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Discrete Current Controller for SPMSMs at Low Sampling-to-Fundamental Frequency Ratios Using Critical Damping Feedback

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IEEE Transactions on Power Electronics 2025年第8期40卷 11787-11798页

作者： Huang, Zhen Zhu, Rui Zhang, Yun Zanchetta, Pericle Nanchang University School of Information Engineering Nanchang330000 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Pavia Electrical Computer and Biomedical Engineering Department Pavia27100 Italy

The discrete current controllers can generally ensure high-performance machine control. However, for high-speed machines associated with low sampling-to-fundamental frequency ratios (SFR), the unavoidable disturbances will seriously affect the control performance. To address this issue, an advanced discrete current controller is proposed in this article. The critical damping feedback is used to achieve the maximum damping ratio of the disturbance loop at any SFR. Compared with the existing discrete controllers, the proposed one can significantly reduce the impact of multiple parameter mismatches on the performance of the controller while improving the current tracking response and enhancing the antidisturbance capability. Finally, the experimental results on a 10 krpm high-speed permanent magnet synchronous machine with the lowest SFR of 8 have proven the feasibility and advantages of the addressed current controller. © 1986-2012 IEEE.

关键词： Damping

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Leveraging machine learning and blockchain in E-commerce and beyond: benefits, models, and application

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Discover Artificial Intelligence 2023年第1期3卷 3页

作者： Jebamikyous, Hrag Li, Menglu Suhas, Yoga Kashef, Rasha Electrical Computer and Biomedical Engineering Department Toronto Metropolitan University Toronto Canada

Blockchain technology (BT) allows market participants to keep track of digital transactions without central recordkeeping. The features of blockchain, including decentralization, persistency, and attack resistance, allow data security and privacy. Machine learning (ML) involves the analytical platform on a massive amount of data to provide precise decisions. Since data reliability, integration, and data security are crucial in machine learning, the emergence of blockchain technology and machine learning has become a unique, most disruptive, and trending research in the last few years, achieving comparable and precise performance. The combination of blockchain and machine learning (BT–ML) has been applied across different applications to assist decision-makers in retrieving valuable data insights while preserving privacy and integration. This paper summarizes the state-of-the-art research in combing BT and ML in e-commerce and other various applications, including healthcare, smart transportation, and the Internet of Things (IoT). The challenges and benefits of integrating machine learning and blockchain technologies are outlined in the paper. We also discuss the advantages and limitations of current algorithms in the BT–ML integration. This paper provides a roadmap for researchers to pave the way for current and future research directions in combing the BT and ML research areas. © The Author(s) 2023.

关键词： Blockchain

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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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Effects of Image Normalization on CNN-Based EEG-EMG Fusion

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IEEE Sensors Journal 2025年第11期25卷 20894-20906页

作者： Tryon, Jacob Guillermo Colli Alfaro, J. Trejos, Ana Luisa Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada Western University Department of Electrical and Computer Engineering School of Biomedical Engineering LondonONN6A 5B9 Canada

The rise in popularity of wearable robotic devices has brought many opportunities in the area of assistive devices for rehabilitation. However, despite their numerous advantages, these wearable mechatronic devices are not widely adopted due to a poor interaction between the device and the wearer. To solve this issue, several studies have proposed the use of sensor fusion of electroencephalography (EEG) and electromyography (EMG) signals using Convolutional Neural Networks (CNN) to detect the motion intention of the user and control the wearable device. Although normalization techniques can be applied during data pre-processing in order to improve performance, the correct normalization methods to apply for combined EEG and EMG data are unknown, hindering the potential improvement of the resulting CNN models. Therefore, in this study, no normalization, subject-wise, speed-wise, image-wise, and channel-wise normalization methods were compared. The effectiveness of these methods was tested on the overall and speed specific accuracies of CNN models trained using a database of combined EEG-EMG data collected during elbow flexion-extension motions at different speeds. The results of this study showed that no normalization improved the performance of the overall accuracy of CNN models, with grouped spectrogram-based models achieving an accuracy of 81.57 ± 7.11%. A similar trend was found for speed specific accuracies, in which no normalization showed to be a slightly better option during slow motions. Overall, these results provide information on the different tools that can be used to improve the performance of CNN-based models used for the control of wearable robotic devices. © 2001-2012 IEEE.

关键词： Electromyography

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Patient-independent epileptic seizure detection using weighted visibility graph features and wavelet decomposition

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Multimedia Tools and Applications 2025年第6期84卷 3197-3221页

作者： Mohammadpoory, Zeynab Nasrolahzadeh, Mahda Amiri, Sekineh Asadi Department of Biomedical Engineering Shahrood University of Technology Shahrood Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Computer Engineering University of Mazandaran Babolsar Iran

Epileptic seizure detection has been a complex task due to the chaos and non-stationariness observed in the electroencephalogram (EEG) signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. This study investigates the patient-independent detection of seizure events using the big dataset CHB-MIT Scalp EEG. This paper proposes a new method based on weighted visibility graph (WVG) features to identify seizures from EEG signals. EEG signals from 22 channels and delta, theta, alpha, beta, and gamma sub-bands of EEGs are mapped into the WVG, and then WVG features are calculated from these WVGs. Then more informative features were selected using a combination of some feature selection methods and were given to five classifiers to investigate the performance of these features to classify the brain signals into seizure free (interictal) and during a seizure (ictal) groups. After that, the post-processing step was used to promote the performance of the method and also detect the seizure onset and offset times detection. The proposed method could detect 163 seizures out of 184, considering patient-independent situations for training classifiers and it could obtain an accuracy, sensitivity, and specificity of 94.02%, 92.31%, and 94.12% respectively. It also could obtain an FDR equal to 0.12/h and an ADL of 5.11 Seconds. Then, the proposed method has promising results in detection of seizures and it may use in online seizure detection applications such as closed-loop therapies. This study proposed extracting some WVG-based features from the signal and its sub-band with a feature selection scheme combining some feature selection methods and using some classifiers. Then a post-processing proposed to process the classifiers output and promote the performance of the proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Electroencephalography

