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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Binhua Yu Ping-An Liu Yichen Liu Hongyu Wang Liang Zhang Yanhua Wang Kun Liu Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target’s pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method.

关键词： Training Sensitivity Image resolution Image recognition Target recognition Databases Imaging Radar applications Signal resolution Dispersion

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Toward UAV-Enabled Stereoscopic UL Heavy NOMA: Joint Resource Allocation and 3D Trajectory Design

Toward UAV-Enabled Stereoscopic UL Heavy NOMA: Joint Resourc...

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IEEE International Conference on Communications (ICC)

作者： Haiyong Zeng Rui Zhang Xu Zhu Yufei Jiang Zhongxiang Wei Fu-Chun Zheng Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We study an unmanned aerial vehicle (UAV)-enabled uplink heavy non-orthogonal multiple access (NOMA) system in this paper, where the UL communication becomes extremely important in some hot spot areas such as live concerts or soccer stadiums, and investigate the joint optimization of bandwidth assignment (BA) and power allocation (PA) with UAV three-dimensional (3D) trajectory design to maximize the minimum average rate among all ground users, while meeting their heterogeneous rate requirements. More specifically, we propose a joint BA and PA algorithm by revealing that the inter-user interference in each NOMA group can be eliminated naturally while deriving the sum rate of users. The algorithm is proposed to get the optimal BA and PA solutions with the help of closed-form results and ellipsoid method. After that, in order to solve the UAV 3D trajectory design problem we introduce the elevation angle as a supplementary variable, and the successive convex approximation method is adopted to obtain the UAV 3D trajectory. The joint BA and PA algorithm and the UAV 3D trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

关键词： NOMA Three-dimensional displays Stereo image processing Autonomous aerial vehicles Approximation algorithms Trajectory Resource management

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Lightweight CNN for Radar HRRP Recognition Using NAS-Based Pruning and Multi-Knowledge Distillation

Lightweight CNN for Radar HRRP Recognition Using NAS-Based P...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Zhilong Zhang Heke Wu Yinhui Xu Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Xi'an Modern Control Technology Research Institute Xi'an China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep convolutional neural network (CNN) has been widely studied in radar target high resolution range profile (HRRP) recognition. However, the CNN with deep structure requires high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we propose a differentiable neural architecture search (NAS)-based channel pruning and multi-knowledge distillation (MKD) to prune and fine-tune the CNN for lightweight. In the pruning stage, NAS method is used to automatically find the good-performance pruning structure by optimizing channel preserve scores. After the search process, these scores are used for global pruning to derive the pruned model. In the fine-tuning stage, MKD method combine the physical scattering knowledge with the category knowledge from pretrained model to restore the pruned model performance. Experimental results on VGG-16 with HRRP dataset inversed from the MSTAR show that the recognition accuracy of the proposed method decreases 1.52% by using 5% model parameters.

关键词： Accuracy Target recognition Neural networks Scattering Radar Data processing Data models Neural architecture search Convolutional neural networks

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Study on the Construction of "One-Bill Coverage" Transport Service System for China-Laos Railway Multimodal Transport 11th

Study on the Construction of "One-Bill Coverage" Transport S...

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11th International Conference on Traffic and Transportation Studies, ICTTS 2024

作者： Du, Xinyi Zhang, Yaqin Wang, Li School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China Beijing Engineering Research Center of Urban Traffic Information Intelligent Sensing andService Technologies Beijing100044 China Key Laboratory of Railway Industry for Operational Active Safety Assurance and Risk Prevention and Control Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796397

The "one-bill coverage" of China-Laos railway multimodal transport is an effective way to promote the growth of bilateral trade between China and Laos railways, and it is an inevitable requirement for the construction of a modern comprehensive transport system. In order to enhance the efficiency and economic benefits of cargo transport on China-Laos Railway, this paper combines the current situation of freight transport on China-Laos Railway and divides the life cycle of "one-bill coverage" of China-Laos Railway multimodal transport into five phases: order submission, domestic consolidation, cross-border transport, foreign evacuation and order signing, etc., and builds a "one-bill coverage" covering all the links, such as loading, transport, transshipment, warehousing, connecting, and customs clearance. This paper can provide a reference for the development of "one-bill coverage" transport service of China-Laos Railway multimodal transport. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Multimodal transportation

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Distortion-Disentangled Contrastive Learning

