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- 저자 : Kwan-yu Chris Lo (검색결과 2 건)
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Ivan King‑Hei Or,Chris Kwan‑Yu Lo,Chi‑Wai Kan 한국섬유공학회 2023 Fibers and polymers Vol.24 No.3
The existing literature review articles in composite materials research field target a particular resin material which cannot show the full picture of the research area. The traditional literature reviews are vulnerable to subjective opinion on selecting, reviewing and analyzing the related articles. In this systematic literature review, keywords were input into Web of Science (WOS) search engine to collect relevant articles from the database. This method can more comprehensively review articles that are related to fiber-reinforced polymer composites. Articles published from 1970 to 2020 were collected from the WOS database. Keywords input were relevant to manufacturing, enhancement and sustainability of fiber-reinforced polymer composites and their variants. 151 articles were selected based on keyword filtering, nature of articles and content, and further examined with citation network analysis. Seven principal clusters were formed and evaluated. Publication year, geographical locations, research areas and journal of the selected articles were presented statistically. Two major clusters, “fabrication and molding methods of composites” and “properties and performance of multiscale composites”, were identified and a few emerging clusters were found to establish their networks including topics about impact response, natural fiber, rapid curing, nanofiller, and hydrothermal aging. Future research and more results are needed in those emerging clusters.
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Senbiao Liu,Yaohui Keane Liu,Kwan-yu Chris Lo,Chi-wai Kan 한국의류학회 2024 Fashion and Textiles Vol.11 No.1
Based on a selection of 101 articles published from 2013 to 2022, this study systematically reviews the application of intelligent techniques and optimization algorithms in textile colour management. Specifically, the study explores how these techniques have been applied to four subfields within textile colour management: colour matching and prediction, colour difference detection and assessment, colour recognition and segmentation, and dye solution concentration and decolourization. Following an introduction to intelligent techniques and optimization algorithms in textile colour management, the study describes the specific applications of these techniques in the field over the past decade. Descriptive statistics are used to analyse trends in the use of these techniques and optimization algorithms, and comparative performances indicate the effectiveness of the techniques and algorithms. The study finds that the primary intelligent techniques used in the field of textile colour management include artificial neural networks (ANN), support vector machines (SVM) such as SVM, LSSVM, LSSVR, SLSSVR, FWSVR, fuzzy logic (FL) and adaptive neuro-fuzzy inference systems (ANFIS), clustering algorithms (e.g., K-means, FCM, X-means algorithms), and extreme learning machines (ELM) such as ELM, OSLEM, KELM, RELM. The main optimization algorithms used include response surface methodology (RSM), genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). Finally, the study proposes a comparison of the performance of intelligent techniques and optimization algorithms, summarizes the relevant research trends, and suggests future research opportunities and directions, besides stating the limitations of this paper.
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