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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Kathleen DeMichele Masoumeh Heidari Kapourchali Christine Waigl Claire A. Richards Micah Hahn Long Zhao Payman Dehghanian Mohammad Heidari Kapourchali Department of Computer Science and Engineering University of Alaska Anchorage Anchorage AK International Arctic Research Center University of Alaska Fairbanks Fairbanks AK Department of Nursing and Systems Science Washington State University Spokane WA Institute for Circumpolar Health Studies University of Alaska Anchorage Anchorage AK Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid City SD Department of Electrical and Computer Engineering George Washington University Washington DC Department of Electrical Engineering University of Alaska Anchorage Anchorage AK

ISBN: (数字)9798331508081

ISBN: (纸本)9798331508098

Accurate prediction of wildfire risk is crucial for power grid decision makers, allowing them to identify areas of high risk and optimize resource allocation through strategic planning. This is particularly critical in regions like Alaska, where the risk of wildfires poses a significant threat to remote and difficult-to-access power infrastructures. In this study, we aggregate multiple sources of regional climate, topographic, lightning, and fire data to analyze the risk to the electricity grid posed by lightning-caused wildfires in Alaska. A focus area is chosen based on notable changes in lightning activity and the occurrences of lightning-induced fires, along with its proximity to densely populated areas and electric utility infrastructure. To identify key factors, we perform a significance analysis on widely recognized contributors to lightning-ignited fires and subsequently eliminate redundant features. The risk of fire occurrence (target variable) is calculated as a weighted average of two factors: the frequency of fires in the region and the distance to the nearest fire. Machine learning models, including logistic regression and multilayer perceptron, are applied. Ultimately, a decision tree regressor is used to enhance the interpretability of the results in predicting fire occurrence risk. Assigning risk values to the power grid in the designated focus area offers electric utilities in wildfire-prone regions valuable insights, enabling them to improve wildfire planning and mitigation strategies.

关键词： Wildfires Prevention and mitigation Merging Lightning Strategic planning Multilayer perceptrons Power grids Power industry Resource management Regression tree analysis

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Dynamic Graph Neural Networks for Joint Terahertz based Sensing and Communication Optimization in Vehicular Networks

Dynamic Graph Neural Networks for Joint Terahertz based Sens...

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IEEE Conference on Wireless Communications and Networking

作者： Xuefei Li Mingzhe Chen Ye Hu Zhilong Zhang Danpu Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijina China Department-of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Industrial and System Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based scheme can improve the number of successfully served vehicles by up to 17% compared to a GNN based algorithm with a fixed neural network model.

关键词： Network topology Simulation Heuristic algorithms Graph neural networks Sensors Data mining Vehicle dynamics

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FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning

arXiv

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arXiv 2023年

作者： Lee, Gihun Jeong, Minchan Kim, Sangmook Oh, Jaehoon Yun, Se-Young Graduate School of AI KAIST Korea Republic of Samsung Advanced Institute of Technology Korea Republic of Department of Electrical and Computer Engineering UBC Canada

Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge, it can also hinder local learning by interfering with local objectives. To address this problem, we propose a novel method, Federated Stabilized Orthogonal Learning (FedSOL), which adopts an orthogonal learning strategy to balance the two conflicting objectives. FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically, FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios. © 2023, CC BY.

关键词： Data privacy

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Integrated Sensing and Communication for Edge Inference with End-to-End Multi-View Fusion

arXiv

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arXiv 2024年

作者： Jin, Xibin Li, Guoliang Wang, Shuai Wen, Miaowen Xu, Chengzhong Vincent Poor, H. School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer and Information Science University of Macau China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Electrical and Computer Engineering Princeton University Princeton United States

Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals. However, this paradigm needs to minimize the inference error and latency under ISAC co-functionality interference, for which the existing ISAC or edge resource allocation algorithms become inefficient, as they ignore the inter-dependency between low-level ISAC designs and high-level inference services. This letter proposes an inference-oriented ISAC (IO-ISAC) scheme, which minimizes upper bounds on end-to-end inference error and latency using multi-objective optimization. The key to our approach is to derive a multi-view inference model that accounts for both the number of observations and the angles of observations, by integrating a half-voting fusion rule and an angle-aware sensing model. Simulation results show that the proposed IO-ISAC outperforms other benchmarks in terms of both accuracy and latency. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio

arXiv

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arXiv 2023年

作者： Li, Jialu Hasegawa-Johnson, Mark McElwain, Nancy L. Department of Electrical and Computer Engineering University of Illinois United States Beckman Institute for Advanced Science and Technology University of Illinois United States Department of Human Development and Family Studies University of Illinois United States

To perform automatic family audio analysis, past studies have collected recordings using phone, video, or audio-only recording devices like LENA, investigated supervised learning methods, and used or fine-tuned general-purpose embeddings learned from large pretrained models. In this study, we advance the audio component of a new infant wearable multi-modal device called LittleBeats (LB) by learning family audio representation via wav2vec 2.0 (W2V2) pretraining. We show given a limited number of labeled LB home recordings, W2V2 pretrained using 1k-hour of unlabeled home recordings outperforms oracle W2V2 pretrained on 960-hour unlabeled LibriSpeech in terms of parent/infant speaker diarization (SD) and vocalization classifications (VC) at home. Extra relevant external unlabeled and labeled data further benefit W2V2 pretraining and fine-tuning. With SpecAug and environmental speech corruptions, we obtain 12% relative gain on SD and moderate boost on VC. Code and model weights are available. © 2023, CC BY.

