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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chan, Pak-Ian Sou, Wai-Kit Lam, Chi-Seng 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 University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper, an improved model predictive control (MPC) is proposed for a single-phase LC-coupling hybrid active power filter (LC-HAPF), which ensures the low steady-state error and fast transient response. Generally, the intrinsic discretized and predictive error of the traditional MPC for the LC-HAPF enlarges the steady-state error. To relax this problem, a signal correction technique is proposed, and therefore reduce the steady-state error. Then, the parameter design of the proposed improved MPC for the LC-HAPF is given based on the stability analysis. Finally, simulated results are provided to verify the effectiveness of the improved MPC compared with the traditional MPC. © 2022 IEEE.

关键词： Model predictive control

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Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

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Remote Sensing 2024年第1期16卷

作者： Chang, Chein-I Chen, Shuhan Zhong, Shengwei Shi, Yidan Information and Technology College Dalian Maritime University Dalian116026 China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States Department of Electrical Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD. © 2023 by the authors.

关键词： Curve fitting

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A Deadbeat Current Controller for Thyristor-Controlled LC-Coupling Hybrid Active Power Filter 48

A Deadbeat Current Controller for Thyristor-Controlled LC-Co...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sou, Wai-Kit Gong, Cheng Wong, Chi-Kong Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal Vlsi University of Macau China University of Macau Institute of Microelectronics China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Thyristor-controlled LC-coupling hybrid active power filter (TCLC-HAPF) is a promising current quality compensator in the medium-voltage-level power system, which is characterized by a wide operational range and low DC-link voltage. Based on the simplified discrete model, this paper proposes a deadbeat current control (DBCC) scheme for the TCLC-HAPF. The proposed controller can lead TCLC-HAPF to be operating at a fixed switching frequency and track the reference compensating current accurately with a fast transient response. Simulation studies under both balanced and unbalanced load conditions are conducted to verify the proposal. © 2022 IEEE.

关键词： Thyristors

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Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM During COVID-19 Pandemic

Short-Term Power Load Forecasting Using MOGOA and ConvBiLSTM...

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2023 International Conference on Power System Technology, PowerCon 2023

作者： Xu, Da Liu, Bowen Lam, Chi-Seng Huang, Zhangyou University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi Macao China Institute of Microelectronics University of Macau Macao China School of Automation China University of Geosciences Wuhan430074 China University of Macau Fst Department of Electrical and Computer Engineering Macao China

ISBN: (纸本)9798350300222

Due to the high restrictions of the global COVID-19 on society, economy and production, the electrical supply-demand balance is facing a great challenge. This paper proposes a short-term load forecasting (STLF) method based on multi-factor fusion during the COVID-19. After fully analyzing and screening the nonlinear coupling interactions among cross-domain weather and public health safety features, a convolutional bi-directional long short-term memory neural network (ConvBiLSTM) is developed based on the convolutional and bidirectional loop enhancements of long short-term memory (LSTM). To avoid early termination of training the global optimal solution, multi-objective grasshopper optimization algorithm (MOGOA) is adopted to select the inherent hyperparameters of the forecasting network. Attention mechanism is further introduced to enhance the learning ability of the key features in temporal data, improving the forecasting accuracy and robustness of ConvBiLSTM. Comparisons on cross-domain COVID-19 dataset over classical methods are performed to verify its effective and superior performances. © 2023 IEEE.

关键词： Coronavirus

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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Direct fractional order MRAC adaptive control design for a class of fractional order commensurate linear systems

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Journal of Control and Decision 2021年第3期8卷 363-371页

作者： Hanane Balaska Samir Ladaci Abdelbaki Djouambi Department of Electrical Engineering LGEA LaboratoryUniversity of Oum El BouaghiOum El BouaghiAlgeria Department of E.E.A. National Polytechnic School of ConstantineConstantineAlgeria SP-LAB Signal Processing Laboratory UMC1 UniversityConstantineAlgeria

In this paper,we introduce a direct fractional order adaptive control design based on model reference adaptive control(MRAC)structure for a class of commensurate fractional order linear systems with an arbitrary relative degree,and whose parameters are *** generalising the application of standard direct MRAC strategy to plants described by fractional order models,we develop a fractional adaptive control scheme(FOMRAC)based on the output *** also define an adaptation control law ensuring the stability of the closed-loop system and the good tracking of the reference *** asymptotic stability of the fractional order control system is proven using an extension of the Lyapunov *** results show the effectiveness of the proposed control method even for plants with model parametric variations and additive noises.

关键词： Fractional order linear commensurate system direct model reference adaptive control fractional adaptive control direct FOMRAC Lyapunov stability

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Bayesian Rule Ontologies For XAI Classification and Regression

Bayesian Rule Ontologies For XAI Classification and Regressi...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Akash Kumar Panda Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute University of Southern California Los Angeles California

A random foam defines a modular rule-based ontology after sampling from a neural or other input-output system. A random foam combines several rule-based systems and averages the systems. It gives a Bayesian posterior over the subsystems. It also gives separate Bayesian posteriors over the rules of each subsystem. The shape of the rules controls how well the random-foam ontology performs in classification and regression. We found that a heterogenous ontology that mixes different rule shapes can perform better than a homogenous ontology based on a single Gaussian or other rule type. Random foams are also universal function approximators. So they can train on a neural black box and act as its explainable proxy system. We prove this uniform approximation theorem for the important case of bump-function random foams with throughput combination. Random foams also measure their output’s uncertainty through the conditional variance. Bump function rules performed better than Cauchy rules at classification while Cauchy rules performed better at regression. Gaussian rules performed best in both classification and regression. A homogeneous Gaussian random foam that trained on a 96.7% accurate neural classifier was itself 95.96% accurate on the MNIST data set. A heterogeneous random foam with two-thirds Gaussian rules and one-third Laplacian rules did better than did the all-Gaussian foam ontology.

