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Data-driven intelligent computational design for products: method, techniques, and applications
Yang Maolin,Jiang Pingyu,Zang Tianshuo,Liu Yuhao 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
Data-driven intelligent computational design (DICD) is a research hotspot that emerged under fast-developing artificial intelligence. It emphasizes utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data and then learn the combination and mapping patterns of these design features for design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attention of both academic and industrial fields. However, as an emerging research subject, many unexplored issues still limit the development and application of DICD, such as specific dataset building, engineering design-related feature engineering, systematic methods and techniques for DICD implementation in the entire product design process, etc. In this regard, a systematic and operable road map for DICD implementation from a full-process perspective is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the common mechanisms and calculation principles during DICD, key enabling technologies for detailed DICD implementation, and three case scenarios of DICD application. The road map can help academic researchers to locate their specific research directions for the further development of DICD and provide operable guidance for the engineers in their specific DICD applications.
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Varying-coefficient partially functional linear quantile regression models
Ping Yu,Jiang Du,Zhongzhan Zhang 한국통계학회 2017 Journal of the Korean Statistical Society Vol.46 No.3
In this paper, we introduce a new varying-coefficient partially functional linear quantile regression model, which combines varying-coefficient quantile regression model with functional linear quantile regression model. The functional principal component basis and regression splines are employed to estimate the slope function and varying-coefficient functions, respectively, and the convergence rates of the estimators are obtained under some regularity conditions. Simulations and an illustrative real example are presented.
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