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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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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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The decline in tropical land carbon sink drove high atmospheric CO2 growth rate in 2023

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National science Review 2024年第12期11卷 203-212页

作者： Yanchen Gui Kai Wang Zhe Jin Heyuan Wang Hanzhi Deng Xiangyi Li Xiangjun Tian Tao Wang Wei Chen Tengjiao Wang Shilong Piao Institute of Carbon Neutrality Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University State Key Laboratory of Tibetan Plateau Earth System Resources and Environment(TPESRE) Institute of Tibetan Plateau Research Chinese Academy of Sciences School of Computer Science Peking University Institute of Computational Social Science Peking University (Qingdao) University of Chinese Academy of Sciences

Atmospheric CO2growth rate（CGR）, reflecting the carbon balance between anthropogenic emissions and net uptake from land and ocean, largely determines the magnitude and speed of global warming. The CGR at Mauna Loa Baseline Observatory reached a record high in 2023. We quantified major components of the global carbon balance for 2023, by developing a framework that integrated fossil fuel CO2emissions data and an atmospheric inversion from the Global Observatio N-based system for monitoring Greenhouse GAses（GONGGA） with two artificial intelligence（AI） models derived from dynamic global vegetation models. We attributed the record high CGR increase in 2023 compared to 2022 primarily to the large decline in land carbon sink(1803 ± 197 TgC year-1), with minor contributions from a small reduction in ocean carbon sink(184 TgC year-1) and a slight increase in fossil fuel emissions(24 TgC year-1). At least78% of the global decline in land carbon sink was contributed by the decline in tropical sink, with GONGGA inversion(1354 TgC year-1) and AI simulations(1578 ± 666 TgC year-1) showing similar declines in the tropics. We further linked this tropical decline to the detrimental impact of El Ni?o-induced anomalous warming and drying on vegetation productivity in water-limited Sahel and southern Africa. Our successful attribution of CGR increase within a framework combining atmospheric inversion and AI simulations enabled near-real-time tracking of the global carbon budget, which had a one-year reporting lag.

关键词： carbon budget land carbon sink atmospheric CO growth rate artificial intelligence El Niño

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Aerial-ground collaborative routing with time constraints

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Chinese Journal of Aeronautics 2023年第2期36卷 270-283页

作者： Jiahao XING Lichen SU Wenjing HONG Lu TONG Renli LYU Wenbo DU School of Electronics and Information Engineering Beihang UniversityBeijing 100091China School of Future Aerospace Technology/Shen Yuan Honors College Beihang UniversityBejing 100091China School of Automation Science and Electrical Engineering Beihang UniversityBejing 100091China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Shenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Research Institute of Frontier Science Beihang UniversityBeijing 100083China CAAC Key Laboratory of General Aviation Operation Civil Aviation Management Institute of ChinaBeijing 100102China

The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery *** reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily *** this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion ***,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality *** numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.

关键词： Aerial-ground collaborative delivery Mixed-integer programming Space-time hybrid heuristic algorithm Vehicle routing problem with drones Time-dependent travel times

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A Novel Deep Learning Approach for Tissue Microstructure Estimation Using Spherical Mean Technique 22

A Novel Deep Learning Approach for Tissue Microstructure Est...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Pan, Yiang Feng, Yuanjing He, Jianzhong Cui, Jianan Xie, Lei Chen, Zan Zeng, Qingrun Zhang, Jiawei Yu, Yan Kukanov, Konstantin K. Wu, Ye Zhejiang University of Technology Institution of Information Processing and Automation Hangzhou310023 China Polenov Neurosurgery Institute The Branch of Almazov National Medical Research Centre Russia School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9798331520526

Multi-shell diffusion magnetic resonance imaging (dMRI) models connect tissue organization with the observed dMRI signals to infer tissue microstructure. Given the highly non-linear nature of these models, several deep network based approaches have been proposed to improve the efficiency and accuracy of tissue microstructure estimation. However, these approaches rely on dMRI data with dense q-space sampling, which can lead to high computational costs. In this work, we propose a novel deep learning framework, termed DeepSMT, which utilizes the spherical mean technique (SMT) to directly estimate NODDI and DKI microstructure. The network comprises an inter-voxel block and a multi-shell feature block that can capture the feature relationship of the average signal between inter-voxels and integrate features from each shell. We validate the effectiveness of our method using the Human Connectome Project dataset. Overall, the DeepSMT has better performance compared to other methods in estimating tissue microstructure. © 2025 IEEE.

