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Chinese automation Congress (CAC)

作者： Jinyang Zhu Zhezhuang Xu Ye Lin College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Using machine vision for meter reading significantly enhances the efficiency of industrial monitoring. However, meters in outdoor environment are often subject to the noise such as rain and fog, which affect the accuracy of meter readings. Additionally, the meter images and labels under noise are hard to obtain. To address these challenges, We propose a method for pointer meter reading under noise based on virtual data generation. Firstly, We utilize Unity to construct an industrial meter scene, which generates the images of meter under noise and theri labels. Secondly, we develop a U-Net network with an ASPP module, which expands the receptive field of network and enhances the ability of segmentation network to capture global semantic information under noise. Finally, the angular method is used to calculate the meter readings. Experimental results show that this method can effectively read meters under noise in outdoor environments.

关键词： Meters Image segmentation Rain Accuracy Noise Semantics Object segmentation Data collection Meter reading Monitoring

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Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo

Simulated Ising Annealing Algorithm with Gaussian Augmented ...

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Chinese control Conference (CCC)

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China School of Mechanical Engineering and Automation Harbin Institude of Technology Shenzhen China

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in combinatorial optimization, which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo (GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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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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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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Predictive control of Irrigation Canals Considering Well-being of Operators

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IFAC-PapersOnLine 2023年第2期56卷 731-736页

作者： R. Ranjbar A. Sadowska J.M. Maestre P.J. van Overloop B. De Schutter Systems and Automation Engineering Department University of Seville Spain Schlumberger Cambridge Research Cambridge United Kingdom Water Management Department Delft University of Technology The Netherlands. Delft Center for Systems and Control Delft University of Technology The Netherlands

We propose a model-predictive control (MPC) approach to solve a human-in-the-loop control problem for a non-automatic networked system with uncertain dynamics. There are no sensors or actuators installed in the system and we involve humans in the loop to travel between various nodes in the network and to provide the remote controller with measurements as well as actuating the system according to the control requirements. We compute the time instants at which the measurements and actuations should take place to yield better performance with respect to current control methods. We present simulation results using a numerical model of a real canal, the West-M canal in Arizona, and we demonstrate the superiority of the new method over previously proposed ones for such setting.

关键词： MPC human-in-the-loop networked system optimisation uncertainty

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Cross-Modality Prompts: Few-Shot Multi-Label Recognition With Single-Label Training

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IEEE Transactions on Multimedia 2025年 27卷 3023-3033页

作者： Ding, Zixuan Zhou, Zihan Chen, Hui Hao, Tianxiang Xiong, Yizhe Zhao, Sicheng Zhang, Qiang Han, Jungong Xidian University State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipments Xi'an710071 China Xidian University Center for Complex Systems School of Mechano-Electronic Engineering Xi'an710071 China Tsinghua University School of Software Beijing100190 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100190 China Tsinghua University Department of Automation Beijing100190 China

Few-shot multi-label recognition (FS-MLR) presents a significant challenge due to the need to assign multiple labels to images with limited examples. Existing methods often struggle to balance the learning of novel classes and the retention of knowledge from base classes. To address this issue, we propose a novel Cross-Modality Prompts (CMP) approach. Unlike conventional methods that rely on additional semantic information to mitigate the impact of limited samples, our approach leverages multimodal prompts to adaptively tune the feature extraction network. A new FS-MLR benchmark is also proposed, which includes single-label training and multi-label testing, accompanied by benchmark datasets constructed from MS-COCO and NUS-WIDE. Extensive experiments on these datasets demonstrate the superior performance of our CMP approach, highlighting its effectiveness and adaptability. Our results show that CMP outperforms CoOp on the MS-COCO dataset with a maximal improvement of 19.47% and 23.94% in mAPharmonic for 5-way 1-shot and 5-way 5-shot settings, respectively. © 1999-2012 IEEE.

