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10th International Conference on Power Electronics, Machines and Drives, PEMD 2020

作者： Juki, Domagoj-Kreimir Varga, Toni Beni, Tin Štil, Vedrana Jerkovi Baruki, Marinko Automation and Control Institute TU Wien Vienna Austria Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (纸本)9781839535635

This paper deals with Permanent Magnet Synchronous Motor modeling and control. The derived nonlinear dq model is a combination of Finite Element Method simulation data with the classical dq model. The so derived model keeps the precision of the Finite Element Method simulations and also the fast execution times of the dq model. The Finite Element Method data is included in the dq model using Artificial Neural Networks. The derived model is also used for controller design. The chosen controller is a PI like Fuzzy Logic Controller, which is tuned using Genetic Algorithm optimization. The Permanent Magnet Synchronous Motor with Fuzzy Logic Controller operation is tested using Real-Time (Model-in-the-Loop) simulations. © PEMD *** right reserved.

关键词： Permanent magnets

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A Pilot Study on the Development and Validation of AI Literacy Test Items for Grade 7 to Grade 9 Students

A Pilot Study on the Development and Validation of AI Litera...

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International Symposium on Educational Technology (ISET)

作者： Yifan Chen King Woon Yau Ching Sing Chai Thomas K. F. Chiu Helen Meng Irwin King Savio Wai-Ho Wong Yeung Yam Faculty of Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong China Department of Curriculum and Instruction The Chinese University of Hong Kong Shatin Hong Kong China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong China Department of Educational Psychology The Chinese University of Hong Kong Shatin Hong Kong China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

ISBN: (数字)9798350361414

ISBN: (纸本)9798350361421

To provide K-12 students with an understanding of AI’s potential opportunities and challenges, AI education has been incorporated into the technology curriculum. Most existing methods for assessing students’ AI literacy rely on self-assessment questionnaires, which may not offer an objective measure of their understanding. This study, therefore, sought to develop and validate a 25 -item multiple-choice test tailored for Hong Kong secondary school students to gauge their AI literacy. A total of 144 students from six secondary schools participated in a pilot test. Item Response Theory (IRT) incorporating a Markov Chain Monte Carlo (MCMC) algorithm, was developed to estimate the difficulty and discrimination indices of test items. The 3-Parameter Logistic (3-PL) model was selected for its effectiveness in providing a more nuanced understanding of item characteristics. Preliminary analysis suggested that the test warrants further revision and enhancement, as evidenced by a Kuder-Richardson Formula 20 (KR-20) reliability coefficient of 0.68. Nonetheless, all test items exhibited satisfactory discrimination capabilities, indicating their proficiency in differentiating among varying levels of respondent aptitude. Thus, this study can provide a foundation for developing a standardized AI literacy test in the future.

关键词： Monte Carlo methods Atmospheric measurements Instruments Educational technology Particle measurements Reliability Iterative methods

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Ivomfuse: An Image Fusion Method Based on Infrared-to-Visible Object Mapping

SSRN

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SSRN 2022年

作者： Zhang, Xiangbo Huang, Lei Liu, Gang Ren, Qin Bavirisetti, Durga Prasad School of Automation Engineering Shanghai University of Electric Power Shanghai201306 China Library Shanghai University of Electric Power Shanghai201306 China School of Software and Microelectronics Peking University Beijing102600 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Due to the significant difference in the structure of infrared and visible images, most of the existing algorithms use globally consistent strategy, which may weaken the contrast of the target region. To solve this problem, we propose an infrared and visible image fusion method based on infrared-to-visible object mapping (IVOMFuse) First of all, we use the probability induced intuitionistic fuzzy C-means algorithm (PIIFCM) to process the infrared image, and extract the target region of the infrared image according to the image structure features. However, because of the poor contrast of the target of the visible image, it can not be segmented directly. For this reason, we map the infrared target region to the visible image, and the target region with the same coordinates of the two images can be obtained very quickly. Secondly, due to the significant differences in image structure, we set a hybrid fusion strategies for different regions. We use the fusion strategy based on the Expectation-Maximization (EM) algorithm to iterate and optimize the probability model for the target region. The fourth-order partial differential equation (FPDE) is used for the background region to decompose the source image into high-frequency texture and low-frequency approximation images respectively. Then, the principal component analysis (PCA) and the average fusion strategy are used to obtain the fused image. Finally, we evaluated our algorithm with thirteen other state-of-the-art image fusion methods on three commonly used datasets, namely TNO, CVC14, and RoadScene. Among the ten evaluation indexes, our algorithm achieved the best six times. © 2022, The Authors. All rights reserved.

