探索在复杂设计中概括设计需求的主题建模

IF 2.5 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY Journal of Engineering Design Pub Date : 2023-10-14 DOI:10.1080/09544828.2023.2268850
Cheng Chen, Beshoy Morkos
{"title":"探索在复杂设计中概括设计需求的主题建模","authors":"Cheng Chen, Beshoy Morkos","doi":"10.1080/09544828.2023.2268850","DOIUrl":null,"url":null,"abstract":"AbstractAs the redesign process progresses in product lifecycle management, effectively managing engineering changes becomes increasingly challenging, often leading to catastrophic and costly project failures. In response, the study provides a framework for generalising design requirements documents into topics that engineers can use to understand complex designs. Based on previous work, this study employs and compares four different models, including latent Dirichlet allocation (LDA), the collapsed Gibbs sampling algorithm for the Dirichlet multinomial mixtures model (GSDMM), LDA-BERT, and GSDMM-BERT to determine the appropriate representation of requirements documents. Both heatmaps and UMAPs are used to illustrate the correlation between topics and words. The results indicate that the combined vector representation of topic modelling and the sentence-BERT model outperforms single topic modelling. This combined model leverages the additional knowledge from a pre-trained sentence-BERT model, thereby improving model performance and word distribution in all three industrial projects. Through this proposed framework, engineers can potentially generalise high-quality requirements topics for large requirements documents.KEYWORDS: Requirement managementrequirement topicscomplex designBERTdesign process Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://radimrehurek.com/gensim/models/ldamodel.html2 https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html","PeriodicalId":50207,"journal":{"name":"Journal of Engineering Design","volume":"20 1","pages":"0"},"PeriodicalIF":2.5000,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring topic modelling for generalising design requirements in complex design\",\"authors\":\"Cheng Chen, Beshoy Morkos\",\"doi\":\"10.1080/09544828.2023.2268850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractAs the redesign process progresses in product lifecycle management, effectively managing engineering changes becomes increasingly challenging, often leading to catastrophic and costly project failures. In response, the study provides a framework for generalising design requirements documents into topics that engineers can use to understand complex designs. Based on previous work, this study employs and compares four different models, including latent Dirichlet allocation (LDA), the collapsed Gibbs sampling algorithm for the Dirichlet multinomial mixtures model (GSDMM), LDA-BERT, and GSDMM-BERT to determine the appropriate representation of requirements documents. Both heatmaps and UMAPs are used to illustrate the correlation between topics and words. The results indicate that the combined vector representation of topic modelling and the sentence-BERT model outperforms single topic modelling. This combined model leverages the additional knowledge from a pre-trained sentence-BERT model, thereby improving model performance and word distribution in all three industrial projects. Through this proposed framework, engineers can potentially generalise high-quality requirements topics for large requirements documents.KEYWORDS: Requirement managementrequirement topicscomplex designBERTdesign process Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://radimrehurek.com/gensim/models/ldamodel.html2 https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html\",\"PeriodicalId\":50207,\"journal\":{\"name\":\"Journal of Engineering Design\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/09544828.2023.2268850\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09544828.2023.2268850","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

摘要随着产品生命周期管理中重新设计过程的进展,有效地管理工程变更变得越来越具有挑战性,常常导致灾难性和昂贵的项目失败。作为回应,该研究提供了一个框架,将设计需求文档概括为工程师可以用来理解复杂设计的主题。在前人工作的基础上,本研究采用并比较了四种不同的模型,包括潜在狄利克雷分配(LDA)、狄利克雷多项混合模型(GSDMM)的崩溃吉布斯抽样算法、LDA- bert和GSDMM- bert,以确定需求文档的合适表示。热图和umap都用于说明主题和单词之间的相关性。结果表明,主题建模和句子- bert模型的组合向量表示优于单一主题建模。这个组合模型利用了来自预训练的句子- bert模型的额外知识,从而提高了所有三个工业项目中的模型性能和单词分布。通过这个建议的框架,工程师可以潜在地概括出大型需求文档的高质量需求主题。关键词:需求管理需求主题复杂设计bert设计过程披露声明作者未报告潜在利益冲突。注1 https://radimrehurek.com/gensim/models/ldamodel.html2 https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Exploring topic modelling for generalising design requirements in complex design
AbstractAs the redesign process progresses in product lifecycle management, effectively managing engineering changes becomes increasingly challenging, often leading to catastrophic and costly project failures. In response, the study provides a framework for generalising design requirements documents into topics that engineers can use to understand complex designs. Based on previous work, this study employs and compares four different models, including latent Dirichlet allocation (LDA), the collapsed Gibbs sampling algorithm for the Dirichlet multinomial mixtures model (GSDMM), LDA-BERT, and GSDMM-BERT to determine the appropriate representation of requirements documents. Both heatmaps and UMAPs are used to illustrate the correlation between topics and words. The results indicate that the combined vector representation of topic modelling and the sentence-BERT model outperforms single topic modelling. This combined model leverages the additional knowledge from a pre-trained sentence-BERT model, thereby improving model performance and word distribution in all three industrial projects. Through this proposed framework, engineers can potentially generalise high-quality requirements topics for large requirements documents.KEYWORDS: Requirement managementrequirement topicscomplex designBERTdesign process Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://radimrehurek.com/gensim/models/ldamodel.html2 https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Engineering Design
Journal of Engineering Design 工程技术-工程:综合
CiteScore
5.00
自引率
33.30%
发文量
18
审稿时长
4.5 months
期刊介绍: The Journal of Engineering Design is a leading international publication that provides an essential forum for dialogue on important issues across all disciplines and aspects of the design of engineered products and systems. The Journal publishes pioneering, contemporary, best industrial practice as well as authoritative research, studies and review papers on the underlying principles of design, its management, practice, techniques and methodologies, rather than specific domain applications. We welcome papers that examine the following topics: Engineering design aesthetics, style and form- Big data analytics in engineering design- Collaborative design in engineering- Engineering concept design- Creativity and innovation in engineering- Engineering design architectures- Design costing in engineering Design education and pedagogy in engineering- Engineering design for X, e.g. manufacturability, assembly, environment, sustainability- Engineering design management- Design risk and uncertainty in engineering- Engineering design theory and methodology- Designing product platforms, modularity and reuse in engineering- Emotive design, e.g. Kansei engineering- Ergonomics, styling and the design process- Evolutionary design activity in engineering (product improvement & refinement)- Global and distributed engineering design- Inclusive design and assistive engineering technology- Engineering industrial design and total design- Integrated engineering design development- Knowledge and information management in engineering- Engineering maintainability, sustainability, safety and standards- Multi, inter and trans disciplinary engineering design- New engineering product design and development- Engineering product introduction process[...]
期刊最新文献
Integrating GRA with intuitionistic fuzzy VIKOR model to explore attractive design solution of wickerwork cultural and creative products Modularity and new product performance: the role of new product development speed and task uncertainty Reconciling platform vs. product optimisation by value-based margins on solutions and parameters Smart industrial information integration: a lightweight privacy protection model in an intelligent manufacturing architecture Lumos: AI-driven prompt optimisation tool for assisting conceptual design
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1