Exploring for Inspiration: A Bayesian Inference Based Approach to Design By Analogy Retrieval

探索灵感：基于贝叶斯推理的类比检索设计方法

• 批准号: 1663204
• 负责人: Katherine Fu
• 金额: $ 37.9万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663204&HistoricalAwards=false
• 关键词: Exploring; Inspiration; Bayesian; Inference; Based

People use analogical reasoning in everyday life to understand new things by drawing parallels with familiar things. The same principle is used by engineering designers in an approach called Design by Analogy (DbA). Many groundbreaking innovations have emerged through DbA, including self-cleaning, outdoor-surface paint that was inspired by the lotus flower and the first printing press, which was inspired by the early wine press. Although analogical inspiration can be arrived at serendipitously, formal approaches for DbA enable reliable the approach to be applied more widely and reliably. To date, most analogy identification techniques have relied on a query-based approaches, such as key-word search. Instead, this research will enable visual techniques for exploring analogies for engineering design innovation and show the superiority of this visual approach over a query-based DbA. The results will advance engineering design and promote engineering innovations by enhancing the ability of engineers to search for analogical inspiration in a way that is more dynamic and exploratory than current techniques. Results will be made available in an open-source format for use, comparison, and integration by researchers across many fields. Outreach efforts will incorporate research ideas about engineering design and DbA into a mechanical engineering Girl Scout badge. The objective of this research is to test the hypothesis that exploration-based approaches are more effective than query-based approaches for retrieval of stimuli for DbA practices. By using Topic Modeling techniques, big data from the US patent database, and visual analytics techniques, the research will transform the way DbA inspiration is found. The results will give designers more intuitive, dynamic access and maneuverability for exploring the design space. Properties of analogical information and external stimuli will be extracted deductively from literature and theory of DbA. Visual, exploration-enabling, manipulable design repositories will be constructed using Latent Semantic Analysis, Latent Dirichlet Allocation, Non-negative Matrix Factorization, techniques in data visualization and Human Computer Interaction design principles; the repositories will be compared to identify which can best support the retrieval stage of DbA. Novel, seeded, hierarchical topic modeling methods will be developed and tested. These DbA properties and techniques will be tested with cognitive studies of the design process. By supporting an exploratory approach at a larger scale of repository size than ever before, transformational effects can be achieved on the path to innovation. This research will enable engineering of complex device designs that lead to products with improved performance, better material use, and fewer resources consumed.

人们在日常生活中使用类比推理，通过与熟悉的事物进行类比来理解新事物。工程设计师在称为类比设计（DbA）的方法中使用相同的原理。 通过DbA出现了许多突破性的创新，包括受莲花启发的自清洁户外表面涂料和受早期葡萄酒印刷机启发的第一台印刷机。虽然类比灵感可以偶然获得，但DbA的正式方法使该方法能够更广泛和可靠地应用。迄今为止，大多数类比识别技术都依赖于基于查询的方法，如关键词搜索。相反，这项研究将使视觉技术探索类比工程设计创新，并显示这种视觉方法的优越性基于查询的DbA。研究结果将通过提高工程师以比当前技术更动态和更具探索性的方式搜索类比灵感的能力来推进工程设计和促进工程创新。结果将以开源格式提供，供许多领域的研究人员使用，比较和整合。推广工作将把有关工程设计和DbA的研究想法纳入机械工程女童子军徽章。本研究的目的是验证以下假设：对于DBA实践的刺激检索，基于探索的方法比基于查询的方法更有效。 通过使用主题建模技术，美国专利数据库的大数据，以及可视化分析技术，研究将改变DbA灵感的发现方式。结果将为设计师提供更直观，动态的访问和可操作性，以探索设计空间。 类比信息和外部刺激的属性将从DbA的文献和理论中演绎提取。可视化，探索，可操作的设计存储库将使用潜在语义分析，潜在狄利克雷分配，非负矩阵分解，数据可视化和人机交互设计原则的技术构建;存储库将进行比较，以确定哪些可以最好地支持DbA的检索阶段。新的，种子，层次主题建模方法将开发和测试。 这些DbA属性和技术将与设计过程的认知研究进行测试。通过在比以往任何时候都更大的存储库规模上支持探索性方法，可以在创新的道路上实现转型效应。这项研究将使复杂设备设计的工程化成为可能，从而使产品具有更好的性能、更好的材料使用和更少的资源消耗。

项目成果

Approaches for Supporting Exploration for Analogical Inspiration With Behavior, Material and Component Based Structural Representations of Patent Databases

通过基于行为、材料和组件的专利数据库结构表示来支持类比灵感探索的方法

• DOI: 10.1115/detc2018-85591
• 发表时间: 2018
• 期刊: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 2A: 44th Design Automation Conference
• 作者: Song, Hyeonik;Fu, Katherine
• 通讯作者: Fu, Katherine

Design-by-Analogy: Exploring for Analogical Inspiration With Behavior, Material, and Component-Based Structural Representation of Patent Databases

• DOI: 10.1115/1.4043364
• 发表时间: 2019-04
• 期刊: J. Comput. Inf. Sci. Eng.
• 作者: H. Song;Katherine K. Fu

LDA v. LSA: A Comparison of Two Computational Text Analysis Tools for the Functional Categorization of Patents

• 发表时间: 2016
• 作者: Tonči Cvitanić;Bumsoo Lee;H. Song;Katherine K. Fu;D. Rosen