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A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Intelligent Automation & Soft Computing 2023年第2期35卷 1721-1736页

作者： Ali Alqahtani Shumaila Akram Muhammad Ramzan Fouzia Nawaz Hikmat Ullah Khan Essa Alhashlan Samar MAlqhtani Areeba Waris Zain Ali College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Department of Medical Imaging King Khaled HospitalNajran61441Saudi Arabia Electrical&Electronics Engineering Department Universiti TeknologiPetronas31750Malaysia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 *** rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its *** is a signiﬁ-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most *** overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of ***-ever,this method burdens the radiologist to examine each ***,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of *** proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 *** proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays *** dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical *** results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

关键词： COVID-19 transfer learning deep learning artiﬁcial intelligence chest X-rays machine learning

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In-situ fabrication of vertical heterogeneous nickel diselenide-molybdenum diselenide architectures as bifunctional overall water-splitting electrocatalyst

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Journal of Materials Science & Technology 2024年第10期177卷 256-263页

作者： He Zhou Jingrui Hou Limin Zhang Dezhi Kong Hui Wang Longhui Zeng Zhiwei Ren Tingting Xu Ye Wang Key Laboratory of Material Physics Ministry of EducationSchool of Physics and MicroelectronicsZhengzhou UniversityZhengzhou 450001China Department of Electrical and Computer Engineering University of California at San DiegoSan DiegoCA 92093USA Department of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHung HomKowloon 999077Hong KongChina

Despite the rapid advances in electrocatalysts based on two-dimensional(2D)transition metal dichalco-genides(TMDs)materials,they are subject to serious aggregation,poor conductivity and the presence of inactive basal ***,we have successfully demonstrated the in-situ construction of NiSe_(2)-MoSe_(2) heterostructure arrays on carbon cloth(NiSe_(2)-MoSe_(2)/CC)by a facile two-step hydrothermal *** presence of the synergistic effect in the heterostructures effectively optimizes the poor conductivity and hydrophilicity,and thus enables fast electron transfer,leading to enhanced electrochemical ***-thermore,density functional theory calculations reveal that the electrons redistribution at the heterojunc-tion interface and the reduced Gibbs free energy of hydrogen adsorption for hydrogen evolution reaction(HER)/the Gibbs free energy change value of rate-determining step for oxygen evolution reaction(OER),thus enhancing the HER/OER catalytic ***,the device displays a good performance with a low overpotential of 98 and 310 mV for HER and OER,respectively,and a low cell voltage of 1.59 V for its corresponding electrolyzer(10 mA cm^(-2)).This work presents the high-performance water splitting of bifunctional electrocatalysts based on 2D TMDs materials and offers a novel design concept of interface engineering.

关键词： Bifunctional electrocatalyst Heterostructures Hydrogen/oxygen evolution reaction Water splitting Synergistic effect

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Relative Permittivity of Natural Ester Oil-Based Nanofluids with Iron Oxide Nanoparticles 58

Relative Permittivity of Natural Ester Oil-Based Nanofluids ...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Tsovilis, Thomas E. Staikos, Evangelos T. Hadjicostas, Alexandros Y. Datsios, Zacharias G. Litsardakis, George Pyrgioti, Eleftheria C. Bakandritsos, Aristeidis Peppas, George D. School of Electrical & Computer Engineering Aristotle University of Thessaloniki High Voltage Laboratory Greece School of Electrical & Computer Engineering Aristotle University of Thessaloniki Laboratory of Materials for Electrotechnics Greece University of Patras High Voltage Laboratory Electrical & Computer Engineering Department Greece Palacký University Olomouc Czech Republic Raycap Sa Industrial Area of Drama Research & Development Department Drama66100 Greece

ISBN: (纸本)9798350316834

The real part of the relative permittivity and the electrical conductivity of a natural ester oil, that serves as a liquid matrix for nanofluids, are measured for a wide frequency and electric field range. The focus of this work is the exploration of the modification of these basic electrical parameters of the liquid matrix after the integration of iron oxide nanoparticles. A series of experiments with the aid of an impedance analyzer, an LCR meter, and a variable-frequency power supply employing two different liquid test fixtures reveal an increase of both real and imaginary parts of relative permittivity with increasing nanoparticles concentration up to 0.5% w/w. © 2023 IEEE.

