检索结果-内蒙古大学图书馆

A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

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Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking

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IEEE Transactions on Biomedical engineering 2025年第6期72卷 1963-1972页

作者： Su, Bai-Quan Ma, Xu-Dong Li, Weihan Kuang, Zi-Ao Gong, Yi Wang, Gang Zhang, Qingqian Liu, Wenyong Li, Changsheng Gao, Li Wang, Junchen Beijing University of Posts and Telecommunications Medical Robotics Laboratory School of Automation Beijing100876 China University of Tokyo School of Mechanical Engineering Tokyo113-0033 Japan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China Peking University General Surgery Department Beijing100191 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Peking University Department of Periodontology National Stomatological Center School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China

The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relies on observing the trajectory of surgical tools on tissue. This observation-based method often lacks clear standards, leading to inaccurate assessments. This paper presents a method for evaluating cutting skill in open surgery through scalpel motion analysis. A 3D multiple-facet ArUco code cube is designed, and a dataset of tip coordinate system poses for various scalpels in the ArUco code coordinate system (ACS) is established using the pivot calibration method. The YOLOv8 model and an image dataset of different scalpels are used to identify the scalpel type and select its tip position. The tip position is then transformed from ACS to a binocular camera coordinate system (BCS), representing the incision curve made by the scalpel. Five assessment metrics are proposed to quantify the surgeon's cutting skill: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk. Experiments involving twenty expert and novice surgeons performing four common incisions (straight line, polyline, semicircle, and cross line) demonstrate the metrics' effectiveness. The metrics provide a clear, objective display of individual cutting skills, and a combined ranking reveals comparative skill levels. This study offers a precise method for evaluating surgeons' cutting skills with a scalpel in open surgery. © 1964-2012 IEEE.

关键词： Surgery

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T-SNVAE: Deep probabilistic learning with local and global structure for industrial process monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1603-1613页

作者： Huang, Jian Liu, Zizhuo Yang, Xu Liu, Yupeng Lv, Zhaomin Peng, Kaixiang Ersoy, Okan K. University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering China University of Science and Technology Beijing Shunde Innovation School Foshan China Shanghai University of Engineering Science School of Urban Railway Transportation China Purdue University Electrical and Computer Engineering West Lafayette United States

Variational autoencoder is a generative deep learning model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t- SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN Embedding. Through minimizing the Kullback-Leibler divergence of joint probabilities between the original data and the reconstructed data, variational autoencoder learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process. © 2020 IEEE.

关键词： Embeddings

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Facilitating Continuous Facial Aging through Latent Age Attribute Modulation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hu, Xiyuan Qu, Jinglei Chen, Chen Beijing University of Technology School of Computer Science Beijing100124 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China

In recent years, facial aging has attracted significant research interest due to its broad applications and potential benefits. While Generative Adversarial Networks (GANs) have achieved notable progress in synthesizing realistic facial images, many GAN-based facial aging methods struggle to accurately capture the continuous progression of age-related changes over time. In this paper, we propose an innovative framework featuring the Latent Age Attribute Module (LAAM), which maps age attributes to a structured latent space that facilitates efficient sampling for precisely age attribute modeling. We further introduce the Age-AdaIN Fusion Module (AFM), which seamlessly integrates age features from LAAM with facial content features, enabling the generation of images that exhibit smooth, continuous age transitions. This framework excels in capturing fine-grained aging details, particularly for elderly individuals. Quantitative and qualitative evaluations on benchmark datasets demonstrate the effectiveness of our approach in generating realistic age-progressed facial images, with a notable improvement in elderly aging accuracy and detail. © 2025 IEEE.

关键词： Generative adversarial networks

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A Noise-robust U2 Scheme for Automatic Speech Recognition of Air Traffic Voice Communication

A Noise-robust U2 Scheme for Automatic Speech Recognition of...

引用

2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Jiang, Yutong Zhang, Xiaoxiao Zhang, Huaiwen Qian, Shengsheng Yang, Yang School of Electronic and Information Engineering Beihang University State Key Laboratory of CNS/ATM Beijing China School of Inner Mongolia University of Computer Science Hohhot China Institute of Automation CAS University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331534738

Air traffic voice communication (ATC Communication) involves the transmission of operational commands between controllers and pilots through voice. This communication is crucial for maintaining the safety and order of air traffic management. However, due to manual execution, errors, omissions and errors often occur, leading to safety accidents. Automatic speech recognition (ASR) solution tailored to the characteristics of air traffic speech is crucial to achieving real-time recognition, reducing ambiguity and preventing errors. This paper presents a Noise-Robust U2 scheme for ATC communication ASR. The scheme enhances the speech under severe noise interference and achieves high efficiency and accuracy while ensuring the robustness of the recognition process. The scheme includes two key components: a noise-robust speech enhancement model based on the two-layer gating cycle unit (GRU), which extracts compact features through the Bark frequency inverted spectrum coefficient (BFCC) and captures short-and long-term dependencies through the GRU;the second is the U2 ASR method, which provides real-time flow identification and optimized output combined with Transformer encoder and Connectionist temporal classification(CTC) decoder and attention-based(AED) decoder. Finally, noise data from Noise-92, clean voice data from Aishell-1 and ATC communication recording data collected by an airport in China were selected to verify the feasibility of the proposed scheme. © 2025 IEEE.

关键词： Air Traffic Voice Communication Automatic Speech Recognition Noise-robust Model U2 Scheme

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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface Technology" as an Example 6th

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th International Conference on computer science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of Things (AIOT), the course "Microcomputer Principles and Interface Technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. The innovative teaching mode of this course has achieved significant results in innovative practice. This innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. This teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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ENS-RFMC: An Encrypted Network Traffic Sampling Method Based on Rule-Based Feature Extraction and Multi-hierarchical Clustering for Intrusion Detection 27th

ENS-RFMC: An Encrypted Network Traffic Sampling Method Based...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Wei, Zi-Xuan Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031783821

Efficiently and quickly identifying malware in encrypted network traffic is a major challenge. This research presents and evaluates a novel approach for sampling encrypted network traffic, called ENS-RFMC. Initially, the method employs a rule-based strategy for extracting relevant metadata features of encrypted traffic from network connections, SSL and certificates log files. Subsequently, a multi-hierarchical clustering algorithm is utilized to divide the dataset into smaller clusters, with benign clusters subsequently eliminated for streamline data processing. Classifier models such as Random Forests, XGBoost and SVM are then utilized to detect attacks and assess performance. Experimental results indicate that the proposed ENS-RFMC sampling method is effective, with the encrypted traffic intrusion detection model employing ENS-RFMC sampling exhibits enhanced accuracy and efficiency in identifying attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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