• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.1-9
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• 2022

Today, artificial intelligence (AI) has emerged as a key driver of national competitiveness, but it is also causing unexpected side effects in society. This study intends to examine major social issues by collecting papers on AI targeting KCI journals in the field of social science. Therefore, we conducted keyword analysis of papers from 2016 to 2020. As a result of the analysis, the keywords for 'robot' and 'education' appeared the most, and the top six clusters (issues) were derived through the keyword network. The main issues are as follows: the background and/or basic concept of AI, AI education, side effects of AI, legal issues of AI-based creations, intention to use AI products/services, and AI ethics. The results of this study can be used to expand the discussion on the social aspects of AI and to find policy directions at the national level.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.77-87
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• 2020

This study aims to suggest basic data for developing forest product in Jeongeup City by surveying and analyzing consumer awareness and purchasing behavior. A survey was conducted focusing on local tourist attractions in Jeongeup City, and the analysis was conducted on 234 people. Among the local specialty processed products that the survey participants had experience in purchasing, food was mainly tea, concentrate solution, liquor, snacks and fruit syrup/enzyme. The therapy was shown in order of soap, aroma oil, and lotion. It was found that the purchase cost was more than 10,000 won and less than 30,000 won. Major purchase uses were for direct use and gifts, and 56.8% of the customers were satisfied with the satisfaction of the products, which were found to be purchased because of their good quality, good gift, and good health functions. In the question of 11 kinds of forest products, the subjects were aware of bokbunja, balloon flower, wild flower, deodeok, bracken, durum, and mal, among which bokbunja and wild flower were recognized as the representative forest products of Jeongeup. A cluster of food and therapy product selection attributes was analyzed to find target consumers. As the group that is interested in forest products and values the safety and quality of products is highly recognized, the value of forest products should be increased in consideration of the quality and safety of forest products when developing products in the future.

• Journal of Korea Technology Innovation Society
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• v.20 no.3
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• pp.576-606
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• 2017

It is more salient in the high-tech industry to cooperate even among competitors in order to promptly respond to the changes in product architecture. In this sense, 'co-opetition,' which is the combination word between 'cooperation' and 'competition,' is the new business term in the strategic management and represents the two concepts "simultaneously co-exist." From this view, this study set up the research purposes as follows: 1) investigating the corporate managerial and technological behavioral characteristics in the co-opetition of the global display industry. 2) verifying the emerging factors during the co-opetition behavior hereafter. 3) suggesting the strategic direction focusing on the co-opetition behavioral characteristics. To this end, this study used co-word network analysis to understand the structure in context level of the co-opetition. In order to understand topics on each network, we clustered the keywords by community detection algorithm based on modularity and labeled the cluster name. The results show that there were increasing patterns of competition rather than cooperation. Especially, the litigations for mutual control against Korean firms much more severely occurred and increased as time passed by. Investigating these network structure in technological evolution perspective, there were already active cooperation and competition among firms in the early 2000s surrounding the issues of OLED-related technology developments. From the middle of the 2000s, firm behaviors have focused on the acceleration of the existing technologies and the development of futuristic display. In other words, there has been competition to take leadership of the innovation in the level of final products such as the TV and smartphone by applying the display panel products. This study will provide not only better understanding on the context of the display industry, but also the analytical framework for the direction of the predictable innovation through analyzing the managerial and technological factors. Also, the methods can support CTOs and practitioners in the technology planning who should consider those factors in the process of decision making related to the strategic technology management and product development.

• Korean Journal of Food Science and Technology
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• v.24 no.3
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• pp.209-214
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• 1992

In order to save foreign currency and to domesticize the dairy products, various fresh cheeses and whey drinks were developed and some physicochemical, microbiological and sensory evaluation were performed. The yield of fresh cheese was 22.3%, while that of whey 77.7%. The pH-values of fresh cheeses were $5.90{\sim}6.49$, while those of whey drinks $6.07{\sim}6.49$, and fermented whey drinks $3.97{\sim}4.91$. The acidities of fresh cheeses were $0.09{\sim}0.26%$, while those of whey drinks $0.09{\sim}0.36%$. The contents of solid substances, protein and lactose in fresh cheeses were $25.67{\sim}34.18%$, $7.45{\sim}9.11%$ and $3.61{\sim}4.14%$, while those of whey drinks $7.39{\sim}7.70%$, $0.88{\sim}0.94%$ and $4.93(\sim}6.17%$, respectively. The lactic acid contents of whey drinks varied from $0.01{\sim}0.38%$, where the content in the fermented sample was the highest. The general colony counts of fresh cheeses were $0{\sim}30/g$, while those of whey drinks $0{\sim}80/ml$. The psychrotrophs counts of fresh cheeses were $0{\sim}20/g$, while those of whey drinks $0{\sim}60/ml$. Lactic acid bacterial counts in both products were not detected except for $97{\sim}401{\times}10^8/ml$ in fermented whey drinks. E. coli and fungi were not detected in both products. In sensory evaluation of both products, the strawberry added fresh cheese was the best of fresh cheeses, while the garlic added fresh cheese was the worst. Pure whey drink was the best of whey drinks, while the ginseng added whey drink was the worst.

