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Conference on Infrared Imaging systems - Design, Analysis, modeling, and Testing XXXV

作者： Watson, Thomas P. Dasgupta, Apratim Foti, Daniel Jacobs, Eddie L. Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)9781510674097;9781510674080

Optical turbulence in the atmosphere causes defocus, blur, and wander of images captured over long distances, which can significantly degrade their quality. Turbulence is a manifestation of variations in the index of refraction, which are caused by local variations in air temperature, pressure, humidity, gas content, and other factors. Turbulence can be quantified by the refractive index structure function parameter C-n(2). simulation of images after propagation through an atmosphere of a specific C-n(2), along with measurement of the observed C-n(2) from images, is thus of interest for a variety of agricultural, environmental, and defense applications. We discuss the generation of simulated imagery after propagation through an atmosphere of a defined C-n(2) using various algorithms, then examine methods to determine the observed C-n(2) from the generated images. Finally, we choose and test an algorithm to generate images and another to estimate C-n(2), then compare and contrast the observed C(n)(2)to the defined C-n(2) in each case to observe how the simulation method and measurement method perform.

关键词： Cn2 optical turbulence phase screens image subtraction image analysis

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PoKER: a probability of kill estimation rate model for air-to-air missiles using machine learning on stochastic targets

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JOURNAL OF defense modeling AND simulation-applications METHODOLOGY TECHNOLOGY-JDMS 2025年

作者： Dantas, Joao P. A. Costa, Andre N. Geraldo, Diego Maximo, Marcos R. O. A. Yoneyama, Takashi Inst Adv Studies Decis Support Syst Subdiv Trevo Coronel Aviador Jose Alberto Albano do Amara BR-12228001 Sao Jose Dos Campos SP Brazil Aeronaut Inst Technol Comp Sci Div Sao Jose Dos Campos Brazil Aeronaut Inst Technol Elect Engn Div Sao Jose Dos Campos Brazil

This work introduces PoKER, a novel probabilistic model engineered to optimize missile launch effectiveness in air-to-air scenarios, specifically within Beyond visual Range (BVR) air combat. Unlike conventional Weapon Engagement Zone (WEZ) models that delineate zones based on static distances such as maximum, minimum, and no-escape ranges, PoKER applies machine learning to predict kill probabilities more accurately by integrating the stochastic behaviors of targets and missile miss distances. This model dynamically evaluates target behavior, greatly expanding the predictive capabilities of engagement analysis. By factoring in elements such as warhead lethality, target and shooter orientations, and the specific conditions of engagement, PoKER provides important insights into engagement dynamics and quantifies success probabilities. Consequently, it can potentially be an important tool for BVR air combat pilots, improving operational decision-making within this specialized combat domain.

关键词： Air combat decision support systems machine learning military simulation weapon engagement zone

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Using cognitive models to design dynamic task allocation systems

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JOURNAL OF defense modeling AND simulation-applications METHODOLOGY TECHNOLOGY-JDMS 2023年第4期20卷 467-480页

作者： Fisher, Christopher R. Frame, Mary E. Stevens, Christopher Air Force Res Lab Wright Patterson Cub Def Wright Patterson AFB OH 45377 USA Air Force Res Lab Wright Patterson Parallax Adv Res Wright Patterson AFB OH 45377 USA Air Force Res Lab Wright Patterson Wright Patterson AFB OH 45377 USA

Many operations in Intelligence, Surveillance, and Reconnaissance (ISR) involve balancing multiple simultaneous interdependent tasks and coordinating between multiple teammates. Recently, autonomous managers (AMs) were proposed as a method for optimizing performance in team-based workflows using dynamic reallocation in response to changes in workload and performance. We demonstrate how cognitive models can be used in the design and evaluation of AMs. Specifically, cognitive models can be used to inform an AM's decision policy, and to stress test an AM under a wide variety of conditions. simulation 1 tested the robustness of numerous AMs across a wide range of cognitive agents. We found that a simpler cognitive model in the AM's decision system was more robust than more complex models. In the second simulation study, we compared dynamic task reallocation and corrective feedback to improve performance of cognitive agents based on the ACT-R cognitive architecture. Our results indicate that both interventions have the potential to improve performance, and that the most robust AM from simulation 1 can improve the performance of a model with realistic learning dynamics. Our simulations demonstrate that cognitive models are useful for designing and evaluating AMs for multiple military applications, including ISR.