关键词： Zero-shot learning

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Intelligent Log Analysis technology for Distributed Energy Production Management System

Intelligent Log Analysis Technology for Distributed Energy P...

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China International Electrical and Energy Conference (CIEEC)

作者： Kaiming Qie Yuanyuan Wang Fuliang Lin Ruizheng Zhao Han Yang Science and Technology on Information Systems Engineering Laboratory Beijing Institute of Control and Electronic Technology Beijing China School of Automation Chongqing University Chongqing China

Aiming at the demand of distributed energy production management system for intelligent operation and maintenance, considering the problems in current log analysis method, we propose an intelligent log analysis technology for distributed energy production management system. This technology applies distributed log framework to achieve the collection, storage, analysis and display of various equipment and service logs in the energy production management system. On the one hand, we use log anomaly detection technology to realize the anomaly detection of single node and the generating of log anomaly alarm, On the other hand, we use rule reasoning technology to realize the analysis and tracing of log anomaly alarm. In addition, we combine with practical application, optimize the traditional rule matching algorithm, which improves the fault tolerance of the matching network, and gets better accuracy and efficiency of the algorithm.

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Electromagnetic Performance and Vibration Analysis of a Special-shaped External-rotor Machine 18

Electromagnetic Performance and Vibration Analysis of a Spec...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Dong, Ziwei Yan, Liang Lu, Zhang Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Science and Technology on Aircraft Control Laboratory Beijing100191 China Tianmushan Laboratory Xixi Octagon City Yuhang District Hangzhou310023 China Beihang University Ningbo Institute of Technology Ningbo315800 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China

ISBN: (纸本)9798350312201

Permanent magnet synchronous machines have advantages such as small size, light weight, high efficiency, etc. It is more and more widely used in robots, electromagnetic propulsion, electric vehicles and so on. High power density and high torque density are urgent need for those applications. The electromagnetic design of machines mostly adopt multi-pole magnet patterns to reduce the stator yoke size. It helps to decrease the mass of the machine with the same power and improve the torque density. As the number of poles grows larger, the frequency and the core loss of per unit volume will also increase with the same speed, which can aggravate the heat problem of the machine. It is easy to generate the low-order vibration with high flux density. Therefore, the performance of a special-shaped external-rotor machine and comparisons with the traditional machine are analyzed in this paper. Numerical simulation is conduced to validated that the special-shaped magnet pattern can reduce the torque ripple, improve the torque output and power density. From the two-dimensional Fourier analysis of the radial electromagnetic force, it is found that the low-order amplitude of the special-shaped machine is greatly reduced, so the vibration can be avoided. Subsequently, the vibration simulation is carried out to indicate that the magnet pattern can satisfy low level in vibration and noise of the machine. © 2023 IEEE.

关键词： Vibration analysis

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Research on Hearing-Impaired EEG Emotion Recognition Based on Deep Residual Shrinkage Networks

Research on Hearing-Impaired EEG Emotion Recognition Based o...

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Chinese control Conference (CCC)

作者： Meiyi Cheng Qiang Gao Zhongli Bai Yirui Chen Yang Liu Yu Song School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin

With the development of affective computing and computer science, electroencephalogram (EEG) based emotion recognition has attracted much more attention. In this paper, we collected EEG signals from fifteen healthy people and fifteen hearing-impaired people when they were watching five kinds of emotional pictures (fear, anger, sadness, happiness and neutral). The collected EEG signals are preprocessed to remove artifacts by independent component analysis (ICA). Then the differential power spectral density, entropy, and wavelet entropy features were extracted (PSD, DE, WE). The Deep Residual Shrinkage Networks (DRSNs) were composed of residual building units (RBU s) and an attention mechanism was used to capture the representative features, and then made a classification. The classification results prove that using DE as a feature for classification is better than other features, with an average accuracy being 76.35% and 80.47% for the hearing-impaired group and the normal group, respectively. Moreover, from the brain topography, we found that there are certain differences in brain function between hearing-impaired and healthy people.

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