• Journal of the Economic Geographical Society of Korea
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• v.27 no.1
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• pp.38-52
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• 2024

Digital transformation industry contributes to the improvement of productivity in overall industrial production, the smart green industry for carbon neutrality and sustainable growth is growing as a future industry. The purpose of this paper is to explore the status and role of the industry in the future industry innovation ecosystem through the analysis of the growth drivers and location pattern changes of the smart green industry. The industry is on the rise in both metropolitan and non-metropolitan areas, and the growth of the industry can be seen in non-metropolitan and non-urban areas. In particular, due to the smart green industrial complex pilot project, the creation of Gwangju Jeonnam Innovation City, and the promotion of new and renewable energy policies, the emergence of core aggregation areas (HH type) in the coastal areas of Honam and Chungcheongnam-do, and the formation of isolated centers (HL type) in the Gyeongsang region, new and renewable energy production companies are being accumulated in non-metropolitan areas. Therefore, the smart green industry is expected to promote the formation of various specialized spokes in non-urban areas in the future industrial innovation ecosystem that forms a multipolar hub-spoke network structure, where policy factors are the triggers for growth.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.172-178
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• 2003

In the recent researches, a variety of ways for determining the locations of RBF centers have been proposed assuming that the number of RBF centers is known. But they have also many numerical drawbacks. We propose a new method to overcome such drawbacks. The strength of our method is to determine the locations and the number of RBF centers at the same time without any assumption about the number of RBF centers. The proposed method consists of two phases. The first phase is to determine the number and the locations of RBF centers using bi-section search method and enhanced k-medoids clustering which overcomes drawbacks of clustering algorithm. In the second phase, network weights are computed and the design of RBF network is completed. This new method is applied to several benchmark data sets. Benchmark results show that the proposed method is competitive with the previously reported approaches for center selection.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.19-29
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• 2010

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.4
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• pp.32-40
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• 2004

This study tried to analyze the characteristic data, gathered by the semantic differential method, of respondents, user manuals and agricultural machines with the traditional statistical approach, i.e., cluster analysis and factor analysis semantic differential methods. Though the existing papers of the traditional sensory engineering only suggested the fragmentary result of analysis, this study tries to analyze the data with step-by-step approach, in which this study is analyzing the data with cluster analysis to get the characteristics of respondents, and then using the factor analysis to condensing the adjectives of describing the manual characteristics into several components. Concludingly, this study suggested a prototype of analyzing the semantic differential data with using cluster analysis and factor analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• Journal of the Korea Computer Industry Society
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• v.5 no.1
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• pp.101-108
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• 2004

As the dependency to network system and demands of efficient storage systems rapidly grows in every networking filed, the current trends initiated by explosive networked data grow due to the wide-spread of internet multimedia data and internet requires a paradigm shift from computing-centric to data-centric in storagesystems. Furthermore, the new environment of file systems such as NAS(Network Attached Storage) and SAN(Storage Area Network) is adopted to the existing storage paradigm for Providing high availability and efficient data access. We describe the design issues and system components of SANiqueTM, which is the cluster file system based on SAN environment. SANiqueTM has the capability of transferring the user data from the network-attached SAN disk to client applications directly We, especially, present the protocol and functionality of the global buffer manager in our cluster file system.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1421-1426
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• 2001

Nowadays, as the quantity of multimedia information increases rapidly, an efficient management for multimedia has become more important. In this paper, to index and search multimedia contents efficiently, we designed the algorithm searching specific image and saving the extracted image using the semantic information extraction scheme based on contents and it is one of the schemes to indexing and searching of video data. After extracting the RGB information from input image, while all frames of video is inspected sequentially, the specific image is saved through referring to the position and distribution of contents from the collection scheme of RGB range. In case of using the proposed image extraction algorithm, because only saved video is searched instead of the whole the searching time can be reduced.

• The Korean Journal of Health Service Management
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• v.5 no.3
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• pp.53-61
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• 2011

This study was to assess the differences in rehabilitation outcomes between the different facilities in Japan, and to determine if there was any variation in patients' functional recovery at hospital discharge across the different facilities. This study focused on acute patients in the rehabilitation ward using the data of 1,830 patients from 8 hospitals after adjusting for triage at admission obtained from the Rehabilitation Patients Databank in Japan (issued in February, 2011) and compared the therapeutic results of each hospital. We estimate the expected value of levels of activities of daily living(ADL) at discharge for rehabilitation patients using regression analysis and Cluster analysis. There were differences among hospitals in their therapeutic results. The differences in the participation of physicians registered as rehabilitation specialists, amount of exercise, self-exercise without therapist, and exercise in wards, were statistically significant differences between hospitals.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.3
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• pp.87-99
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• 2008

This study investigates the management performance of primary medical centers on the basis of power process perspective to suggest the direction for their competitive advantages. To do so, this study conducts the questionnaire survey to middle and top managers of primary medical centers in Seoul, Incheon, and Kyoungki Province and analyzes the data by using cluster analysis and variance analysis of SPSS statistical package. According to the results, the types of primary medical centers are determined by the interaction of power determinants variables, and the type of primary medical centers which has the strongest interaction of power determinants variables shows the highest management performance compared to other types.