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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Xu, Jiaxin Ren, Cheng Chen, Cailian Ma, Yehan Guan, Xinping Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798331527471

During the flight control system testing (FCST), it is crucial to accurately measure the deflection angles of flight control surfaces to determine whether they respond precisely to commands. However, the traditional measurement method, which relies on the manual use of angle measuring rulers for inspections, is inefficient, prone to wear, and lacks precision. To address this issue, we introduce a vision-based angle measurement method for FCST that replaces manual measurements, thereby significantly enhancing testing efficiency and accuracy. Our proposed triangular mesh alignment based angle measurement algorithm (TMA-AM) is a non-contact measurement method that involves two key procedures, capturing 3D coordinates from images and calculating deflection angles. The TMA-AM algorithm converts deflected angles into angular differences between two coordinate systems, while accounting for the curved characteristics of control surfaces. We evaluate TMA-AM algorithm on a 3D-printed wing test platform and an aircraft wing test platform. Experimental results demonstrate that our method achieves high accuracy, with an average angular measurement error below 0.05◦ © 2024 IEEE.

关键词： Mesh generation

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Preventing Frequency Instability Using Large-Scale Photovoltaic Resources 8

Preventing Frequency Instability Using Large-Scale Photovolt...

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8th International Conference on Technology and Energy Management, ICTEM 2023

作者： Yari, Siavash Khoshkhoo, Hamid Rouzbehi, Kumars Emran, S. M. Ale Sahand University of Technology Faculty of Electrical Engineering Sahand Iran University of Seville Department of System Engineering and Automatic Control Seville Spain Faculty of Engineering Canada

ISBN: (纸本)9781665452854

Various control methods have been presented to prevent the occurrence of instability in power systems, among which, it seems that methods based on the special protection system (SPS) are more effective and efficient than other ones. In this article, a SPS using the capabilities of large-scale photovoltaic power plants (LSPVPPs) is presented to prevent frequency increase and instability in power systems. According to the presented algorithm, when frequency increases, the proposed SPS uses the ability of photovoltaic power plants to quickly change their generation powers based on the operating point and frequency changes to prevent frequency instability. To confirm the proposal, LSPVPPs along with their controllers are modeled in the DSL environment of DIgSILENT PowerFactory software. The proposed SPS is implemented and tested on the IEEE 39-bus test system and the results of dynamic simulations show that this SPS prevents frequency instability in different conditions by taking timely measures. © 2023 IEEE.

关键词： Electric power system protection

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Multi-physics Based Temperature Distribution Simulation of HVAC Submarine Cable at Various Velocity 4th

Multi-physics Based Temperature Distribution Simulation of H...

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4th International Symposium on Insulation and Discharge Computation for Power Equipment, IDCOMPU 2023

作者： Zhang, Zhen Zhou, Yuanxiang Chen, Jianning Li, Yuhang Dai, Yasong The Wind Solar Storage Division of State Key Laboratory of Power System and Generation Equipment School of Electrical Engineering Xinjiang University Urumqi830047 China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9789819974009

The temperature distribution of submarine cables is an important parameter reflecting its operation status. The acquisition of temperature distribution trends for submarine cables under varying seawater flow rates holds important engineering significance. In this paper, the geometric model is constructed using a 220 kV three-core AC armored submarine cable laid on the seabed. The simulation model of AC three-core submarine cable temperature field is proposed based on the coupling of electro-thermal-flow multi-physics field. Finally, the study investigates how the steady-state temperature distribution of submarine cables varies with different seawater flow velocities. The results show that the temperature is radially distributed at the seawater velocity of 0m/s, and the temperatures of different phase conductors are different by the relative position, which are 89.9 °C, 89.6 °C and 89.08 °C, respectively. As the seawater flow velocity increases, the heat taken away from the cable surface by convection increases. When the seawater flow velocity is 1 × 10−6m/s, 0.0001 m/s, 0.001 m/s, 0.001 m/s and 0.001 m/s, the maximum conductor temperatures are 69.5 °C, 44.6 °C, 38.5 °C, 33.6 °C and 32.7 °C, and the ampacity is 871.7 A, 1216 A, 1382.8 A, 1590.7 A and 1640.9 A, respectively. The simulation results can provide reference for submarine cable transmission engineering design. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Temperature distribution

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Skewed Pins Detection of Aviation Connectors Based on Multi-Modal Cooperative Perception

Skewed Pins Detection of Aviation Connectors Based on Multi-...