关键词： DoD task assignment multiagent simulation

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Multi-Agent Collaboration Environment simulation

Multi-Agent Collaboration Environment Simulation

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Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and applications 2023

作者： Haley, Joshua Tucker, Jonathan Nesper, Jonathan Daniel, Brian Fish, Trisha Elbit Systems of America: Advanced Capabilities Center 12600 Challenger Pkwy STE 155 OrlandoFL United States

ISBN: (纸本)9781510661721

Machine Learning (ML) and Artificial intelligence (AI) have increased automation potential within defense applications such as border protection, compound security, and surveillance applications. Advances in low-size weight and power (SWAP) computing platforms and unmanned aerial systems (UAS) have enabled autonomous systems to meet the critical needs of future defense systems. Recent academic advances in deep learning aided computer vision yielding impressive results on object detection and recognition, necessary capabilities to enable autonomy in defense applications. These advances, often open-sourced, enable the opportunistic integration of state-of-the-art (SOTA) algorithms. However, these systems require a large amount of object-relevant data to transfer from general academic domains to more relevant situations. Additionally, UAS systems require costly verification and validation of autonomy logic. These challenges can lead to high costs for both training data generation and costly field autonomy integration and testing activities. To address these challenges, in conjunction with partners, Elbit America has developed a multipurpose synthetic simulation environment capable of generating synthetic training data and prototyping, verifying, and validating autonomous distributed behaviors. We integrated a thermal modeling capability into Unreal Engine to create realistic training data by enabling the real-time simulation of SWIR, MWIR, and LWIR sensors. This radiometrically correct sensor model capability enables the simulation-based training data generation for our object recognition and classification pipeline, called Rapid Algorithm Development and Deployment (RADD). Several drones were instantiated using emulated flight controllers to enable end-to-end autonomy training and development before hardware availability. Herein, we describe an overview of the simulation environment and its relevance to detection, classification, and distributed autonomous decision-ma

关键词： Object recognition

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AI/ML Digital Twins of Hardware-in-the-Loop systems

AI/ML Digital Twins of Hardware-in-the-Loop Systems

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2024 NDIA Michigan Chapter Ground Vehicle systems Engineering and Technology Symposium, GVSETS 2024

作者： Colley, Wesley N. Banyai, Joel Gordy, Joshua Mills, Matthew Warren, Randall Torch Technologies Inc. HuntsvilleAL United States

Proprietary, black box, and other hard-to-model subsystems are a leading source of schedule and labor cost across simulation supported analysis and lifecycle management. Using AI/ML technologies to rapidly develop and deploy digital twins of Hardware in the Loop (HWIL) and software systems reduces the Non-Recurring Engineering (NRE) in modeling and simulation (M&S) and supports validation of existing software digital twins. This approach also allows for portability of obsolete or proprietary components into a broader range of simulations or applications without exposing critical technologies. We present results of multiple case studies applying AI to black box components of interest to the ground vehicle community. © Rights reserved by the National defense Industrial Association (NDIA) Michigan Chapter, authors and their respected organizations.

关键词： Hardware-in-the-loop simulation

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Optimizing Underwater Acoustic Survey Strategies with Autonomous Underwater Vehicles: A simulation-Based Framework 21st

Optimizing Underwater Acoustic Survey Strategies with Autono...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Lagudi, Antonio Severino, Umberto Bruno, Fabio Univ Calabria Dept Mech Energet & Management Engn I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783031809453;9783031809460

Ocean exploration and inspection missions involving underwater robotic systems have gained increasing popularity in various domains such as marine science, archaeology, and defense in recent years. However, deploying vessels and robotic solutions for such research activities entails significant costs for acquisition and maintenance. This is primarily due to the logistical challenges involved in transporting equipment between locations, which necessitates a substantial financial investment, in addition to expenses associated with crew and research personnel. Effective planning of survey campaigns is crucial to avoid costly missteps arising from poor organization. It ensures that expenditures are justified and that objectives are achieved without unnecessary setbacks or errors. In this context, the utilization of simulation methodologies adds invaluable value in mitigating costs and exploring various solutions before the mission is carried out in the real world. Their utility extends beyond the examination of specific sensors or setups;they are also a valuable tool for planning different campaigns with different mission objectives. In this paper, a simulation framework that integrates an Autonomous Underwater Vehicle with a Multibeam Echo Sounder as the perception sensor is presented. The objective is to streamline acoustic survey mission planning and generate synthetic acoustic image data incorporating the modeling of typical error sources, simplifying the evaluation of output data for real applications. This system can also be utilized as a Software-in-the-loop system for testing and verification of control systems and algorithms.

