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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Rafay, Abdul Maqsood, Adnan National University of Sciences & Technology Research Center for Modeling & Simulation Islamabad44000 Pakistan

ISBN: (纸本)9798331516680

The stability coefficients and derivatives are used to determine a vehicle's motion and are considered critical for the design of flight vehicles. In this research, the dynamic pitch derivatives of an axisymmetric projectile prototype are examined using Computational Fluid Dynamics (CFD). For this purpose, a methodology is established by validating two test cases and then applied to a newly designed prototype. The dynamic damping derivatives are calculated using the forced oscillation approach. The pitch damping coefficients are simulated for angles of attack from 0° to 20°. A decent agreement is observed between the CFD and experimental data. The test cases are also analyzed through DATCOM for comparative analysis. A conceptual projectile geometry is designed and evaluated at a Mach number of 1.5. A parametric analysis is also conducted for the variation of Mach number and reduced pitch rate on pitch damping coefficients for the conceptual geometry. This study establishes the use of CFD to determine fast and consistent estimates for the aerodynamic stability parameters. © 2024 IEEE.

关键词： Computational fluid dynamics

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An Intelligent Ranking Evaluation Method of simulation Models Based on Graph Neural Network 15th

An Intelligent Ranking Evaluation Method of Simulation Model...

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15th EAI International Conference on simulation Tools and Techniques, SIMUTools 2023

作者： Yang, Fan Ma, Ping Zhang, Jianchao Cheng, Huichuan Li, Wei Yang, Ming Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Complex System Modeling and Simulation Harbin150080 China Chinese Aeroengine Research Institute Beijing101304 China

ISBN: (纸本)9783031575228

To validate the alternative simulation models and select the most credible one when the models have multivariate and correlated outputs, an intelligent ranking evaluation method of simulation models based on Graph Neural Network (GNN) is proposed. The process of ranking evaluation is divided into three parts: graph structure conversion for evaluation data, feature extraction based on Graph Representation Learning (GRL) and ranking evaluation based on feature distance. A graph structure modeling method is presented to provide the pre-define graph structure for further GRL primarily. Next the interdependencies and dynamic evolutionary patterns among variables are captured by GNN so that the graph representations of evaluation data can be obtained. Then ranking evaluation is achieved by similarity measurement of the graph representations. In the end, the effectiveness of the proposed method on feature extraction of evaluation data and simulation models ranking is illustrated through an application example on a prediction model for aerodynamic parameters of a certain flight vehicle. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Feature extraction

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An Intelligent simulation Result Validation Method Based on Variational Autoencoder 23rd

An Intelligent Simulation Result Validation Method Based on ...

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23rd Asia simulation Conference, AsiaSim 2024

作者： Yang, Fan Ma, Ping Li, Wei Tao, Chao Yang, Ming Zhang, Jianchao Cheng, Huichuan Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Complex System Modeling and Simulation Harbin150080 China Chinese Aeroengine Research Institute Beijing101304 China

ISBN: (纸本)9789819772247

Validation for complex system simulation faces the challenges of a large amount of validation data, which comes in the form of extensive samples and prolonged time series. To address this issue, this paper proposes an intelligent simulation result validation method based on Variational Autoencoder (VAE), achieving model validation through intelligent feature extraction and feature consistency metrics. Initially, we present the overall validation framework, which consists of feature extraction and simulation result validation, and detail the steps of the validation process. Subsequently, the principles and procedures of the feature extraction and simulation result validation methods are thoroughly discussed. The feature extraction is achieved by learning the distribution parameters of the latent space using a VAE model. Feature differences across various aspects are measured by three distance metrics and are further converted into simulation credibility. Finally, the effectiveness and superiority of this method are demonstrated through a case study on validating a drone delivery model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital elevation model

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Flight Mechanics of a Supersonic Projectile: A Parametric Investigation

Flight Mechanics of a Supersonic Projectile: A Parametric In...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Abdul Rafay Adnan Maqsood Research Center for Modeling & Simulation National University of Sciences & Technology Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

关键词： Damping Geometry Computational fluid dynamics Projectiles Computational modeling Prototypes Aerodynamics Stability analysis Vehicle dynamics Oscillators

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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Computer modeling in Engineering & Sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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CONSTRAINED BI-LEVEL OPTIMIZATION: PROXIMAL LAGRANGIAN VALUE FUNCTION APPROACH AND HESSIAN-FREE ALGORITHM 12

CONSTRAINED BI-LEVEL OPTIMIZATION: PROXIMAL LAGRANGIAN VALUE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Yao, Wei Yu, Chengming Zeng, Shangzhi Zhang, Jin National Center for Applied Mathematics Shenzhen SUSTech China Department of Mathematics SUSTech China Department of Mathematics and Statistics UVic Canada CETC Key Laboratory of Smart City Modeling Simulation and Intelligent Technology The Smart City Research Institute of CETC China

