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IEEE Transactions on Energy Markets, policy and Regulation 2024年第2期2卷 186-199页

作者： Gautam, Arnav Nock, Destenie Pandey, Amritanshu Carnegie Mellon University Department of Engineering and Public Policy PittsburghPA15213 United States University of Vermont Department of Electrical and Biomedical Engineering BurlingtonVT05405 United States

Sustained power outages are growing in scale and number primarily due to i) the increasing number and intensity of disasters and ii) decarbonization-and electrification-related grid changes. Outage mitigation technologies (e.g., backup diesel generators, and solar panels) increasingly provide vital electricity access during disasters. However, their adoption is inequitable due to individual-or community-level barriers and historic underinvestment in certain communities. We postulate that community-based Resilience Hubs (RHs), which are being increasingly deployed to provide on-site services during disasters, can be expanded to address this inequity by supplying backup power to vulnerable communities through islanded operations. To that end, we present Grid-Aware Tradeoff Analysis (GATA) framework to identify the best backup power systems for expanded RHs. To include technical, economic, and social facets in the framework, we will use three-phase power flow (TPF) and multi-criteria decision analysis (MCDA). TPF will enforce the electrical feasibility of islanded RH operation, and MCDA will quantify the economic, environmental, and equity-weighted outage mitigation performance. As a use case for GATA, we will evaluate multiple representative RHs in Richmond, California, and highlight the non-dominated systems for the electrically feasible RHs. We show the value of GATA's detailed grid simulation, its ability to quantify tradeoffs across scenarios, and its possible extensions. © 2023 IEEE.

关键词： Outages

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images 27

Classifying Bicycle Infrastructure Using On-Bike Street-Leve...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Backman, Kal Beck, Ben Kulic, Dana School of Public Health and Preventive Medicine Monash University Department of Electrical and Computer Systems Engineering Australia

ISBN: (纸本)9798331505929

While cycling offers an attractive option for sus-tainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hi-erarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis. © 2024 IEEE.

关键词： Bicycles

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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods 9

Predicting COVID-19 Transmission in Southern California with...

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9th International Conference on Big Data Analytics, ICBDA 2024

作者： Li, Han Wei, Ran Wang, Wenyu Yu, Nanpeng School of Computer Science and Engineering University of California Riverside Riverside United States School of Public Policy University of California Riverside Riverside United States University of California Riverside Department of Electrical and Computer Engineering Riverside United States

ISBN: (纸本)9798350352962

The COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region. © 2024 IEEE.

关键词： COVID-19

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Machine Learning and Deep Learning Approach for Predicting Neonatal Mortality in Indonesia 10

Machine Learning and Deep Learning Approach for Predicting N...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Hamid, Nur Rumiati, Fidya Prayogo, Rizal Dwi Nambo, Hidetaka Kanazawa University Division of Electrical Engineering and Computer Science Japan Universitas Indonesia Faculty of Public Health Department of Epidemiology Indonesia

ISBN: (纸本)9798350394924

This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and CNN. We found that low birth weight and early breastfeeding becomes the most significant features related to neonatal mortality in Indonesia. For prediction task, implementing feature importance task as feature selection can improve prediction performance and reduce algorithm complexity. LSTM and CNN achieved the best prediction model with 90.91% accuracy. © 2024 IEEE.

关键词： Forecasting

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A Comprehensive Exploration of Renewable Energy Education Landscape in Ethiopian public Universities

A Comprehensive Exploration of Renewable Energy Education La...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Temesgen, Adugnaw Lake Bekele, Getachew Broto, Vanesa Castan Mulugeta, Yacob School of Electrical and Computer Engineering Addis Ababa University Addis Ababa Ethiopia Urban Insctitute University of Sheffield Sheffield United Kingdom University College London Department of Science Technology Engineering and Public Policy London United Kingdom

