This paper presents LLM4SecHW, a novel framework for hardware debugging that leverages domain specific Large Language Model (LLM). Despite the success of LLMs in automating various software development tasks, their application in the hardware security domain has been limited due to the constraints of commercial LLMs and the scarcity of domain specific data. To address these challenges, we propose a unique approach to compile a dataset of open source hardware design defects and their remediation steps, utilizing version control data. This dataset provides a substantial foundation for training machine learning models for hardware. LLM4SecHW employs fine tuning of medium sized LLMs based on this dataset, enabling the identification and rectification of bugs in hardware designs. This pioneering approach offers a reference workflow for the application of fine tuning domain specific LLMs in other research areas. We evaluate the performance of our proposed system on various open source hardware designs, demonstrating its efficacy in accurately identifying and correcting defects. Our work brings a new perspective on automating the quality control process in hardware design.

The automotive industry was the first to promote the development of applications of model-based systems technology on a broad scale and, as a result, has produced some of the most advanced prototypes and products. In this article, we illustrate the features and benefits of model-based systems and qualitative modeling by prototypes and application systems that were developed in the automotive industry to support on-board diagnosis, design for diagnosability, and failure modes and effects analysis. Car manufacturers and their suppliers face increasingly serious challenges particularly related to fault analysis and diagnosis during the life cycle of their products. On the one hand, the complexity and sophistication of vehicles is growing, so it is becoming harder to predict interactions between vehicle systems, especially when failures occur. On the other hand, legal regulations and the demand for safety impose strong requirements on the detection and identification of faults and the prevention of their effects on the environment or dangerous situations for passengers and other people.

The eighteenth annual International Workshop on Principles of Diagnosis was held in Nashville, Tennessee, May 29-31, 2007. Papers presented at the workshop covered a variety of theories, principles, and computational techniques for diagnosis, monitoring, testing, reconfiguration, fault-adaptive control, and repair of complex systems. Before deployment they are subjected to strict testing and validation. Although these procedures reduce the likelihood of initial system failures, degradation and faults in system components still occur because of wear and tear from sustained operations. Industry sources, service agencies, and the military report that down time, due to maintenance and repairs of equipment, is still a significant cost of daily operations.

Through the technology of expert systems, the expertise of highly skilled personnel can be automated and used to assist lesser skilled personnel in the diagnosis and repair of complex machines. Expert systems that incorporate causal reasoning represent a second-generation approach to the provision of diagnostic assistance. The technology involved performs postdiction by reasoning from first principles. This article is based on research in qualitative physics and the philosophy of causality. A new implementation vehicle for causal reasoning is described, one that embodies hypothetical or counterfactual reasoning (Roach, Eichelman, and Whitehead 1985) in a language called Wif (What IF).

The traditional reliability design methods are imperfect because the designed systems aim at fewer faults, but once a fault happens, the systems might hard fail. To solve this problem, we present a self-maintenance machine (SMM), one that can maintain its functions flexibly even though faults occur. To achieve the capabilities of diagnosing and repair planning, a model-based approach that uses qualitative physics was proposed. Regarding the repair-executing capability, a control-type repair strategy was followed. A prototype of the SMM was developed, and it succeeded in maintaining its functions if the structure did not change.

This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space. As NASA and other space agencies around the world formulate and deploy missions to return to the moon and explore Mars and beyond, the realization is emerging that smarter mobile systems that are themselves instruments of knowledge and understanding must be ...

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system--comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment--is manifested as an out-ofbound parameter elsewhere in the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. In such cases, the normal procedure is to run AGETS self-diagnostics on the abnormal parameter. However, because the self-diagnostics only test the parameter specified, it will pass because parameter tests are local tests that cannot uncover malfunctions in other parts of the system.

This article describes a common diagnostic framework used to evaluate these algorithms. These competitions, started in 2009, have significantly helped shape subsequent diagnostic algorithms. Diagnostic algorithms (DAs) (1) detect malfunctioning systems, (2) isolate the faulty component or components that cause the malfunction, and possibly (3) repair the system to restore its functionality. The fundamental challenge of diagnosis is that the system is only partially observable. Therefore, diagnostic algorithms must reason backwards from symptoms to causes.