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42nd Chinese control Conference, CCC 2023

作者： Zhao, Xinyi Zhao, Linhui Deng, Zhichong Ji, Guohuang Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Intelligent Power System Co. Ltd. Wuhan430056 China

ISBN: (纸本)9789887581543

The Automotive Headlamp Leveling system (ALS), aims to ensure the stability of the illumination range of the headlamp by vertically rotate the headlamp using motors when the body attitude changes. This paper proposes an ALS scheme which obtains the body attitude from body height sensors and accordingly calculates the output angles of left headlamp and right headlamp respectively based on Euler transformation. Different from existing ALS, this scheme uses several strategies to improve the performance of ALS, including anti-shake function, working mode division and rough road identification. The purpose of rough road identification is to avoid unexpected vibration of headlamp caused by ALS on rough road and decision tree model is chosen for identification to improve the accuracy. Moreover, several field tests are performed to verify the validity and stability of the system. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Decision trees

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Capacity Optimization Configuration of Hybrid Energy Storage system Using a Modified Grey Wolf Optimization 7

Capacity Optimization Configuration of Hybrid Energy Storage...

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7th IEEE International Conference on Power and Renewable Energy, ICPRE 2022

作者： Meng, Defang Yu, Wei Shanghai University of Electric Power College of Automation Engineering Shanghai China HuaDian Electric Research Institute Department of Control Engineering Hangzhou China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

Aiming at the randomness and intermittent characteristics of renewable energy power generation, a capacity optimization method of a hybrid energy storage system is proposed to ensure the economical and reliable operation of wind and solar power supply systems. The optimization method takes the minimum life cycle cost of the hybrid energy storage system as the optimization goal, takes the load power shortage rate and the energy storage capacity as the constraints, and establishes the optimal configuration model of the hybrid energy storage capacity. Based on this model, the modified gray wolf algorithm (MGWO) is used to solve the optimal capacity configuration of the hybrid energy storage system. Finally, the optimization results of MGWO are compared with the basic GWO and particle swarm algorithm (GWO) through a numerical example, and it is verified that MGWO can configure the hybrid energy storage capacity more reasonably. © 2022 IEEE.

关键词： Wind power

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NONLINEAR STEAM GENERATOR FEEDWATER system IDENTIFICATION BASED ON WAVELET NEURAL NETWORK 10

NONLINEAR STEAM GENERATOR FEEDWATER SYSTEM IDENTIFICATION BA...

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10th International Symposium on Test automation and Instrumentation, ISTAI 2024

作者： Wang, Hongrui Yuan, Mingzhe Zhang, Pengbin Xing, Kunpeng Guangzhou Institute of Industrial Intelligence Guangzhou China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (纸本)9781837242672

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability. © The Institution of engineering & Technology 2024.

关键词： Nuclear power plants

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Technical-economic feasibility analysis of a large-scale parabolic trough collectors solar power plant in Brazil

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 337-344页

作者： Lopes, M.E. Azevedo, G.F. Menezes, B.A. Rabelo, O.H.F. Brito, L.B. Dias, A.B. Filho, A.L.F. Domingues, E.G. Department of Electrical Engineering University of Brasília Darcy Ribeiro University Campus Gleba A Avenida L3 Norte DF Brasília70.910-900 Brazil Electrical Engineering/Control and Automation Engineering Program Federal Institute of Goiás Câmpus Goiânia Rua 75 nº46-Centro GO Goiânia74055-110 Brazil

This study aims to present the results of the application of a computational tool developed to perform a technical-economic feasibility analysis of large-scale Parabolic Trough Collectors (PTC) solar power plants in Brazil. The gross revenue from cash flow is obtained through an energy trading model for the Regulated Contracting Environment of the Brazilian Electricity Market. The economic viability analysis uses the Net Present Value, Modified Internal Rate of Return, Discounted Payback, and Levelized Cost of Energy indicators. The parametric structural optimization method is used to optimize the technical parameters of the plant. A case study was conducted to analyse the feasibility of implementing a 100 MW PTC in the five regions of Brazil. The results indicated that the best region in Brazil to receive this technology is the northeastern region. However, the project is economically unfeasible in all cities analysed. Therefore, a sensitivity analysis of the main parameters that affect the project's net present value is carried out to evaluate the impact of the economic parameters on the economic viability of an optimized PTC plant located in the city with the best technical performance. An optimistic scenario was evaluated considering the system's response to the parameters investigated in the sensitivity analysis. For this scenario, the results pointed to the project's economic viability. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Sensitivity analysis

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A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

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IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

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Human Following Robot Based on Object Detection for Aerospace Application 1

Human Following Robot Based on Object Detection for Aerospac...