关键词： Iron oxides

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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition 46

BioPoint: Enhancing Human-Computer Interaction through Singl...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Isabel, Xavier Campbell, Evan Olivier, Loïc Gagné, Gabriel Scheme, Erik Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laval University Dept. of Electrical and Computer Engineering QC Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B 5A3 Canada University of Oslo Dept. of Technology Systems Oslo Norway

ISBN: (纸本)9798350371499

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero."Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process's appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG's potential for human-computer interaction. © 2024 IEEE.

关键词： Electromyography

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Resilience of gas interchangeability in hydrogen-blended integrated electricity and gas systems:A transient approach with dynamic gas composition tracking

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iEnergy 2023年第2期2卷 143-154页

作者： Sheng Wang Hongxun Hui Pierluigi Siano State Key Laboratory of Internet of Things for Smart City University of MacaoTaipaMacao SARChina Department of Electrical and Computer Engineering University of MacaoTaipaMacao SARChina Department of Management&Innovation Systems University of SalernoFisciano 84084Italy Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburg 2006South Africa

Green hydrogen can be produced by consuming surplus renewable *** can be injected into the natural gas networks,accelerating the decarbonization of energy ***,with the fluctuation of renewable energies,the gas composition in the gas network may change dramatically as the hydrogen injection *** gas interchangeability may be adversely *** investigate the ability to defend the fluctuated hydrogen injection,this paper proposes a gas interchangeability resilience evaluation method for hydrogen-blended integrated electricity and gas systems(H-IEGS).First,gas interchangeability resilience is defined by proposing several novel ***,A two-stage gas interchangeability management scheme is proposed to accommodate the hydrogen *** steady-state optimal electricity and hydrogen-gas energy flow technique is performed first to obtain the desired operating state of the ***,the dynamic gas composition tracking is implemented to calculate the real-time traveling of hydrogen contents in the gas network,and evaluate the time-varying gas interchangeability ***,to improve the computation efficiency,a self-adaptive linearization technique is proposed and embedded in the solution process of discretized partial derivative ***,an IEEE 24 bus reliability test system and Belgium natural gas system are used to validate the proposed method.

关键词： Integrated electricity and gas systems gas composition hydrogen resilience renewable energy

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Preventive Power Outage Estimation Based on A Novel Scenario Clustering Strategy

Preventive Power Outage Estimation Based on A Novel Scenario...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Wang, Xiaofei Liu, Weijia Ding, Fei Yao, Yiyun Zhao, Junbo Power System Engineering Center National Renewable Energy Laboratory Golden United States Department of Electrical and Computer Engineering University of Connecticut Storrs United States

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy. © 2024 IEEE.

关键词： Mobile energy resources (MERs) power system resilience preventive outage evaluation scenario clustering

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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An IDM-Based Two-Stage Robust Balance Analysis Model for High-proportion Renewable Energy Integration through HVDC System 8

An IDM-Based Two-Stage Robust Balance Analysis Model for Hig...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Qi, Jie Guo, Jincheng Zhang, Yaodan Xi'An Jiaotong University School of Electrical Engineering Xi'an China Case Western Reserve University Department of Electrical Computer and Systems Engineering Cleveland United States

ISBN: (纸本)9798350328813

Under the circumstance of the new-type power system, uncertainties on both sides of supply and demand lead to prominent characteristics of supply-demand probabilistic matching and multi-regional complementarity, which greatly increased the balance risk during system operation. In this regard, this paper proposes a short-term electric power balance analysis model for a renewable energy power base transmitted through a high-voltage direct current (HVDC) system. First, a detailed cascade hydropower system (CHS) and HVDC model are constructed , and different linearization techniques are applied to linearize different nonlinear constraints, reducing computational complexity. Second, to effectively capture the uncertainty of renewable energy, an Imprecise Dirichlet model (IDM) based method for constructing uncertainty sets is designed, greatly reducing the conservation of the robust optimization. Finally, a two-stage robust optimization model with mixed-integer recourse is established to analyze the potentially unbalanced state by minimizing the operational and curtailment costs. The proposed two-stage robust optimization model is a solved column-and-constraint generation (C&CG) algorithm by using customized nested. The result of the numerical verifies the effectiveness of the proposed model and algorithm. © 2023 IEEE.

关键词： Cascade hydropower Customized nested C&CG HVDC Imprecise Dirichlet model Two-stage robust optimization

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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

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Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

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