Sunghae Jun
Cheongju University
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Publication
Featured researches published by Sunghae Jun.
Industrial Management and Data Systems | 2012
Sunghae Jun; Sang Sung Park; Dong Sik Jang
Purpose – The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new approaches to patent mapping and clustering. In addition, the paper aims to introduce a matrix map and K‐medoids clustering based on support vector clustering (KM‐SVC) for vacant TF.Design/methodology/approach – TF is an important research and development (R&D) policy issue for both companies and government. Vacant TF is one of the key technological planning methods for improving the competitive power of firms and governments. In general, a forecasting process is facilitated subjectively based on the researchers knowledge, resulting in unstable TF performance. In this paper, the authors forecast the vacant technology areas in a given technology field by analyzing patent documents and employing the proposed matrix map and KM‐SVC to forecast vacant technology areas in the management of technology (MOT).Findings – The paper examines...
Expert Systems With Applications | 2014
Sunghae Jun; Sang Sung Park; Dong Sik Jang
This study proposes new method to overcome sparsity problem of document clustering.We build combined method using dimension reduction, K-means clustering, and SVC.In particular, we attempt to overcome the sparseness in patent document clustering.First, we conduct experiment using news data from UCI machine learning repository.Second, using retrieved patent documents, we carry out patent clustering. Many studies on developing technologies have been published as articles, papers, or patents. We use and analyze these documents to find scientific and technological trends. In this paper, we consider document clustering as a method of document data analysis. In general, we have trouble analyzing documents directly because document data are not suitable for statistical and machine learning methods of analysis. Therefore, we have to transform document data into structured data for analytical purposes. For this process, we use text mining techniques. The structured data are very sparse, and hence, it is difficult to analyze them. This study proposes a new method to overcome the sparsity problem of document clustering. We build a combined clustering method using dimension reduction and K-means clustering based on support vector clustering and Silhouette measure. In particular, we attempt to overcome the sparseness in patent document clustering. To verify the efficacy of our work, we first conduct an experiment using news data from the machine learning repository of the University of California at Irvine. Second, using patent documents retrieved from the United States Patent and Trademark Office, we carry out patent clustering for technology forecasting.
Technology Analysis & Strategic Management | 2014
Seong-Yong Choi; Sunghae Jun
Vacant technology forecasting (VTF) is a technology forecasting approach to find technological needs for given industrial field in the future. It is important to know the future trend of developing technology for the R&D planning of a company and a country. In this paper, we propose a new Bayesian model for patent clustering. This is a VTF methodology based on patent data analysis. Our method is composed of Bayesian learning and ensemble method to construct the VTF model. To illustrate the practical way of the proposed methodology, we perform a case study of given technology domain using retrieved patent documents from patent databases in the world.
FGIT-DTA/BSBT | 2011
Sunghae Jun
Patent documents are the results of researched and developed technologies. Patent is a protecting system of inventors’ right for their technologies by a government. Also, patent is an important intellectual property of a company. R&D strategy has been depended on patent management. For efficient management of patent, we need to analyze patent data. In this paper, we propose a method for analyzing international patent classification (IPC) code as a patent analysis. We introduce association rules and maps for IPC code analysis. To verify our improved the performance, we will make experiments using searched patent documents of database technology.
Advanced Engineering Informatics | 2015
Jong-Min Kim; Sunghae Jun
Apple is a leading company of technological evolution and innovation. This company founded and produced the Apple I computer in 1976. Since then, based on its innovative technologies, Apple has launched creative and innovative products and services such as the iPod, iTunes, the iPhone, the Apple app store, and the iPad. In many fields of academia and business, diverse studies of Apples technological innovation strategy have been performed. In this paper, we analyze Apples patents to better understand its technological innovation. We collected all applied patents by Apple until now, and applied statistics and text mining for patent analysis. By using graphical causal inference method, we created the causal relations among Apple keywords preprocessed by text mining, and then we carried out the semiparametric Gaussian copula regression model to see how the target response keyword and the predictor keywords are relating to each other. Furthermore, Gaussian copula partial correlation was applied to Apple keywords to find out the detailed dependence structure. By performing these methods, this paper shows the technological trends and relations between Apples technologies. This research could make contributions in finding vacant technology areas and central technologies for Apples R&D planning.