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Anomaly-Focused Augmentation Method for Industrial Visual Inspection

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IEEE Access 2025年 13卷 89118-89139页

作者： Melvinlara, Melvin A. Hameed, Ibrahim A. Koziorek, Jiri Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava70800 Czech Republic Department of Mechanical Engineering and Technology Management Ås1433 Norway

In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images. © 2013 IEEE.

关键词： Photointerpretation

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Reflection-mode diffraction tomography of multiple-scattering samples on a reflective substrate from intensity images

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Optica 2025年第3期12卷 406-417页

作者： Li, Tongyu Zhu, Jiabei Shen, Yi Tian, Lei Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States

We introduce a reflection-mode diffraction tomography technique that enables the simultaneous recovery of forward and backward-scattering information for high-resolution 3D refractive index reconstruction. Our technique works by imaging a sample on a highly reflective substrate and employing a multiple-scattering model and a reconstruction algorithm. It combines the modified Born series as the forward model, Bloch and perfect electric conductor boundary conditions to handle oblique incidence and substrate reflections, and the adjoint method for efficient gradient computation in solving the inverse-scattering problem. We validate the technique through simulations and experiments, achieving accurate reconstructions in samples with high refractive index contrasts and complex geometries. Forward scattering captures smooth axial features, while backward scattering reveals complementary interfacial details. Experimental results on dual-layer resolution targets, 3D randomly distributed beads, phase structures obscured by highly scattering fibers, fixed breast cancer cells, and fixed C. elegans demonstrate its robustness and versatility. This technique holds promise for applications in semiconductor metrology and biomedical imaging. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Multiple scattering

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EEG Prediction with Deep Recurrent Learning

EEG Prediction with Deep Recurrent Learning

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zou, Jiadao Zhang, Qingxue Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering IN United States Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering Biomedical Engineering IN United States

ISBN: (纸本)9798350324136

Electroencephalogram (EEG) reveals the brain dynamics that are of a great value in clinical applications, e.g., epilepsy diagnosis, cognitive status, and other disorders. Intracranial Electroencephalogram (iEEG) is encoding rich dynamics, with its invasive electrodes that go deep into the brain. Despite the high precision, the iEEG surgery may limit its use in daily lives due to the risk, setup time and cost. In this study, we propose a deep learning framework that can reconstruct iEEG from scalp EEG, thereby demonstrating the potential of convenient deep brain monitoring in daily lives. We leverage a long short-Term memory (LSTM) model for ultra-deep iEEG prediction from the non-invasive scalp EEG. Our simulation has demonstrated that, our deep learning model, with the nonlinear dynamics mining capability, facilitates the deep source signal estimation performance, compared with the linear model. Furthermore, we find that the estimation performance may be improved with selection of more EEG measurement sites. This study will greatly advance brain dynamics decoding and broadly benefit diverse brain-computer-interface and smart health applications. © 2024 IEEE.

关键词： Brain computer Interface Electroencephalogram Recurrent Learning Smart Health

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Computational coherent Raman scattering imaging:breaking physical barriers by fusion of advanced instrumentation and data science

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eLight 2023年第1期3卷 211-229页

作者： Haonan Lin Ji-Xin Cheng Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02215USA

Coherent Raman scattering(CRS)microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular *** date,endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical ***,the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system,which trades off speed,signal fidelity and spectral *** synergistic combination of instrumentation and computational approaches offers a way to break the *** this review,we first introduce coherent Raman scattering and recent instrumentation developments,then discuss current computational CRS imaging methods,including compressive micro-spectroscopy,computational volumetric imaging,as well as machine learning algorithms that improve system performance and decipher chemical *** foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy.

关键词： Coherent anti-Stokes Raman scattering Stimulated Raman scattering Computational imaging Hyperspectral imaging Deep learning

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A Biasing Scheme for the Gain Compression Point Optimization of HBT Cascode D-Band LNAs

A Biasing Scheme for the Gain Compression Point Optimization...

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2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium, BCICTS 2024

作者： Serra, Lorenzo De Filippi, Guglielmo Piotto, Lorenzo Mazzanti, Andrea University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798331541248

Cascode amplifiers with a matching network between the common-emitter (CE) and the common-base (CB) HBTs are popular in the sub-THz band for their enhanced gain. The stage is analyzed in this paper with focus on the large-signal behavior. It is first observed that the CE-CB interstage network can push the CB out of the linear class-A regime, limiting the input power at 1 dB gain compression. It is then shown that the issue can be avoided with a suitable biasing scheme, leading to enhanced dynamic range with superior power efficiency. The concept is applied to the design of a D-band LNA in BiCMOS technology. With a quiescent power consumption of 20 mW from 2 V supply, the LNA shows 17.5 dB gain and 5.5 dB NF at 170 GHz. The output-referred 1dB compression point is 4.2dBm with 9% power efficiency at 175 GHz. Compared to previously reported LNAs in the same band, measured results demonstrate a remarkable dynamic range improvement with the the highest power efficiency. © 2024 IEEE.

关键词： BiCMOS technology

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