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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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Performance Analysis of Fixed Broadband Wireless Access in mmWave Band in 5G

arXiv

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arXiv 2023年

作者： Banerjee, Soumya Gochhayat, Sarada Prasad Shetty, Sachin Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Department of Electrical and Computer Engineering Villanova University VillanovaPA United States

An end-to-end fiber-based network has the potential to offer multi-gigabit fixed access to end-users. However, a fiber network’s biggest hurdle is delivering that fiber access to the end-user. Especially in places where fiber is non-existent, it can be time-consuming and costly to deploy, resulting in Operators experiencing a long delay in realizing a return on their investment. This work investigates transmission data from fixed broadband wireless Access in mmWave band in 5G. With the increasing interest in this domain, it is worth investigating the transmission characteristic of the data and utilizing the same to build more sophisticated capabilities. Existing datasets for mmWave band are detailed but generated from simulated environments. In this work, we introduce a dataset built from the collection of real-world transmission data from Fixed Broadband Wireless Access in mmWave Band device(RWM6050). The goal of this data is to enable self-configuration capability based on transmission characteristics. Towards this goal, we present an online machine learning-based approach that can classify transmission characteristics with real-time training. We also present two more advanced temporal models for more accurate classifications. We demonstrate that it is possible to detect the transmission angle and distance directly from the analysis of transmission data with very high accuracy. We achieved up to 99% accuracy on the combined classification task. Finally, we outline some interesting future research scopes based on the collected data. © 2023, CC BY-NC-ND.

关键词： Fibers

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A Computational Pipeline for Patient-Specific Prediction of the Post-operative Mitral Valve Functional State 1

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Functional Imaging and modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Liu, Hao Simonian, Natalie T. Pouch, Alison M. Gorman, III, Joseph H. Gorman, Robert C. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences and the Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Departments of Radiology and Bioengineering University of Pennsylvania PhiladelphiaPA19104 United States Gorman Cardiovascular Research Group Department of Surgery Smilow Center for Translational Research Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (数字)9783031353024

ISBN: (纸本)9783031353017

Mitral valve (MV) repair is safer than replacement for mitral regurgitation (MR) treatment, but long-term outcomes remain suboptimal and poorly understood. Moreover, preoperative optimization is complicated due to the heterogeneity of MR presentations and potential repair configurations. We thus developed a patient-specific MV computational pipeline to quantitatively predict the post-repair MV functional state using standard-of-care preoperative imaging data alone. First, we built a finite-element model of the full patient-specific MV apparatus by quantifying the MV chordae tendinae (MVCT) distributions from 5 CT-imaged excised human hearts and incorporating this data with patient-specific MV leaflet geometries and and MVCT origin displacements from preoperative 3D echocardiography. We then calibrated the leaflet and MVCT pre-strains by simulating preoperative MV closure in order to tune the functionally equivalent, patient-specific mechanical behavior. With this fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by modifying the annular displacement to match the applied ring size. In all patient cases, the postoperative geometries were predicted to within 1 mm of the target, and the MV leaflet strain fields demonstrated very good global and local correspondence to results from a previous heavily validated pipeline. Additionally, our model predicted increased postoperative posterior leaflet tethering in a recurrent patient, which is the likely driver of long-term MV repair failure. This pipeline allows us to predict postoperative outcomes using strictly preoperative clinical data, which lays the foundation for quantitative surgical planning, personalized patient selection, and ultimately, more durable MV repairs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Echocardiography

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Energy-Efficient Trajectory and Age of Information Optimization for Urban Air Mobility

Energy-Efficient Trajectory and Age of Information Optimizat...

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IEEE Symposium on Network Operations and Management

作者： Hyeonsu Kim Yu Min Park Pyae Sone Aung Md. Shirajum Munir Choong Seon Hong Department of Artificial Intelligence Kyung Hee University Yongin Republic of Korea Department of Computer Science and Engineering Kyung Hee University Yongin Republic of Korea Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Urban air Mobility (UAM) has been conceived as a new form of transportation. UAM ultimately aims to operate unmanned, so it needs to select its trajectory and periodically send its status to the base station (BS). As an status indicator, the age of information (AoI) signifies the freshness of the information, and it is crucial for applications like real-time control systems. In this article, we address two main challenges: optimizing the UAM’s trajectory and updating the AoI between the UAM and the BS. We formulate an algorithm to maximize the energy efficiency of each UAM’s trajectory and jointly minimize the AoI cycle. As a complicated and non-convex problem, we approach proximal policy optimization (PPO) as our solution in this paper. Experiment results show that our proposed method outperformed the direct trajectory baseline in similar energy efficiency but achieved 46% increased efficiency in average AoI.

关键词： Three-dimensional displays Trajectory planning simulation Transportation Information processing Information age Energy efficiency

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Conceptual Development of a Probabilistic Graphical Framework for Assessing Port Resilience 23rd

Conceptual Development of a Probabilistic Graphical Framewor...

