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MXene Nanosheets-Decorated Paper as a Green Electronics Material for Biosensing

ACS Measurement Science Au 2024年第1期4卷 81-91页

作者： Yu, Shan-Chu Huang, Tzu-Yen Lin, Tzu-En Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Kaohsiung Medical University Hospital Kaohsiung Medical University Kaoshiung 807378 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

This research delves into the development and optimization of MXene nanosheet-based paper electrodes, emphasizing their adaptability in green electronics and diverse applications. Xuan paper, a cellulose-based material, was identified as an ideal substrate for its mechanical attributes and capacity to accommodate MXene, further yielding outstanding electrical conductivity. The MXene paper electrode demonstrated consistent performance under various conditions, showing its potential in the field of wearable electronics and medical devices. Notably, its impressive electrothermal capabilities and environmentally conscious decomposition mechanism make it a promising candidate for future green electronic applications. Overall, this study underscores the electrode’s harmonization of performance and environmental sustainability, paving the way for its integration into futuristic electronic solutions. © 2023 The Authors. Published by American Chemical Society.

关键词： electrothermal performance green electronics MXene nanosheets paper electrode sustainable design wearable devices Xuan paper

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An economic centralised combinational load shedding scheme to protect power system in case cascade and major disturbances occur

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International Journal of Ambient Energy 2022年第1期43卷 8949-8958页

作者： Mohammadzadeh, Hossein Mohammadzadeh, Mohammad Department of Electrical Engineering Shahid Chamran University of Ahvaz Ahvaz Iran School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL United States

This paper introduces an economic centralised combinational load shedding scheme for self-healing of the power system against cascading significant disturbances. The proposed method utilises the voltage stability risk index (VSRI) to maintain the power system's voltage and frequency stability margin. In this algorithm, the protection against cascading disturbances when the spinning reserve of the system cannot recover has been considered. Moreover, it considered the voltage and economic cost of loads to find the proper location of load shedding and minimised the number of customers being turned off to increase social welfare. Furthermore, a voltage stability index based on the maximum power limit of the transmission lines (L index) and frequency steps is used to offset issues associated with VSRI. According to the simulation results that have been performed over the 39-bus New England system, the proposed scheme has a better performance than the previous methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electric load shedding

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Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

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Journal of Energy Chemistry 2024年第4期91卷 512-521页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chunsheng Hu School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernization The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Advanced Interdisciplinary Studies Ningxia UniversityYinchuan 750000NingxiaChina

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured *** data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low *** response,this study integrates domain knowledge into deep learning to enhance the RUL prediction *** demonstrate accurate RUL prediction using only a single charging ***,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging *** parameters inform a deep neural network(DNN)to predict RUL with high accuracy and *** trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged *** data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,*** its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and *** inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

关键词： Lithium-ion batteries Remaining useful life Physics-informed machine learning

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Latency and Cost Minimization for Task Offloading with Energy Harvesting in UAV-MEC Network

Latency and Cost Minimization for Task Offloading with Energ...

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2023 IEEE Symposium on Wireless Technology and Applications, ISWTA 2023

作者： Basharat, Mehak Naeem, Muhammad Anpalagan, Alagan Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Comsats University Islamabad Department of Electrical Engineering Wah Campus Pakistan

ISBN: (纸本)9798350310955

Unmanned aerial vehicles (UAVs) are recently considered for various Internet of things (IoT) applications. Furthermore, mobile edge computing offers low-latency data computation and improves network performance by bringing computing resources closer to IoT devices. UAVs equipped with MEC capabilities are expected to play a critical role in providing reliable and efficient communication in future wireless networks. The low-power IoT devices can harvest energy for sustainable operations in different applications such as public safety. Resource management and efficient task offloading are the major challenges to take full advantage of UAV-MEC networks. In this paper, we formulate an optimization problem to minimize latency and cost as well as maximize the number of IoT devices connected with UAVs. We consider constraints on computation, caching, energy harvested, latency, and the maximum number of connections a given UAV allows. A heuristic with learning placement (HLP) algorithm is proposed based on an unsupervised learning algorithm and an iterative rounding algorithm to solve the problem with less complexity. Simulation results show the effectiveness of the proposed HLP algorithm in UAV-MEC networks compared with the branch and bound algorithm (optimal results). © 2023 IEEE.

