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Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems

IEEE ACCESS 2025年 13卷 58443-58464页

作者： Amadei, Federico quercio, Michele Riganti Fulginei, Francesco Univ Roma Tre Dept Ind Elect & Mech Engn I-00146 Rome Italy

This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. With the growing demand for wireless charging applications such as electric vehicles (EVs), IoT devices, and medical implants, WPT systems face challenges in terms of coil alignment, foreign object detection, and power efficiency. The use of ML algorithms, particularly neural networks and reinforcement learning has emerged as a promising solution to address these challenges. We explore how ML can optimize the geometric and structural design of WPT coils, predict the optimal parameters for inductive couplers, and enhance coupling efficiency under varying conditions. Additionally, ML is being used for foreign object detection (FOD) to ensure safety by identifying metallic and living objects that may interfere with power transmission. The article discusses various approaches, including supervised learning, regression models, and q-learning algorithms, highlighting their ability to reduce design time, improving system efficiency, and mitigate risks associated with misalignment and object interference. By reviewing recent advancements and ongoing research, this paper provides a comprehensive overview of the potential and limitations of integrating ML into WPT systems, paving the way for smarter, safer, and more efficient wireless charging technologies.

关键词： Optimization Machine learning Coils Wireless power transfer Tuning Magnetic fields Accuracy Heuristic algorithms Computational modeling Adaptation models Electric vehicle (EV) charging foreign object detection (FOD) inductive coupling machine learning (ML) magnetic field optimization misalignment compensation neural networks power efficiency power transfer efficiency q-learning algorithm regression learning smart charging technologies wireless charging wireless power transfer (WPT)

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Research on q-learning based Channel Access Control algorithm for Internet of Vehicles

Research on Q-Learning based Channel Access Control Algorith...

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International Computer Symposium (ICS)

作者： Zhao Hai-tao Du Ai-qian Zhu Hong-bo Li Dapeng Liu Nan-jie Nanjing Univ Posts & Telecommun Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509034383

A q-learning based back-off algorithm was proposed in this paper because the traditional DCF approach used for IEEE 802.11p MAC protocol to access the channel has some problems of the low packet delivery rate, high delay and the poor scalability in VANETs. The proposed algorithm which is quite different from the traditional BEB algorithm was adopted by the nodes(agents) to interact with surroundings continuously and learn from each other. The vehicle nodes adjust the size of CW(Contention Window) dynamically according to the results learned from the surroundings so that the nodes can access the channel with the optimal CW eventually minimizing the packet collisions and end-to-end delay. The simulation results show that the communication nodes using the proposed algorithm can adapt to the unknown vehicular environment rapidly, and simultaneously the high packet delivery ratio, low end-to-end delay and high fairness can be achieved for vehicular network with various load.

关键词： VANETs DCF BEB algorithm CW q-learning algorithm

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q-learning based multi-objective immune algorithm for fuzzy flexible job shop scheduling problem considering dynamic disruptions

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Chen, Xiao-long Li, Jun-qing Xu, Ying Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China HengXing Univ Sch Informat Engn Qingdao 266199 Peoples R China Yunnan Normal Univ Dept Math Kunming 650500 Yunnan Peoples R China

Confronted with complex industrial environments, dynamic disruptions like new job arrival and machine breakdown bring significant challenges to the robustness and stability of the manufacturing process, making the static production depart from the original scheduling scheme. To address this problem, a flexible job shop scheduling problem with fuzzy processing time, dynamic disruptions, and variable processing speeds is considered simultaneously. As well as three objectives of maximum completion time, total energy consumption, and average agreement index are demonstrated in this study. Then, a predictive-reactive dynamic/static rescheduling model is developed, where the off-line based mixed integer linear programming model and the on- line based rescheduling heuristics are proposed. Next, a multi-objective immune algorithm combined with a q learning algorithm (q-MOIA) is developed. In the proposed algorithm, an active decoding heuristic based on the interval insertion mechanism is used to optimize the initial solutions. After that, the clonal selection-based immune algorithm and the q-learning algorithm are adopted to improve the exploration and exploitation ca- pabilities, respectively, where four objective-thiten Tierghborifood structures are designet: Everftually, extensive computational experiments were conducted on 27 instances under static and dynamic scenarios to demonstrate the superiority and stability of the proposed predictive-reactive dynamic static rescheduling model and the q- MOIA. Comparative analysis with four state-of-the-art approaches revealed that proposed q-MOIA outperformed in approximately 51.9, 66.7, and 83.3 % of the instances for the three multi-objective metrics.

