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Annual Conference of Industrial Electronics Society

作者： Pak-Ian Chan Wai-Kit Sou Chi-Seng Lam State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

In this paper, a multi-quasi-proportional-resonant control (MQPRC) for a three-phase capacitive-coupling grid-connect inverter (CGCI) with accurate active power injection technique is proposed to mitigate the current harmonics and improve the active power injection accuracy. Then, a detail parameter design of the proposed MQPRC is provided. And the proposed accurate active power injection is verified by the simulation. Subsequently, to verify the effectiveness of the proposed MQPRC, the simulation results are presented in comparison to the quasi-proportional-resonant control (QPRC).

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Harmonic Transfer Function Modeling of LCC-S Inductive Power Transfer System

Harmonic Transfer Function Modeling of LCC-S Inductive Power...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Cheng Gong Zhaoyi Ding Chi-Seng Lam State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

Mathematical model of the inductive power transfer (IPT) systems is essential for stability analysis and control design. Conventional modeling approaches for IPT systems result in high-order models, as each resonant voltage or current is represented by two slowly changing real-valued variables. Alternative modeling methods may decrease the model order, but they are often plagued by drawbacks such as the absence of analytical expressions, reduced generality, and incomplete modeling. Moreover, the harmonic AC components of the IPT system are not well modeled in the existing methods, which affects the analysis accuracy and control optimality. In this paper, a harmonic transfer function (HTF) modeling of the IPT system is proposed, which has good mathematical properties and improved accuracy. First, the circuit configuration and the linear time-varying (LTV) large-signal model are proposed. Then, the derivation of the proposed HTF is comprehensively discussed. Finally, the accuracy of the proposed HTF is verified by simulation and experimental results in comparison with other representative modeling methods, which shows an improved accuracy.

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A Multiple Energy Conversion Channels Fusion Grid-Connected Inverter for Large-Scale PV Active Power Injection Preventing Power Loss Caused by Insufficient MPP PV Voltage

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IEEE Transactions on Power Electronics 2025年

作者： Pang, Ying Yang, Hao Ji, Yanju Zhang, Xinhao Zhu, Yongji Li, Gang Xiang, Zeng Gong, Cheng Qiu, Shilin Zhou, Haigen Wong, Man-Chung Lin, Jun Jilin University State Key Laboratory of Deep Earth Exploration and Imaging College of Instrumentation and Electrical Engineering Changchun130026 China Jilin University College of Instrumentation and Electrical Engineering Changchun130021 China Jilin University Department of Laboratory Animals College of Animal Sciences Changchun China Tongda Electromagnetic Energy Co. Ltd. Changsha410000 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Macao999078 China University of Macau Institute of Microelectronics Macao999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Macao999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Macao999078 China

In the context of the increasing global demand for renewable energy and the rapid development of large-scale photovoltaic (PV) power generation, efficient grid connection of PV energy has become crucial. However, existing gridconnected inverters (GCI) face issues such as high-power losses issue of inductive GCI (IGCI) or limited operating ranges issue of capacitive GCI (CGCI). This paper introduces a multiple energy conversion channels fusion GCI (MC-FGCI) for largescale PV power injection. First, MC-FGCI is proposed based on a new topology circuit configuration. Second, an equivalent circuit model of IGCI and CGCI is introduced to calculate the critical point for channel switching. The IGCI and CGCI efficient operating boundaries are determined based on the critical point. Third, based on the efficient operating boundaries, a finite state machine (FSM) based method is proposed to choose the optimal VSI for high-efficiency grid connection. Finally, experimental results show that compared to previous multiple parallel IGCI (MP-IGCI), the proposed MC-FGCI saves around 11% power loss by eliminating the lossy DC/DC link. Compared to MP-CGCI, the proposed MC-FGCI expands the active power injection range under low maximum power point (MPP) voltage. © 1986-2012 IEEE.

关键词： Equivalent circuits

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Optimizing Inductive Power Transfer Circular Pad Coils for Electric Vehicles Using Multi-Objective Particle Swarm Optimization

Optimizing Inductive Power Transfer Circular Pad Coils for E...

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Annual Conference of Industrial Electronics Society

作者： Hou-Wa Wong Chi-Fong Ieong Chi-Seng Lam State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

The wireless power transfer (WPT) technology has gained significant attention in recent years due to its potential to provide a convenient and efficient method to charging electronic devices without the need for physical connections. In wireless charging, the inductive coils are significantly important as they are the component that power transfer takes place. In this context, Multi-Objective Particle Swarm Optimization (MOPSO) has emerged as a promising technique for designing WPT coils with improved performance. Aiming to verify the performance of the designed coil, LCC-S compensation topology was utilized as it can provide practical battery charging specifications.