FGIT-DTA/BSBT | 2011
Sunghae Jun
Many results of the developed technologies have applied for patents. Also, an issued patent has exclusive rights granted by a government. So, all companies in the world have competed with one another for their intellectual property rights using patent application. Technology forecasting is one of many approaches for improving the technological competitiveness. In this paper, we propose a forecasting model for technological trend using unsupervised learning. In this paper, we use association rule mining and self organizing map as unsupervised learning methods. To verify our improved performance, we make experiments using patent documents. Especially, we focus on image and video technology as the technology field.
Industrial Management and Data Systems | 2016
Sangsung Park; Juhwan Kim; Hongchul Lee; Dong-Sik Jang; Sunghae Jun
Purpose – An increasing amount of attention is being paid to three-dimensional (3D) printing technology. The technology itself is based on diverse technologies such as laser beams and materials. Hence, 3D printing technology is a converging technology that produces 3D objects using a 3D printer. To become technologically competitive, many companies and nations are developing technologies for 3D printing. So to know its technological evolution is meaningful for developing 3D printing in the future. The paper aims to discuss these issues. Design/methodology/approach – To get technological competitiveness of 3D printing, the authors should know the most important and essential technology for 3D printing. An understanding of the technological evolution of 3D printing is needed to forecast its future technologies and build the R & D planning needed for 3D printing. In this paper, the authors propose a methodology to analyze the technological evolution of 3D printing. The authors analyze entire patent documents...
Archive | 2012
Sunghae Jun
In this paper, a central technology is defined as a key technology that is connected to most other technologies and that significantly affects them. Accordingly, we can build an R&D policy effectively if we can forecast central technologies. We propose a central technology forecasting model that uses social network analysis (SNA). A social network is a social structure of diverse items as well as of human beings. In this study, we set each technology as a node in an SNA graph and analyze the linkages between them. Thus, we forecast central technologies from SNA results. To verify the performance of our model, we conducted a case study using patent data related to nanotechnology.
The Journal of the Korea Contents Association | 2010
Sunghae Jun; Sangsung Park; Young-Geun Shin; Dong-Sik Jang; Ho-Seok Chung
Patent analysis is the extracting knowledge which is needed for the company`s research and development strategy through accumulated worldwide patent database. In order to set the future direction of corresponding technology which is scheduled to be developed, the technology trends and deployment processes are identified by analyzing results of present patent applications. The patent analysis provides the required results for analyzing present patent applications. In this paper, we will carry out technology classification for related patent analysis methods and systems. Moreover we will investigate and analyze related domestic patents, U.S. patents and IEEE papers. Due to the characteristics of technology sector, not only patents are applied but also research papers are released actively about patent analysis system. We will analyze patents according to the technology classification by using the final searching results which come from the selected search words in this study. To find necessary niche technology which is needed for patent analysis system, matrix analysis was performed to all of valid patents and papers. Identifying the technology development trends of registered patent analysis systems, and presenting the future direction of technology development which is related to patent analysis system. To figure out the technology which is developed relatively weak based on domestic patents, U.S patent and research papers by analyzing the valid patents and papers with statistical test and self-organizing map quantitatively. Then, presenting the necessity of this technology development.
Technology Analysis & Strategic Management | 2016
Sunghae Jun; Sangsung Park
ABSTRACT The Korean car market has increased in size. BMW and Hyundai are the top-ranked imported and domestic vehicle brands in Korea, respectively. Thus, it is important to understand these companies and the Korean car market, because technology is most significant in the vehicle industry. In this paper, we compare BMW with Hyundai from the technological perspective. Our research is focused on an analysis of the technological competition between BMW and Hyundai based on their developed technologies. We use all BMW and Hyundai patents from worldwide patent databases to analyse the two companies’ technologies. In addition, we apply statistical methods and machine learning algorithms to the patent analysis. In our conclusion, we show the technological differences and competition between BMW and Hyundai, and find their relative strengths and weaknesses.