关键词： Energy harvesting

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Investigating Deep Learning for Classification of Psychiatric Disorders on Brain MRI Data Using GAN-Based Augmentation

Investigating Deep Learning for Classification of Psychiatri...

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2023 International Conference on electrical and Information Technology, IEIT 2023

作者： Masengi, Gracia Angelina Jeniffer Sardjono, Tri Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798350327298

One of the difficulties in diagnosing patients in the field of psychiatry is the lack of objective biomarker measurements. In general, diagnosis is based on traditional methods relying on behavioral symptoms. Currently, diagnostic approaches predominantly rely on traditional methods grounded in behavioral symptoms. The evolution of neuroimaging techniques has prompted extensive investigations into biological indicators of psychiatric disorders. Among these endeavors, the application of deep learning for classification has demonstrated substantial promise. Through generative models like the generative adversarial network (GAN), deep learning is being investigated more in the field of medical image analysis to address the problems of small data samples and class imbalance. In this study, deep learning is implemented for automatic detection of psychiatric diseases from brain magnetic resonance imaging (MRI). Convolutional neural networks (CNN) are used for the diagnosis to categorize brain images into one of the classes: (1) normal, (2) schizophrenia, and (3) bipolar disorder. Image synthesis as data augmentation was performed with GAN. The suggested approach aims to advance the state-of-the-art in psychiatric diagnosis and promote additional investigation into the use of brain imaging in diagnostic plans. The findings show that the amount of data and subject variability significantly affect the efficacy of approaches for diagnosing psychiatric disorders. In this study, the data scarcity problem CNN training was not successfully resolved by GAN-based augmentation. It is determined that in order to develop a successful automatic psychiatric disease diagnostic system, a sufficient data sample must be collected while taking into account variables like subject variability. © 2023 IEEE.

关键词： Generative adversarial networks

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IMU-Trans: imputing missing motion capture data with unsupervised transformers

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Neural Computing and Applications 2025年第7期37卷 5699-5717页

作者： Avdan, Goksu Onal, Sinan Lu, Chao Department of Engineering Technology Missouri Western State University St. JosephMO64507 United States Department of Industrial Engineering Southern Illinois University Edwardsville EdwardsvilleIL62026 United States Department of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States

Motion capture (mocap) systems are extensively utilized in healthcare for monitoring rehabilitation programs, facilitating clinical gait assessments for early Alzheimer’s diagnosis, managing walking disorders, and developing exoskeleton suits. However, like many other healthcare technologies, mocap systems have some flaws, like missing markers and occlusions. Given mocap data’s sequential and temporal nature, understanding marker relationships and capturing global dependencies are crucial for effective human motion recovery applications. To address these challenges, we proposed an unsupervised transformers framework for human motion recovery, called IMU-Trans. We evaluated our framework’s generalizability across two clinical datasets and tested its robustness by adjusting the missing marker rates, comparing its performance against low-dimensional Kalman filtering, long short-term memory (LSTM), and gated recurrent unit (GRU) models. Our experimental results demonstrated that IMU-Trans outperforms state-of-the-art models by training in an unsupervised manner. The closest competitor, GRU, demonstrated an RMSE of 1.35 ± 0.82, 2.36 ± 1.26, 3.43 ± 1.73, and 4.39 ± 2.18 cm for 20%, 30%, 40%, and 50% missing rates, respectively. IMU-Trans outperformed GRU with an RMSE of 1.26 ± 0.60, 2.06 ± 0.88, 2.68 ± 1.04, and 3.05 ± 1.22 for the same rates. Notably, our framework performs well even with higher missing data rates, creating opportunities for advancements in data analytics and indicating a promising future for motion capture in healthcare. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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Enhanced healing of critical-sized bone defects using degradable scaffolds with tailored composition through immunomodulation and angiogenesis

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Bioactive Materials 2025年第2期44卷 371-388页

作者： Juncen Zhou Negar Akrami Hanbo Wang Liang Fang Jie Shen Cunjiang Yu Ben Zhang Donghui Zhu Department of Biomedical Engineering University of Stony Brook100 Nicolls RdStony BrookNY11794USA Department of Orthopaedic Surgery Washington UniversitySt.LouisMO63110USA Department of Electrical&Computer Engineering University of Illinois Urbana-ChampaignUrbanaIL61801USA

