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2024 International Ural Conference on Electrical Power engineering, UralCon 2024

作者： Karandaev, Alexandr S. Loginov, Andrey B. Nikolaev, Andrey A. Moscow Polytechnic University Department of Automation and Control Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia

ISBN: (纸本)9798350386028

This report reviews an example of acoustic positioning of partial discharges (PD) in the tank of a high-voltage oil-filled transformer. The literature review confirms the importance of PD monitoring and justifies the feasibility of positioning discharge activity centers. The precise positioning of PD sources is required to determine the location of insulation defects. The authors provide brief information on the development of PD positioning methods and note that their practical application has been insufficiently covered in publications. They present the results of electromagnetic and acoustic PD positioning in a 110 kV transformer. Some details are provided on the devices and measurement methods used, including the localization of areas with increased PD intensity using one acoustic sensor and the subsequent center localization through the installation of 4 sensors at even intervals. The authors provide PD signals and defect impulses received from each of the sensors and demonstrate the difference in the registration time of these signals. They explain the principle behind PD center positioning, which is determined considering delays in signal reception. The authors demonstrate the usage of the Inva (Portable) software for the processing and visualization of acoustic positioning results. They note that changes in the propagation rate of acoustic waves in a heterogeneous medium result in the reduced accuracy of results. The authors also outline the prospects of the research into the elimination of occurring defects. © 2024 IEEE.

关键词： Oil filled transformers

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Frequency Domain Auto-tuning of Structured LPV controllers for High-Precision Motion control 63

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Broens, Yorick Butler, Hans Toth, Roland Asml Veldhoven Netherlands Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Hunren Institute for Computer Science and Control Systems and Control Laboratory Hungary

ISBN: (纸本)9798350316339

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype. © 2024 IEEE.

关键词： Continuous time systems

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An analysis towards integrated control of wastewater collecting and treatment systems 27

An analysis towards integrated control of wastewater collect...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Vasiliev, Iulian Luca, Laurentiu Barbu, Marian Vilanova, Ramon Caraman, Sergiu Department of Automation and Electrical Engineering Dunărea de Jos University of Galati Galati Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain

ISBN: (纸本)9798350337983

This paper deals with an analysis regarding the effect of Sewer Network operation on the performances of the integrated wastewater collecting and treatment system. The analysis is done by considering various control actions for the output storage tank of the sewer network, comparing a series of performance indicators defined for both subsystems with their "no control" value. Two cases have been considered regarding the control actions for the output storage tank of the sewer network: 1. keeping the output valve at a constant opening and 2. keeping the output valve at the maximum opening possible such as the outflow is limited to a constant value. While the second case did not prove advantageous, the first one showed that lowering the sewer network output valve opening would improve the integrated system performances. © 2023 IEEE.

关键词： Wastewater treatment

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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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Practical Predefined-Time Consensus control for High-Order Multiagent systems Via Output Feedback

Practical Predefined-Time Consensus Control for High-Order M...

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2024 China automation Congress, CAC 2024

作者： Li, Xiaoting Cao, Xiaotong Li, Yueti Jiao, Dexin Wang, Chen Zou, An-Min College of Engineering Shantou University Shantou China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368604

This paper considers the problem of practical predefined-time (PPT) consensus control for a class of high-order nonlinear multiagent systems. With the help of the smooth time-varying function-based technique, the leader’s state and the follower’s unmeasurable states are estimated by introducing PPT observers. Then, a PPT output feedback consensus control algorithm with no singularity and discontinuity is proposed based on the observers. Finally, the effectiveness of the designed output feedback PPT consensus control scheme is validated by virtue of an illustrative numerical example. © 2024 IEEE.

关键词： Feedback

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Global-Local Adaptive Attention-Based U-Net for Single Image Deblurring

Global-Local Adaptive Attention-Based U-Net for Single Image...

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2024 China automation Congress, CAC 2024

作者： Yu, Xinran Li, Yueti Peng, Xuanzhi Wang, Chen Zou, An-Min College of Engineering Shantou University Shantou China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368604

Image deblurring is a classical ill-posed problem aimed at recovering a clear image from a blurred one. The recent application of Transformers to image restoration tasks has significantly improved performance, thanks to their powerful long-range dependency modeling capabilities. However, the high computational cost of self-attention mechanisms limits their practical use. Additionally, the training cost for Transformer models is considerable. To address the issues of high computational cost and training cost, this paper proposes a novel Global Local Attention (GLA) mechanism that combines global and local adaptive attention. The mechanism uses 1D adaptive pooling operations to capture global context information, while fixed-size 1D pooling with different kernel sizes (7 and 11) is used to capture local details, effectively addressing both long-range dependencies and fine-grained information. Furthermore, a larger receptive field is crucial for effective image deblurring. To this end, we introduce a Feature Enhancement Module (FEM) with an expansive convolutional kernel size that serves as a bottleneck layer to further improve recovery quality. Experimental results demonstrate that our proposed model achieves competitive performance on several benchmark datasets, advancing the field of image restoration. © 2024 IEEE.