关键词： Flexible job shop scheduling problem Fuzzy processing time Dynamic disruptions Predictive-reactive dynamic/static reacheduling model Multi-objective immune algorithm q-learning algorithm

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q-learning embedded sine cosine algorithm (qLESCA)

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期193卷 116417-116417页

作者： Hamad, qusay Shihab Samma, Hussein Suandi, Shahrel Azmin Mohamad-Saleh, Junita Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Penang Malaysia Univ Teknol Malaysia Fac Engn Sch Comp Soft Comp Res Grp Johor Baharu 81310 Johor Malaysia Univ Informat Technol & Commun UOITC Baghdad Iraq

Sine Cosine algorithm (SCA) was recognized as a lightweight, efficient, and has a clear math principal optimizer. However, SCA still suffers from a set of problems such as stagnation at local optima, a slow convergence curve, and a lack of efficient balancing between exploration and exploitation search modes. To mitigate these limitations and improve SCA performance, this study introduces a new version of SCA called qLESCA that smartly controls SCA parameters through an embedded q-learning algorithm at run time. Each qLESCA agent evolves independently, and it has its own q-table. The q-table contains nine different states computed based on population density and distance from the micro population leader. As such, nine different actions are generated by q-table to control qLESCA parameters, namely r(1) and r(3). These qLESCA parameters are responsible for adaptive switching from exploration/exploitation and vice versa. For each q-table action, a reward value is given to a well-performing agent and a penalty to a non-performing agent. To verify the proposed algorithm's performance, qLESCA was evaluated with 23 continuous benchmarks, 20 large scale benchmark optimization functions, and three engineering design problems. In addition, the conducted analysis was compared with various SCA variant algorithms and other state-of-the-art swarm-based optimization methods. The numerical results demonstrate that the qLESCA was superior in terms of achieved fitness value. Statistical results confirm that qLESCA significantly outperforms other optimization algorithms. Additionally, the convergence curve outcomes show that the proposed qLESCA optimization obtains fast convergence against other conducted algorithms.

关键词： Since cosine optimizer Swarm intelligence q-learning algorithm Optimization algorithms Metaheuristic algorithm Large-scale problems

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qqLMPA: A quasi-opposition learning and q-learning based marine predators algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Zhao, Shangrui Wu, Yulu Tan, Shuang Wu, Jinran Cui, Zhesen Wang, You-Gan Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Australian Catholic Univ Inst Learning Sci & Teacher Educ Brisbane 4000 Australia Changzhi Univ Dept Comp Sci Changzhi 046011 Shanxi Peoples R China

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Levy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and q-learning based marine predators algorithm (qqLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by qqLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer.

关键词： q-learning algorithm Marine predators algorithm Meta-heuristics quasi-opposition based learning

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A q-Cube Framework of Reinforcement learning algorithm for Continuous Double Auction among Microgrids

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ENERGIES 2019年第15期12卷 2891页

作者： Wang, Ning Xu, Weisheng Shao, Weihui Xu, Zhiyu Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Educ Technol & Comp Ctr Shanghai 200092 Peoples R China

Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both bidding price and quantity as actions, a new q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A q-cube framework is designed to describe the q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed q-cube framework is capable of making rational bidding decisions and raising the microgrids' profits.

关键词： microgrids continuous double auction q-learning algorithm battery energy storage system q-cube framework bidding strategy

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Output-only structural damage identification based on q-learning hybrid evolutionary algorithm and response reconstruction technique

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MEASUREMENT 2024年 224卷

作者： Zhang, Guangcai Kang, Jianfei Wan, Chunfeng Xie, Liyu Xue, Songtao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Tohoku Univ Grad Sch Engn Sendai 9808572 Japan Tongji Univ Res Inst Struct Engn & Disaster Reduct Coll Civil Engn Shanghai Peoples R China Tohoku Inst Technol Dept Architecture Sendai Japan

Various structural damage identification methods have been developed and employed, while the absence of input excitation measurements may pose a huge challenge in their application since the input forces such as seismic load, traffic load, wind load, are not directly measurable. To address this issue, in the present paper, a novel output-only structural damage detection approach based on q-learning hybrid evolutionary algorithm (qHEA) and response reconstruction technique is presented. On the one hand, the external excitation and structural acceleration responses are reconstructed with the aid of response reconstruction and Tikhonov regularization techniques in time domain. Structural damage identification can be formulated as an optimization-based inverse problem. On the other hand, a new optimization framework qHEA integrating Jaya algorithm, differential evolution, q-learning algorithm is developed as search tool. The unknown structural parameters and unmeasured input force are iteratively updated by using two different measurement sets until the reconstructed acceleration responses agree well with the measured responses. Numerical examples involving a cantilever beam structure under single excitation and a simply-supported 51-bar truss structure under multiple excitations, as well as an experimental five-floor steel frame structure in the laboratory are carried out to validate the effectiveness of the proposed method. The final results demonstrate that the proposed method can accurately detect damage locations and quantify damage extents without the information of input excitation. In addition, the superiority of qHEA over other heuristic algorithms, the uncertainties of measurement noise and modeling errors on damage identification results are further examined.