关键词： Image segmentation

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ECM-OPCC: Efficient Context Model for Octree-Based Point Cloud Compression

ECM-OPCC: Efficient Context Model for Octree-Based Point Clo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yiqi Jin Ziyu Zhu Tongda Xu Yuhuan Lin Yan Wang Institute for AI Industry Research (AIR) Tsinghua University Department of Automation Tsinghua University Department of Computer Science Tsinghua University

Recently, deep learning methods have shown promising results in point cloud compression. However, previous octree-based approaches either lack sufficient context or have high decoding complexity (e.g. > 900s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a segment-constrained multi-group coding strategy to exploit the autoregressive context while maintaining decoding efficiency. Then, we propose a dual transformer architecture to utilize the dependency of current node on its ancestors and siblings. We also propose a random-masking pre-train method to enhance our model. Experimental results show that our approach achieves state-of-the-art performance for both lossy and lossless point cloud compression, and saves a significant amount of decoding time compared with previous octree-based SOTA compression methods.

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TATBformer: A Divide-and-Conquer Approach to Ship Trajectory Prediction Modeling 11

TATBformer: A Divide-and-Conquer Approach to Ship Trajectory...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Xia, Chongjun Qu, Dong Zheng, Yonghang Shang Hai University School of Computer Engineering and Science Shanghai200444 China School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Research Institute of Artificial Intelligence Shanghai University Shanghai20444 China

ISBN: (纸本)9798350333664

Ship trajectory prediction, a cornerstone in time series forecasting, demands increasing precision amidst the complexities of modern maritime navigation. Traditional prediction methodologies have been constrained by the non-uniformity of AIS data and often overlook the intrinsic ship information. Addressing this oversight, our work integrates ship-specific data, proposing a divide-and-conquer model architecture. For trajectory data, we employ the Temporal Convolutional Network to extract implicit temporal insights from the vessel, which are subsequently channeled through a transformer for feature extraction. Simultaneously, ship-specific data is processed through a three-layer Multi-Layer Perceptron, recognizing its pivotal role as a reference for individual vessel predictions. This approach not only introduces a richer data set but also counters the potential computational surge induced by transformers. Additionally, we introduce an innovative composite loss function, infusing norm-based regularization to reduce dependencies on non-critical parameters. Empirical evaluations confirm that our approach surpasses classical time series forecasting techniques, delivering superior prediction accuracy with minimized errors. © 2023 IEEE.

关键词： deep learning Divide and Conquer TCN trajectory precdict transformer

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Generalized Poisson Formula for Second Order Elliptic Systems

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Journal of Mathematical sciences (United States) 2024年第4期281卷 625-631页

作者： Soldatov, A.P. Federal Research Center “Computer Science and Conrol” RAS 40 Vavilova St Moscow 119333 Russian Federation Institute of Applied Mathematics and Automation 89-A Shortanova St Nalchik 360000 Russian Federation Academy of Sciences of the Republic of Sakha (Yakutia) 33 Lenin Av. Yakutsk 677007 Russian Federation

In the unit disc, we consider the Dirichlet problem for second order elliptic system with only higher order constant coefficients. We establish the unique solvability of this problem under the assumption that the problem is Fredholm, and obtain an explicit formula for the solution. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

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PANSA: A Framework for Threat Knowledge Graph Construction

PANSA: A Framework for Threat Knowledge Graph Construction

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： TianXiang Chen Yi Zhuang Tao Xu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Automation Research Institute Lianyungang China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Cyber intelligence provides new prevention ideas for cyberattacks, but it is difficult to use effectively due to its multi-source heterogeneity and fragmentation. To address this problem, we propose a threat knowledge graph construction framework based on Pretrained Adversarial NER and relationship Subgraph-based Alignment called PANSA. First, the cyber threat domain ontology model is constructed in combination with the existing cyber threat intelligence standards. Secondly, SecBABC threat entity recognition model is designed, which incorporates pre-training and adversarial training modules to extract high-quality threat intelligence data. Then the Multi-Relation GAT for Entity Alignment framework (MRGEA) is designed. MRGEA considers the impact of different relationships on entities, and uses a multi-relation graph attention network to mine deep information. Finally, the cyber threat knowledge graph is visualized. Experimental results show that PANSA effectively integrates security intelligence and outperforms existing methods in knowledge extraction and entity alignment.

关键词： Training Knowledge engineering Prevention and mitigation Knowledge graphs Ontologies Cyber threat intelligence Security Data mining Standards Software engineering

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