• Journal of Technology Innovation
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• v.25 no.3
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• pp.115-154
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• 2017

This article offers a review of the major literature about sectoral innovation studies of Korea and its implications over the past 30 years. The literature on the sectoral innovation studies in Korea has focused on analysing successful technological catch-up from an evolutionary perspective and most of research has centered on the issues about entry strategies, learning mechanisms. Recently "Emerging economies" like Korea in the 2000s face major challenges as they make a transition from (a) a phase of economic development characterised by 'catching up' with the global technological frontier, involving technological "imitation", to (b) a phase of continuing development based on the development of new knowledge for globally leading (post catch-up) product and process innovation. This paper reviews those bodies of literature of patterns of sectoral innovation, technological capability accumulation and catch-up process, catch-up innovation and institutions, and patterns of growth dynamics. Finally, given the importance of sectoral innovation studies, we suggest that industrial upgrading, transition towards leadership, dark side of catch-up issues are needed for future research directions.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.441-451
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• 2017

In this paper, we review principal component analysis (PCA) procedures in the frequency domain and apply them to analyze sea surface temperature data. The classical PCA defined in the time domain is a popular dimension reduction technique. Extending the conventional PCA to the frequency domain makes it possible to define PCA in the frequency domain, which is useful for dimension reduction as well as a feature extraction of multiple time series. We focus on two PCA methods in the frequency domain, Hilbert PCA (HPCA) and frequency domain PCA (FDPCA). We review these two PCAs in order for potential readers to easily understand insights as well as perform a numerical study for comparison with conventional PCA. Furthermore, we apply PCA methods in the frequency domain to sea surface temperature data on the tropical Pacific Ocean. Results from numerical experiments demonstrate that PCA in the frequency domain is effective for the analysis of time series data.

• Journal of KIISE
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• v.43 no.9
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• pp.1008-1014
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• 2016

Data such as Twitter, Facebook, and customer reviews belong to the informal document group, whereas, newspapers that have grammar correction step belong to the formal document group. Finding consistent rules or patterns in informal documents is difficult, as compared to formal documents. Hence, there is a need for additional approaches to improve informal document analysis. In this study, we classified Twitter data, a representative informal document, into ten categories. To improve performance, we revised and expanded features based on LDA(Latent Dirichlet allocation) word distribution. Using LDA top-ranked words, the other words were separated or bundled, and the feature set was thus expanded repeatedly. Finally, we conducted document classification with the expanded features. Experimental results indicated that the proposed method improved the micro-averaged F1-score of 7.11%p, as compared to the results before the feature expansion step.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.97-102
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• 2017

In this paper, we have interested in cooperative intelligent transport system and autonomous driving system, and focused on analysis of the characteristics of Cooperative Awareness Message (CAM) and Decentralized Environmental Notification Basis Service (DENM) message, which is key delivery message among cooperative intelligent transport system (C-ITS) characteristics for research objectivity. For research method, we also described V2X communication, and also analyzed the security certificate and header structure of CAM and DENM messages. We described CAM message, which is a message informing the position and status of the vehicle. And the DENM message is presented a message informing an event such as a vehicle accident, and analysis security communication, which is supported services. According to standard analysis result, 186 bits or 275 bits are used. In addition to the security header and the certificate format used for vehicle communication, we have gained the certificate verification procedure for vehicles and PKI characteristics for vehicles. Also We derived the characteristics and transmission capability of the security synchronization pattern required for V2X secure communication. Therefore when it is considered for communication service of DENM and CAM in the C-ITS environment, this paper may be meaningful result.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.470-479
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• 2010

In relation to the extraction of opinion words, it may be difficult to directly apply most of the methods suggested in existing English studies to the Korean language. Additionally, the manual method suggested by studies in Korea poses a problem with the extraction of opinion words in that it takes a long time. In addition, English thesaurus-based extraction of Korean opinion words leaves a challenge to reconsider the deterioration of precision attributed to the one to one mismatching between Korean and English words. Studies based on Korean phrase analyzers may potentially fail due to the fact that they select opinion words with a low level of frequency. Therefore, this study will suggest the k-Structure (k=5 or 8) method, which may possibly improve the precision while mutually complementing existing studies in Korea, in automatically extracting opinion words from a simple sentence in a given Korean product review. A simple sentence is defined to be composed of at least 3 words, i.e., a sentence including an opinion word in ${\pm}2$ distance from the attribute name (e.g., the 'battery' of a camera) of a evaluated product (e.g., a 'camera'). In the performance experiment, the precision of those opinion words for 8 previously given attribute names were automatically extracted and estimated for 1,868 product reviews collected from major domestic shopping malls, by using k-Structure. The results showed that k=5 led to a recall of 79.0% and a precision of 87.0%; while k=8 led to a recall of 92.35% and a precision of 89.3%. Also, a test was conducted using PMI-IR (Pointwise Mutual Information - Information Retrieval) out of those methods suggested in English studies, which resulted in a recall of 55% and a precision of 57%.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Design Convergence Study
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• v.15 no.6
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• pp.195-214
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• 2016

