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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Lakouraj, Mohammad Mansour Hosseinpour, Hadis Livani, Hanif Benidris, Mohammed University of Nevada Reno Department of Electrical & Biomedical Engineering RenoNV United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350320169

This study proposes a new method for event sit-uational awareness in distribution grids using Synchro Wave-form Measurement Units (SWMUs). An efficient feature-extracting technique named the Short-Time Matrix Pencil method (STMPM) is used to capture the oscillation modes and distortion of voltage and current measurements under common events such as transient switching events and challenging high impedance faults. The extracted features from the waveform data are then used as the input to Graph Neural Network (GNN) as the event classifier. GNN captures the spatial relationship between SWMUs and physical features of the network to enhance event classification accuracy. The proposed grid-aware waveform analytics is tested for classifying different events, and the superior performance of the proposed method with respect to other approaches is verified using the classification merits, such as accuracy, Fl-score, precision, and recall. © 2023 IEEE.

关键词： Graph neural networks

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A GPU-Based Model for Composite Power System Reliability Evaluation and Sensitivity Analysis

A GPU-Based Model for Composite Power System Reliability Eva...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Olowolaju, Joshua Thapa, Jitendra Hossain, Rakib Benidris, Mohammed Livani, Hanif University of Nevada Department of Electrical & Biomedical Engineering RenoNV89557 United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350320169

Decarbonizing the grid by incorporating more renewable energy sources has made power system reliability more critical. Although Monte Carlo simulation is a traditional method for power system reliability assessment, the computational costs limit its utility as power grid complexity increases. As a result, this study introduces a Graphics Processing Unit (GPU) based Convolution Neural Network (CNN) model to a non-sequential Monte Carlo Simulation (MCS) model to improve computation efficiency while maintaining high evaluation accuracy during reliability evaluation. Furthermore, this study explores the viability of the proposed model for sensitivity analysis of power system components on the overall system reliability condition. The proposed model is tested using the IEEE Reliability Test System 79 and is compared to the baseline Monte Carlo simulation approach. Results show that the model provides more than 80% time savings during reliability analysis while maintaining good evaluation accuracy for sensitivity assessments. © 2023 IEEE.

关键词： Neural network models

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Functional Arm Movement Classification in Stroke Survivors Using Deep Learning with Accelerometry Data

Functional Arm Movement Classification in Stroke Survivors U...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Tran, Tan Chang, Lin-Ching Lum, Peter S. The Catholic University of America School of Engineering Department of Electrical Engineering and Computer Science WashingtonDC United States The Catholic University of America School of Engineering Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350324457

The rehabilitation of the upper extremity (UE) plays a pivotal role in the recovery process for stroke survivors. Despite the routine practice of UE rehabilitation, assessing its effectiveness poses a significant challenge. Evaluation methods vary, ranging from clinical assessments of UE motor skills and patient feedback to the utilization of wearable technology equipped with accelerometer or gyro sensors. In this study, we employed deep learning approaches such as Multi-Layer Perceptron, Long Short-term Memory, and Gated Recurrent Units, in conjunction with data from accelerometry sensors embedded in wrist-wearable Inertial Measurement Units. Our findings demonstrate the successful differentiation between functional and non-functional UE movements in individuals recovering from strokes with 90% and 74% of classification accuracy for intra-subject and inter-subject models, respectively. The methodologies employed in this study exhibit consistency and reliability, providing precise outcomes for individual and acceptable outcomes for cross-participant evaluations. Notably, our advanced models directly utilized raw accelerometry data instead of manual extracted features that was often used in traditional machine learning algorithms. These innovative techniques offer cost-effective and adaptable tools for monitoring UE functionality in real-world settings. The insights derived from this study hold the potential to be transformative in tailoring rehabilitation strategies for adults affected by strokes. © 2023 IEEE.