关键词： Learning systems

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Exploring the development of Machine Learning Innovation Technology for Data Mining in Smart Healthcare

Exploring the development of Machine Learning Innovation Tec...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Rahul Joshi Krishna Pandey Suman Kumari Shashi Kant Gupta Mitali Mohanty Ayodeji Olalekan Salau Department of Journalism & Mass Communication SMEH Manav Rachna International Institute of Research & Studies Haryana India Computer Science and Engineering Eudoxia Research University New Castle Delaware USA Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

In this research, machine learning (ML) modernization technology is developed for Data Mining (DM) in smart healthcare. Internet-of-things (IoT) and ML are used in contemporary technologies to support DM approaches for predicting and interpreting data from different sources. The collection of data and its application domain have an impact on these techniques’ effectiveness. The objective of this research is to examine the ML approach functions in smart healthcare and to analyze the DM technique for predicting breast cancer for smart healthcare. Initially, the datasets for breast cancer were gathered from public sources. The preprocessing of raw data samples is carried out using the min-max normalization technique. Then, follow the feature extraction stage with independent component analysis (ICA). A novel augmented beetle swarm-intelligent-driven quadratic support vector machine (ABS-QSVM) strategy is proposed to examine breast cancer. The Python platform is applied to implement the proposed method, and various metrics are analyzed. The experimental outcomes demonstrated that the ABS-QSVM approach achieved greater accuracy in healthcare examinations than other existing approaches.

关键词： Support vector machines Technological innovation Accuracy Sensitivity Medical services Machine learning Sensitivity and specificity Feature extraction Breast cancer Data mining

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Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

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Plant Phenomics 2024年第1期6卷 79-93页

作者： Faina Khoroshevsky Kaining Zhou Sharon Chemweno Yael Edan Aharon Bar-Hillel Ofer Hadar Boris Rewald Pavel Baykalov Jhonathan EEphrath Naftali Lazarovitch Department of Industrial Engineering and Management Ben-Gurion University of the NegevBeer ShevaIsrael The Jacob Blaustein Center for Scientific Cooperation The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael The Albert Katz International School for Desert Studies The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael Department of Communication Systems Engineering School of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer ShevaIsrael Institute of Forest Ecology Department of Forest and Soil SciencesUniversity of Natural Resources and Life SciencesVienna(BOKU)ViennaAustria Faculty of Forestry and Wood Technology Mendel University in BrnoBrnoCzech Republic Vienna Scientific Instruments GmbH AllandAustria French Associates Institute for Agriculture and Biotechnology of Drylands The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael

Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental *** conventional manual methods used to analyze MR images are time-consuming,limiting their *** study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring *** data were derived from manual annotations in Rootfly,commonly used software for MR image *** compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the ***,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR *** models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic *** datasets are made publicly available as part of this *** coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and *** analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning *** approaches can support precision agriculture by providing real-time root growth information.

关键词： Image Root image

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Near-infrared electroluminescence of AlGaN capped InGaN quantum dots formed by controlled growth on photoelectrochemical etched quantum dot templates

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Photonics Research 2022年第1期10卷 33-40页

作者： XIONGLIANG WEI SYED AHMED AL MUYEED HAOTIAN XUE ELIA PALMESE RENBO SONG NELSON TANSU JONATHAN J.WIERER,JR. Center for Photonics and Nanoelectronics Department of Electrical and Computer EngineeringLehigh UniversityBethlehemPennsylvania 18015USA School of Electrical and Electronic Engineering The University of AdelaideAdelaideSA 5005Australia Institute for Photonics and Advanced Sensing The University of AdelaideAdelaideSA 5005Australia

Near-infrared electroluminescence of InGaN quantum dots(QDs)formed by controlled growth on photoelectrochemical(PEC)etched QD templates is *** QD template consists of PEC InGaN QDs with high density and controlled sizes,an AlGaN capping layer to protect the QDs,and a GaN barrier layer to planarize the *** transmission electron microscopy(STEM)of Stranski-Krastanov(SK)growth on the QD template shows high-In-content InGaN QDs that align vertically to the PEC QDs due to localized strain.A highAl-content A1_(0.9)Ga_(0.1)N capping layer prevents the collapse of the SK QDs due to intermixing or decomposition during higher temperature GaN growth as verified by *** of low-temperature(830℃)p-type layers is used to complete the p-n junction and further ensure QD ***,electroluminescence shows a significant wavelength shift(800 nm to 500 nm),caused by the SK QDs’tall height,high In content,and strong polarization-induced electric fields.

关键词： AlGaN polarization template

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