Predictive analysis is based on a probabilistic learning algorithm called pattern recognition or machine learning. Therefore, if users want to extract more information from the data, they are required high statistical knowledge. In addition, it is difficult to find out data pattern and characteristics of the data. This study conducted statistical data analyses and visual data analyses to supplement prediction analysis's weakness. Through this study, we could find some implications that haven't been found in the previous studies. First, we could find data pattern when adjust data selection according as splitting criteria for the decision tree method. Second, we could find what type of data included in the final prediction model. We found some implications that haven't been found in the previous studies from the results of statistical and visual analyses. In statistical analysis we found relation among the multivariable and deducted prediction model to predict high box office performance. In visualization analysis we proposed visual analysis method with various interactive functions. Finally through this study we verified final prediction model and suggested analysis method extract variety of information from the data.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.29-45
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• 2015

Systematic theory, concepts, and methodology for the biological evolution have been developed while patterns and principles of the evolution have been actively studied in the past 200 years. Furthermore, they are applied to various fields such as evolutionary economics, evolutionary psychology, evolutionary linguistics, making significant progress in research. In addition, existing studies have applied main biological evolutionary models to artifacts although such methods do not fit to them. These models are also limited to generalize evolutionary patterns of artifacts because they are designed in terms of a subjective point of view of experts who know well about the artifacts. Unlike biological organisms, because artifacts are likely to reflect the imagination of the human will, it is known that the theory of biological evolution cannot be directly applied to artifacts. In this paper, beyond the individual's subjective, the aim of our research is to present evolutionary patterns of a given artifact based on peeping the idea of the public. For this, we propose a text mining approach that presents a systematic framework that can find out the evolutionary patterns of a given artifact and then visualize effectively. In particular, based on our proposal, we focus mainly on a case study of mobile phone that has emerged as an icon of innovation in recent years. We collect and analyze review posts on mobile phone available in the domestic market over the past decade, and discuss the detailed results about evolutionary patterns of the mobile phone. Moreover, this kind of task is a tedious work over a long period of time because a small number of experts carry out an extensive literature survey and summarize a huge number of materials to finally draw a diagram of evolutionary patterns of the mobile phone. However, in this work, to minimize the human efforts, we present a semi-automatic mining algorithm, and through this research we can understand how human creativity and imagination are implemented. In addition, it is a big help to predict the future trend of mobile phone in business and industries.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.433-442
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• 2009

People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7485-7493
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• 2015

With the popularity of Korean Wave, making cultural goods specific for Hallyu tourists is getting more important. However, there are mainly daily life goods using celebrity character-based ones. Remarkably, there are only a few cultural goods especially in practicality-based clothing category. In particular, few cultural goods related to children's wear have been developed. Therefore, if children's wear is developed as Korean Wave cultural goods considering Chinese consumers' pattern and Korean Wave cultural goods, it will be helpful for revitalizing the Korean Wave and Korea's fashion market. In this regard, the purpose of this study is to develop children's wear design as Korean Wave cultural goods, thereby presenting empirical research results and fulfilling its following objectives: First, it is to identify the concept of Korean Wave cultural goods, to analyze the current status to finally establish data to develop Korean Wave cultural goods needed at this time. Second, it is to make real-life size works through development of designs to provide the empirical data for Korean Wave cultural goods market. For the research method and contents the review of the previous research, in-depth interview for qualitative research, and empirical research using market research and development of work were performed. Through the final research outcomes, Korean Wave cultural goods, the children's wear that can meet the consumer's needs were presented as empirical data. The study can be used as basic data for domestic fashion market and cultural product market and it is meaningful as a reference for the analysis on the Chinese consumers' needs.