• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.140-145
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• 2010

In this work, to theoretically analyze the nonlinear charging characteristic, a Radial Basis Function Neural Network (RBFNN) is adopted. Based on the RBFNN, an charging characteristic tendency of a Linear Type Magnetic Flux Pump (LTMFP) is analyzed. In the paper, we developed the LTMFP that generates stable and controllable charging current and also experimentally investigated its charging characteristic in the cryogenic system. From these experimental results, the charging current of the LTMFP was also found to be frequency dependent with nonlinear quality due to the nonlinear magnetic behaviour of superconducting Nb foil. On the whole, in the case of essentially cryogenic experiment, since cooling costs loomed large in the cryogenic environment, it is difficult to carry out various experiments. Consequentially, in this paper, we estimated the nonlinear characteristic of charging current as well as realized the intelligent model via the design of RBFNN based on the experimental data. In this paper, we view RBF neural networks as predominantly data driven constructs whose processing is based upon an effective usage of experimental data through a prudent process of Fuzzy C-Means clustering method. Also, the receptive fields of the proposed RBF neural network are formed by the FCM clustering.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.1-14
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• 2021

Innovation and Scale-up of Start-up companies are becoming important national tasks. In the past, it was spread the start-up policy paradigm such as 'Start-up America', 'Start-up Chile', 'Start-up Britain' to overcome the recession globally. However as the economic recovery has become more visible recently in advanced economies, it is shifting from a start-up support policy to a scale-up oriented policy paradigm such as 'Scale-up America', Scale-up UK', 'Scale-up Denmark'. It is necessary to enter the scale-up phase beyond the start-up phase to increase the number of high-quality jobs and to continue economic growth. Therefore, it is necessary to grow the start-up into a strong medium-sized company and to lay the foundation for survival. Therefore, the purpose of this study is to consider the antecedent factors that influence the scale-up aspiration for the start-up firm to grow into a scale-up company, and empirically identifies the differences between the stages of economic development and entrepreneurs in the country. In order to accomplish the purpose, this study predicted scale-up by aspiration which is a predictor of scale-up behavior because it is difficult to achieve visible growth in a short period of time due to the characteristics of start-up companies. In order to empirically explore these relationships, the data were collected from nascent entrepreneurs who have less than 3.5 years of the Adult Population Survey(APS) among the subjects surveyed by the Global Entrepreneurship Monitor(GEM) and the national economic development stage are divided into Innovation-driven, Efficiency-driven, Factor-driven type economies. For the test hypotheses, this study adopted the multi-level model analysis for comparison between national economic development stages and using the R 3.5.0 program. The results of this study are as follows. There is difference between the national economic development and the entrepreneur in the relationship between innovation orientation of entrepreneurs and scale-up aspirations. As the economy of the country develops, the innovation activity of the entrepreneur becomes more active. Since start-ups are heavily influenced by entrepreneurs, there is a difference in the degree of aspiration depending on how innovative an entrepreneur is in the same environment. In terms of the relationship between innovation orientation and scale-up aspiration, the fear of failure was found to differ between national economic development and entrepreneurs. The fear of failure differ from country to country, and this is one of the important factors affecting entrepreneurial activities. It is expected that the factors influencing the growth of the start-up companies which are identified through the results of these studies, will be used to create a suitable scale-up ecosystem according to the national economic development stage.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Management & Information Systems Review
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• v.39 no.1
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• pp.93-114
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• 2020

