A structure mapping–based representation of knowledge transfer in conceptual design process

Liu, Hongwei; Li, Yan; Chen, Jian; Tao, Ye; Xia, Wenhan

doi:10.1177/0954405419883070

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…(Paek et al 1996 ), and the common method is to extract and index design elements through a searchable case library (Goel et al 2015 ). The current commonly used retrieval algorithms include analogy (Liu et al 2019a ), serial/parallel search (Huan et al 2008 ) and hierarchical search (Lim et al 2012 ). The adaptive design can also adopt a case-based design method.…”

Section: Development Of Dt-related Technology In the Infrastructure Fieldmentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

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Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

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Section: Development Of Dt-related Technology In the Infrastructure Fieldmentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

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“…Also, pioneers benefit from using analogies to generate innovative ideas through searching for analogies, IR [52]. Hence, [53] applied analogical designing, clustering of patents having similar design cases, to prompt new product design idea generation [53].…”

Section: Characterization Of Textual Data For Idea Extractionmentioning

confidence: 99%

“…Hence, [54] applied analogical designing, clustering of patents having similar design cases, to prompt new product design idea generation [54].…”

Section: Characterization Of Textual Data For Idea Extractionmentioning

confidence: 99%

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A Systematic Literature Review about Idea Mining: The Use of Machine-Driven Analytics to Generate Ideas

Ayele

Juell-Skielse

2021

Advances in Intelligent Systems and Computing

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Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally serve as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored.

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“…Knowledge networks play an important role in how students acquire and share knowledge. Connecting students to knowledge requires an analysis of its complex structural features [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Niu¹

2023

Int. J. Emerg. Technol. Learn.

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Knowledge networks play an important role in the process of knowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge. Existing research lacks insight into the internal structural features of knowledge networks constructed from expertise. There is also a lack of effective methods for constructing personalised knowledge networks for students' cognitive states. This paper analyses the categories and structures of expertise for students' cognitive states, and presents in detail a grey prediction algorithm to identify students' cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students' cognitive states, and analyses the knowledge network structure from the perspectives of paths and statistical properties. After that, the paper gives a method for analysing the knowledge flow of the expertise network. The experimental results validate the effectiveness of the proposed method.

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A structure mapping–based representation of knowledge transfer in conceptual design process

Cited by 8 publications

References 39 publications

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

A Systematic Literature Review about Idea Mining: The Use of Machine-Driven Analytics to Generate Ideas

An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

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