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3rd International Conference on electrical Machines and Drives, ICEMD 2023

作者： Eikani, Alireza Amirkhani, Mohammad Jafari, Hossein Vaez-Zadeh, Sadegh Mirsalim, Mojtaba Khaburi, Davood Arab University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency. © 2023 IEEE.

关键词： Energy transfer

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Comparative Study of Alternative-Pole Flux Switching and Inter-Modular Biased-Flux Permanent Magnet Motors 3

Comparative Study of Alternative-Pole Flux Switching and Int...

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3rd International Conference on electrical Machines and Drives, ICEMD 2023

作者： Afrank, Mohammad Eikani, Alireza Farahani, Ehsan Farmahini Mirsalim, Mojtaba Amirkabir University of Technology Department of Electrical Engineering Tehran Iran University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Newcastle University Electrical Power Research Group School of Engineering Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798350393743

This paper executes a comparative analysis between alternative-pole flux-switching and inter-modular biased-flux permanent magnet (PM) motors. Despite their close structural similarities, the former puts magnets not only between stator modules, but also close to the armature windings. However, the inter-modular PM (IMPM) motor only occupies spaces between stator modules. This type of occupation allows this structure to provide a safe operation point for PMs. Not considering the PM's recoil-phenomenon, the torque density of the IMPM motor is not as significant as the alternative-pole flux-switching PM (AP-FSPM) motor. This is because of the four-operating mode of the AP-FSPM structure which enables it to increase the torque density by utilizing the PMs in every moment of the operation. But, when the demagnetization of the AP-FSPM's PMs is taken into account, the torque density and torque per PM values of the AP-FSPM structure plummets. A complete full-load demagnetization analysis is done in order to get a better understanding of how the performance of the IMPM structure supersedes the AP-FSPM structure. 2D finite element analysis is used to extract the most important indexes of both topologies. © 2023 IEEE.

关键词： Permanent magnets

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Optimization and Improvement of Reduced Ferrite DWPT Magnetic Link Structure with New Passive Reflection Windings Topology

Optimization and Improvement of Reduced Ferrite DWPT Magneti...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Maryam Pakzaban Sadegh Vaez-Zadeh Sina Navaiyan-Kalat Advanced Motion Systems Research Lab School of Electrical and Computer Engineering Collage of Engineering University of Tehran Tehran Iran Advanced Motion Systems Research Lab and The Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Charging electric vehicles via dynamic wireless power transfer (DWPT) can be more cost-effective and safe than other charging methods if different system parts enjoy a proper design for a long way. Therefore, structural simplicity and, at the same time, the utilization of economic components in the magnetic link of such systems are significant advantages. Maximum reduction of expensive and brittle ferrite cores in the structure and their replacement with reflection windings will be an optimization strategy. In this paper, in order to achieve a robust and economic DWPT system with the ability to transfer suitable power while introducing a new topology of passive reflection windings, optimized rectangular pads (RP-RP) with 90-degree rotation are used as primary and secondary main pads.

关键词： Ferrites Couplings Coils Magnetic flux Magnetic cores Windings Reflection

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Robust Synchronizing Method for Unbalanced Weak Grid Connected Converters with High Power Quality

Robust Synchronizing Method for Unbalanced Weak Grid Connect...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Pooriya Jamallo Sadegh Vaez-Zadeh Alireza Jabbarnejad Advanced Motion Systems Research Lab School of Electrical and Computer Engineering Collage of Engineering University of Tehran Tehran Iran Advanced Motion Systems Research Lab and The Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Renewable energy sources are usually installed far from the power grid. In these cases, transmission lines have a high impedance which causes voltage distortion at the point of common coupling (PCC). In addition, unbalanced grid voltage conditions may occur due to unbalanced loads and lines impedance. This problem increases the challenge of control system design in weak grid conditions. Moreover, any difference between the real amount of the line impedance and its amount in the control system causes the selection of improper voltage vectors. This paper proposes a sensorless direct power control based on the line impedance estimation that synchronizes the converter with the grid of the proposed PCC. It leads to improving the system performance under unbalanced weak grid conditions. The effectiveness of the proposed control system in comparison with the existing methods are shown by extensive analytical and simulation studies.

关键词： Couplings Renewable energy sources Reactive power Power transmission lines Simulation system performance Estimation

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux Motors: A Comparative Analysis

Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux...

