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International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD

作者： Yannan Dong Lei Gao Shaohua Ma Xin Yao Xiangjun Li School of Electrical Engineering Shenyang University of Technology Shenyang China State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China

The power quality of the rectifier power supply and the load characteristics influence the specific energy consumption (SEC) of PEM water electrolyzer. This paper establishes a PEM water electrolyzer model, and examines the harmonic distribution of the stack current for 6-pulse and double star rectifiers. It then calculates the ripple factors (RF) and power factors (PF) of the PEM water electrolyzer, and analyzes the SEC of the water PEM electrolyzer under the two rectifier types. The results indicate that the double star rectifier shows greater improvements in PF, RF, and harmonic distribution. Therefore, to reduce the cost of electricity for hydrogen production, the double star rectifier may be considered in PEM systems.

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Deep Learning Enabled Semantic Communication systems for Video Transmission

arXiv

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arXiv 2023年

作者： Zhang, Zhenguo Yang, Qianqian He, Shibo Chen, Jiming College of Control Science and Engineering Zhejiang University Hangzhou 310007 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310007 China The State Key Laboratory of Industrial Control Technology Hangzhou 310007 China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Zhejiang University Hangzhou 310007 China

Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system's robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Sparse Gaussian Feature Learning for Nonlinear industrial Fault Diagnosis

Sparse Gaussian Feature Learning for Nonlinear Industrial Fa...

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第34届中国控制与决策会议

作者： Jianfeng Zhang Chunhui Zhao Jinliang Ding Youxian Sun Xianling Li State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research Institute

ISBN: (纸本)9781665478977

With the increasing complexity of modern industrial processes,the high-dimensional industrial data exhibits strong nonlinearity,bringing considerable challenges to the fault diagnosis of *** efficiently extract deep meaningful features that are crucial for fault diagnosis,a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label *** feature space is described by the one-hot encoding of the fault category label as an orthogonal *** this way,the sparse Gaussian features related to fault categories can be gradually learned from the raw data by *** the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian *** sparse Gaussian features are linearly separable in the feature space,which can improve the accuracy of the downstream fault classification *** effectiveness of the proposed SGFE is verified by the Tennessee-Eastman(TE) benchmark process.

关键词： Nonlinear fault diagnosis sparse Gaussian feature learning Gaussian feature extractor

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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Multivalent Display of Lipophilic DNA Binders for Dual-Selective Anti-Mycobacterium Peptidomimetics with Binary Mechanism of Action

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CCS Chemistry 2022年第11期4卷 3573-3586页

作者： Jianxue Wang Junfeng Song Xianhui Chen Rey-Ting Guo Yingjie Wang Guopu Huang Nan Zheng Peilei Hu Xinxin Feng Yugang Bai State Key Laboratory of Chem-/Bio-Sensing and Chemometrics Hunan Provincial Key Laboratory of Biomacromolecular Chemical BiologySchool of Chemistry and Chemical EngineeringHunan UniversityChangshaHunan 410082 State Key Laboratory of Biocatalysis and Enzyme Engineering School of Life SciencesHubei UniversityWuhanHubei 430062 Institute of Systems and Physical Biology Shenzhen Bay LaboratoryShenzhenGuangdong 518055 Department of Polymer Science and Engineering School of Chemical EngineeringDalian University of TechnologyDalianLiaoning 116024 Hunan Institute for Tuberculosis Control Hunan Chest HospitalChangshaHunan 410013

We made oligoamidine-based peptidomimetics highly specific for mycobacteria eradication by introducing and arraying lipophilic DNA binding motifs on macromolecular *** short poly(amidino-phenylindole)(PAPI)structures feature an alternating amphiphilic structure with cationic,lipophilic DNA-binding moieties,enabling fast and selective eradication of mycobacteria through binary,membrane-and DNA-selective mechanisms of *** importantly,PAPIs address the primary treatment challenge by combating mycobacteria in eukaryotic cells and working as a sensitizer for conventional antibiotics,in bothways promoting more thorough removal of pathogens and reducing the mycobacteria’s resistance generation rate during *** optimizationwas achieved to counter specific pathogens,including Mycobacterium tuberculosis,in the Mycobacterium *** of the hit peptidomimetics was evaluated in a zebrafish-based aquatic infection model using Mycobacterium fortuitum and a mice tail infection model using Mycobacterium marinum,both revealing excellent in vivo performance.

关键词： peptidomimetic mycobacteria antimicrobial membrane disruption DNA binding

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Temperature control System of Thermoelectric Cooler Based on Improved ADRC

Temperature Control System of Thermoelectric Cooler Based on...