关键词： simulation AUV Underwater Robotics Acoustic Imaging Unity3D Robot Operating System

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24th International Conference on Embedded Computer systems: Architectures, modeling, and simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th International Conference on Embedded Computer systems: Architectures, modeling, and simulation, SAMOS 2024

ISBN: (纸本)9783031783760

The proceedings contain 38 papers. The special focus in this conference is on Embedded Computer systems: Architectures, modeling, and simulation. The topics include: QCEDA: Using Quantum Computers for EDA;Real-Time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for Embedded Processing;A Novel System simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;vitamin-V: Serverless Cloud Computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced Real-Time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G systems: The Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.

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24th International Conference on Embedded Computer systems: Architectures, modeling, and simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th International Conference on Embedded Computer systems: Architectures, modeling, and simulation, SAMOS 2024

ISBN: (纸本)9783031783791

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Design Of Intelligent Countermeasure System for Power System Network Security defense

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JOURNAL OF ELECTRICAL systems 2023年第4期19卷 227-240页

作者： Hang, Feilu Xie, Linjiang Zhang, Zhenhong Hu, Jian Digital Secur Ctr Informat Ctr Yunnan Power Grid Kunming 650011 Yunnan Peoples R China

In an increasingly interconnected world, the convergence of power system networks and biometric-based biomedical applications presents unique challenges for data protection and privacy. This research endeavors to conceptualize and design an intelligent countermeasure system that serves as a robust defense mechanism for enhancing security in this complex ecosystem. The proposed system incorporates biometric authentication techniques to fortify user access controls, implements advanced encryption methods for safeguarding sensitive biomedical data, and intrusion detection and prevention mechanisms to thwart cyber threats. This paper proposed an Integrated Probabilistic Regression Cryptographic Classifier (IPRCC) for data protection and privacy in biometric data for power system devices for biomedical applications. The IPRCC combines probabilistic regression techniques for data analysis with cryptographic methods to fortify the security and privacy of biometric data used within power system devices for biomedical applications. To secure biometric data, IPRCC integrates cryptographic techniques. Cryptography involves encoding information in a way that only authorized parties can decode and understand it. IPRCC incorporates a classifier as part of its security framework. The classifier is used to make decisions or classifications based on the analyzed biometric data. The IPRCC includes enhanced data protection, improved privacy, and increased security for biometric data. The Integrated Probabilistic Regression Cryptographic Classifier (IPRCC) is a sophisticated security system that combines probabilistic regression modeling and cryptographic techniques to protect biometric data used in biomedical applications, especially when integrated with power system devices. simulation results demonstrated that the proposed IPRCC model exhibits an improved attack detection rate of 99%.

关键词： Cryptographic Model Data Privacy Biomedical Application Biomedical data Power System Probabilistic Regression

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Estimation of cyber network risk using rare event simulation

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JOURNAL OF defense modeling AND simulation-applications METHODOLOGY TECHNOLOGY-JDMS 2022年第1期19卷 37-55页

作者： Krall, Alexander L. Kuhl, Michael E. Yang, Shanchieh J. Penn State Univ Leonhard Bldg310 South Barnard St University Pk PA 16802 USA Rochester Inst Technol Rochester NY 14623 USA

Inherent vulnerabilities in a cyber network's constituent machine services can be exploited by malicious agents. As a result, the machines on any network are at risk. Security specialists seek to mitigate the risk of intrusion events through network reconfiguration and defense. When dealing with rare cyber events, high-quality risk estimates using standard simulation approaches may be unattainable, or have significant attached uncertainty, even with a large computational simulation budget. To address this issue, an efficient rare event simulation modeling and analysis technique, namely, importance sampling for cyber networks, is developed. The importance sampling method parametrically amplifies certain aspects of the network in order to cause a rare event to happen more frequently. Output collected under these amplified conditions is then scaled back into the context of the original network to provide meaningful statistical inferences. The importance sampling methodology is tailored to cyber network attacks and takes the attacker's successes and failures as well as the attacker's targeting choices into account. The methodology is shown to produce estimates of higher quality than standard simulation with greater computational efficiency.

关键词： Cyber security network risk rare event simulation importance sampling

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