• Knowledge Management Research
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• v.24 no.4
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• pp.103-129
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• 2023

This study aims to analyze the network structure of technological innovation resources in SMEs, especially manufacturing firms, and reveal the differences between innovative and non-innovative firms. The study first analyzes connection centrality, flow-mediated centrality, and power centrality for all firms, and derives structural equivalence through CONCOR analysis. Then, the network structure of innovative and non-innovative firms was compared and analyzed according to innovation performance and creation. The results show that entrepreneurship and corporate innovation strategy have a significant impact on the analysis of technological innovation resources of all firms. According to the CONCOR analysis, the innovation resources of SMEs are organized into seven clusters, which can be defined as intrinsic product innovation resources, competitive advantage promotion resources, cooperative activities resources, information system resources, and innovation protection resources. The network analysis of innovative and non-innovative firms showed that innovative firms focused on enhancing competitiveness and improving quality, while non-innovative firms tended to focus more on existing products and customers. In addition, innovative firms had eight clusters, while non-innovative firms had six clusters, suggesting that innovative firms utilize resources diversely to pursue structural change and new value creation, while non-innovative firms operate technological innovation resources in a more stable form. This study emphasizes the importance of entrepreneurship and corporate innovation strategy in SMEs' technological innovation, and suggests that strong internal efforts are needed to increase innovativeness. These findings have important implications for strategy formulation and policy development for technological innovation in SMEs.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Music and Human Behavior
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• v.10 no.2
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• pp.35-54
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• 2013

The purpose of this study was to examine how Korean music therapy students experience growth under clinical supervision. The investigator conducted in-depth qualitative interviews with 9 students from 3 different universities in Seoul who had at least three semesters of clinical supervision. Data was analyzed using a modified grounded theory approach to construct the growth experience of music therapy supervisees. Results suggest that growth can be understood in terms of both personal and professional domains and includes four types of experiences: growth hindering, fostering, mediating, and revealing. In the personal domain, hindering factors are defensiveness, narcissistic trauma, avoidance and anxiety whereas growth fostering and mediating factors include reflection on self, musical self, unconscious drives and conflicting issues as well as self-driven problem solving skills. As a result, growth in the personal domain is associated with increased self-acceptance and self-awareness. Growth in the professional domain is hindered by having trust issues, performance anxiety, identity crisis, and being hypersensitive to the judgment of others. On the other hand, growth is fostered and mediated by opening the self and interacting more with others, building trusting relationships with peers and supervisors, and establishing a new relationship with music, which leads to improved attitude, increased motivation, and more efficient and effective training.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.34.2-35
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• 2018

Galaxy transition from star-forming to quiescent, accompanied with morphology transformation, is one of the key unresolved issues in extragalactic astronomy. Although several environmental mechanisms have been proposed, a deeper understanding of the impact of environment on galaxy transition still requires much exploration. My Ph.D. thesis focuses on which environmental mechanisms are primarily responsible for galaxy transition in different environments and looks at what happens during the transition phase using multi-wavelength photometric/spectroscopic data, from UV to mid-infrared (MIR), derived from several large surveys (GALEX, SDSS, and WISE) and our GMOS-North IFU observations. Our multi-wavelength approach provides new insights into the *late* stages of galaxy transition with a definition of the MIR green valley different from the optical green valley. I will present highlights from three areas in my thesis. First, through an in-depth study of environmental dependence of various properties of galaxies in a nearby supercluster A2199 (Lee et al. 2015), we found that the star formation of galaxies is quenched before the galaxies enter the MIR green valley, which is driven mainly by strangulation. Then, the morphological transformation from late- to early-type galaxies occurs in the MIR green valley. The main environmental mechanisms for the morphological transformation are galaxy-galaxy mergers and interactions that are likely to happen in high-density regions such as galaxy groups/clusters. After the transformation, early-type MIR green valley galaxies keep the memory of their last star formation for several Gyr until they move on to the next stage for completely quiescent galaxies. Second, compact groups (CGs) of galaxies are the most favorable environments for galaxy interactions. We studied MIR properties of galaxies in CGs and their environmental dependence (Lee et al. 2017), using a sample of 670 CGs identified using a friends-of-friends algorithms. We found that MIR [3.4]-[12] colors of CG galaxies are, on average, bluer than those of cluster galaxies. As CGs are located in denser regions, they tend to have larger early-type galaxy fractions and bluer MIR color galaxies. These trends can also be seen for neighboring galaxies around CGs. However, CG members always have larger early-type fractions and bluer MIR colors than their neighboring galaxies. These results suggest that galaxy evolution is faster in CGs than in other environments and that CGs are likely to be the best place for pre-processing. Third, post-starburst galaxies (PSBs) are an ideal laboratory to investigate the details of the transition phase. Their spectra reveal a phase of vigorous star formation activity, which is abruptly ended within the last 1 Gyr. Numerical simulations predict that the starburst, and thus the current A-type stellar population, should be localized within the galaxy's center (< kpc). Yet our GMOS IFU observations show otherwise; all five PSBs in our sample have Hdelta absorption line profiles that extend well beyond the central kpc. Most interestingly, we found a negative correlation between the Hdelta gradient slopes and the fractions of the stellar mass produced during the starburst, suggesting that stronger starbursts are more centrally-concentrated. I will discuss the results in relation with the origin of PSBs.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.11-15
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• 2016