ISBN: (纸本)9798350389388

The renewable energy sector has experienced a rapid expansion, driven by rising fossil fuel costs, increasing concerns about energy security, and the imperative to electrify rural areas. This growth creates a demand for skilled professionals, and higher education institutions must respond by providing comprehensive and practical renewable energy education. This study aims to analyze energy education in Ethiopian public universities, considering program offerings, content, geographical distribution, challenges, and opportunities. To achieve this, a multifaceted methodology involving systematic curriculum reviews, targeted surveys, data compilation, and interactive spatial mapping is employed. Findings indicate a diverse landscape, with both standalone programs and integrated courses available. Among the 45 public universities, only 22% of them have Institutes of Technologies (IoTs), indicating a significant technology-focused institute gap. None of these universities provide standalone energy programs at the BSc level, and less than 10% offer such programs at the MSc level. Notably, key energy courses like 'Energy Conversion and Rural Electrification' and 'Hydropower engineering' have been identified, taught in 78% of public universities, and integrated within electrical Power engineering programs at the BSc level. However, the evident lack of practical training and experiential learning in energy education has implications for the sector's development in Ethiopia. © 2024 IEEE.

关键词： Clean energy

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Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, policy and Regulation 2024年第4期2卷 519-535页

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Unel, Burcin Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States New York University School of Law Institute for Policy Integrity New YorkNY10012 United States StanfordCA94305 United States Johns Hopkins University Departments of Electrical and Computer Engineering and Civil and Systems Engineering BaltimoreMD21218 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. © 2024 IEEE.

关键词： Investments

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Market-Based Environmental Policies in the Power Sector: State of the Art and Future Research Needs

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Current Sustainable/Renewable Energy Reports 2023年第3期10卷 59-74页

作者： Siddiqui, Afzal S. Tanaka, Makoto Chen, Yihsu Department of Computer and Systems Sciences Stockholm University Kista Sweden Department of Mathematics and Systems Analysis Aalto University Espoo Finland National Graduate Institute for Policy Studies Tokyo Japan Department of Electrical and Computer Engineering and Environmental Studies Department University of California Santa CruzCA United States

Purpose of Review: So-called Green New Deals aim to decarbonise the power sector via market-based instruments. Consequently, engineering-economic models for analysing the sustainable-energy transition have proliferated. This review categorises existing approaches and identifies areas for extending the state of the art. Recent Findings: We cluster the extant literature into two groups: engineering/operational research (M1) and environmental economics (M2). While M1 focuses on the power sector’s technical and spatio-temporal aspects, M2’s emphasis is on incentives and externalities. Depending on the nature of the research question, either perspective (or both) may be suitable. Summary: Since the envisaged electrification of the wider economy implies tighter coupling between formerly distinct sectors, e.g., power and heat, both M1 and M2 will have to adapt to the new paradigm in terms of methodology and application areas. Here, eliciting coordinating mechanisms, enhancing algorithms for solving hierarchical models, soft linking bottom-up and top-down models, and crafting robust environmental policy in face of uncertainty over externalities are some of the vistas for future research. © 2023, The Author(s).

关键词： Game theory

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Enhanced Multiclass Brain Tumor Detection in MRI Using Similarity-Navigated Graph Neural Networks with Banyan Tree Growth Optimization 4

Enhanced Multiclass Brain Tumor Detection in MRI Using Simil...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu Rao, K. Singh, Pallavi Verma, Gargi Shankar Kannan, L. Mohana Kadam, Swaraj Mangalore Institute of Technology & Engineering Department of Computer Science and Engineering Karnataka Mijar India Saraswati Dental College & Hospital Department of Public Health Dentistry Uttar Pradesh Lucknow India Krishna's Vikash Institute of Technology Department of Computer Science and Engineering Chhattisgarh Raipur India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India Dr. D Y Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune India

