• Journal of Digital Convergence
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• v.9 no.6
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• pp.137-151
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• 2011

This dissertation aims to empirically analyze the effect of cultivation of software industry on the local economy through Inter-regional Software Input-Output Analysis. The temporal range of analysis of effect of software industry on the local economy shall be for the year 2005 since analysis is made on the basis of the Regional Industrial Input-Output Table published by the Bank of Korea in 2005, and spatial domain shall be limited to the 16 metropolitan cities and provinces, which are the standards for each administrative zone. Results of analysis of this dissertation are as follows. Firstly, average inverse matrix coefficient of software industry for each region was computed to be 1.6248, which is lower than the average inverse matrix coefficient of 1.7979 for the entire industries. Secondly, among these, inverse matrix coefficient of software industry for each region on other industry within the same region was 0.1794, which is higher than that of entire industries at 0.1382. However, average inverse matrix coefficients of software industry for each region on self-industry within the same region and entire industries in other regions were found to be 1.0119 and 0.4335, respectively, which is lower than those of entire industries at 1.0982 and 0.5616, respectively. Thirdly, domestic produces induced by final demand items of software industry for each region was the highest for Seoul with 17.3309 trillion Korean won, accounting for 81.0% of the total, followed by Gyeonggi with 2.3370 trillion Korean won, 10.9% of the total. Fourthly, distribution ratios of domestic produces induced by final demand items of software industry for each region were found to be 19.1%, 72.1% and 8.8% with respect to the weight of consumption, investment and export, respectively, thereby illustrating very high level of distribution ratios of domestic produces being induced by investment in comparison to the distribution ratios of domestic produces being induced for the entire industries at 47.3%, 19.8% and 32.9%, respectively.

• Cartoon and Animation Studies
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• s.26
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• pp.131-156
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• 2012

As the Global Age has arrived, the domain of festivals has expanded to fulfill the role of being not only a tourist attraction but of being a factor that determines the image and identity of cities, and the factor of enhancing the brand value of a particular city is being focused upon. The city of Bucheon, which aims to be a culture oriented city, is attempting to utilize the Bucheon International Comics Festival as a cultural asset for the revitalization of the city. This study has as its purpose the development of an evaluation index model on the brand value of the Bucheon International Comics Festival and research being conducted based on the developed evaluation index model on the awareness level of the citizens of Bucheon of the festival. In regards to this, the theoretical background was examined and the index model was developed based on precedent research. Based on this, a survey of 1,000 citizens of Bucheon was conducted in this study. This study conducted a survey targeting 500 persons, dividing them into 2 groups according to whether they participated in the festival. The survey of this study established 9 evaluation categories for the International Comics Festival evaluation index model which consists of demographic research and participation motivation, value of comics, festival brand awareness and association image, perceived product quality and loyalty for the festival, internationality of the festival and urban activation. Each survey question is composed of 5 points scale measurement. As a result of the survey, 'for an education of children' was the highest for the participation motivation, and 'not knowing of the festival information' was the highest for the reason of not having participated. The industrial value was evaluated as the highest among the value of comics by the both two groups, and it was studied that there was perception gap for the festival according to whether they participated in the festival for each survey question. It was revealed that the level of awareness of the Bucheon International Comics Festival was "normal," the "city revitalization" index and the "value of comics" index were relatively high and the "international character of the festival" index was the lowest. Furthermore, it was shown that there were differences in the awareness of the established categories of the developed evaluation index model based on whether or not there was participation in the festival. This study comprehensively organizes these analytical results and derives implications which can be used as data for the criteria of the development of future strategy for the Bucheon International Comics Festival.

• Microbiology and Biotechnology Letters
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• v.45 no.1
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• pp.63-70
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• 2017

A functional screen of 60,672 fosmid metagenomic clones amplified from marine sediment obtained from the Dokdo islets in Korea identified the gene EstES1, whose product, EstES1, displayed lipolytic properties on tributyrin-supplemented media. EstES1 is a 576 amino acid protein with a predicted molecular weight of 59.4 kDa including 37 N-terminal leader amino acids. EstES1 exhibited the highest sequence similarity (44%) to a carboxylesterase found in Haliangium ochraceum DSM14365. Phylogenetic analysis indicated that EstES1 belongs to a currently uncharacterized family of lipases. Within the conserved domain, EstES1 retains the catalytic triad that consists of the consensus penta-peptide motif, GESAG. EstES1 demonstrated a broad substrate specificity toward the long acyl group of ethyl esters (C2-C12), and its optimal activity was recorded toward p-Nitrophenyl butyrate (C4) at pH 9.0 and $40^{\circ}C$ (specific activity of 255.4 U/mg). The enzyme remained stable in the ranges of $60-65^{\circ}C$ and pH 9.0-10.5 and in the presence of methanol, ethanol, isopropanol, and dimethyl sulfoxide. Therefore, EstES1 has potential for use in industrial applications involving high temperature, organic solvents, and/or alkaline conditions.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Industry Promotion Research
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• v.7 no.2
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• pp.43-49
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• 2022

This study aims to study Esping-Anderson's theory of welfare state system, develop a model of welfare state suitable for Korea's situation, and apply it to reality. In this research method, basic research and analysis of ideology is used, focusing on Esping-Anderson's welfare state system theory, and applying it appropriately to the Korean situation. Studies on the model of the welfare state have been studied after the classification of complementary and institutional models asserted by Willensky and Lebo in 1965. In addition, Esping-Andersen asserts three things as a model of the welfare state according to ideology. First, the role of the market is central to the liberal welfare system that best fits the image of classical capitalism, and individualistic solidarity through the market. The role of the state or family, which can be a hindrance, is actually marginalized. In addition, in order to maximize individualistic solidarity through the market, de-commodification in the national domain tends to be minimized. Second, the conservative welfare system has a strong familistic element, so the source of social solidarity is the family, and the state plays a role of supporting and supplementing the characteristics of this family. In the conservative system, de-commodification appears to be high among household heads, or the welfare system takes on a corporatist and nationalistic form, it can be said that these characteristics are reflected. Third, in the social democratic welfare system, the source of social solidarity is the state. Therefore, the role of the state is large, the state has a high possibility of decommodification, and it has the characteristics of substitutes for the family and the market through universalist intervention. This study applies Esping-Anderson's three welfare state models to study a model suitable for the Korean situation. In conclusion, Esping-Anderson's three welfare state models can be classified into a market-oriented model based on a liberal welfare system, a status-oriented model based on a conservative corporatist welfare system, and a solidarity-oriented model based on a social-democratic welfare system, presented a compromise between liberalism and conservatism as a Korean model.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.