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23rd International Conference on Harbor, Maritime and Multimodal Logistics Modelling and simulation, HMS 2021

作者： Smith, Katherine Diaz, Rafael Shen, Yuzhong Longo, Francesco Virginia Modeling Analysis & Simulation Center Old Dominion University 1030 University Blvd SuffolkVA23435 United States Department of Computational Modeling and Simulation Engineering Old Dominion University 1300 Engineering & Computational Sciences Building NorfolkVA23529 United States Mechanical Department University of Calabria Via Pietro Bucci Arcavacata CS Rende87036 Italy

ISBN: (纸本)9788885741591

Technological advances such as cyber physical systems and autonomous vehicles combined with increased disruptions including the Covid-19 pandemic and coastal natural disasters have heightened the importance of port risk analysis methodologies and frameworks that can accurately quantify and optimize resilience. This work presents the conceptual development of a novel combination of analysis methodologies linking a probabilistic graphic approach on a network of risk events with a functional dependency approach on a system network. Key advantages of these two methodologies are the ability to model and learn causal interactions rather than simply correlations and a high level of computational efficiency. This combination of robustness and flexibility offers the ability to quickly analyze multiple port configurations in order to invest in efforts that maximize the resilience-cost ratio. In addition, the methodology opens the door for real-time anomaly detection and causal analysis in order to enhance efforts in the protecting against attacks on infrastructure and in particular cyber physical systems. © 2021 The Authors.

关键词： Cyber Physical System

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Automatic classification of activities in classroom videos

引用

Computers and Education: Artificial Intelligence 2024年 6卷

作者： Foster, Jonathan K. Korban, Matthew Youngs, Peter Watson, Ginger S. Acton, Scott T. Department of Educational Theory and Practice University at Albany United States C.L. Brown Department of Electrical and Computer Engineering University of Virginia United States Department of Curriculum Instruction and Special Education University of Virginia United States Virginia Modeling Analysis and Simulation Center Old Dominion University United States

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning. © 2024 The Authors

关键词： Computer vision

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Sequential solution strategies for the Cahn-Hilliard-Biot model

arXiv

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arXiv 2024年

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin A. Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied sciences Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © 2024, CC BY.

关键词： Computational efficiency

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GPT-4 Generated Narratives of Life Events using a Structured Narrative Prompt: A Validation Study

arXiv

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arXiv 2024年

作者： Lynch, Christopher J. Jensen, Erik Munro, Madison H. Zamponi, Virginia Martínez, Joseph O’Brien, Kevin Feldhaus, Brandon Smith, Katherine Reinhold, Ann Marie Gore, Ross Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA United States Electrical and Computer Engineering Department Old Dominion University NorfolkVA United States College of Engineering Montana State University BozemanMT United States

Large Language Models (LLMs) play a pivotal role in generating vast arrays of narratives, facilitating a systematic exploration of their effectiveness for communicating life events in narrative form. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 narratives using OpenAI’s GPT-4. From this dataset, we manually classify 2,880 narratives and evaluate their validity in conveying birth, death, hiring, and firing events. We observe that 87.43% of the narratives sufficiently convey the intention of the structured narrative prompt. To automate the identification of valid and invalid narratives, we train and validate nine Machine Learning models on the classified datasets. Leveraging these models, we extend our analysis to predict the classifications of the remaining 21,120 narratives. The ML models all excelled at classifying valid narratives as valid, but experienced challenges at simultaneously classifying invalid narratives as invalid. Our findings advance the study of LLM capabilities, limitations, and validity and also offer practical insights for narrative generation and natural language processing applications. © 2024, CC BY.

关键词： Zero-shot learning

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A New MS engineering Program with A Base in Computer engineering

A New MS Engineering Program with A Base in Computer Enginee...

引用

2021 Winter simulation Conference, WSC 2021

作者： Leathrum, James F. Shen, Yuzhong Gonzalez, Oscar Old Dominion University Department of Computational Modeling and Simulation Engineering 1300 Engineering and Computational Sciences Building NorfolkVA23529 United States Old Dominion University Department of Electrical and Computer Engineering NorfolkVA23529 United States

ISBN: (纸本)9781665433112

The reality of the current academic climate, in particular faced with drops in enrollment over the next decade as a result from drops in birth rates, is forcing hard choices for small programs such as the modeling simulation engineering (MSE) program at Old Dominion University (ODU). The quality of the program and its benefit to its constituents do not offset the impracticality of continuing such programs. Two primary options for such programs are closure or consolidation. ODU decided on the latter course of action for MSE. Due to the computational nature of the existing program, a decision was made to place MSE as a major under the Computer engineering degree. This paper presents the justification for this decision and the resulting curriculum and the hard decisions made to allow it to fit under computer engineering. A discussion of feedback from constituents such as the industrial advisory board for the existing MSE program is included. © 2021 IEEE.

关键词： Drops

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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