This paper presents a new approach and algorithm for solving a class of constrained Bi-Level Optimization (BLO) problems in which the lower-level problem involves constraints coupling both upper-level and lower-level variables. Such problems have recently gained significant attention due to their broad applicability in machine learning. However, conventional gradient-based methods unavoidably rely on computationally intensive calculations related to the Hessian matrix. To address this challenge, we devise a smooth proximal Lagrangian value function to handle the constrained lower-level problem. Utilizing this construct, we introduce a single-level reformulation for constrained BLOs that transforms the original BLO problem into an equivalent optimization problem with smooth constraints. Enabled by this reformulation, we develop a Hessian-free gradient-based algorithm-termed proximal Lagrangian Value function-based Hessian-free Bilevel Algorithm (LV-HBA)-that is straightforward to implement in a single loop manner. Consequently, LV-HBA is especially well-suited for machine learning applications. Furthermore, we offer non-asymptotic convergence analysis for LV-HBA, eliminating the need for traditional strong convexity assumptions for the lower-level problem while also being capable of accommodating non-singleton scenarios. Empirical results substantiate the algorithm's superior practical performance. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Machine learning

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Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture

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Journal of Advanced research 2024年 62卷 155-163页

作者： Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Kharel, Rupak Das, Ashok Kumar Shetty, Sachin Rodrigues, Joel J.P.C. School of Psychology and Computer Science University of Central Lancashire Preston United Kingdom Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Punjab Sahiwal 57000 Pakistan Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center and Center for Cybersecurity Education and Research Old Dominion University Suffolk 23435 VA United States COPELABS Lusófona University Campo Grande 376 Lisbon 1749-024 Portugal

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. Methods: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. Results: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. Conclusion: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed. © 2024

关键词： Digital twins Industrial Internet of Things (IIoT) Key agreement Mutual authentication Physically Unclonable Functions (PUFs)

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No Time to Lose: Enabling Real-Time Fluorescence Lifetime Imaging on Resource-constrained FPGAs Through Efficient Scheduling 25

No Time to Lose: Enabling Real-Time Fluorescence Lifetime Im...

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Proceedings of the 2025 ACM/SIGDA International Symposium on Field Programmable Gate Arrays

作者： Ismail Erbas Aporva Amarnath Vikas Pandey Karthik Swaminathan Naigang Wang Xavier Intes Center for Modeling Simulation & Imaging in Medicine Rensselaer Polytechnic Institute Troy NY USA IBM T.J. Watson Research Center Yorktown Heights USA

ISBN: (纸本)9798400713965

Fluorescence lifetime imaging (FLI) is a widely used technique in the biomedical field for measuring the decay times of fluorescent molecules, providing insights into metabolic states, protein interactions, and ligand-receptor bindings [1, 5]. However, its broader application in fast biological processes, such as dynamic activity monitoring, and clinical use, such as in guided surgery, is limited by long data acquisition times and computationally demanding data processing. While deep learning has reduced post-processing times, time-resolved data acquisition remains a bottleneck for real-time applications. To address this,we propose a method to achieve near realtime FLI using an FPGA-based hardware accelerator. Specifically,we implemented a GRU-based sequence-to-sequence (Seq2Seq) model [3] on an FPGA board compatible with time-resolved cameras. The GRU model balances accurate processing with the resource constraints of FPGAs, which have limited DSP units and BRAM. The limited memory and computational resources on the FPGA require efficient scheduling of operations and memory allocation to deploy deep learning models for low-latency *** address these challenges by using STOMP, a queue-based discrete-event simulator that automates and optimizes task scheduling and memory management on hardware [4]. By integrating a GRU-based Seq2Seq model and its compressed version, called Seq2SeqLite [2], generated through knowledge distillation (KD), we were able to process multiple pixels in parallel, reducing latency compared to sequential processing. We explore various levels of parallelism to achieve an optimal balance between performance and resource utilization. Our results indicate that the proposed techniques achieved a 17.7x and 52.0x speedup over manual scheduling for the Seq2Seq model and the Seq2SeqLite model, respectively. Furthermore, the Seq2SeqLite model achieved a 98% reduction in parameters compared to the Seq2Seq model.

关键词： computer simulation

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Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging

arXiv

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arXiv 2024年

作者： Erbas, Ismail Pandey, Vikas Amarnath, Aporva Wang, Naigang Swaminathan, Karthik Radev, Stefan T. Intes, Xavier Center for Modeling Simulation Imaging in Medicine Rensselaer Polytechnic Institute TroyNY12206 United States Center for Modeling Simulation Imaging in Medicine Rensselaer Polytechnic Institute TroyNY United States IBM T.J. Watson Research Center Yorktown HeightsNY United States

Fluorescence lifetime imaging (FLI) is an important technique for studying cellular environments and molecular interactions, but its real-time application is limited by slow data acquisition, which requires capturing large time-resolved images and complex post-processing using iterative fitting algorithms. Deep learning (DL) models enable real-time inference, but can be computationally demanding due to complex architectures and large matrix operations. This makes DL models ill-suited for direct implementation on field-programmable gate array (FPGA)-based camera hardware. Model compression is thus crucial for practical deployment for real-time inference generation. In this work, we focus on compressing recurrent neural networks (RNNs), which are well-suited for FLI time-series data processing, to enable deployment on resource-constrained FPGA boards. We perform an empirical evaluation of various compression techniques, including weight reduction, knowledge distillation (KD), post-training quantization (PTQ), and quantization-aware training (QAT), to reduce model size and computational load while preserving inference accuracy. Our compressed RNN model, Seq2SeqLite, achieves a balance between computational efficiency and prediction accuracy, particularly at 8-bit precision. By applying KD, the model parameter size was reduced by 98% while retaining performance, making it suitable for concurrent real-time FLI analysis on FPGA during data capture. This work represents a big step towards integrating hardware-accelerated real-time FLI analysis for fast biological processes. Copyright © 2024, The Authors. All rights reserved.

关键词： Recurrent neural networks

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