• Korea Trade Review
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• v.47 no.5
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• pp.253-272
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• 2022

As the digital era accelerates, traditional perspectives have limitations in explaining the success or failure of export performance. The purpose of this study is to analyze new factors affecting export performance from the perspective of customer experience, which has emerged as an important factor in securing a competitive advantage and generating organizational performance. After deriving hypotheses based on literature review and discussion, a research model is designed in which three factors of customer experience such as understanding customer's objectives, customer value creation capability, and customer journey management are the antecedents, and export performance is the dependent variable. This model also includes organizational agility and personal contact as the moderating variables. To verify the hypotheses, multiple regression analysis was conducted on the collected data drawn from 198 SME exporters. According to the analysis results, it was found that all three antecedents positively affected export performance. In particular, the organizational agility and personal contact were confirmed to have a moderating effect that creates better export performance by interacting with customer value creation capability. The theoretical significance of this study is to find that effective customer experience management can be a key factor in creating export performance. The results suggest that checking the overall customer journey, exporters should select and intervene to intensively manage key touch points that can have a decisive impact on the quality of customer experience. At the end of the paper, practical implications to be considered in creating export performance through effective customer experience management are presented.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.479-486
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• 2013

The data model that can be said as skeleton of the information system constitutes important 2 axles in the information system together with the process model. There is entity, properties, relation as key factors of the data model, and entity is the most fundamental factor in the data model, and thus total data model becomes vague if not deriving entity definitely. This study dealt with entity deduction only. Deducing methods of existing entity depended on experiences, task knowledge of designers and clear procedures were not suggested, so there were many difficulties in approaching them from beginners or unskilled persons. For giving helps in solving the problem, this study proposes entity- deducing procedures based on tasks that can derive entity with a systematic process at previously derived target businesses through suggested methods from advancing researches. And the study enabled proposing procedures on imaginary tasks to be applied, objecting to undergraduates who had not experiences on the data modeling, and then verified suggesting process through a similarity checking between best answers with deduced entity by students after taking impossible points of comparing existing methods with suggesting process into consideration. By doing so, deducing entity closely to the best answer was confirmed accordingly. Therefore, a fact could be confirmed that beginners were able to deduce entity closely to the best answer even if letting beginners who had not experiences on the data modeling be applied to unfamiliar tasks. Regarding researches on properties and relation deduction besides entity, this study leaves them to next time.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.612-620
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• 2013

In this study, time series analysis was tried, which is widely applied to demand forecast of diverse fields such as finance, economy, trade, and so on, different from previous regression analysis. Future marine traffic volume was forecasted on the basis of data of the number of ships entering Incheon port from January 1996 to June 2013, through courses of stationarity verification, model identification, coefficient estimation, and diagnostic checking. As a result of prediction January 2014 to December 2015, February has less traffic volume than other months, but January has more traffic volume than other months. Also, it was found out that Incheon port was more proper to ARIMA model than exponential smoothing method and there was a difference of monthly traffic volume according to seasons. The study has a meaning in that future traffic volume was forecasted per month with time series model. Also, it is judged that forecast of future marine traffic volume through time series model will be the more suitable model than prediction of marine traffic volume with previous regression analysis.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.653-662
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• 2016

This paper describes a test case generation algorithm for Simulink/Stateflow models based on the Rapidly exploring Random Tree (RRT) algorithm that has been successfully applied to path finding. An important factor influencing the performance of the RRT algorithm is the metric used for calculating the distance between the nodes in the RRT space. Since a test case for a Simulink/Stateflow (SL/SF) model is an input sequence to check a specific condition (called a test target in this paper) at a specific status of the model, it is necessary to drive the model to the status before checking the condition. A status maps to a node of the RRT. It is usually necessary to check various conditions at a specific status. For example, when the specific status represents an SL/SF model state from which multiple transitions are made, we must check multiple conditions to measure the transition coverage. We propose a unique distance calculation metric, based on the observation that the test targets are gathered around some specific status such as an SL/SF state, named key nodes in this paper. The proposed metric increases the probability that an RRT is extended from key nodes by imposing penalties to non-key nodes. A test case generation algorithm utilizing the proposed metric is proposed. Three models of Electrical Control Units (ECUs) embedded in a commercial vehicle are used for the performance evaluation. The performances are evaluated in terms of penalties and compared with those of the algorithm using a typical RRT algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.493-498
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• 2017

