algorithms converging to local optimal solutions. Therefore, an enhanced solution is provided for the discrete hybrid time window problem, achieving superior optimization outcomes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Genetic algorithms

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Stabilization for Discrete-Time Stochastic Systems with Multiple Input Delays

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Journal of Systems science & Complexity 2023年第5期36卷 1961-1980页

作者： LI Lin ZHANG Huanshui College of Mechanical and Electronic Engineering Shandong Agricultural UniversityTaian 271018China School of Information Science and Engineering Shandong Agricultural UniversityTaian 271018China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

A stabilization problem for stochastic systems with multiple input delays is investigated herein.A new method to construct an optimal control problem with input constraints is adopted.A sufficient stabilization condition is established via Riccati-type equations,whose verification is computationally simple because the variable dimension of the equations does not increase with regard to the delay.A stabilization controller is proposed,and it is a feedback of the state and a history *** controller uses less history information than those with distributed *** numerical examples illustrate the effectiveness of the obtained results.

关键词： Riccati-type equations stabilization stochastic systems time-delay systems

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CS2DIPs: Unsupervised HSI Super-Resolution Using Coupled Spatial and Spectral DIPs

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IEEE Transactions on Image Processing 2024年 33卷 3090-3101页

作者： Fang, Yuan Liu, Yipeng Chi, Chong-Yung Long, Zhen Zhu, Ce University of Electronic Science and Technology of China School of Communication and Information Engineering Chengdu611731 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan

In recent years, fusing high spatial resolution multispectral images (HR-MSIs) and low spatial resolution hyperspectral images (LR-HSIs) has become a widely used approach for hyperspectral image super-resolution (HSI-SR). Various unsupervised HSI-SR methods based on deep image prior (DIP) have gained wide popularity thanks to no pre-training requirement. However, DIP-based methods often demonstrate mediocre performance in extracting latent information from the data. To resolve this performance deficiency, we propose a coupled spatial and spectral deep image priors (CS2DIPs) method for the fusion of an HR-MSI and an LR-HSI into an HR-HSI. Specifically, we integrate the nonnegative matrix-vector tensor factorization (NMVTF) into the DIP framework to jointly learn the abundance tensor and spectral feature matrix. The two coupled DIPs are designed to capture essential spatial and spectral features in parallel from the observed HR-MSI and LR-HSI, respectively, which are then used to guide the generation of the abundance tensor and spectral signature matrix for the fusion of the HSI-SR by mode-3 tensor product, meanwhile taking some inherent physical constraints into account. Free from any training data, the proposed CS2DIPs can effectively capture rich spatial and spectral information. As a result, it exhibits much superior performance and convergence speed over most existing DIP-based methods. Extensive experiments are provided to demonstrate its state-of-the-art overall performance including comparison with benchmark peer methods. © 1992-2012 IEEE.

关键词： Tensors

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A Multi-Agent Based Model for User Interest Mining on Sina Weibo

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China Communications 2022年第2期19卷 225-234页

作者： Meijia Wang Qingshan Li School of Electronic Information and Artifical Intelligence Shaanxi University of Science and TechnologyXi’an 710021China Software Engineering Institude Xidian UniversityXi’an 710071China

User interest mining on Sina Weibo is the basis of personalized recommendations,advertising,marketing promotions,and other *** great progress has been made in this area,previous studies have ignored the differences among users:the varied behaviors and habits that lead to unique user data *** is unreasonable to use a single strategy to mine interests from such varied user ***,this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data,microblogs and *** method has the ability to select the appropriate strategy based on each user’s data *** experimental results show that the proposed method performs better than the baselines.

关键词： multi-agent system user interest mining adaptive model Sina Weibo online social network

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A lightweight attention-guided semantic segmentation network using a group convolution pyramid 2

A lightweight attention-guided semantic segmentation network...

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2nd International Conference on Computing, Communication, Perception and Quantum technology, CCPQT 2023

作者： Yu, Siyu Huang, Dandan Wang, Guixian Yao, Anni Changchun University of Science and Technology School of Electronic Information Engineering Changchun China Name of Changchun University of Science and Technology School of Electronic Information Engineering Changchun China

ISBN: (纸本)9798350342697

Semantic segmentation is a key part of computer vision tasks, but its application is greatly limited by memory capacity and computational cost. This paper proposes a lightweight attention-guided semantic segmentation network (LAGNet) that adopts a joint group convolution pyramid strategy. Specifically, we introduced a lightweight symmetric double attention module (LS-DAM) and an adaptive selection interaction module (ASIM). In the LS-DAM, the self-attention adopts adaptive maximum pooling and average pooling instead of its previous form, in which the pixel-by-pixel similarity is calculated. Moreover, The ASIM has the capability to seamlessly integrate both high-level semantic information and low-level geometric information, leading to a remarkable enhancement in the performance of semantic segmentation. We evaluate our proposed LAGNet model using mean intersection over union (mIoU) on PASCAL VOC 2012 and Cityscapes, two commonly used datasets in the field of semantic segmentation, and finally achieve state-of-the-art performance of 81.73% and 81.90%, respectively. © 2023 IEEE.

关键词： Semantic Segmentation

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Readout electronics for the gamma detector of the HIRFL-CSR external target facility

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Nuclear science and Techniques 2025年第2期36卷 71-81页

作者： Xian-Qin Li Hai-Bo Yang Xiao-Meng Ma Chao-Jie Zou Tao Liu Xian-Cai Zhou Duo Yan Yang-Zhou Su Shu-Wen Tang Shi-Tao Wang Yu-Hong Yu Zhi-Yu Sun Cheng-Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China School of Physics and Electronic Engineering Northwest Normal UniversityLanzhou 730070China School of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China

The Cooling Storage Ring of the Heavy Ion Research Facility in Lanzhou(HIRFL-CSR)was constructed to study nuclear physics,atomic physics,interdisciplinary science,and related *** External Target Facility(ETF)is located in the main ring of the *** gamma detector of the ETF is built to measure emitted gamma rays with energies below 5 MeV in the center-of-mass frame and is planned to measure light fragments with energies up to 300 *** readout electronics for the gamma detector were designed and *** readout electronics consist of thirty-two front-end cards,thirty-two readout control units(RCUs),one common readout unit,one synchronization&clock unit,and one sub-trigger *** using the real-time peak-detection algorithm implemented in the RCU,the data volume can be significantly *** addition,trigger logic selection algorithms are implemented to improve the selection of useful events and reduce the data *** test results show that the integral nonlinearity of the readout electronics is less than 1%,and the energy resolution for measuring the 60 Co source is better than 5.5%.This study discusses the design and performance of the readout electronics.

关键词： HIRFL-CSR Gamma detector External target facility Readout electronics Readout control unit Common readout unit Peak-detection algorithm

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Non-Hermitian polarization quantized by time-reversal symmetry

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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