Gastrointestinal cancers remain the most prevalent cancers in many developing countries such as Iran. The aim of this study was to estimate incidence, prevalence and mortality, as well as time trends for gastrointestinal cancers in Isfahan province of Iran for the period 2001 to 2010 and to project these estimates to the year 2020. Estimates were driven by applying the MIAMOD method (a backward calculation approach using mortality and relative survival rates). Mortality data were obtained from the Ministry of Health and the relative survival rate for all gastrointestinal cancers combined was derived from the Eurocare 3 study. Results indicated that there were clear upward trends in age adjusted incidence (males 22.9 to 74.2 and females 14.9 to 44.2), prevalence (males 52.6 to 177.7 and females 38.3 to 111.03), and mortality (males 14.6 to 47.2 and females 9.6 to 28.2) rates per 100,000 for the period of 2001 to 2010 and this upward state would persist for the projected period. For the entire period, the male to female ratio increased slightly for all parameters (incidence rate increased from 1.5 to 1.7, prevalence from 1.4 to 1.6, and mortality from 1.5 to 1.7). In males, totals of 2,179 incident cases, 5,097 prevalent cases and 1,398 mortality cases were predicated to occur during the study period. For females the predicted figures were 1,379, 3,190 and 891, respectively. It was concluded that the upward trend of incidence alongside increase in survival rates would induce a high burden on the health care infrastructure in the province of Isfahan in the future.

• Molecules and Cells
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• v.28 no.4
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• pp.383-388
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• 2009

The dehydration responsive element binding protein 2C (DREB2C) is a dehydration responsive element/C-repeat (DRE/CRT)-motif binding transcription factor that induced by mild heat stress. Previous experiments established that overexpression of DREB2C cDNA driven by the cauliflower mosaic virus 35S promoter (35S:DREB2C) resulted in increased heat tolerance in Arabidopsis. We first analyzed the proteomic profiles in wild-type and 35S:DREB2C plants at a normal temperature ($22^{\circ}C$), but could not detect any differences between the proteomes of wild-type and 35S: DREB2C plants. The transcript level of DREB2C in 35S: DREB2C plants after treatment with mild heat stress was increased more than two times compared with expression in 35S:DREB2C plants under unstressed condition. A proteomic approach was used to decipher the molecular mechanisms underlying thermotolerance in 35S:DREB2C Arabidopsis plants. Eleven protein spots were identified as being differentially regulated in 35S:DREB2C plants. Moreover, in silico motif analysis showed that peptidyl-prolyl isomerase ROC4, glutathione transferase 8, pyridoxal biosynthesis protein PDX1, and elongation factor Tu contained one or more DRE/CRT motifs. To our knowledge, this study is the first to identify possible targets of DREB2C transcription factors at the protein level. The proteomic results were in agreement with transcriptional data.

• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.367-377
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• 2015

ADCPs have been highlighted so far for measuring steramflow discharge in terms of their high-order of accuracy, relatively low cost and less field operators driven by their easy in-situ operation. While ADCPs become increasingly dominant in hydrometric area, their actual measurement accuracy for velocity and bathymetry measurement has not been sufficiently validated due to the lack of reliable bench-mark data, and subsequently there are still many uncertain aspects for using ADCPs in the field. This research aimed at analyzing inter-comparison results between ADCP measurements with respect to the detailed ADV measurement in a specified field environment. Overall, 184 ADV points were collected for densely designed grids for the given cross-section that has 6 m of width, 1 m of depth, and 0.7 m/s of averaged mean flow velocity. Concurrently, ADCP fixed-points measurements were conducted for each 0.2m and 0.02m of horizontal and vertical spacing respectively. The inter-comparison results indicated that ADCP matched ADV velocity very accurately for 0.4~0.8 of relative depth (y/h), but noticeable deviation occurred between them in near surface and bottom region. For evaluating the capacity of measuring bathymetry of ADCPs, bottom tracking bathymetry based on oblique beams showed better performance than vertical beam approach, and similar results were shown for fixed and moving-boat method as well. Error analysis for velocity and bathymetry measurements of ADCP can be potentially able to be utilized for the more detailed uncertainty analysis of the ADCP discharge measurement.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.335-347
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• 2013

There are various types of real world signals. For example, an electrocardiogram(ECG) represents myocardium activities (contraction and relaxation) according to the beating of the heart. ECG can be expressed as the fluctuation of ampere ratings over time. A signal is a composite of various types of signals. An orchestra (which boasts a beautiful melody) consists of a variety of instruments with a unique frequency; subsequently, each sound is combined to form a perfect harmony. Various research on how to to decompose mixed stationary signals have been conducted. In the case of non-stationary signals, there is a limitation to use methodologies for stationary signals. Huang et al. (1998) proposed empirical mode decomposition(EMD) to deal with non-stationarity. EMD provides a data-driven approach to decompose a signal into intrinsic mode functions according to local oscillation through the identification of local extrema. However, due to the repeating process in the construction of envelopes, EMD algorithm is not efficient and not robust to a noise, and its computational complexity tends to increase as the size of a signal grows. In this research, we propose a new method to extract a local oscillation embedded in a signal by utilizing the second derivative.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.751-760
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• 2021

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.