• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.279-286
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• 2015

The rapid increase of the current marine accidents is mainly due to the human execution errors. In an effort to address this, various kinds of researches such as construction of the digital vessels and vessel information monitoring systems have been conducted. But for safe navigation of vessels, it lack on systems study which can efficiently store, utilize and manage the mass data accepted by the vessel. In this paper, we propose a VWS(Virtual World System) that is based on the architecture of intelligent systems RVC(Reactive Layer-Virtual World-Considerative Layer) model of intelligent autonomous navigation system. VWS is responsible to store all the necessary information for safe navigation of the vessel and the information services to the sub-system of intelligent autonomous navigation system. VWS uses topology database to express the specific problem area, and utilizes a scheduling to reflect the characteristics of the real-time processing environment. Also, Virtual World defines API for the system to reflect the characteristics of the distributed processing environment. As a case study, the VWS is applied to a intelligent ship autonomous navigation system, and simulation is done to prove the effectiveness of the proposed system.

• Journal of Korea Technology Innovation Society
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• v.10 no.3
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• pp.383-405
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• 2007

In this paper we estimate knowledge production function for 15 South Korean industry sectors using panel data. To accommodate the influence of inter-sectoral interactions on the creation of knowledge, we estimate parameters for related knowledge production functions using the Dynamic Seemingly Unrelated Regression(DSUR) model proposed by Mark et al. (2005). We find the elasticity of knowledge production with respect to the size of research staff to be 0.25 and that with respect to the existing stock of knowledge to be 0.35. The fact that the elasticity of new knowledge creation with regard to the existing knowledge stock is below 1 in South Korea corroborates the view that the rate of long-term growth of her economy is chiefly determined by the elasticity related to production functions of goods and services and the rate of population growth, and that her government policy, to ensure a continued growth for the Korean economy, must shift the focus of R&D policies from the current direct intervention-centered model to one consisting of indirect measures, namely supporting knowledge management and diffusion and the creation of a knowledge sharing system. In terms of R&D policy implications it could be consider that the national knowledge production system should strengthen the cumulative process of knowledge accumulation and population for research and development. Our country R&D policy, also, need to adopt a global approach to increase knowledge stock at the highest levels of a country.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.109-119
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• 2017

This paper is to analyse empirically using the data collected from Korea portal Naver's ad management system and show online ad platform may not be two-sided market. It is aim of this study to propose the appropriate approach to define the market, based on the empirical result. Here are two research questions to be reviewed. First, is there any consistency between business model of search advertising and definition of two-sided market which Rochet-Tirole proposed in 2006? Second, do indirect network externalities exist significantly in search advertising market? if so, this study is going to estimate the level of it through empirical measurement. Based on Luchetta's paper which suggested that google may be one-sided market, it performed the correlation & regression analysis to prove his suggestion. The result is that online search advertising costs increased by more than 50 won when advertisers increased by one unit. However, there was no significant correlation and regression between the search frequency and online search advertising cost. It means that there is little possibility to identify two-sidedness in online search advertising service(market) because of no(or little) indirect network externalities which are a necessary condition for two-sided market. This result has three implications, such as the availability to adapt traditional market definition tools to online search advertising market, the possibility enhancement to find the fundamental competition elements in defined market and promotion of the powers of persuasion in competitive market reality. It is significant that the gap between legal scholars including regulatory practitioners and economists can be overcome to some extent. who have shown the different perspective on the two-sided market.

• Korean journal of applied entomology
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• v.51 no.4
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• pp.383-388
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• 2012

This study was conducted to assess damage to the welsh onion by Acrolepiopsis sapporensis Mastumura (Lepidoptera: Acrolepiidae) during the summer season, and to estimate the economic injury level (EIL) which results, providing basic data for pest management. The adult peaks of A. sapporensis in Suwon were observed in March, April, May, and September of 2009 and 2010 through the use of pheromone traps. The feeding of A. sapporensis results in white lines on the surface, and holes within welsh onions. During the entire larval period, one larva was able to damage 1.6 leaves and punch 11.9 holes, resulting in a total damaged leaf area of $1,321.6mm^2$. Through cage experiments and larval releases, a reduction of the mean number of leaves, increased percentage of leaf damage, and reduction in gross weight and marketable weight were observed. We could obtain a regression equation that showed a clear positive correlation between pest density and percentage of damaged leaf. Using this equation, the EIL of A. sapporensis on welsh onion was calculated to one larva per 10 plants based on 7% leaf damage observed for welsh onion.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.639-651
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• 2013

This study aims to improve caregiver's labor conditions, to provide a high-quality care service and to offer basic research information for developing a standardized job content of caregiver. The study establishes basic principles for a standardized job content with a literature review and then collects basic information of caregiver's task with an observation technique. The study based on the observation technique develops 11 job categories and 72 duty categories expected to do by the caregiver. The study examines acute care hospitals that provide joint care services in Seoul and Gyeonggi province. Sixty caregivers who work at these hospitals are carried out a survey regarding a specific task of caregiver, an importance of task and a frequency of task. Collected data is analyzed using statistical package SPSS 18.0, and descriptive statistics (frequency, percentage, average, standard deviation) is reported. An analysis of frequency is used to analyze target for research. From the collected information, tasks of caregiver are sorted into 10 job categories and 60 duty categories. As a result of the study, tasks of caregiver are sorted into 10 job categories and 68 duty categories based on the observations technique. One job category and four duty categories are added to the final survey based on an interview with professionals and officials in the field of care service but, one additional job category and four duty categories based on the interview are excepted from the result of the analysis. The interview with professionals and officials is nothing meaningful on the task analysis of caregiver.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.33-33
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• 2020