关键词： Mapping

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Editorial: Modeling and application of computational intelligence in sustainable energy systems

引用

Frontiers in Energy Research 2025年 13卷

作者： Wang, Yirui Gao, Shangce Yu, Yang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Faculty of Engineering University of Toyama Toyama Japan College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

In recent years, the study of computational intelligence in sustainable energy systems has gained increasing attention due to its effectiveness and sophistication. Sustainable energy systems are designed, modeled and optimized by various computational intelligence methods, such as evolutionary computation and machine learning. These methods show the great value and significant contributions in energy research. As researchers continue to explore this area, new insights are being uncovered that have the potential to improve the role and significance of computational intelligence methods in modeling and optimizing sustainable energy systems. In this Research Topic, several studies have contributed significantly to the advancement of computational intelligence in sustainable energy systems. In [1], this study focused on optimizing the configuration and scheduling of integrated energy systems with flexible load resources to maximize energy efficiency and reduce environmental impact. By using Weibull and Beta distribution models to account for uncertainties in wind and solar power, and proposing an enhanced Kepler Optimization Algorithm (EKOA), the approach improved search scope and efficiency. A case study in southeastern China demonstrated that this method reduced energy curtailment and costs, while enhancing renewable energy use. Despite the success, the study only considered hydrogen for single-use and did not explore market benefits, leaving room for future research on hydrogen demand response and market integration. In [2], multi-energy microgrids (MEM) integrate various energy sectors to enhance renewable energy use while maintaining balance. This study analyzed the technical challenges of increasing renewable penetration, using an artificial neural network-based model for solar power forecasting, integrated with the EnergyPLAN tool. A case study on Norway revealed that combined heat and power plants hindered renewable integration, while heat pumps enhanced it. A

关键词： aritificial neural networks computational intelligence evolutionary compuation machine learning metaheuristics optimizaiton algorithms sustainable energy systems

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Collaboration Energy Efficiency with Mobile Edge Computing for Target Tracking in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Yu, Gang Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786205

In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing target tracking effects. We deign the sensor selection scheme for carrying out the tracking tasks according to energy distribution of the sensor node. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency without reducing the accuracy of target tracking. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the target state. Simulations demonstrate the efficiency for tracking performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Target tracking

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Collaboration Energy Efficiency with Mobile Edge Computing for Data Collection in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786144

In this paper, we investigated collaboration energy efficiency with mobile edge computing (MEC) mechanism in internet of things (IoT), which is a challenge issue. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing energy balance. We deign the sensor selection scheme for collecting data according to Pareto optimality. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the data state. Simulations demonstrate the efficiency for the performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Krasovskii and Shifted Passivity Based Output Consensus

arXiv

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arXiv 2022年

作者： Kawano, Yu Cucuzzella, Michele Feng, Shuai Scherpen, Jacquelien M.A. Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia PV Pavia27100 Italy School of Automation Nanjing University of Science and Technology Nanjing210094 China Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., power demand. This output consensus problem is solved by a simple distributed output feedback controller if a system is either Krasovskii or shifted passive, which is the only essential requirement. The effectiveness of the proposed controller is shown in simulation on an islanded DC power network. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving

arXiv

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arXiv 2022年

作者： Azam, Shoaib Munir, Farzeen Kyrki, Ville Jeon, Moongu Pedrycz, Witold Department of Electrical Engineering and Automation Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Learning contextual and spatial environmental representations enhances autonomous vehicle’s hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA’s Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions. © 2022, CC BY.

关键词： Autonomous vehicles

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Share of Additional Losses in Total Core Losses in the Remagnetization Process of Amorphous FeB-Based Alloys

Share of Additional Losses in Total Core Losses in the Remag...

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作者： Jeż, B. Stambula, S. Kukula, O. Wyslocki, J.J. Razak, N.R.A. Manea, L.R. Nabialek, M. Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Częstochowa42-200 Poland Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Częstochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis Taman Muhibbah Perlis Arau02600 Malaysia Faculty of Industrial Design & Business Management "Gheorghe Asachi" Technical University of Iasi 29 D. Mangeron Blv. Iasi70005 Romania

In the process of remagnetization of magnetic materials at certain frequencies, there are losses that reduce their energy expenditure, which are referred to as total losses. These losses include hysteresis loop losses, eddy current losses, and additional losses. The factor of additional losses is very often overlooked. Its contribution to total losses is considered to be marginal. Even in the case of materials showing the minimization of total losses, such as amorphous materials, this loss factor is important and should not be neglected when studying this phenomenon. The paper presents the results of tests on remagnetization losses of a ferromagnetic amorphous alloy at frequencies from 50 to 1000 Hz. The share of additional losses was separated, and their total contribution to total core losses was determined as a percentage. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

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Model-free power optimization of wind farm based on nelder-mead method 2

Model-free power optimization of wind farm based on nelder-m...

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2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020

作者： Xu, Zhiwei Geng, Hua Chu, Bing Tsinghua University Beijing National Research Center for Information Science and Technology Department of Automation Beijing China University of Southampton School of Electronics and Computer Science Faculty of Physical Sciences and Engineering Southampton United Kingdom

ISBN: (纸本)9781728166117

Wake effects generated by wind turbines causes the strong wake interactions among turbines and thus significantly lowers the power output of wind farm. However, it is generally difficult to model the interactions between turbines due to its complexity. To mitigate the wake interactions and thus improve the power output of wind farm, this paper proposes a model-free optimization scheme. The power optimization problem of wind farm is divided into several optimization sub-problems according to the power efficiencies of wind farm under different wind directions. Each optimization sub-problem is solved approximately by developed power optimization algorithm based on Nelder-Mead method. Simulation results show that the proposed scheme can quickly and effectively improve the power output of wind farm in complex wind condition without modeling the wake interactions among the turbines. © 2020 IEEE.

关键词： Optimization

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