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arXiv 2024年

作者： Nam, Juno Liu, Sulin Winter, Gavin Jun, KyuJung Yang, Soojung Gómez-Bombarelli, Rafael Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Corrector to locally correct unphysical geometries, and incorporates an adaptive prior based on the Maxwell–Boltzmann distribution to account for chemical and thermal conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of lithium diffusion across 4,186 solid-state electrolyte (SSE) candidates at four temperatures. The model obtains a consistent Spearman rank correlation of 0.7–0.8 for lithium mean squared displacement (MSD) predictions on unseen compositions. Furthermore, LiFlow generalizes from short training trajectories to larger supercells and longer simulations while maintaining high accuracy. With speed-ups of up to 600,000× compared to first-principles methods, LiFlow enables scalable simulations at significantly larger length and time scales. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

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Economic Feasibility and Sensitivity Analysis on Integration of Solar PV and Electric Vehicles in Renewable Energy Community

Economic Feasibility and Sensitivity Analysis on Integration...

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Compatibility in Power Electronics (CPE)

作者： Shoaib Ahmed Amjad Ali Sikandar Abdul Qadir Antonio D’Angola Department of Information and Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Via Giovanni Paolo II 132 Fisciano SA Italy Department of Engineering University of Basilicata Via dell’Ateneo Lucano 10 Potenza PZ Italy Interdisciplinary Research Centre for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

Renewable Energy Communities (RECs) have emerged recently because of numerous advantages, as these are characterized by locally generated electricity from renewable energy sources (RESs) controlled by individuals, enterprises, and public administrations. This article focuses on the development of a mathematical model to investigate the time-dependent behavior of RECs with an overview of the technical, economic, and environmental scenario. The economic viability and overall profitability of incorporating electric vehicles (EVs) into a photovoltaic (PV)-based REC that consists of consumers and prosumers are assessed in this study. Key economic parameters, for instance Net Present Value (NPV), Levelized cost of energy (LCOE), and payback period (PP), are included in the mathematical model, and, additionally, CO 2 emission analysis has been considered. A sensitivity analysis of the REC has been carried out to focus on the impact of various parameters considered in our configuration. Moreover, the saving of CO 2 emissions and economic benefits have been evaluated. The mathematical and numerical model could represent a useful tool for policymakers, the Government, stakeholders, and participants to analyze the advantages of building a REC.

关键词： Photovoltaic systems Renewable energy sources Power engineering Sensitivity analysis Profitability Mathematical models Power electronics Numerical models Stakeholders Carbon emissions

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On the Consistency of Renewable-to-Hydrogen Pricing

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CSEE Journal of Power and Energy systems 2022年第2期8卷 392-402页

作者： Litao Zheng Jianxiao Wang Yang Yu Gengyin Li Ming Zhou Qing Xia Guizhi Xu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Global Energy Interconnection Research Institute co. Ltd.State Grid Corporation of ChinaBeijing 102211China

With the proposal of carbon neutrality goals and hydrogen energy development strategies in various countries,the development and construction of hydrogen supply chains have become important ***,existing research has paid little attention to the hydrogen market and ***,a hydrogen pricing method based on marginal pricing theory is proposed in this paper,which adapts to hydrogen systems with renewable-to-hydrogen as a major source,in the future.A hydrogen energy market is established to define the industrial chain of hydrogen and the hydrogen trading *** hydrogen market-clearing model is formulated considering a dynamic line *** to its nonconvexity,the model is equivalently converted into mixed-integer second-order cone programming,and the optimality gap is minimized by introducing a penalty *** on the clearing solution,the concept and calculation method of the locational marginal hydrogen price(LMHP)are proposed with respect to the locational marginal price(LMP)in electricity *** studies based on a modified Belgium 20-node gas network and Pennsylvania,New Jersey,and Maryland(PJM)market operation data demonstrate the consistency between LMHP and LMP.

关键词： Hydrogen market locational marginal price renewable-to-hydrogen second-order cone programming Weymouth equation

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

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Medical Technologies National Conference (TIPTEKNO)

作者： Ayse Gurluk Hafiz Muhammad Bilal Elif Hocaoglu Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Türkiye Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Türkiye Department of Electrical and Electronics Engineering Living Robotics Laboratory Istanbul Medipol University Istanbul Türkiye

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

This study presents a more accurate evaluation method for assessing ankle spasticity compared to conventional goniometric techniques by utilizing measurements obtained from Inertial Measurement Units (IMU) and surface electromyography (sEMG) devices. Integrating IMU and EMG sensors provides substantial benefits for accurately determining the range of motion (ROM) associated with spasticity during targeted movements. In this study, the proposed method provides precise measurement and an objective evaluation of onset time detection of antagonistic muscle co-contraction, and accordingly the ROM of the actuated joint. Moreover, the implementation of cost-effective wearable sensors enables the monitoring of muscle spasticity in clinical environments and can be particularly valuable in regions with limited access to experienced therapists, providing a more efficient approach to tracking spasticity levels.

关键词： Ankle Measurement units Inertial navigation Muscles Electromyography Sensors Reliability Motion control Read only memory Monitoring

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Ensemble Multifeatured Deep Learning Models and Applications: A Survey

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IEEE Access 2023年 11卷 107194-107217页

作者： Abimannan, Satheesh El-Alfy, El-Sayed M. Chang, Yue-Shan Hussain, Shahid Shukla, Saurabh Satheesh, Dhivyadharsini Mumbai410206 India King Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence Information and Computer Science Department Dhahran34464 Saudi Arabia King Fahd University of Petroleum and Minerals Interdisciplinary Research Center of Intelligent Secure Systems Information and Computer Science Department Dhahran34464 Saudi Arabia National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan School of Business MaynoothW23 F2H6 Ireland Lucknow226002 India Vellore632014 India

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models. © 2013 IEEE.

