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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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A real-time, robust and versatile visual-SLAM framework based on deep learning networks

arXiv

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arXiv 2024年

作者： Zhang, Xiao Dong, Hongbin Zhang, Haoxin Zhu, Xiaozhou Li, Shuaixin Deng, Baosong Harbin Engineering University College Of Computer Science And Technology National Engineering Laboratory for E-Government Modeling and Simulation China Intelligent Game and Decision Laboratory Chinese Academy of Military Science Beijing China Sun Yat-sen University China

In this letter, we investigate the paradigm of deep learning techniques to enhance the performance of visual-based SLAM systems, particularly in challenging environments. By leveraging deep feature extraction and matching methods, we propose a robust, versatile hybrid visual SLAM framework, Rover-SLAM, aimed at improving adaptability in adverse conditions, such as dynamic lighting conditions, areas with weak textures, and significant camera jitter. Building on excellent learning-based algorithms of recent years, we designed from scratch a novel system that uses the same feature extraction and matching approaches for all SLAM tasks. Our system supports multiple modes, including monocular, stereo, monocular-inertial, and stereo-inertial configurations, offering flexibility to address diverse real-world scenarios. Through comprehensive experiments conducted on publicly available datasets and self-collected data, we demonstrate the superior performance of our Rover-SLAM system compared to the SOTA approaches. We also conducted an in-depth analysis of the integration of visual SLAM with deep learning methods quantitatively to provide insights for future research endeavors in this domain. The experimental results showcase the system’s capability of achieving higher localization accuracy and robust tracking performance. To facilitate further research and foster community collaboration, we have made the source code of our system publicly available at https://***/zzzzxxxx111/SLslam. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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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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A Biased-Randomized Discrete-Event Heuristic for the Hybrid Flow Shop Problem with Batching and Multiple Paths

A Biased-Randomized Discrete-Event Heuristic for the Hybrid ...

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2021 Winter simulation Conference, WSC 2021

作者： Laroque, Christoph Leissau, Madlene Copado, Pedro Panadero, Javier Juan, Angel A. Schumacher, Christin Institute for Management and Information University of Applied Sciences Zwickau Zwickau08056 Germany TU Dortmund University Informatik 4-Modeling and Simulation Department of Computer Science DortmundD-44221 Germany

ISBN: (纸本)9781665433112

Based on a real-life use-case, this paper discusses a manufacturing scenario where different jobs be processed by a series of machines. Depending on its type, each job must follow a pre-defined route in the hybrid flow shop, where the aggregation of jobs in batches might be required at several points of a route. This process can be modeled as a hybrid flow shop problem with several additional but realistic restrictions. The objective is to find a good permutation of jobs (solution) that minimizes the makespan. Discrete-event simulation can be used to obtain the makespan value associated with any given permutation. However, to obtain high-quality solutions to the problem, simulation needs to be combined with an optimization component, e.g., a discrete-event heuristic. The proposed approach can find solutions that significantly outperform those provided by employing simulation only and can easily be extended to a simheuristic to account for random processing times. © 2021 IEEE.

关键词： Discrete event simulation

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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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Multi2human: Controllable Human Image Generation with Multimodal Controls

SSRN

引用

SSRN 2023年

作者： Gu, Xiaoling Xu, Shengwenzhuo Wong, Yongkang Wu, Zizhao Yu, Jun Fan, Jianping Kankanhalli, Mohan S. Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China National University of Singapore Singapore School of Digital Media Technology Hangzhou Dianzi University China

Generating high-quality and diverse human images presents a substantial difficulty within the field of computer vision, especially in developing controllable generative models that can utilize input from various modalities. Such models could enable innovative applications like digital human, fashion design, and content creation. In this study, we introduce Multi2Human, a two-stage image synthesis framework for controllable human image generation with multimodal controls. In the first stage, a novel WAvelet-vqVaE (WAVE) architecture is designed to embed human images using a learnable codebook. The WAVE model enhances the conventional Vector Quantized Variational Autoencoder (VQVAE) by integrating wavelets throughout the encoder, thereby enhancing the quality of image reconstruction and synthesis. In the second stage, a new Multimodal Conditioned Diffusion Model (MCDM) is de- signed to estimate the underlying prior distribution within the discrete latent space using a discrete diffusion process, thus allowing for human image generation condi- tioned on multimodal controls. Quantitative and qualitative analysis demonstrates that the proposed method has the ability to create high-quality, lifelike full-body human images while satisfying the specified multimodal controls. Our code is available at https://***/gxl-groups/Multi2Human. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Sequential Solution Strategies for the Cahn-Hilliard-Biot Model

Sequential Solution Strategies for the Cahn-Hilliard-Biot ...

引用

European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2023

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

ISBN: (纸本)9783031861680

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

引用

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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Wearable Sensor Gait Analysis of Fall Detection using Attention Network

Wearable Sensor Gait Analysis of Fall Detection using Attent...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Yhdego, Haben Li, Jiang Paolini, Christopher Audette, Michel Old Dominion University Computational Modeling and Simulation Engineering Norfolk United States Old Dominion University Electrical and Computer Engineering Norfolk United States San Diego State University Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9781665401265

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding-founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models. © 2021 IEEE.

关键词： Fall detection

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