Platform is considered as an alternative strategy to the traditional linear pipeline based business. Moreover, in the 4th industrial revolution period, efficiency driven pipeline business model needs to be changed to platform business. We have such success stories about platform as Apple, Google, Amazon, Uber, and so on. However, for those smaller corporations, it is not easy to find out the transformation strategy. The essence of platform business is to leverage network effect in management. Thus platform based management can be rephrased as network management across the business functions. Research on platform business is popular and related to diverse facets. But few scholars cover what the research trend of the domain is. The main purpose of this paper is to identify the research trend on platform business in Korea. To do that we first propose the analytical model for platform architecture whose components are consumers, suppliers, artifacts, and IT platform system. We conjecture that mapping of the research work on platform to the components of the model will make us understand the hidden domain of platform research. We propose three hypotheses regarding the characteristics of research and one proposition for the transitional path from pipeline to platform business model. The mapping is based on the research articles filtered from the Korea Citation Index, using keyword search. Research papers are searched through the keywords provided by authors using the word of "platform". The filtered articles are summarized in terms of the attributes such as major component of platform considered, platform type, main purpose of the research, and research method. Using the filtered data, we test the hypotheses in exploratory ways. The contribution of our research is as follows: First, based on the findings, scholars can find the areas of research on the domain: areas where research has been matured and territory where future research is actively sought. Second, the proposition provided can give business practitioners the guideline for changing their strategy from pipeline to platform oriented. This research needs to be considered as exploratory not inferential since subjective judgments are involved in data collection, classification, and interpretation of research articles.

• Spatial Information Research
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• v.21 no.3
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• pp.45-53
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• 2013

Web GIS, based on the latest web-technology, has evolved to provide efficient and accurate spatial information to users. Furthermore, Web GIS Server has improved the performance constantly to respond user web requests and to offer spatial information service. This research aims to create a designed and implemented Web GIS Server that is named as Nodemap which uses the emergent technology, Node.js, which has been issued for an event-oriented, non-blocking I/O model framework for coding JavaScript on the server development. Basically, NodeMap is Web GIS Server that supports OGC implementation specification. It is designed to process GIS data by using DBMS, which supports spatial index and standard spatial query function. And NodeMap uses Node-Canvas module supported HTML5 canvas to render spatial information on tile map. Lastly, NodeMap uses Express module based connect module framework. NodaMap performance demonstration confirmed a possibility of applying Node.js as a (next/future) Web GIS Server development technology through the benchmarking. Having completed its quality test of NodeMap, this study has shown the compatibility and potential for Node.js as a Web GIS server development technology, and has shown the bright future of internet GIS service.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.2
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• pp.217-225
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• 2007

This paper proposes the real-time hybrid shaking table testing methods to simulate the dynamic behavior of a soil-structure interaction system with dynamic soil stiffness by using only a structure model as the physical specimen and verifies their effectiveness for experimental implementation. Experimental methodologies proposed in this paper adopt such a way that absolute accelerations measured from the superstructure and shaking table are feedback to the shaking table controller, and then the shaking table is driven by the calculated motion of the absolute acceleration (acceleration feedback method) or the absolute velocity (velocity feedback method) of foundation that is required to simulate the dynamic behavior of a whole soil-structure interaction system. The shaking table test is implemented by reflecting the dynamic soil stiffness, which are differently approximated from the theoretical one depending on the feedback methods, on the shaking table controller to calculate soil part. The effectiveness of the proposed experimental methods is verified by comparing the response measured from the test on a foundation-fixed structural model and that obtained from the experiment of a soil-interaction system under the consideration in this paper and by matching the dynamic soil stiffness reflected on the shaking table controller with that identified using the experimentally measured data.

• Journal of Distribution Science
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• v.13 no.10
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• pp.83-91
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• 2015