关键词： deep learning rehabilitation stroke upper extremity

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Direct ink writing of gradient shear-stiffening elastomer for enhanced toughness and impact resistance

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Journal of Industrial and engineering Chemistry 2025年 149卷 593-603页

作者： Gong, Liping Zhao, Chunyu Lu, Hongda Zhang, Qingtian Chen, Zexin Yang, Junjie Yang, Jinyu Sun, Shuaishuai Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong2522 Australia CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong2522 Australia

Shear-stiffening materials have garnered significant attention due to their inherent ability to rapidly respond to changes in strain rate. However, conventional studies on these materials exhibit limited mechanical performance and insufficient structural design, hindering applications in material science and smart mechatronics. Inspired by the gradient structure of a squid beak, this study uses a direct-ink-writing (DIW) technology to develop a novel gradient shear-stiffening elastomer (GSSE). Comprehensive characterizations were conducted to optimize the printability of the shear-stiffening elastomer under various testing conditions, including rheology, compression, and tension. Additionally, the toughness and impact resistance of the elastomer were systematically investigated. It is demonstrated that the biomimetic GSSE exhibits enhanced flexibility, efficient energy dissipation, and outstanding impact resistance. Consequently, this work advances the field of shear-stiffening elastomers for diverse applications by leveraging the gradient structure design strategy to achieve superior mechanical performance under complex loading conditions. © 2025 The Author(s)

关键词： Strain rate

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Machine Learning Prediction Models of Optimal Time for Aortic Valve Replacement in Asymptomatic Patients

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Intelligent Automation & Soft Computing 2023年第7期37卷 455-470页

作者： Salah Alzghoul Othman Smadi Ali Al Bataineh Mamon Hatmal Ahmad Alamm Biomedical Engineering Department The Hashemite UniversityZarqaJordan Department of Electrical and Computer Engineering Norwich UniversityNorthfieldVermont05663USA Department of Biochemistry and Molecular Biology The Hashemite UniversityZarqaJordan National Centre for Big Data Science and Artificial Intelligence AmmanJordan

Currently,the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis(AS)is made by healthcare professionals based on the patient’s clinical biometric records.A delay in surgical aortic valve replacement(SAVR)can potentially affect patients’quality of *** using ML algorithms,this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following *** study represents a novel approach that has the potential to improve decision-making and,ultimately,improve patient *** analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or were indicated for *** divide the data into two groups:those who died within the first year after SAVR and those who survived for more than one year or were still alive at the last *** then use six different ML algorithms,Support Vector Machine(SVM),Classification and Regression Tree(C and R tree),Generalized Linear(GL),Chi-Square Automatic Interaction Detector(CHAID),Artificial Neural Net-work(ANN),and Linear Regression(LR),to generate predictions for the best timing for *** results showed that the SVM algorithm is the best model for predicting the optimal timing for SAVR and for predicting the post-surgery survival *** optimizing the timing of SAVR surgery using the SVM algorithm,we observed a significant improvement in the survival period after *** study demonstrates that ML algorithms generate reliable models for predicting the optimal timing of SAVR in asymptomatic patients with moderate-to-severe AS.

关键词： Aortic stenosis aortic valve replacement machine learning survival period enhancement artificial intelligence in cardiology

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Adaptive Indoor People-Counting System Based on Edge AI Computing

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第1期8卷 255-263页

作者： Yen, Mao-Hsu Lin, Bor-Shyh Kuo, Yu-Lun Lee, I-Jung Lin, Bor-Shing National Taiwan Ocean University Department of Computer Science and Engineering Keelung City202301 Taiwan National Yang Ming Chiao Tung University Institute of Imaging and Biomedical Photonics Tainan71150 Taiwan Chi Mei Medical Center Department of Medical Research Tainan71004 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237303 Taiwan

Since the outbreak of COVID-19 in 2019, people counting in confined spaces has become essential for controlling the flow of people and reducing viral spread. Many people-counting systems use a fisheye lens to achieve a wide viewing range. However, the images captured with a fisheye lens can have different sizes and deformations due to different camera heights, which consequently affects the accuracy of people counting. In this study, a Raspberry Pi 4 development board attached to a fisheye lens was used to construct an edge computing platform for people counting by using artificial intelligence. A modified You Only Look Once (YOLO) model, which has three output layers and a spatial pyramid pooling module, is proposed to recognize people in images captured at various heights. For efficiently running this deep learning model on the proposed edge computing platform, a TensorFlow Lite framework was used to reduce the model size. The final experimental results indicated that the mean average error and mean squared error of the modified tiny version of the YOLO v4 model were 0.249 and 0.292, respectively, and the detection speed of the platform was 2 frames per second. The results indicate that the proposed edge computing platform can efficiently and accurately count the number of people. Because the proposed system uses edge computing, it can effectively solve personal privacy and transmission bandwidth problems. © 2017 IEEE.

