Measuring functional independence in design with deep-learning language representation models
Measuring functional independence in design with deep-learning language representation models
复制标题
使用深度学习语言表示模型衡量设计中的功能独立性
DOI：
10.1016/j.procir.2020.02.210
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Kim, Sang-Gook
中科院分区：
文献类型：
--
作者：
Akay, Haluk;Kim, Sang-Gook
测量复杂系统中的功能耦合是良好设计实践的重要任务，尽管历史上它一直是一种主观判断的艺术。随着深度学习和自然语言处理的最新进展，以单词和句子表示的功能需求（FRs）和设计参数（dp）可以在向量空间中表示。本文采用句子嵌入模型BERT对FRs和dp进行矢量化，计算功能独立性，并研究如何增强功能耦合度量指标。研究发现，在向量空间中表示的FRs和dp之间的语义相似性可以用来计算功能独立性度量的定量值。研究还发现，耦合明确的设计案例产生了最好的结果，而需要定义FR-DP关系的物理定律不能很好地转换为用于表达FR-DP的自然语言的案例突出了模型在当前状态下的局限性。然而，这项研究展示了一个很好的机会来开发一个健壮的、微调的设计语言表示模型，以准确地测量功能独立性，作为我们努力提高设计智能的一部分。
Measuring functional coupling in complex systems is an important task for good design practice, though historically it has been an art of subjective judgement. With the recent advancements in Deep Learning and Natural Language Processing, functional requirements (FRs) and design parameters (DPs), which are expressed as words and sentences, can be represented in a vector space. The sentence embedding model, BERT, was used in this paper to vectorize FRs and DPs, to calculate functional independence and to study how metrics for functional coupling measurement can be enhanced. It was found that semantic similarity among FRs and DPs, represented in vector space, could be used to compute quantitative values for metrics of functional independence. It was also found that design cases where coupling was unambiguous yielded the best results, while cases where laws of physics needed to define the FR-DP relationship did not transliterate well to the natural language used to express the FR-DP highlighted the limitations of the model in its current state. This study, however, demonstrates a great opportunity to develop a robust, fine-tuned design language representation model for accurately measuring functional independence as a part of our effort to enhance design intelligence.
DOI：
10.1016/s0007-8506(07)63323-x
发表时间：
1982
期刊：
CIRP Annals
影响因子：
--
作者：
N. Suh;J. Rinderle
通讯作者：
J. Rinderle
人工智能设计：虚拟设计助理
DOI：
10.1016/j.cirp.2019.03.024
发表时间：
2019
期刊：
CIRP Annals
影响因子：
--
作者：
Kim, Sang-Gook;Yoon, Sang Min;Yang, Maria;Choi, Jungwoo;Akay, Haluk;Burnell, Edward
通讯作者：
Burnell, Edward