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simulation Winter Conference

作者： Imran Mahmood Syed Hassan Askari Hessam S. Sarjoughian Center for Research in Modeling & Simulation School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Arizona Center for Integrative Modeling & Simulation School of Computing Information and Decision Systems Engineering Arizona State University Tempe Arizona USA

ISBN: (纸本)9781665433129

The discipline of component-based modeling and simulation offers promising gains including reductions in development cost, time, and system complexity. This paradigm promotes the use and reuse of modular components for adequate development of complex simulations. Achieving effective and meaningful model reuse through the composition of components still remains a daunting challenge. “Composability”, an integral part of this challenge, is the capability to select and assemble model components in various combinations to satisfy specific user requirements. In this paper, we propose the use of Colored Petri Nets for component-oriented model development, model composition, and the verification of composed models using state-space analysis technique. We present a case study of an elevator model as a proof of concept. Our case study explains the proposed process of developing and composing CPN-based model components and verifying the composed model using state-space analysis.

关键词： Analytical models Costs Petri nets Elevators Complexity theory Complex systems

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Chances and Challenges of Chatgpt and Similar Models for Education in M&S 23

Chances and Challenges of Chatgpt and Similar Models for Edu...

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Proceedings of the Winter simulation Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz The MITRE Corporation Modeling and Analysis Innovation Center Charlottesville USA L3Harris Corporation Mission Analysis Group Herndon USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta USA University of Central Florida School of Modeling Simulation and Training Orlando USA University of Virgina Department of Systems and Information Engineering Charlottesville USA Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn USA

ISBN: (纸本)9798350369663

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within modeling and simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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2023 Winter simulation Conference, WSC 2023

作者： Tolk, Andreas Barry, Philip Loper, Margaret L. Rabadi, Ghaith Scherer, William T. Yilmaz, Levent The Mitre Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

ISBN: (纸本)9798350369663

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Securing the light escaping in a Li-Fi network environment

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Procedia computer Science 2022年 201卷 684-689页

作者： D.D. Diambeki R.E. Mandiya K. Kyamakya S.K. Kasereka University of Kinshasa Mathematics and Computer Science Department Kinshasa DR Congo Artificial intelligence Big data and modeLing simulation research center (ABIL) Kinshasa Democratic Republic of the Congo Alpen-Adria-Universitaet Klagenfurt Institute of Smart Systems Technologies Department of Mathematical Sciences Klagenfurt Austria

Nowadays, everyone knows Wi-Fi. The technology that ensures connection of computer equipment. It is now positioned as the easiest way to interconnect multiple end users because It allows user mobility in workspaces, avoids cable clutter within the network coverage, such as offices, homes and so on. Despite these advantages, several scientific studies have shown that this technology can raise security and safety issues and is dangerous to human health, and therefore not suitable for hospital networks. In an attempt to overcome these difficulties, Li-Fi technology based on visible light is beginning to interest researchers. This technology is considered more secure than Wi-Fi because the light can be contained in a physical space and waves can’t pass through walls and other objects. Unfortunately, when the physical environment contains spaces that can escape light to the outdoors, this can be a security risk for Li-Fi because it uses light sources to transmit data. That why, in this paper, the main differences between Wi-Fi and Li-Fi technologies are explained, and a Li-Fi architecture provided with an authentification captive portal and a data transfer security mechanism between end users based on the AES algorithm is proposed. The developed prototype and the architecture proposed guarantee the security of the Li-Fi network even if the physical environment is provided with multiple light escape spaces.

关键词： Li-Fi (Light-Fidelity) VLC (Visible Light Communication) LED (Light Emitting Diode) Photodetector LDR (Light Dependent Resistor) Arduino AES algorithm Captive portal

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network

A Novel Smartphone-Based Human Activity Recognition Approach...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Dipanwita Thakur Sandip Roy Suparna Biswas Edmond S. L. Ho Samiran Chattopadhyay Sachin Shetty Department of Computer Science Banasthali Vidyapith Jaipur Rajasthan India Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Kolkata India School of Computing Science University of Glasgow Scotland United Kingdom Department of Information Technology Jadavpur University Kolkata India

In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, Autoencoder and LSTM architectures for efficient smartphone-based HAR operation. There is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. The simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.

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

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

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

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.

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Battery Management System for On-Board Data-Driven State of Health Estimation for Aviation and Space Applications

Battery Management System for On-Board Data-Driven State of ...

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IEEE Space Computing Conference (SCC)

作者： Steffen Bockrath Johannes Wachtler Martin Wenger Radu Schwarz Marco Pruckner Vincent R. H. Lorentz Fraunhofer IISB Institute for Integrated Systems and Device Technology Schottkystraße 10 Erlangen Germany Modeling and Simulation Institute of Computer Science University of Würzburg Am Hubland Würzburg Germany Chair of Electronics for Electrical Energy Storage Bavarian Center for Battery Technology (BayBatt) University of Bayreuth Weiherstrasse 26 Bayreuth Germany

To ensure a safe and economically valuable operation of a battery system over the whole lifetime, a battery management system is used for measuring and monitoring battery parameters and controlling the battery system. Since the battery performance decreases over its lifetime, a precise on-board aging estimation is needed to identify significant capacity degradation endangering the functionality and safety of a battery system. Especially for aviation and space applications, this can result in catastrophic scenarios. Therefore, in this work, a generic battery management system approach is presented considering aerospace application requirements. The modular hardware and software architecture and its components are described. Moreover, it is shown that the developed battery management system supports the execution of data-driven state of health estimation algorithms. For this purpose, aging estimation models are developed that only receive eight high-level parameters of partial charging profiles as input without executing further feature extraction steps and can thus be easily provided by a battery management system. Three different neural network architectures are implemented and evaluated: a fully connected neural network, a 1D convolutional neural network and a long short-term memory network. It is shown that all three aging models provide a precise state of health estimation by only using the obtained high-level parameters. The achieved fully connected neural network provides the best tradeoff between required memory resources and accuracy with an overall mean absolute percentage error of 0.41%.

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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IEEE International Conference on Communications in China (ICCC)

作者： Mengchu Xu Yan Zeng Meiting Xue Ji-Lin Zhang Jian Wan Mingyao Zhou Yilin Wen Yukun Shi School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms.

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Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

TechRxiv

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TechRxiv 2023年

作者： Adhikary, Apurba Munir, Md. Shirajum Raha, Avi Deb Qiao, Yu Han, Zhu Hong, Choong Seon Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Virginia Modeling Analysis and Simulation Center Department of Electrical and Computer Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Artificial Intelligence School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensing-communication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communicatio

关键词： Beamforming

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