Purpose - The purpose of this study is to identify whether the operation of cultural centers in discount stores contributes to their profitability. Thus, this study is aimed at exploring how the lifestyles of customers who use the cultural centers influence their intention to use the discount stores. Specifically, the effect of shopping value on the correlation between the lifestyle types and usage intention of the customers were examined through a structured research model. To verify the effect, a survey on 139 customers of the Cultural Center of Nonghyup Hanaro Club's S branch was conducted and the valid questionnaires were used for analysis. Research design, data, and methodology - The findings are as follows. First, the lifestyles seeking self-realization had a positive effect on utilitarian value, and lifestyles seeking pop cultures had a positive effect on hedonic value. Second, the mediating effect of shopping value on the correlation between the lifestyle types and usage intention of the customers is as follows. Utilitarian value had a mediating effect only on the lifestyles seeking self-realization. In case of lifestyles seeking pop cultures, the use of Cultural Center had no effect on the intention to use the discount store. Third, an analysis of a revised research model revealed that the store usage intention of lifestyles seeking pop cultures can be enhanced by boosting the utilitarian value through hedonic value. Results - The findings suggest the following. Customers with lifestyles seeking self-realization, who value what is beneficial to them with little attention to the perceptions of others, are highly interested in the benefits they can gain from shopping. As for customers with lifestyles seeking pop cultures, they are highly likely to consume products popular in a particular culture such as new products and sports, based on financial stability they pursue. Thus, they prefer more subjective, personal experience, unlike consumers pursuing utilitarian value. Conclusions - As a result, the former pursues hedonic value gained in the process of shopping with fun and joy, rather than doing shopping with a particular purpose in mind. Therefore, Cultural Centers need to offer information that fits the lifestyles of the users so that they are more likely to use the discount stores. However, if the Cultural Centers offer unified, profit-driven products and information, just to increase their store sales, it can backfire, which occurred in the past. On the other hand, if they provide information that fits the lifestyles of the users, it can actually increase the sales. Also, the findings suggest that sophisticated marketing strategies that can boost the hedonic value of customers by linking the educational contents of Cultural Centers to actual shopping, which is beneficial to consumers, should be set and operated by discount stores. In particular, customers with lifestyles seeking self-realization can be encouraged to use the stores by making them recognize the utilitarian value. However, the use of Cultural Centers doesn't necessarily lead to higher sales among customers with lifestyles seeking pop cultures. As mentioned previously, unified marketing strategy is not as effective for Cultural Centers of large discount stores.

• The KIPS Transactions:PartD
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• v.17D no.2
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• pp.157-166
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• 2010

More recently, RFID (Radio Frequency Identification) systems have begun to find greater use in various industrial fields. The use of RFID system in these application domains has been promoted by efforts to develop the RFID tags which are low in cost, small in size, and high in performance. The RFID applications enable the real-time capture and update of RFID tag information, while simultaneously allowing business process change through real-time alerting and alarms. These be developed to monitor person or objects with RFID tags in a place and to provide visibility and traceability of the seamless flows of RFID tags. In this time, the RFID readers should be placed in diverse locations, the RFID flows between these readers can be tested based on various scenarios. However, due to the high cost of RFID readers, it may be difficult to prepare the similar environment equipped with RFID read/write devices. In this paper, we propose a simulator to allow RFID application testing without installing physical devices. It can model the RFID deployment environment, place various RFID readers and sensors on this model, and move the RFID tags through the business processes. This simulator can improve the software development productivity by accurately testing RFID middleware and applications. In addition, when data security cannot be ensured by any fault, it can decide where the problem is occurred between RFID hardware and middleware.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.1-10
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• 2021

The purpose of this study is to explore a significant HPWS(High Performance Work System) model for the entertainment industry. HPWS is one of the most studied themes for managing human resources as well as a set of practices to elicit employees' commitment to an organization. Recently, the entertainment industry is growing rapidly, but it is difficult for entertainment firms to retain a stable profit unlike the manufacturing industry. This is because the performance of entertainment business tends to rely heavily on the capabilities and synergy of human resources. In order to suggest a systematic way to manage these, this research identified an effective HPWS model for entertainment business and provides a competitive advantage to entertainment firms, using ANP(Analytic Network Process). ANP is a multicriteria decision making technique that allows dependences and feedbacks among decision elements in the hierarchical or network structures in a holistic manner. The pairwise comparison data that prioritized the criteria of HPWS was collected from 28 team leaders in entertainment firms. According to our results, the most critical factor for HPWS in entertainment business is "employee involvement in decision-making." The sub-factors such as "open communication," "distributive decision-making," and "performance-driven reward" have a greater effect. These findings could provide implications for entertainment firms to determine which practices should be taken into account to accomplish HPWS.