Distortion-Disentangled Contrastive Learning

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jinfeng Wang Sifan Song Jionglong Su S. Kevin Zhou School of AIAC Xi’an Jiaotong-Liverpool University Suzhou China School of BME & Suzhou Institute for Advanced Research Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) University of Science and Technology of China Suzhou China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Self-supervised learning is well known for its remarkable performance in representation learning and various downstream computer vision tasks. Recently, Positive-pair-Only Contrastive Learning (POCL) has achieved reliable performance without the need to construct positive-negative training sets. It reduces memory requirements by lessening the dependency on the batch size. The POCL method typically uses a single objective function to extract the distortion invariant representation (DIR) which describes the proximity of positive-pair representations affected by different distortions. This objective function implicitly enables the model to filter out or ignore the distortion variant representation (DVR) affected by different distortions. However, some recent studies have shown that proper use of DVR in contrastive can optimize the performance of models in some downstream domain-specific tasks. In addition, these POCL methods have been observed to be sensitive to augmentation strategies. To address these limitations, we propose a novel POCL framework named Distortion-Disentangled Contrastive Learning (DDCL) and a Distortion-Disentangled Loss (DDL). Our approach is the first to explicitly and adaptively disentangle and exploit the DVR inside the model and feature stream to improve the representation utilization efficiency, robustness and representation ability. Experiments demonstrate our framework’s superiority to Barlow Twins and Simsiam in terms of convergence, representation quality (including transferability and generalization), and robustness on several datasets.

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Automation 5.0: The key to Systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Mimicking synaptic plasticity based on TiO2/MgO film resistive switching device

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Materials Science in Semiconductor processing 2025年 197卷

作者： Pan, Jinyan Li, Jing He, Ruotong Huang, Qiao Li, Tiejun Yan, Lingsen Liu, Yan He, Hongyang Gao, Yunlong School of Ocean Information Engineering Jimei University Fujian Xiamen361021 China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Fujian Xiamen361021 China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Fujian Xiamen361102 China

The TiO2/MgO composite film was prepared via atomic layer deposition, exhibiting superior resistive switching (RS) performance compared to pure MgO film, including a higher resistance ratio (Roff/Ron) and enhanced retention. The Ag/TiO2/MgO/ITO device exhibited gradual RS characteristics, making it suitable for neuromorphic applications. The device also displayed excellent multilevel RS properties analogous to synaptic plasticity. When a series of positive or negative scanning voltages with progressively increasing amplitudes were applied, the resistance of the device switched and remained at correspondingly higher or lower states, effectively mimicking synaptic potentiation or depression in an amplitude-dependent manner. Furthermore, long-term potentiation and depression behaviors were observed under the time-dependent stimulation. As the pulse voltage amplitude increased, fewer pulses were required to set the device to a specific state. Conversely, lower voltage necessitated more pulses to facilitate Ag ions migration and conductive filament formation. This dynamic mimics synaptic weight modulation, where the evaluation of filaments over time corresponds to synaptic adjustment. Therefore, applying higher or lower amplitude voltages for longer or shorter durations aligns with the observation, where varying stimuli to the presynaptic membrane modulate synaptic weight, thereby strengthening (and weakening) the synaptic signal. The pulse amplitude and duration thresholds modulate the RS behavior of the composite TiO2/MgO film effectively mimic the learning and forgetting processes of human brain memory. © 2025

关键词： Composite films

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Design method for bionic topology optimization of high-specific-stiffness mirrors based on additive manufacturing

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Applied Optics 2025年第17期64卷 4801-4809页

作者： Wencong Chen Siyuan Li Lijun Sun Yueqi Zhu Jiajun Long Yichun Wang Bingliang Hu Shuang Wang Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an 710119 China University of Chinese Academy of Sciences Beijing 100049 China Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing Xi’an 710119 China School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 China sunlijun@***

Aiming at the demand for high-specific stiffness of space optical mirrors, this study proposes a bionic topology optimization design method for mirrors based on additive manufacturing. First, the principle and process of the Voronoi structure bionic topology optimization method are presented. The optimal design and analysis of the mirrors were then conducted using this method. Subsequently, the mirror blanks were fabricated using selective laser melting (SLM) technology. The modal testing results demonstrated that the first-order natural frequencies of the two mirrors designed in this study were enhanced by 31.78% and 60.20%, respectively, compared to traditional mirrors with the same lightweight ratio. This method offers a novel design and optimization approach, to our knowledge, for space optical mirrors.

关键词： Finite element method Laser beams Mirrors Optical aberration Optical components Remote sensing

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A Fractional-Order Logarithmic Adaptive Filtering Algorithm Under $\alpha$-Stable Noise

A Fractional-Order Logarithmic Adaptive Filtering Algorithm ...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Dazhi Sun Canping Yu Li Li Yingsong Li Wudang Xiao Yong Chen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China

In practical applications, noise is more appropriately modelled as an a-stable random process than as a Gaussian process. To cope with a-stable noise, a fractional-order stochastic gradient descent (SGD) method is invented to adaptive filtering (AF) algorithms. When both input signal and noise follow α-stable random processes, the AF algorithms based on mean square error (MSE) criteria can be affected. Nevertheless, the fractional-order AF (FoAF) algorithm can effectively address this issue. For the purpose of better stability performance of the FoAF algorithm, different cost functions (CFs) are introduced into fractional-order SGD method. Based on the fractional-order derivative of a logarithmic CF (LCF), a fractional-order logarithmic adaptive filtering (FoLAF) algorithm is formed in this condition. Trials show that the FoLAF algorithm achieves faster convergence, excellent steady-state performance and superior tracking performance.

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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