• Journal of Food Hygiene and Safety
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• v.38 no.1
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• pp.1-11
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• 2023

Skin sanitizers are effective in killing or removing pathogenic microbial contaminants from the skin of food handlers, and the progressive growth of consumer interest in personal hygiene tends to drive product diversification. This review covers the advances in the application of efficacy tests for hand sanitizers to suggest future perspectives to establish an assessment system that is optimized to each product type (gel, liquid, and wipes). Previous research on the in vivo simulative test of actual consumer use has adopted diverse experimental conditions regardless of the product type. This highlights the importance of establishing optimal test protocols specialized for the compositional characteristics of sanitizers through the comparative analysis of test methods. Although the operational conditions of the mechanical actions associated with wiping can affect the efficacy of the removal and/or the inactivation of target microorganisms from the skin's surface, currently there is a lack of standardized use patterns for the exposure of hand sanitizing wipes to skin. Thus, major determinants affecting the results from each step of the overall assessment procedures [pre-treatment - exposure of sanitizers - microbial recovery] should be identified to modify current protocols and develop novel test methods. The ex vivo test, designed to overcome the limited reproducibility of in vivo human trials, is also expected to replicate the environment for the contact of sanitizers targeting skin microorganisms. Recent progress in the area of skin microbiome research revealed distinct microbial characteristics and distribution patterns after the application of sanitizers on hands to establish the test methods with the perspectives on the antimicrobial effects at the community level. The future perspectives presented in this study on the improvement of efficacy test methods for hand sanitizers can also contribute to public health and food safety through the commercialization of effective sanitizer products.

• Management & Information Systems Review
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• v.39 no.2
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• pp.181-193
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• 2020

This study is a study that analyzes consumer perception by utilizing text mining, which is a recent issue. we analyzed the consumer's perception of Samsung Galaxy by analyzing consumer reviews of Samsung Galaxy YouTube ads. for analysis, 1,819 consumer reviews of YouTube ads were extracted. through this data pre-processing, keywords for advertisements were classified and extracted into nouns, adjectives, and adverbs. after that, frequency analysis and emotional analysis were performed. Finally, clustering was performed through CONCOR. the summary of this study is as follows. the first most frequently mentioned words were Galaxy Note (n = 217), Good (n = 135), Pen (n = 40), and Function (n = 29). it can be judged through the advertisement that consumers "Galaxy Note", "Good", "Pen", and "Features" have good functional aspects for Samsung mobile phone products and positively recognize the Note Pen. in addition, the recognition of "Samsung Pay", "Innovation", "Design", and "iPhone" shows that Samsung's mobile phone is highly regarded for its innovative design and functional aspects of Samsung Pay. second, it is the result of sentiment analysis on YouTube advertising. As a result of emotional analysis, the ratio of emotional intensity was positive (75.95%) and higher than negative (24.05%). this means that consumers are positively aware of Samsung Galaxy mobile phones. As a result of the emotional keyword analysis, positive keywords were "good", "good", "innovative", "highest", "fast", "pretty", etc., negative keywords were "frightening", "I want to cry", "discomfort", "sorry", "no", etc. were extracted. the implication of this study is that most of the studies by quantitative analysis methods were considered when looking at the consumer perception study of existing advertisements. In this study, we deviated from quantitative research methods for advertising and attempted to analyze consumer perception through qualitative research. this is expected to have a great influence on future research, and I am sure that it will be a starting point for consumer awareness research through qualitative research.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.10 no.1
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• pp.1-7
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• 2005

Tidal flats have been regarded to carry out transformation and removal of land-derived organic matter, and this purifying capability of organic matter by tidal flats is one of very important reasons for their conservation. However, integral biogeochemical studies on production and decomposition of organic matter by benthic microbes in tidal flats have been absent in Korea, although the information is indispensable to quantification of the purifying capability. Our major goals in this multidisciplinary research were to understand major biogeochemical processes and rates mediated by diverse groups of microbes dominating material cycles in the tidal flats, and to assess the contribution of benthic microbes to removal of organic matter and nutrients in the tidal flats. Our study sites were Ganghwa and Incheon north-port tidal flats that had been regarded as naturally well reserved and organically polluted, respectively. Our research group measured over 3 years primary production, biomass and community structure of primary producers, abundance and production of bacteria, enzyme activities, distribution of protozoa and protozoan grazing rates, rates of denitrification and sulfate reduction, early sediment diagenesis, primary production and respiration based on oxygen microelectrode. We analyzed major features of each biogeochemical process and their interactions. The results are compiled in the following articles in this special issue: An (2005), Hwang and Cho (2005), Mok et at. (2005), Na and Lee (2005), Yang et at. (2005), and Yoo and Choi (2005).