Capsule endoscopy is specialized for the observation of small intestine that is difficult to access by general endoscopy. The diagnostic procedure through capsule endoscopy consists of three stages: examination of indicant, endoscopy, and diagnosis. At this time, key information needed for diagnosis includes indicant, lesions, and suspected disease information. In this paper, these information are defined as semantic features and the extracting process is defined as semantic-based analysis. It is performed in whole capsule endoscopy. First, several symptoms of patient are checked before capsule endoscopy to get some information on suspected disease. Next, capsule endoscopy is performed by checking the suspected diseases. Finally, diagnosis is concluded by using supporting information. At this time, some association are used to conclude diagnosis. For example, there are the disease association between the symptom and the disease to identify the expected disease, and the anatomical association between the location of the lesion and supporting information. However, existing knowledge models such as MST and CEST only lists the simple term related to endoscopy and cannot consider such semantic associations. Therefore, in this paper, we propose association-based knowledge model for supporting diagnosis of capsule endoscopy. The proposed model is divided into two; a disease model and anatomical model of small intestine, interesting area(organs) of capsule endoscopy. It can effectively support diagnosis by providing key information for capsule endoscopy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Journal of Digital Contents Society
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• v.12 no.4
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• pp.457-465
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• 2011

The annual R&D investment in Korea increased by an average of 12.2percent during the last 5 years. Therefore, prevention of duplicate projects being performed became an important factor in promoting the efficiency of R&D investment and the originality of R&D projects. On measuring the similarity of projects, the measurement model used to estimate the accuracy of the similarity is crucial. In this paper, we propose an advanced measurement model on checking the similarity of R&D projects for promoting the efficiency of R&D investment. The proposed model is made up of the following steps for the model measurement, sampling and analyzing. During the sampling step, we append the abstract of R&D reports on the search engine based on document vector. We then measure the similarity on projects to use research title network which is consists of the compound keyword and the weight of items on during the analysis. The proposed method improved the accuracy for measuring the similarity of projects by an average of 0.19 over the existing search engine and by 9.25 over the simple keyword search on R&D projects. On searching the similarity with the appending conditions and high sampling, it improved the accuracy of measuring the similarity of R&D projects.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.473-486
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• 2005

The Hippocratic database model recently proposed by Agrawal et at. incorporates privacy protection capabilities into relational databases. The authors have subsequenty proposed the Hippocratic XML daかabase model[4], an extension of the Hippocratic database model for XML databases. In this paper, we propose a new concept that we cail the dynamic predicate(DP) for effective access control in the Hippocratic XML database model. A DP is a novel concept that represents a dynamically constructed rendition that tan be adapted for determining the accessibility of elements during query execution. DPs allow us to effectively integrate authorization checking into the query plan so that unauthorized elements are excluded in the process of query execution. Using synthetic and real data, we have performed extensive experiments comparing query processing time with those of existing access control mechanisms. The results show that the proposed access control mechanism improves the wall clock time by up to 219 times over the top-down access control strategy and by up to 499 times over the bottom-up access control strategy. The major contribution of our, paper is enabling effective integration of access control mechanisms with the query plan using the DP under the Hippocratic XML database model.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.19-36
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• 2016

The purpose of this study was to analyze domestic and overseas cases of flipped learning instructional design model and actual classes in K-12 Education, and find out educational implications in order to design effective flipped learning. Papers, 14 articles in domestic and international journals, were collected. As results of the analysis, first, flipped learning instructional model was presented as flipped learning that applied to ADDIE model and 8C model etc. Second, 'Activities before classroom' consisted of watching lecture videos, lecture notes etc. 'Activities during classroom' was checking prior learning in early stage, individual activities and cooperative activities in middle stage, and solving quizzes, reviewing in later stage. After class, students performed tasks and questions&answers. Third, in case of creating lecture video, they used application such as Screencast-o-matic, Explain Everything; In contrast, some cases utilized online web-sites such as YouTube or Phet. Fourth, positive results were shown in learners' academic achievement, motivation and learning attitude etc. This research has a significance in terms of analyzing the flipped learning instructional model and flipped learning activities, and providing the preliminary data to facilitate the design for the effective flipped learning.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.88-99
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• 2007

Soil moisture is one of the essential components in determining surface hydrological processes such as infiltration, surface runoff as well as meteorological, ecological and water quality responses at watershed scale. This paper discusses soil moisture transfer processes measured at hillslope scale in the Gwangneung forest catchment to understand and provide the basis of stochastic structures of soil moisture variation. Measured soil moisture series were modelled based upon the developed univariate model platform. The modeling consists of a series of procedures: pre-treatment of data, model structure investigation, selection of candidate models, parameter estimation and diagnostic checking. The spatial distribution of model is associated with topographic characteristics of the hillslope. The upslope area computed by the multiple flow direction algorithm and the local slope are found to be effective parameters to explain the distribution of the model structure. This study enables us to identify the key factors affecting the soil moisture distribution and to ultimately construct a realistic soil moisture map in a complex landscape such as the Gwangneung Supersite.