关键词： Uncertainty Laplace equations Shape Measurement uncertainty Machine learning Ontologies Aerospace electronics

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Transclaw U-Net: Claw U-Net With Transformers for Medical Image Segmentation 5

Transclaw U-Net: Claw U-Net With Transformers for Medical Im...

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5th International Conference on Information Communication and signal processing, ICICSP 2022

作者： Yao, Chang Hu, Menghan Li, Qingli Zhai, Guangtao Zhang, Xiao-Ping East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Ryerson University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9781665485890

In medical image analysis, the long-range spatial features are often not accurately obtained by the traditional convolutional neural networks. Hence, we propose a TransClaw U-Net network structure. The transformer part is added after three convolution operations to fuse shallow features extracted by convolution operations for maximally encoding the long-range spatial features between patches. The 'Claw' in TransClaw U-net means that we add the bottom upsampling part to retain the deepest feature information for detail segmentation. In addition, the modified three-channel global attention mechanism to blend the outputs of three channels (the encoding part, the bottom upsampling part and the decoding part) to effectively extract image contours. The experimental results on Synapse Multi-organ Segmentation Dataset show that TransClaw U-Net performs better than other networks. The results of ablation experiments prove the effectiveness of the three improved components of the network and influence of input image size and skip-connection numbers on network performance. The source code will be publicly available once the paper is accepted. © 2022 IEEE.

关键词： Convolution

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Suppression of Mainlobe Deceptive Jammers with SF-RDA Radar

Suppression of Mainlobe Deceptive Jammers with SF-RDA Radar

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General Assembly and Scientific Symposium, URSI

作者： Lan Lan Guisheng Liao Shengqi Zhu Jingwei Xu Hing Cheung So National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

This paper addresses the problem of suppressing main-lobe deceptive jammers based on a novel stepped frequency (SF)-receive delay array (RDA) radar. In our system design, the linear frequency modulation waveform is transmitted with a SF across the transmit antenna elements, and the received echo is delayed with a time shift between adjacent receive elements. In this regard, the range-angle-dependent transmit and receive steering vectors are obtained, making it possible to distinguish the true target and the main-lobe deceptive jammers, especially those generated in the same transmit pulse behind the true target. Furthermore, a joint transmit-receive beamforming method is developed, including the data-independent transmit beamforming and the dimension-reduced data-dependent receive beamforming. Therefore, the jammers are suppressed by nulling in the joint transmit-receive spatial domain. Numerical simulations are provided to demonstrate the effectiveness of the proposed method in suppressing mainlobe deceptive jammers.

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Towards the Improvement of the Cognitive Process of the Synthesized Speech of Mathematical Expression in MathML: An Eye-Tracking 2

Towards the Improvement of the Cognitive Process of the Synt...

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2nd International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2022

作者： Souza, Adriana Freitas, Diamantino Bahia Federal Institute of Education Science and Technology Bahia Brazil Doctoral Program in Digital Media Faculty of Engineering of the University of Porto Porto Portugal Faculty of Engineering of the University of Porto Speech Processing Laboratory Electrical and Computer Engineering Department Porto Portugal

ISBN: (数字)9781665470162

ISBN: (纸本)9781665470162

Being one essential part of the solutions we are developing to provide accessibility for blind persons, synthesized speech of mathematical content, although having evolved in naturalness in recent years, still keeps a characteristic of generating very long outputs, which makes it difficult for a person who uses a screen reader to memorize. Eye-gaze tracking has been useful for studying cognitive processes and student's effort in performing tasks therefore it seems attractive as a means to understand how people analyze, process, and break down extensive math expressions into smaller ones. This understanding can help to build a similar model of auditory rendering of MathML capable of avoiding cognitive overload. Therefore, we carried out an experiment to do a gaze-based analysis of reading and memorizing mathematical expressions. Our sample consisted of fourteen participants, and seven mathematical expressions were used in the experiment. The results showed that the computed complexity of the expressions and the average duration of eye-gaze fixations have a strong and significant relationship. Therefore, we conclude that the complexity variable must always be considered in the speech synthesis process of mathematical expressions. However, when considering why participants requested the repetition of an expression with not larger complexity during the memorization process, a tentative interpretation based on the number of MathML tree nodes and the computed complexity of the expressions was not enough to explain it, indicating that other factors need to be investigated. Although some answers to the research questions were not yet conclusive, we consider this research relevant because it points to directions for the work in order to minimize the cognitive processing and mental load of the synthesized speech rendering of mathematical expressions. © 2022 IEEE.

关键词： Eye tracking

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