确保机载机器学习模型功能不变，实现可靠部署。
Implementation of airborne ML models with semantics preservation
- 提出模型描述（MLMD）以精确定义模型行为
- 强调语义保持，确保模型在迁移中性能不衰减
- 工业案例验证，适合航空系统开发者参考
机器学习（ML）可能为机载系统带来新能力，但如所有机载系统一样，基于ML的系统必须保证安全运行，其开发需符合相关规范。目前，欧洲航空安全局（EASA）已发布概念文件，ED-324项目正由EUROCAE/SAE推进，两者均提出高阶目标：确认模型实现预期功能，并在目标环境中维持训练性能。本文旨在厘清机器学习模型与其明确描述之间的差异，引入机器学习模型描述（MLMD）概念。进一步细化语义保持机制，以确保模型在转换与部署过程中准确复现。通过多个工业用例，构建并对比若干目标模型，验证方法有效性。
原文摘要 · Abstract (English)
Machine Learning (ML) may offer new capabilities in airborne systems. However, as any piece of airborne systems, ML-based systems will be required to guarantee their safe operation. Thus, their development will have to be demonstrated to be compliant with the adequate guidance. So far, the European Union Aviation Safety Agency (EASA) has published a concept paper and an EUROCAE/SAE group is preparing ED-324. Both approaches delineate high-level objectives to confirm the ML model achieves its intended function and maintains training performance in the target environment. The paper aims to clarify the difference between an ML model and its corresponding unambiguous description, referred to as the Machine Learning Model Description (MLMD). It then refines the essential notion of semantics preservation to ensure the accurate replication of the model. We apply our contributions to several industrial use cases to build and compare several target models.
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