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Browse AllThis SAE Aerospace Standard (AS) establishes guidelines for enhancement to IEEE 1394-2008 Beta (formerly IEEE 1394b) PHYsical (PHY) layer. It encompasses enhancements to the IEEE 1394-2008 Beta PHY to reduce port connection times and increase port connection reliability. Therefore, this document contains extensions/restrictions to “off-the-shelf” IEEE 1394 standards and assumes that the reader already has a working knowledge of IEEE 1394. The enhancements covered in this document include: Detect loss of descrambler synchronization Fast-ReTrain (FRT) Fast Power-on Re-connect (FPR) Fast Connection Tone Debounce (FTD) Programmable invalidCount Bus Reset Cause This document does not identify specific environmental requirements (electromagnetic compatibility, temperature, vibration, etc.); such requirements will be vehicle-specific and even LRU-specific. One should refer to the appropriate sections of MIL-STD-461E for their particular LRU and utilize handbooks such as MIL-HDBK-454A and MIL-HDBK-5400 for guidance. This document is referred to as a “slash sheet” and accompanies the AS5643B base standard.
Military ground vehicles are transitioning from manually operated platforms to highly automated systems, which fundamentally alters the cognitive demands placed on crew members. Rather than reducing workload, automation frequently redistributes it, introducing challenges associated with supervisory monitoring, autonomy mode calibration, and situational awareness. Augmented cognition (AugCog) offers a closed-loop framework in which real-time physiological and behavioral measures of operator cognitive state can be used to dynamically adapt system interfaces and autonomy levels. This paper reviews six nonintrusive sensor modalities suitable for military ground vehicle crew stations: remote photoplethysmography (rPPG), facial thermography, eye tracking and pupillometry, dry-electrode electroencephalography (EEG), Facial Action Coding System (FACS) action unit (AU) analysis, and elastic electrodermal activity (EDA) sensors. A conceptual multimodal crew station integration framework is proposed, and future research directions are outlined.
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming Computer-Aided Engineering (CAE) workflows by enabling faster design iterations and reducing computational costs. This paper presents the application of Ansys SimAI and Ansys GeomAI in modelling an automotive side impact scenario using high-fidelity data from LS-DYNA simulations. Two AI models are trained on datasets with systematically varied parameters: one encompassing pole impact position and door beam configurations, and another focusing on rocker panel reinforcements. Both models exhibit strong predictive performance, reliably capturing deformation patterns and force-time histories for previously unseen configurations. The datasets are subsequently merged to train a comprehensive surrogate model capable of simultaneously representing variations in pole position, door beam geometry, and rocker reinforcement design, demonstrating robust generalization across a multidimensional design space. To address the emerging bottleneck of geometry creation, GeomAI’s geometry exploration functionality is employed to generate new rocker reinforcement geometries from existing ones, which are then rapidly validated using the pre-trained surrogate model. The results confirm that LS-DYNA simulations can be leveraged effectively to build AI models that dramatically reduce design exploration time. With SimAI and GeomAI in the loop, CAE workflows can evolve from simulation-driven design toward AI-augmented autonomous engineering, where geometry generation, simulation, validation, and optimization converge into an intelligent closed loop.
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This research is meant to enhance the analytic capabilities of OneSAF for usage as a Monte Carlo-style data generator for use in large problem space trade studies. Using novel ground vehicle data such as: RHA armor values, weapon penetration prediction models, and sensor values, new models can be developed in OneSAF for use in data generation and analysis. This process and companion software developed for this purpose enables the rapid construction and evaluation of differing vehicle variants in a fraction of the time of the baseline process, improving the efficiency of using OneSAF as a data analysis tool. This approach facilitates a more comprehensive virtual experimentation approach that can use manufactured data as a part of the process.