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Iranian Conference on electrical engineering (ICEE)

作者： Mohammad Amirkhani Ehsan Farmahini Farahani Alireza Eikani Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory Amirkabir University of Technology Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyses three prominent biased-flux permanent magnet (BFPM) motors and compares them with each other. The structures of these BFPMs are different from each other, but they share the same operation principle. These structures are, namely, hybrid-excited multi-tooth slot-PM (HETSPM), multi-tooth variable flux reluctance (TVFR), and inter-modular PM (IMPM). The reason for this selection of structure is because of complementary features that each one possesses. For example, the HETSPM motor places in between the TVFR and IMPM motors in terms of PM consumption and demagnetization. However, the IMPM structure produces high amount torque at high efficiency. The demagnetization of the HETSPM and IMPM structures is also investigated and is shown that, in the similar situation, the HETSPM provides a safer operation environment for the PMs. Two-dimensional finite element method is used to extract the important profiles of the mentioned structures.

关键词： electrical engineering Magnetic flux Torque Motors Permanent magnets Finite element analysis Topology

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Subject-independent emotion recognition of EEG signals using graph attention-based spatial-temporal pattern learning 41

Subject-independent emotion recognition of EEG signals using...

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第41届中国控制会议

作者： Yiwen Zhu Yeshuang Guo Wenzhe Zhu Lare Di Zhong Yin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Engineering Research Center of Optical Instrument and System Ministry of EducationShanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Electroencephalography(EEG) reveals human brain activities and becomes an essential solution for exploring human intrinsic emotional *** this study,we proposed a graph attention-based spatial-temporal pattern learning method called TAGAT to take full advantage of spatial structure of EEG channels and take the nonstationary of emotions into *** attention mechanism is applied to compute weight coefficients of different spatial and temporal *** alleviate differences between subjects,a domain discriminator is added to the model based on domain adaptation to tackle subject-independent EEG emotion recognition *** on the DEAP database,our method achieves the accuracy of 56.56% and 58.91% of arousal and valence dimensions for subject-independent emotion *** effectiveness of T-AGAT method has been demonstrated when compared to other existing common methods.

关键词： Emotion recognition electroencephalography deep learning transfer learning pattern learning

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Comparative Study of Alternative-Pole Flux Switching and Inter-Modular Biased-Flux Permanent Magnet Motors

Comparative Study of Alternative-Pole Flux Switching and Int...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Afrank Alireza Eikani Ehsan Farmahini Farahani Mojtaba Mirsalim Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K

This paper executes a comparative analysis between alternative-pole flux-switching and inter-modular biased-flux permanent magnet (PM) motors. Despite their close structural similarities, the former puts magnets not only between stator modules, but also close to the armature windings. However, the inter-modular PM (IMPM) motor only occupies spaces between stator modules. This type of occupation allows this structure to provide a safe operation point for PMs. Not considering the PM’s recoil-phenomenon, the torque density of the IMPM motor is not as significant as the alternative-pole flux-switching PM (AP-FSPM) motor. This is because of the four-operating mode of the AP-FSPM structure which enables it to increase the torque density by utilizing the PMs in every moment of the operation. But, when the demagnetization of the AP-FSPM’s PMs is taken into account, the torque density and torque per PM values of the AP-FSPM structure plummets. A complete full-load demagnetization analysis is done in order to get a better understanding of how the performance of the IMPM structure supersedes the AP-FSPM structure. 2D finite element analysis is used to extract the most important indexes of both topologies.

关键词：

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Deep Q-learning Sampling Based on Advantages 5

Deep Q-learning Sampling Based on Advantages

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5th International Conference on Intelligent Robotics and Control engineering, IRCE 2022

作者： Xie, Ming Ren, Xinrui Yu, Jianbo Shu, Feng Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement Academy for Engineering and Technology Fudan University Shanghai China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Microelectronics Fudan University State Key Laboratory of ASIC and System Shanghai200433 China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

Deep Q-learning (DQN) has shown recent success on a wide range of complicated sequential decision-making issues, especially in the classic control area. However, in most DQN training, the sampling policies, particularly the ϵ-greedy policy and its derivation rules, produce noise erroneously due to the unsteadiness and unreliability of Q values utilized for searching for optimum actions. We begin with a fundamental hypothesis: complete advantages-based exploitation may be employed to help in successful exploration, and the gap between advantages on outstanding and poor actions tends to extend during the early phases of training before stabilizing. We then present a novel DQN architecture with a superior sampling policy that is totally and directly based on advantage values. This new DQN is named A-sampling DQN, and it requires minor code changes based on the original DQN codes. In most cases, A-sampling DQN and its derivatives outperformed the traditional DQN method. © 2022 IEEE.

关键词： Deep learning

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