关键词： Cameras

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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Resolution enhancement with deblurring by pixel reassignment

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Advanced Photonics 2023年第6期5卷 59-71页

作者： Bingying Zhao Jerome Mertz Boston University Department of Electrical and Computer EngineeringBostonMassachusettsUnited States Boston University Department of Biomedical EngineeringBostonMassachusettsUnited States

Improving the spatial resolution of a fluorescence microscope has been an ongoing challenge in the imaging *** address this challenge,a variety of approaches have been taken,ranging from instrumentation development to image *** example of the latter is deconvolution,where images are numerically deblurred based on a knowledge of the microscope point spread ***,deconvolution can easily lead to noise-amplification *** by postprocessing can also lead to negativities or fail to conserve local linearity between sample and *** describe here a simple image deblurring algorithm based on pixel reassignment that inherently avoids such artifacts and can be applied to general microscope modalities and fluorophore *** algorithm helps distinguish nearby fluorophores,even when these are separated by distances smaller than the conventional resolution limit,helping facilitate,for example,the application of single-molecule localization microscopy in dense *** demonstrate the versatility and performance of our algorithm under a variety of imaging conditions.

关键词： image deblurring microscopy bio-imaging image reconstruction optical resolution

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FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction 11

FOOTBALLTrace: An AI-Based System for Football Player Tracki...

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11th International Conference on Sport Sciences Research and Technology Support, icSPORTS 2023

作者： Mostafa, Abdelrahman H. Rushdi, Muhammad A. Basha, Tamer A. Sayed, Khaled Department of Systems and Biomedical Engineering Faculty of Engineering Cairo University Giza Egypt Department of Computer Science New Giza University Giza Egypt Department of Electrical and Computer Engineering and Computer Science University of New Haven West HavenCT United States

ISBN: (纸本)9789897586736

Data analytics have had a significant impact on tactical and workload planning in football. Football data is divided into two categories: event data, which captures on-the-ball events like passes and shots, and tracking data, which captures off-the-ball movements. However, traditional methods of collecting tracking data are expensive and inconvenient. Recently, AI solutions have emerged as low-cost and user-friendly alternatives to track players' movements from video streams. This paper introduces FOOTBALLTrace, an end-to-end AI system for tracking multiple football players from a panoramic game view. The system also incorporates a novel algorithm that detects potential occlusion events and ensures trajectory continuity for occluded players. The workflow involves five stages: panoramic view creation, player detection, player ID association, occlusion detection, and trajectory correction. The system utilizes YOLOv7 for multiple object detection and employs a pre-trained deep affinity network to assign unique IDs to players throughout the game. Occlusion detection and trajectory correction are achieved by extracting geometric features from discontinuous player trajectories. The system's performance was evaluated on full-length video data of a football game, with occlusion events manually extracted for training and testing the occlusion detection and trajectory correction algorithm. The system achieved an 87.5% trajectory correction rate for occluded trajectories. © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Trajectories

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SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for Identifying AMD Grades

SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： El-Den, Niveen Nasr Elsharkawy, Mohamed Ghazal, Mohammed Mahmoud, Ali Sandhu, Harpal Mahdi, Hani El-Baz, Ayman Department of Computer and System Engineering Ain Shams University Egypt Department of Bioengineering University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

Accurate early diagnosis is crucial in addressing Age-related Macular Degeneration (AMD), a chronic retinal disease that is a leading cause of blindness among the elderly. Medical imaging, particularly fundus imaging, is essential in facilitating timely detection and intervention. Due to the variability in image sizes within our dataset, this paper introduces the SABiT-MNet model, which effectively discriminates between healthy retinas, dry AMD, and wet AMD. The model integrates a novel scale-adaptive (SA) approach by combining an autoencoder with Big Transfer (BiT) as its backbone. Unlike traditional resizing methods, which often result in the loss of critical diagnostic information, the SA model dynamically adjusts to varying image sizes, preserving key retinal features essential for accurate diagnosis. The primary aim of this architecture is to retain crucial details in fundus images to ensure precise classification. In this study, 648 subjects were recruited through the Comparisons of AMD Treatments Trials study group, sponsored by the University of Pennsylvania. Experimental results demonstrate that the proposed SABiT-MNet model outperforms state-of-the-art approaches, including transformer-based models, achieving superior diagnostic accuracy. The model recorded performance metrics of 94% accuracy, 97% sensitivity, and 93.94% specificity. To further validate the robustness of the system, we tested it on the public ODiR dataset, where it achieved similarly promising results, confirming the effectiveness of our approach. © 2025 IEEE.

关键词： AMD Deep Learning fundus photography

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