Watermelon (Citrullus lanatus) is a non-seasonal, economically important, cucurbit cultivated throughout the world with Asia as a continent contributing the most. As part of the effort in diversifying watermelon genetic resources in the already cultivated group, this study was devoted to providing baseline data on morphological quality traits and health-beneficial phytonutrients of watermelon germplasm collections, thereby promoting watermelon research and cultivation programs. To this end, we reported morphological traits, citrulline, and arginine levels of watermelon genetic resources obtained from the gene bank of Agrobiodiversity Center, Republic of Korea, and discussed the relationship between each other. Diverse characteristics were observed among many of the traits. But, most of the genetic resources (>90%) were either red or pink-fleshed. Korean origin fruits contained intermediate levels of soluble solid content (SSC) while The USA, Russian, Tajikistan, Turkmenistan, Taiwan, and Uruguay originated had generally the highest levels of soluble solids. The citrulline and arginine contents using HPLC method were ranged from 6.9 to 52.1 mg/g (average, 27.3 mg/g) and 1.8 to 21.3 mg/g (average, 9.8 mg/g), respectively. The citrulline content determined using Citrulline Assay Kit was ranged from 6.5 to 42.8 mg/g (average, 27.0 mg/g). Resources with high citrulline and arginine levels contained low SSC. Whereas, red- and pink-colored flesh samples had less citrulline compared to yellow and orange. In addition to the profiling of morphological characters and phytonutrients, molecular marker characterization and identification of sources of resistance to diseases and pests are recommended for a more complete diversity analysis of watermelon genetic resources.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Industrial Convergence
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• v.1 no.1
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• pp.41-70
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• 2003

Many researches on brand equity have been focused in definition about it, factors of it, and the process of formation. Most of them have been used by voluntary production category, as is durable goods or nondurable goods. But this study, using the model is developed by Foote, Cone & Be1ding(FCB) Company, classified four fields, high-low involvement, rationality(rational or sensitive) involvement. The selected goods is a sensitive high involvement(casual wear). This study investigate the effects of brand equity and search the influences of brand equity formation according to factors of marketing mix. To this goals, this study kept a literature survey and a demonstrative research. In literature survey, there are several definitions of brand and brand equity. The research model is derived from selected factors of marketing mix and former study. This study used the regression analysis to verify effects from brand equity through the selected marketing mix. The research data is collected from the capital area. The focus of this study is effects of brand equity according to marketing mix. The followings are results and suggestions of this study. First, in the price factors, the affirmative effects are revealed the perceived quality and the brand awareness in a rational high involvement goods, the perceived quality and the brand associations in a sensitive high involvement goods, all factors of brand equity in a rational low involvement goods, and the perceived quality in a sensitive low involvement goods. As summary, the important characteristics is the price factors to consumers, and consumers recognize that a high price means a high quality. Second, in the store image factors, the affirmative effects are revealed all brand equity factors in a rational high involvement and a sensitive high-low involvement. A good store image incites more interest, contact, and visit from potential consumer. And such store offers more consumer satisfaction, simulates more active and positive conversation to consumers. Third, in advertising spending factors, the affirmative effects are revealed the brand awareness and associations in a rational high involvement and a sensitive high involvement, all brand equity factors in a rational low involvement and a sensitive low involvement. An advertisement increases not only a brand awareness but also strong brand associations. Forth, in price promotion factors, the affirmative effects are revealed the brand associations in a rational high involvement, the negative effects are revealed all brand equity in sensitive high involvement. According the result about the effects of brand royalty through the brand equity factors, a perceived quality and brand associations have positive effects to brand royalty in all factors. Consumers choice a deep perceived quality than other competitive brand. So, brand equity will increase according to a qualitative grade of a perceived brand by consumers. Brand associations represent a quality and a degree of involvement. In conclusion, brand associations and equity have a positive relation each other. According to the analysis results about a brand royalty of selected marketing mix factors, the affirmative effects are revealed the store image and price promotion factors in a rational high involvement, the price and store image in a sensitive high involvement, and the price and advertising spending in a rational low involvement. The results about the affect of selected marketing mix factors according to brand equity, are the perceived quality in a high involvement, and all brand equity factors in a low involvement. The affirmative effects about a store image are revealed all equity factors in high-low involvement. In advertising spending factors, the affirmative effects are revealed the brand awareness and associations in a high involvement goods, and the perceived quality and the brand awareness in a low involvement goods. In price promotion factors, the affirmative effects are revealed the brand awareness in a low involvement goods, and the negative effects are revealed the brand awareness in a high involvement goods. According to a degree of involvement, the results of a brand royalty through a brand equity factors are following. The affirmative effects are revealed the perceived quality and the brand royalty in a high involvement goods, and the brand awareness are revealed a negative effect. The affirmative effects are revealed the perceived Quality and the brand associations in a low involvement goods. So, in a high involvement goods, the brand royalty is built by strong brand associations, but, in a low involvement goods, the brand royalty is built also by a perceived Quality and a brand awareness. This study have some concept of limitation. So, this study presents a future direction of research. First, a future study has to have more deep analysis for this study analyzed through a limited marketing mix factors. Second, a future research has to get mutual effects about brand equity of marketing mix factors for this study has an individual marketing decision factors. Third, for the future, a brand equity needs a research about a several goods such as services, profit or nonprofit, industrial products, culture, and so on. Forth, the research have to diversify a various data for population.