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Multi-Granularity Prosodic Speech Synthesis with Grammar Information

SSRN

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SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

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Exploration of Optimizing FPGA-based Qubit Controller for Experiments on Superconducting Quantum Computing Hardware

Exploration of Optimizing FPGA-based Qubit Controller for Ex...

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IEEE International Conference on Electro-Information Technology

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Superconducting Quantum Materials and Systems Center (SQMS) Fermi National Accelerator Laboratory Batavia IL U.S.A.

This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.

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Improving signal-to-Noise Ratio (SNR) for Readout signals Using Adaptive Filters on Reconfigurable Controls Hardware

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Us...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Fermi National Accelerator Laboratory Superconducting Quantum Materials and Systems Center (SQMS) Batavia IL U.S.A.

This study investigates the optimization of signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

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A Double-Side Self-Tuning IPT Converter Immune to Parameters Variation Under Misalignment Condition

A Double-Side Self-Tuning IPT Converter Immune to Parameters...

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Annual Conference of Industrial Electronics Society

作者： Bowei Zou Zhicong Huang Io-Wa Iam Lam Chi-Seng Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

For inductive power transfer (IPT) systems, the loosely coupled transformer (LCT) is a crucial component. Variations in the air gap can lead to fluctuations in the parameters of the LCT, such as self-inductance and mutual inductance, caused by positional deviations of the ferrite cores on both sides. However, such variable LCT parameters can damage the resonant tank, thus impacting transmission efficiency and output stability. To address this issue, this paper proposes a self-tuning S/S IPT system based on switch-controlled capacitors (SCCs). The system utilizes gradient descent methods independently on double-side to achieve decoupled tuning control. This approach effectively mitigates the impact of LCT parameter fluctuations without requiring wireless communication. Furthermore, all switches in the system operate at fixed frequencies and implement soft switching. Finally, a prototype is constructed to validate the effectiveness of the proposed system.

关键词： Wireless communication Fluctuations Capacitors Air gaps Prototypes Switches Transformer cores Transformers Steady-state Tuning

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Editorial

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2024年第3期6卷 289-289页

作者： Liao, Shengcai Liu, Xiaoming Tistarelli, Massimo Chellappa, Rama Kittler, Josef United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East LansingMI48824 United States University of Sassari Computer Vision Laboratory Sassari07100 Italy Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD21218 United States University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

The 2022 International Joint Conference on Biometrics (IJCB 2022) was held at Abu Dhabi, United Arab Emirates, from October 10 to October 13, 2022. This marked the first biometrics conference held in the Middle East. The IJCB 2022 conference combined two major biometrics conferences: the IEEE Biometrics Theory, Applications, and Systems (BTAS) conference and the International Conference on Biometrics (ICB). The IJCB 2022 conference was technically co-sponsored by both IAPR TC4 (Technical Committee on Biometrics) and the IEEE Biometrics Council. © 2019 IEEE.

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Deep Learning based Fault Diagnosis in a Grid-Connected Photovoltaic Systems 19

Deep Learning based Fault Diagnosis in a Grid-Connected Phot...

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19th IEEE International Multi-Conference on Systems, signals and Devices, SSD 2022

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kasserine Tunisia Electrical and Computer Engineering Program Texas A&m University at Qatar Doha23874 Qatar Prince Sultan University Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Signal Processing and Image National Engineering School of Monastir Research Laboratory of Automation 5019 Tunisia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Photovoltaic (PV) system fault diagnosis is crucial because it helps PV system operators reduce energy and income losses. It also decreases the risk of fire and electric shock from PV system failures. Thus, the implementation of fault diagnosis tool is required. The most well-known techniques are machine learning models. Although these techniques provided well in terms of diagnosis and classification accuracy, they rely on manual feature extraction, which is time consuming, expensive, and requires diagnostic skill. Manual feature extraction is a problem that has to be addressed. This paper therefore presents a PV system fault diagnosis techniques that can automatically extract features from raw data in order to classify PV system faults. The implemented methods include long short-term memory (LSTM) bidirectional long short-term memory (BiLSTM) networks, which are deep learning based algorithms. Finally, the presented fault diagnosis approaches showed a good diagnosis performances when applied to an emulated GCPV system. © 2022 IEEE.

关键词： Failure analysis

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