Browsing by Author "Fan, C. Y."

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E-discover State-of-the-art Research Trends of Deep Learning for Computer Vision
(2019-01-01)
Li, Annie A. S.
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Trappey, Amy J. C.
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Trappey, Charles V.
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Fan, C. Y.
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交大名義發表
Computer vision (CV) attempts to mimic human eyes for image processing and identifications of detailed visual information, such as object positions, features of appearances, and even human emotions and behaviors. In this research, more than one hundred literatures, relating to applying deep learning (DL) methodologies in advanced computer visions (2010 similar to 2018), are reviewed and analyzed. The objective is to discover the state-of-the-art DL methods, topics, and trends for CV and their practical applications. DL algorithms aim at representing multilevels of distributed neural networks. Because of the enhancement of high speed computational power, DL modeling, based on accumulated big data analytics, has found practical applications for non-supervised intelligent decision supports, such as detection of product defects and prognosis of machine malfunctions based on real-time signal or feature data analyses. There are a vast number of literature, describing DL related researches, developments, and implementations for problem solving. For the comprehensive mining of the related literature, we integrate Latent Dirichlet Allocation (LDA), K-means (Clustering), and normalized term frequency-inverse document frequency (NTF-IDF) approaches to discover, or called technology mining, of the major trends in DL for computer visions, specifically for key applications in object detection, semantic segmentation, image retrieval, and human pose estimation.
Mining the Customer's Voice and Patent Data for Strategic Product Quality Function Deployment
(2017-01-01)
Trappey, A. J. C.
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Trappey, C. V.
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Fan, C. Y.
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Lee, I. J. Y.
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管理科學系
The goal of research and development for new product and service development is to satisfy customers. A company that satisfies customers with short life cycle products like smartphones that match or exceed expectations builds brand equity and a global competitive advantage. Companies are challenged with the task of identifying the market demand that evolves with technological innovations. Since many of the short product life cycle communications products are continuously changing, capturing and measuring the satisfaction of customers is increasingly difficult. One approach to accurately identify market demand and customer satisfaction with the functions of products is to listen to what is said among social networks. The Internet has empowered customers to express their opinions, attitudes, beliefs, and purchase intentions about products using community platforms, social media, customer blogs, and other networks. The words, expressed on these platforms, represent the voice of customers and provide collective and dynamic intelligence about the users' purchase intentions as well as experience using the products. Patent documents disclose the technological evolutions of a domain, and contribute to the features and characteristics of products that differentiate between brands and build customer expectations and loyalty. This research proposes a systematic methodology to combine collective intelligence using Internet web crawling and text mining to access the voice of customers. Patent information (retrieved from global patent search engines and analytic function mining of the patent content) provides critical information for planning strategic product repositioning and improvements that match the voice of customers and increase brand loyalty. The systematic extended QFD approach provides intelligent and dynamic demand-compliant strategies for developing new products and services.