关键词： Computer vision

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Interactive Facial Expression Editing with Non-linear Blendshape Interpolation

Interactive Facial Expression Editing with Non-linear Blends...

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43rd Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2022

作者： Roh, Ji Hyun Kim, Seong Uk Jang, Hanyoung Seol, Yeongho Kim, Jongmin Department of Computer Science and Engineering Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Game AI Lab NC Korea Republic of NVIDIA Korea Republic of

ISBN: (纸本)9783038681694

The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization. © 2022 The Author(s).

关键词： Interpolation

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Learning from 2D: machine learning of 3D effective properties of heterogeneous materials based on 2D microstructure sections

arXiv

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arXiv 2023年

作者： Hu, Guangyu Latypov, Marat I. Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States

Microstructure–property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. 3D microstructure data required as input to these models are typically obtained from either 3D characterization experiments or digital reconstruction based on statistics from 2D microstructure images. In this work, we present machine learning (ML) approaches to modeling effective properties of heterogeneous materials directly from 2D microstructure sections. To this end, we consider statistical learning models based on spatial correlations and convolutional neural networks as two distinct ML strategies. In both strategies, models are trained on a dataset of synthetically generated 3D microstructures and their properties obtained from micromechanical 3D simulations. Upon training, the models predict properties from 2D microstructure sections. The advantage of the presented models is that they only need 2D sections, whose experimental acquisition is more accessible compared to 3D characterization. Furthermore, the present models do not require digital reconstruction of 3D microstructures. © 2023, CC BY.

关键词： Microstructure

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Robust Visual Servoing Algorithm-based Cucumis-melo Harvesting Robot

Robust Visual Servoing Algorithm-based Cucumis-melo Harvesti...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Woojun Park Yonghyun Park Changjo Kim Hyoung Il Son Department of Convergence Biosystems Engineering Chonnam National University Gwangju Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National University Gwangju Korea Research Center for Biological Cybernetics Chonnam National University Gwangju Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Harvesting robots are performed in unpredictable and unstructured environments. Visual servoing is being researched and developed to enhance the ability to perform tasks in real-time within such environments. It is essential to accurately estimate the pose and move to the desired location to harvest the fruit. In this study, we utilized image stabilization(IS) to obtain stable images and proposed a curvature-based modeling method for the fruit and pedicel to apply it to various fruits and vegetables. The main objective of this study is to perform detection and 6D pose estimation that can be applied to various fruits using deep learning-based fruit detection. The proposed algorithm will be validated by evaluating its performance on cucumis-melo in a laboratory environment. Through this evaluation, it has been confirmed that it can be applied to cucumis-melo, and if the proposed system is used, it can be applied to other fruits. The experimental results will serve as foundational data for assessing the possibility of applying this system in real farms in the future.

关键词： Automation Pose estimation Laboratories Feature extraction Control systems Visual servoing Real-time systems Robots

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Workforce Development and Training Needs for Behavioral Health Telehealth Use in the Post COVID-19 Era

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Journal of Technology in Behavioral Science 2024年第1期9卷 131-139页

作者： Fleddermann, Kathryn Chwastiak, Lydia Fortier, Ashley Gotham, Heather Murphy, Ann Navarro, Rachel Tapscott, Stephanie Vjorn, Olivia Molfenter, Todd Center for Health Enhancement Systems Studies Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI United States Department of Psychiatry and Behavioral Sciences University of Washington School of Medicine Seattle WA United States Western Interstate Commission for Higher Education (WICHE) Boulder CO United States Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA United States Department of Psychiatric Rehabilitation and Counseling Professions Rutgers University New Brunswick NJ United States Department of Education Health and Behavior Studies Counseling Psychology Ph.D. Program University of North Dakota Grand Forks ND United States Department of Health and Policy Management Rollins School of Public Health Emory University Atlanta GA United States

Temporary regulatory changes early in the COVID-19 pandemic facilitated telehealth use, but with an increased return to in-person care in some settings, understanding provider attitudes about the practice and benefits of telehealth may help to inform policy and practice. This mixed methods study seeks to identify areas of training needed for effective telehealth provision in this new era. An online survey was distributed by five Mental Health Technology Transfer Center (MHTTC) regional service centers, which provide workforce capacity-building training and technical assistance, and the MHTTC Network Coordinating Office’s national listserv. Three hundred and sixty-five respondents from 43 states and Puerto Rico participated. The majority of respondents were clinical providers (69.3%). Eighty-five percent of respondents indicated they provided at least one telehealth service. Most indicated telehealth has improved their organizations’ ability to meet patient needs and increased access for underserved populations but suggested significant needs for continued training on telehealth use. These needs were consistent across organization locations and provider types. Qualitative analysis of free response questions identified key areas for ongoing training, including administrative topics (e.g., billing, documentation);integration of telehealth with existing systems;telehealth use with specific populations, especially children;and methods to increase access to technology for less-connected populations. Despite many providers receiving exposure to telehealth use during COVID-19, training in telehealth administration is still needed and may better serve those in certain roles and areas. While ongoing training may effectively address many needs identified, other significant concerns facing behavioral health providers may not be easily addressed via training (e.g., increasing technology access) and may require other approaches to facilitate the continued use of telehealth. © 202

关键词： Behavioral health Digital divide Telehealth Training

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