关键词： Restoration

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Model Predictive control of a wastewater treatment process using neural networks 28

Model Predictive Control of a wastewater treatment process u...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Vasiliev, Iulian Luca, Irina Condrachi, Larisa Luca, Laurentiu Barbu, Marian Vilanova, Ramon Caraman, Sergiu Dunǎrea de Jos University of Galati Department of Automation and Electrical Engineering Galati Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain

ISBN: (纸本)9798350339918

This paper deals with the increasing of the operation efficiency of a wastewater treatment plant by reducing the level of pollutant concentration in the effluent. In essence, the main goal is the reduction of the concentration of organic substrate, Model Predictive control algorithm being used for this, considering as a model of the wastewater treatment process a neural network model based on a simplified mathematical model of the 4th order. For the implementation of the control algorithm, two neural networks were trained. In the first version of the neural network, it was considered that all 4 state variables are measurable and in the second version, only the organic substrate was considered measurable. Regarding the training of the two neural networks, good results were obtained in the case of both versions. Instead, the control algorithm gave better results when the second neural network was used (lower values of the substrate, lower variations of the aeration rate, which means a lower cost of operation). © 2023 IEEE.

关键词： Neural networks

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Electrocardiogram Signal Secure Transmission via a Wireless Communication Protocol of Chaotic systems Based on Adaptive Sliding Mode control and Disturbance Observer

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IEEE Access 2023年 11卷 145373-145385页

作者： Chien Le, Minh Giap, Van Nam Nguyen, Quang Dich Huang, Shyh-Chour Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi100000 Viet Nam National Kaohsiung University of Science and Technology Department of Mechanical Engineering Kaohsiung807618 Taiwan

This paper presents a new application of the encryption and decryption techniques for securing the electrocardiogram (ECG) signal. The secure communication system (SCS) is embedded two Chen chaotic systems with different initial conditions, which were named master and slave, respectively. An ECG signal is encrypted by using master chaotic system. Which can be sent to the far destination to serving the heath care monitoring process. To obtain the original data, the state of the slave system is used to decrypt the signal. To obtain the goal, the master and slave need to be synchronized. Therefore, to softening the calculation of synchronization control design, the Takagi-Sugeno fuzzy (TSF) was used to remodel the master and slave into the fuzzy format, which are consists of the sublinear systems and fuzzy outer membership functions. Second, the disturbance observer (DOB) was proposed on slave system, which is used to reject the parameters variations and disturbances during the transmission. Third, the sliding mode control (SMC) was designed to synchronize the master and slave systems. Fourth, the stability of SCS is proved based on the Lyapunov condition. Finally, the simulation by using MATLAB software on computer and experiment by using the WIFI microchip ESP8266 were provided to show the power of the proposed methods. © 2013 IEEE.

关键词： Time domain analysis

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection

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IEEE Transactions on Multimedia 2025年 27卷 3057-3068页

作者： Mei, Yunpeng Sun, Jian Peng, Zhihong Deng, Fang Wang, Gang Chen, Jie Beijing Institute of Technology State Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Tongji University Department of Control Science and Engineering Shanghai201804 China

Robotic grasping is a crucial topic in robotics and computer vision, with broad applications in industrial production and intelligent manufacturing. Although some methods have begun addressing instance-level grasping, most remain limited to predefined instances and categories, lacking flexibility for open-vocabulary grasp prediction based on user-specified instructions. To address this, we propose RoG-SAM, a language-driven, instance-level grasp detection framework built on Segment Anything Model (SAM). RoG-SAM utilizes open-vocabulary prompts for object localization and grasp pose prediction, adapting SAM through transfer learning with encoder adapters and multi-head decoders to extend its segmentation capabilities to grasp pose estimation. Experimental results show that RoG-SAM achieves competitive performance on single-object datasets (Cornell and Jacquard) and cluttered datasets (GraspNet-1Billion and OCID), with instance-level accuracies of 91.2% and 90.1%, respectively, while using only 28.3% of SAM's trainable parameters. The effectiveness of RoG-SAM was also validated in real-world environments. © 1999-2012 IEEE.

关键词： Robot vision

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