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1st International Conference of Adisutjipto on Aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Sumari, Arwin Datumava Wahvudi Aryandika, Ahnaf Bagus Purnami, Ndaru Atmi Dermawan, Denny Syamsiana, Ika Noer State Polytechnic of Malang Cognitive Artificial Intelligence Research Group Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Aerosp. Digit. Syst. Microprocessor Microcontroller and Programmable Logic Devices Research Group Yogyakarta Indonesia

ISBN: (纸本)9798331507930

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications. © 2024 IEEE.

关键词： Travel time

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Design and implementation of intelligent LiDAR SLAM for autonomous mobile robots using evolutionary normal distributions transform

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Soft Computing 2024年第6期28卷 5321-5337页

作者： Huang, Hsu-Chih Xu, Sendren Sheng-Dong Lin, Hsien-Chan Xiao, Yuan-Sheng Chen, Yu-Xiang Department of Electrical Engineering National Ilan University Yilan260007 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei106335 Taiwan

This paper presents a method that employs an evolutionary normal distributions transform (NDT) for simultaneous localization and mapping (SLAM) using light detection and ranging (LiDAR) for autonomous mobile robots. An adaptive inertia weight and two genetic operators were employed in the Taguchi-based whale optimization algorithm (WOA) to improve the search diversity and avoid local optima. NDT was used to model the environment of differential-drive mobile robots and WOA was applied to optimize the scan matching for the robotic SLAM problem. The NDT-WOA determines the SLAM pose estimation using sensed data from the physical world. A nonholonomic mobile robot was steered to achieve the NDT-WOA SLAM task with the derived robot kinematics and actuator dynamics. The proposed method was implemented in a TurtleBot3 Burger robotic development kit, which includes a single-board computer and an OpenCR control board. The Robot Operating system (ROS) was utilized to implement the evolutionary NDT-WOA SLAM system due to its flexibility, open source, and client library. Simulation and comparisons were conducted to illustrate the efficiency of the proposed NDT-WOA SLAM method compared to other SLAM paradigms. The experimental results show the effectiveness of the proposed evolutionary NDT-WOA SLAM for autonomous mobile robots. The results could have theoretical and practical significance for robotics research. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Mobile robots

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Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 264-280页

作者： Liu, Ruonan Zhang, Quanhu Han, Te Yang, Boyuan Zhang, Weidong Yin, Shen Zhou, Donghua Shanghai Jiao Tong University Department of Automation Shanghai200240 China Tianjin University College of Intelligence and Computing Tianjin300350 China Beijing Institute of Technology Center for Energy and Environmental Policy Research Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Beijing Laboratory for System Engineering of Carbon Neutrality Beijing100081 China Nanjing University Center for Advanced Control and Smart Operations Suzhou215163 China Hainan University School of Information and Communication Engineering Haikou570228 China Shanghai Jiaotong University Department of Automation Shanghai200240 China Norwegian University of Science and Technology Trondheim7491 Norway

Industrial Cyber-Physical systems (ICPS) integrating disciplines such as computer science, communication technology, and engineering, have become a crucial component of modern manufacturing and industry. However, ICPS faces numerous challenges during long-term operation, including equipment faults, performance degradation, and security threats, etc. To achieve efficient maintenance and management, prognostics and health management (PHM) has been widely applied in the critical tasks of ICPS such as fault prediction, health monitoring, and maintenance decision-making. The emergence of large-scale foundation models (LFMs) like BERT and GPT marks a significant advancement in artificial intelligence (AI) technology, demonstrating substantial application potential in multiple fields. The accumulation of AI technology, rapid development of LFMs, and the abundance of industrial data and industrial process knowledge provide the foundational conditions for the construction and advancement of industrial LFMs. However, there is currently a lack of consensus on applying LFMs of PHM in ICPS, necessitating a systematic review and roadmap to clarify future development directions. To bridge this gap, this survey provides a comprehensive survey and understanding of the recent advances in LFMs of PHM in ICPS. It provides valuable references for decision makers and researchers in the industry, and helps to further improve the reliability, availability and safety of ICPS. © 2023 IEEE.

关键词： Cyber Physical system

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Closed loop systems

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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