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2024 Frontiers in Optics, FiO 2024

作者： Foteinopoulou, Stavroula Tsitsas, Nikolaos L. Department of Electrical and Computer Engineering University of New Mexico NM87131-0001 United States School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

We present a GSS4T1-based metagrating designed to exhibit a homogeneous-optical-medium response, when GSS4T1 amorphous, or a judicious Fano response, when GSS4T1 crystalline, which enables a negative beam steering tha... 详细信息

关键词： Photonics

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Alzheimer’s Disease and Frontotemporal Dementia: Differential Diagnosis Using Electroencephalogram Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Rostamikia, Mehran Sarbaz, Yashar Farzamnia, Ali Biomedical System Modeling Lab Biomedical Engineering Department Electrical and Computer Engineering Faculty University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Dementia is a neurological disorder that affects a person’s cognitive and social skills, leading to a decline in their overall mental functioning. Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) are two common types of dementia. Frontotemporal dementia mostly affects the frontal and temporal lobes of the brain. These areas are responsible for executive functions, decision-making, language, behavior regulation, and personality traits. Alzheimer’s disease primarily damages the cerebral cortex, which is responsible for higher cognitive functions such as memory, language, and perception. The electroencephalogram (EEG) signal has many advantages in diagnosing these disorders, including low cost and high temporal resolution. This study compares AD and FTD patients with healthy subjects by extracting specific features from EEG signals. Two machine learning algorithms were used for the separation, Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs), and 10-Fold Cross-Validation was applied to validate the performance of this method and an accuracy of 91.2% (sd = 7.8) was achieved using the SVM classifier for diagnosing the disease and 71.1% (sd = 8.6) for classifying AD and FTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement 22

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Glucina, Matko Zigulic, Nikola Frank, Domagoj Lorencin, Ivan Matika, Dario Istrian University of Applied Sciences Department of Engineering Pula Croatia University of Rijeka Faculty of Engineering Rijeka Croatia University North Koprivnica Department of Computer Science and Informatics Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical Engineering Varazdin Croatia

ISBN: (纸本)9798350385601

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996. © 2024 IEEE.

关键词： Neural networks

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A hybrid deep learning approach for phenotype prediction from clinical notes

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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Composite Power System Reliability Assessment Considering Uncertainty of Electric Vehicle Charging and PV Power Generation

Composite Power System Reliability Assessment Considering Un...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

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

ISBN: (纸本)9798350320169

Modern power systems are facing several challenges with the increasing penetration of inverter-based resources (IBRs) and electric vehicles (EVs). The problem arises due to inter-mittency and uncertainties associated with IBR-based resources and electric vehicles. It is indispensable from the perspective of power system planning and operation to consider these factors in reliability assessment. The reliability assessment of modern power systems that are heavily penetrated with renewable generators should consider their uncertainties to quantify the reliability index and include EVs charging load in their framework as well. Therefore, this paper proposes an approach for composite power system reliability assessment combined with the probabilistic prediction interval of PV power along with the integration of electric vehicles. An electric vehicle load model developed considering the different locations of charging stations, types of EV, and drivers' behavior is integrated. Finally, a sequential Monte Carlo simulation is used to determine the range of the reliability index after an interval forecast of the PV and EV load model is developed. The impact of different levels of EV penetration on the reliability of power system integrated with PV is also investigated. The significance of this work is to provide an accurate reliability assessment framework that takes PV uncertainties and electric vehicles into consideration. © 2023 IEEE.

关键词： Forecasting

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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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Bioactive superparamagnetic iron oxide-gold nanoparticles regulated by a dynamic magnetic field induce neuronal Ca^(2+)influx and differentiation

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Bioactive Materials 2023年第8期26卷 478-489页

作者： Elias Georgas Muzhaozi Yuan Jingfan Chen Ya Wang Yi-Xian Qin Department of Biomedical Engineering The State University of New York at Stony BrookStony BrookNYUnited States J.Mike Walker’66 Department of Mechanical Engineering Texas A&M UniversityCollege StationTXUnited States Department of Biomedical Engineering Texas A&M UniversityCollege StationTXUnited States Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUnited States

Treating neurodegenerative diseases,e.g.,Alzheimer’s Disease,remains a significant challenge due to the limited neuroregeneration rate in the *** objective of this study is to evaluate the hypothesis that external magnetic field(MF)stimulation of nerve growth factor functionalized superparamagnetic iron oxide-gold(NGF-SPIO-Au)nanoparticles(NPs)can induce Ca^(2+)influx,membrane depolarization,and enhance neuron differentiation with dynamic MF(DMF)outperforming static MF(SMF)*** showed the that total intracellular Ca^(2+)influx of PC-12 cells was improved by 300%and 535%by the stimulation of DMF(1 Hz,0.5 T,30min)with NGF-SPIO-Au NPs compared to DMF alone and SMF with NGF-SPIO-Au NPs,respectively,which was attributed to successive membrane *** uptake performed with the application of sodium azide proved that DMF enhanced cellular uptake of NGF-SPIO-Au NPs via *** addition,DMF upregulated both the neural differentiation marker(β3-tubulin)and the cell adhesive molecule(integrin-β1)with the existence of NGF-SPIO-Au NPs,while SMF did not show these *** results imply that noninvasive DMF-stimulated NPs can regulate intracellular Ca^(2+)influx and enhance neuron differentiation and neuroregeneration rate.

关键词： Calcium influx Membrane potential Dynamic magnetic fields Neuroregeneration SPIO-Au Core-shell nanoparticles

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Grid-Aware Waveform Analytics for Event Classification in Distribution Grids

Grid-Aware Waveform Analytics for Event Classification in Di...

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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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