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Chinese control and Decision Conference, CCDC

作者： Xiyuan Shan Tao Cai Zhicheng Zong Hongbo Mu School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781665478977

Aiming at the problems containing complex working conditions, modeling difficulties and long-time delay of thermoelectric cooler temperature control system, an improved ADRC control method combining ADRC and Smith predictor is proposed in this paper. ADRC deals with the disturbances and uncertain dynamics in the system, SP compensates the time delay to improve the control performance. Compared with the traditional PID controller, the proposed control method has faster response speed and stronger anti-disturbance ability, meanwhile, overcomes the dependence of Smith predictor on object parameters to a certain extent. Through simulation and experimental verification, a good control effect is obtained, which provides a positive guidance of the related application of thermoelectric cooler.

关键词： Robust control Employee welfare Delay effects Simulation Perturbation methods Predictive models control systems

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Privacy-Preserving Optimal state Estimation with Low Complexity via Cramér-Rao Lower Bound Approach

arXiv

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arXiv 2024年

作者： Guo, Liping Wang, Jimin Zhao, Yanlong Zhang, Ji-Feng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

This paper addresses the optimal state estimation problem for dynamic systems while preserving private information against an adversary. To dominate the adversary’s estimation accuracy about private information in the mean square error (MSE) sense, the Cramér-Rao lower bound (CRLB) is employed to evaluate privacy level. The problem is formulated as a constrained optimization, which minimizes the MSE of the state estimate with a constraint on privacy level, achieving a trade-off between privacy and utility. To solve the constrained optimization problem, an explicit expression for CRLB is first provided using the information inequality. To overcome the increasing sizes of the involved matrices over time, a low-complexity approach is then proposed to achieve online calculation for CRLB, significantly reducing computational complexity. Next, the optimization problem is relaxed to a semi-definite programming problem, and a relaxed solution is provided. Finally, a privacy-preserving state estimation algorithm with low complexity is developed and proved to achieve differential privacy. Two illustrative examples, including a practical case of building occupancy, demonstrate the effectiveness of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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GAT-Net: A Network using Grid Attention and Transformer for Dynamic Facial Expression Recognition

GAT-Net: A Network using Grid Attention and Transformer for ...

引用

Chinese control and Decision Conference, CCDC

作者： Wenxin Dai Yaping Dai Kaoru Hirota Shuai Shao School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781665478977

Dynamic facial expression recognition (DFER) in the wild has received widespread attention recently. There are complex factors such as face occlusion and pose variation in the wild. Facial expression recognition has a subtle competition between capturing local features of a human face and obtaining a global feature representation. This paper proposes an end-to-end DFER network GAT-Net based on the grid attention module and Transformer, which improves the robustness and accuracy of DFER in the wild. Specifically, GAT-Net is divided into two components: spatial feature extraction and temporal feature processing. The grid attention module of the spatial feature extraction component guides the network to pay attention to the local salient features of the face, which reduces the interference of field occlusion and non-frontal poses. The Transformer in the temporal feature processing component guides the network to learn the temporal relationship of high-level semantic features and the global representation of facial expression features. These two components balance the subtle competition between local features and global feature representations of facial expressions. The ablation experiment has proved the effectiveness of the grid attention module and Transformer. Experiments demonstrate that our GAT-Net outperforms state-of-the-art methods on DFEW and AFEW benchmarks with accuracies of 67.53%, and 50.14% respectively.

关键词： Face recognition Semantics Interference Benchmark testing Transformers Feature extraction Robustness

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Dynamic Free-Space Roadmap for Safe Quadrotor Motion Planning

arXiv

引用

arXiv 2022年

作者： Guo, Junlong Xun, Zhiren Geng, Shuang Lin, Yi Xu, Chao Gao, Fei The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China The Huzhou Institute of Zhejiang University Huzhou313000 China

Free-space-oriented roadmaps typically generate a series of convex geometric primitives, which constitute the safe region for motion planning. However, a static environment is assumed for this kind of roadmap. This assumption makes it unable to deal with dynamic obstacles and limits its applications. In this paper, we present a dynamic free-space roadmap, which provides feasible spaces and a navigation graph for safe quadrotor motion planning. Our roadmap is constructed by continuously seeding and extracting free regions in the environment. In order to adapt our map to environments with dynamic obstacles, we incrementally decompose the polyhedra intersecting with obstacles into obstacle-free regions, while the graph is also updated by our well-designed mechanism. Extensive simulations and real-world experiments demonstrate that our method is practically applicable and efficient. © 2022, CC BY-NC-ND.

关键词： Motion planning

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