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2024 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Li, Jiaming Ren, Cheng Xu, Jiaxin Wang, Yao Xu, Hanlin Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9798331507886

Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and time-consuming. Due to the unique structural characteristics of aviation connectors, single-modality sensors often struggle to simultaneously detect both pins and holes. To address this issue, we propose a 3D skewed pins detection (3D-SPD) algorithm that integrates both 2D and 3D modals features. Due to the low quality of the raw connector data, we propose a connector point cloud enhancement (CPCE) algorithm that combines multi-view point clouds for point cloud fusion enhancement. Based on enhanced data, we perform skewed pin detection. Experimental results indicate that CPCE and 3D-SPD are effective in detecting skewed pins, achieving an accuracy rate exceeding 99%. Our algorithm can be deployed as an AI assistant on the edge device for the skewed pin detection. © 2024 IEEE.

关键词： defect detection machine vision multi-modal cooperative perception skewed pin

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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 388-399页

作者：刘增敏王申涛姚莉秀蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China JD Business Growth Department Beijing 100176China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is ***,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking *** the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking *** simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

关键词： moving unmanned aerial vehicle(UAV)platform small object feature extraction image registration multi-object tracking

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Fuzzy Tool for Crowdfunding Readiness Level Assessment for Innovative Projects 18

Fuzzy Tool for Crowdfunding Readiness Level Assessment for I...

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18th Iberian Conference on Information systems and Technologies, CISTI 2023

作者： Cseminschi, Stanislav Ionica, Andreea Cristina Leba, Monica Florea, Vlad Alexandru University of Petrosani Department of Management and Industrial Engineering Petrosani Romania University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Mechanical Engineering Petrosani Romania

ISBN: (纸本)9789893347928

Stimulating creativity in technological entrepreneurial education leads to knowledge and connection to current trends in the development of innovative technological projects and the creation of innovative start-up and spin-off enterprises. Their success or failure depends on a number of factors such as the market, the team, the technological level of the product, but also on the funding methods, including crowdfunding. The paper proposes a Fuzzy tool for preliminary assessment of the opportunity of crowdfunding campaigns for innovative projects resulting from the stimulation of entrepreneurial creativity through gamification-based education. © 2023 ITMA.

关键词： engineering education

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Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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The Study of the Voltage Frequency Effects on Partial Discharge Characteristics 9

The Study of the Voltage Frequency Effects on Partial Discha...

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9th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2023

作者： Marukatat, Nattapon Mai-Eiam, Thanatorn Chaisiri, Punyavee Raxsa, Jedsada Nimsanong, Phethai Yutthagowith, Peerawut School of Engineering King Mongkut's Institute of Technology Ladkrabang Ladkrabang Bangkok10520 Thailand Power System Analysis Section Metropolitan Electricity Authority Power System Control Department Bangkok10330 Thailand

ISBN: (纸本)9798350322040

In this paper, the partial discharge (PD) characteristics under the voltage at frequencies in the range of 20 Hz to 300 Hz are studied. Three main types of PD being corona, surface and internal discharges are simulated by an arrangement of electrodes in test cells. The effects of voltage frequency on the PD characteristics in terms of PD inception voltage (PDIV), PD extinction voltage (PDEV), and phase-resolved PD (PRPD) patterns have been reported from the experimental results. It is found that in the frequency range of 20 Hz to 300 Hz there is no frequency effect on the PD characteristics (PDIV, PDEV, and PRPD) found in cases of corona and surface discharge. However, the frequency effects are found in the case of the internal discharge representing the higher frequency increase the higher PDIV and PDEV are. © 2023 IEEE.

关键词： Partial discharges

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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction systems in High-Speed Trains

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Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach 25

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Demidova, Galina Privalov, Daniil Semenov, Denis Lukichev, Dmitry Liu, Zhitao Anuchin, Alecksey Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9798350389234

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than 99%. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance. © 2024 IEEE.

关键词： Machine learning

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