• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.101-114
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• 2022

Recently, various studies on the entrepreneurial ecosystem have been conducted. The entrepreneurial ecosystem is composed of various elements such as entrepreneurs, governments, and infrastructure, and these factors interact to contribute to economic development. The purpose of this study was to analyze differences in importance and performance of the entrepreneurial ecosystem for startups using the importance-performance analysis (IPA) method. Based on this, the importance and current level of the components of the entrepreneurial ecosystem were identified and policy implications were presented. The results of the study are as follows. The importance ranking was in the order of startup support program(4.43), startup funding (4.39), market accessibility(4.30). The ranking of performance was startup support program(3.81), ease of starting a business(3.76), support for startup support institutions(3.66), and startup funding(3.66). All elements of the entrepreneurial ecosystem showed higher importance than performance. This means that the components of the entrepreneurial ecosystem in Korea are recognized as important, but do not play a significant role in terms of performance for startups. In addition, the factors with the highest improvement in the importance-performance matrix were 「safety nets for startup failure」, 「culture of acceptance of failure」, 「ease of market entry」, 「ease of startup survival」, and 「ease of exit」. This study suggested improvement measures such as establishing a social safety net, improving awareness of startup failure culture, matching successful startups, strengthening scale-up support by growth stage, easing regulations in new business fields, and diversifying investment recovery strategies.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.1
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• pp.1-16
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• 2024

Alvin Toffler mentioned that it is important for future society to keep pace with synchronization and that time deviations can hinder social development. As we experience the new normal era of untact, we have experienced an increase in non-face-to-face contact and accelerated digital transformation. Amid these rapid changes, we can maintain the need for synchronization or change in space. Therefore, we would like to study what kind of settlements people create and choose. We looked at the metaverse as an object that could indirectly find out about this, and used the content called "Animal Crossing" to collect data related to the spatial form of the metaverse. Sampling utilized a judgment sampling method during non-probability sampling to alleviate differences due to the progress of the game. The collected data was classified according to floor plan and location type and briefly organized through descriptive statistics. After matching each facility by use, data was constructed by setting coordinates for each cluster and listing them. This data was interpreted graphically on the coordinate plane for each cluster, and Euclidean analysis was performed to analyze the relationships between clusters and residential choice using a Euclidean matrix. As a result of the analysis, it could be interpreted that efficiency was pursued by arranging similar functions in close proximity. Nevertheless, when choosing a residence, it was interpreted that the intention was to create a community through arrangement adjacent to residents rather than efficiency or convenience. Due to the differences between the metaverse and the real world, it is expected that there will be limitations in equating it with reality. However, through the space expressed in the virtual world by people who are far away from the constraints of reality, we can indirectly know the wishes that we have not been able to express due to our lack of awareness.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.