ISBN: (纸本)9798331529635

The diagnosis of brain tumors is admittedly difficult because of their varied and complicated nature. Therefore, one has to get the right diagnosis and categorization for it to be treated well. This is a situation where identification of multiple classes such as;meningioma, pituitary, glioma and no tumor is crucial in taking the right course of action. Therefore, a new deep learning architecture is suggested in this work to increase the specificity between different types of tumors by detecting brain tumors with high accuracy in MRI scans. The proposed methodology starts with the Cumulative Curve Fitting Approximation (CCFA) preprocessing algorithm to improve the quality of MRI images. Preprocessed images are used as inputs for feature extraction based on geometric algebra transformers. These transformers include geometric relationships within the data, ensuring more distinctive selection. The categorization is further carried out with assistance of a Similarity-Navigated Graph Neural Network (SNGNN) incorporating the Banyan Tree Growth Optimizer (BTGO) model, which is constructed for efficient detection of the subtlest patterns and their connection between classes of brain tumors. It integrates very well in terms of the accuracy shown by its performance metrics. It shows effectiveness in a multiclass scenario of detecting brain tumors, which helps in the precise and early diagnosis and even tailors proper treatment plans. © 2024 IEEE.

关键词： Graph neural networks

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UAV weaknesses against deauthentication based hijacking attacks 22

UAV weaknesses against deauthentication based hijacking atta...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Krasnyanszki, Bruno Brassai, Sandor Tihamer Nemeth, Andras Óbuda University Under Graduate Student in Computer Science Engineering Budapest Hungary Sapientia Hungarian University of Transylvania Department of Electrical Engineering Corunca Romania National University of Public Service Department of Electronic Warfare Budapest Hungary

ISBN: (纸本)9798350317206

Unmanned Aerial Vehicles (UAVs) and Unmanned Aerial Systems (UAS) are more important than ever before and we're using it daily for agriculture, photo making, film industry, Search & Rescue, long range cable quality control, Geo Informatics, cartography, and state use (including border protection, disaster recovery and the military). Most of the UAV/UAS devices are using digital communication protocols nowadays, especially the IEEE 802.11 Wireless Local Area of Network for the cheaper and middle price UAV/UAS. Unfortunately, the protocols specified by the previously mentioned IEEE standard are vulnerable against some attacks. This research paper proposes a method that can interrupt the normal operation of the UAV/UAS that are using the IEEE 802.11 protocol and hijack the interrupted UAV/UAS permanently. For this method, deauthentication attack has been performed against the UAV/UASs. The attack vector is capable of denying the communication between the UAV and the control station. With sufficient prior knowledge it is possible to hijack the UAV that communicates on a 802.11 based protocol. Deauthentication based attacks are much more sophisticated than for example traditional electronic jamming, because the target can be precisely chosen and the attack will not interrupt other devices using the same communication method. The research has unfortunately some limitations in equipment, measurements conducted only in three different types of UAVs from three price categories, but in the future the authors would like to extend the range of UAVs. From this three UAVs two are hijackable by this method and the third should also be hijackable with some modifications. This research could be a base later for UAV/UAS counter measure systems for the safety of all of us. © 2024 IEEE.

关键词： Drones

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Implementation of Robots in Autism Spectrum Disorder Research: Diagnosis and Emotion Recognition and Expression 12

Implementation of Robots in Autism Spectrum Disorder Researc...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Kollias, Konstantinos-Filippos Silva, Luis Miguel Maia Marques Torres E. Sarigiannidis, Panagiotis Syriopoulou-Delli, Christine K. Fragulis, George F. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Porto Faculty of Engineering Porto Portugal University of Macedonia Department of Educational and Social Policy Thessaloniki Greece

ISBN: (纸本)9798350321074

Early Autism Spectrum Disorder (ASD) diagnosis and intervention with state-of-the-art instruments, such as Artificial Intelligence and Robots is a rather challenging and promising research area which can optimise outcomes regarding the quality of life of ASD individuals and their families. Autistic individuals tend to show, among others, deficits in emotion recognition and expression. The present review study, aiming to review the literature concerning the contribution of Robotics to ASD research, found 27 review studies conducted from 2016 to February 2023 on PubMed and Web of Science databases in which robots were either employed in ASD diagnosis, or emotion recognition and expression. Results pointed out beneficial effects of robots' employment as far as ASD diagnosis and emotion recognition and expression are concerned. Limitations and recommendations for future research are also involved, i.e., studies combining the Internet of Things (IoT) and Robotics with an aim to early ASD diagnosis, assessment, intervention, and inclusion. © 2023 IEEE.

关键词： Emotion Recognition

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