The impact of orthopedic scaffolds on bone defect healing,particularly the late-stage bone remodeling process,is pivotal for the therapeutic *** study applies fadditively manufactured scaffolds composed of hydroxyapatite-doped poly(lactide-co-glycolide)-b-poly(ethylene glycol)-b-poly(lactide-co-glycolide)(HAPELGA)with varying properties to treat rat calvarial defects,elucidating their significant role in bone remodeling by modulating physiological *** engineered two scaffolds with different polylactic acid(PLA)to polyglycolic acid(PGA)ratio(9/1 and 18/1)to vary in hydrophobicity,degradation rate,mechanical properties,and structural *** variations influenced physiological responses,including osteogenesis,angiogen-esis,and immune reactions,thereby guiding bone *** findings show that the HA-PELGA(18/1)scaffold,with a slower degradation rate,supported bulk bone formation due to a stable ***,the HA-PELGA(9/1)scaffold,with a faster degradation rate and more active interfaces,facilitated the formation of a thin bone layer and higher bone *** study demonstrates these degradable scaffolds help to promote bone healing and reveals how scaffold properties influence the bone remodeling process,offering a potential strategy to optimize scaffold design aiming at late-stage bone defect healing.

关键词： Additive manufacturing Polymer scaffolds Biodegradable Bone defect healing Bone remodeling

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Deep learning augmented microscopy: a faster, wider view, higher resolution autofluorescenceharmonic microscopy

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Light(Science & Applications) 2022年第5期11卷 746-747页

作者： Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Deep learning enables bypassing the tradeoffs between imaging speed,field of view,and spatial resolution in autofluorescence-harmonic microscopy.

关键词： harmonic Deep faster

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Brain Fingerprinting-Based Community Structure 22

Brain Fingerprinting-Based Community Structure

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Athamnah, Sema Aviyente, Selin Michigan State University Department of Biomedical Engineering Department of Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9798331520526

The ability to uniquely characterize individual subjects based on their functional connectome (FC), i.e., fingerprinting, is key for progress toward precision medicine. Over the past few years, a variety of methods have been proposed for fingerprinting. While early approaches focused on using either the whole brain connec-tivity or pre-defined subnetwork connectivity to discriminate between subjects, more recently different feature extraction methods such as principal component analysis (PCA) have been used to ex-tract discriminative features. However, these methods do not iden-tify functionally interpretable network components, i.e., commu-nities, that contribute most to discrimination across subjects. In this paper, we introduce a fingerprinting approach based on community structure. In particular, we introduce a signed multilayer modularity optimization approach to identify the community structure across a group of subjects from resting state fMRI FCs. The optimal community structure for identifying subjects and discrimi-nating between different tasks is learned through a community in-dicator vector which quantifies the relevance of each community for a subject. The proposed framework is tested on the Midnight Scan Club (MSC) data across different tasks. High identification rates between resting state and different tasks indicate that an indi-vidual's community indicator vector is intrinsic to that individual independent of the task. The proposed framework also provides interpretability to fingerprinting by determining the most discrimi-native brain regions between task pairs. © 2025 IEEE.

关键词： community detection fingerprinting Functional connectivity multilayer graphs signed graph

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An Enhanced YOLO Failure Detection Method 22

An Enhanced YOLO Failure Detection Method

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Senarathna, Danushka Ferdous, Rezoan Tragoudas, Spyros School of Electrical Computer and Biomedical Engineering Southern Illinois University CarbondaleIL United States

ISBN: (纸本)9798350345346

A novel method is proposed in this paper to detect failures in the YOLO object detection network. The proposed method is derived based on the features extracted by the YOLO network and uses a secondary neural network to predict misdetections. Subsequently, a novel Recursive Feature Elimination (RFE) based approach is proposed to make the secondary network more lightweight by selecting important features for a target class(es). Hence the computational cost is reduced with a minimum loss of accuracy. Experimental evaluation was done using a YOLO network trained on the COCO dataset considering four of the most frequently appeared classes in the dataset. The proposed failure detection method achieved an 89.79% accuracy when a single class was considered, and a 16% improvement was observed in the accuracy compared to an existing method. By using the proposed feature selection method, an 88.89% accuracy with a 56% reduction in the inference time was achieved. Feature selection was 62 times faster and achieved almost the same failure detection accuracy with a lesser number of features compared to the conventional RFE approach. Moreover, the proposed failure detection framework was evaluated by considering multiple classes together as well, and high accuracy was observed. © 2023 IEEE.

关键词： Deep Neural Network (DNN) Failure Detection Object Detection Recursive Feature Elimination (RFE) YOLO

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