关键词： Damage identification Evolutionary algorithm Response reconstruction Output-only method Pool strategy q-learning algorithm

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Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and q-learning techniques

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Scientific reports 2025年第1期15卷 16603页

作者： Soraya Khanmohmmadi Toktam Khatibi Golnaz Tajeddin Elham Akhondzadeh Amir Shojaee Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran. Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran. toktam.khatibi@modares.ac.ir. Faculty of Medical Science Tarbiat Modares University Tehran Iran.

Adequate sleep is crucial for maintaining a healthy lifestyle, and its deficiency can lead to various sleep-related disorders. Identifying these disorders early is essential for effective treatment, which traditionally relies on polysomnogram (PSG) tests. However, diagnosing sleep disorders with high accuracy based solely on electroencephalogram (EEG) signals, rather than using various signals in a complex PSG, can reduce the time and cost required, and the need for specialized signal devices, as well as increase accessibility and usability. Previous studies have focused on traditional machine learning (ML) methods such as K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), and ensemble learning methods for sleep disorders analysis. However, these models require manual methods for feature extraction, and the prediction accuracy greatly depends on the type of feature extracted. Additionally, the EEG signal datasets are small and heterogeneous, challenging traditional machine learning and deep learning models. The study proposes an innovative multi-task learning convolutional neural network with a partially shared structure that uses frequency-time images generated from EEG signals to address these limitations. The proposed technique makes two predictions using non-shared features from time-frequency images created through Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT), one prediction from shared features, and the final prediction is a combination of these three predictions. The weights for this combination were optimized using the genetic algorithm and the q-learning algorithm, aiming to minimize loss and maximize accuracy. The study utilizes a dataset involving 26 participants to examine the impact of Partial Sleep Deprivation (PSD) on EEG recordings. The outcomes demonstrated that the multi-task learning model using these two optimization methods, attained 98% accuracy on the test data for predicting partial sleep deprivation. Th

关键词： Electroencephalogram Genetic algorithm Multi-Task learning q-learning algorithm Sleep disorders

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Optimizing cultural heritage tourism routes using q-learning: a case study of Macau

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Sustainable Communities 2025年第1期2卷

作者： Junxin Song Yile Chen a Institute for Research on Portuguese Speaking Countries City University of Macau Taipa Macau China b Faculty of Humanities and Arts Macau University of Science and Technology Taipa Macau China

IntroductionThis study proposes a q-learning-based optimization method for cultural heritage tourism routes, using the Historic Centre of Macau as a case study. The goal is to efficiently visit multiple attractions within a limited *** and methodsCoordinates of 25 heritage sites were obtained through the Google Maps API, and the Haversine formula was used to calculate distances. We designed a state space, action space, and reward function based on distance and time for dynamic route *** and conclusionThe results show that the q-learning algorithm creates the best route that includes all the attractions while shortening the whole path and achieving rapid convergence. The optimized routes improve visit efficiency and balance attraction utilization, preventing overcrowding in popular areas. This approach provides practical implications for intelligent cultural heritage tourism planning. By designing an intelligent tourism route planning system, this helps tourists explore the Historic Centre of Macau more efficiently. Future research will focus on refining the reward function by incorporating visitor preferences and real-time traffic conditions for greater personalization and applicability.

关键词： Cultural heritage tourism Historic Centre of Macau intelligent tourism planning q-learning algorithm route optimization sustainable tourism

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Control the population of free viruses in nonlinear uncertain HIV system using q-learning

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2018年第7期9卷 1169-1179页

作者： Gholizade-Narm, Hossein Noori, Amin Shahrood Univ Technol Dept Control Engn Shahrood Iran Sadjad Univ Technol Mashhad Iran

This paper surveys a new method to reduce the infected cells and free virus particles (virions) via a nonlinear HIV model. Three scenarios are considered for control performance evaluation. At first, the system and initial conditions are considered known completely. In the second case, the initial conditions are taken randomly. In the third scenario, in addition to uncertainty in initial condition, an additive noise is taken into account. The optimal control method is used to design an effective drug-schedule to reduce the number of infected cells and free virions with and without uncertainty. By using the q-learning algorithm, which is the most applicable algorithm in reinforcement learning, the drug delivery rate is obtained off-line. Since q-learning is a model-free algorithm, it is expected that the performance of the control in the presence of uncertainty does not change significantly. Simulation results confirm that the proposed control method has a good performance and high functionality in controlling the free virions for both certain and uncertain HIV models.

关键词： Uncertain system dynamics Drug therapy Human immunodeficiency virus q-learning algorithm Reinforcement learning

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