융합센서 다중객체 추적 및 예측데이터를 기반으로, 요소간의 의미적 관계 파악이 가능한 데이터로 LLM의 약점을 보완하기 위해 온톨로지, RAG 로 정확도를 높이고, 휴먼 가공과 검수, 그리고 피드백 과정을 통해 언어모델의 편향이나 오류를 수정해서 활용도 높은 이미지·텍스트 기반 데이터를 구축하였습니다.

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots. It was initially developed within the Multimodal Information Group (MIG) at the National Institute of Standards and Technology (NIST).This software is documented, configured to be installed as an R package, and comes with an example SEMAIT script with an example (system, dataset, metrics, score) ML tuple set that we constructed ourselves.

In this paper, we present an architecture and a formal framework to be used for systematic benchmarking of monitoring and diagnostic systems and for producing comparable performance assessments of different diagnostic technologies. The framework defines a number of standardized specifications, which include a fault catalog, a library of modular test scenarios, and a common protocol for gathering and processing diagnostic data. At the center of the framework are 13 benchmarking metric definitions. The calculation of metrics is illustrated on a probabilistic model-based diagnosis algorithm utilizing Bayesian reasoning techniques. The diagnosed system is a real-world electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The proposed benchmarking approach shows how to generate realistic diagnostic data sets for large-scale, complex engineering systems, and how the generated experimental data can be used to enable “apples to apples” assessments of the effectiveness of different diagnostic and monitoring algorithms.

IKOS is a C++ library designed to facilitate the development of sound static analyzers based on Abstract Interpretation. Specialization of a static analyzer for an application or family of applications is critical for achieving both precision and scalability. Developing such an analyzer is arduous and requires significant expertise in Abstract Interpretation.

This dataset provides the NIST Software Assurance Metrics And Tool Evaluation (SAMATE) Software Assurance Reference Dataset (SARD) - a set of programs with known security flaws. This will allow end users to evaluate tools and tool developers to test their methods.

The lecture of the [Stanford-IVHM lecture series](https://dashlink.arc.nasa.gov/group/stanford-ivhm-lecture-series/) will give an overview of the approaches in building diagnostic solutions for networks and complex systems. The conventional rule-based approach and the top-down analysis will be compared with other innovative solutions based on information modeling and codebook correlation. One specific solution pioneered by research done in Columbia University and later implemented by SMARTS/EMC will be presented in more detail as an example of a consistent approach to diagnostics. Speaker: Yuri Rabover, Ph.D. VMTurbo Dr. Yuri Rabover, is a co-founder and Director of Product Strategy of VMTurbo, a startup in a stealth mode. Prior to VMTurbo Yuri spent 12 years working for SMARTS as director of engineering, product management and technology partnership. After EMC acquired SMARTS for $275M in 2005, Yuri was managing the Advanced Solution Group in the EMC Corporate CTO Office developing prototypes and proof of concepts of new innovative solutions. He is a seasoned technologist, strategist and researcher in the wide area of system, network and storage management with more than 20 years of industry and academia experience.

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