• The Journal of Bigdata
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• v.7 no.1
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• pp.89-97
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• 2022

Various efforts are needed to prevent accidents because ship collisions can cause various negative situations such as economic losses and casualties. Therefore, research to prevent accidents is being actively conducted, and in this study, new leading indicators for preventing ship collision accidents is proposed. In previous studies, the risk of collision was expressed in consideration of the distance between ships in a specific sea area, but there is a disadvantage that a new model needs to be developed to apply this to other sea areas. In this study, the density-based ship domain DESD (Density-based Empirical Ship Domain) including the environment and operating characteristics of the sea area was defined using AIS (Automatic Identification System) data, which is ship operation information. Deep clustering is applied to two-dimensional DESDs created for each sea area to cluster the seas with similar operating environments. Through the analysis of the relationship between clustered sea areas and ship collision accidents, it was statistically tested that the occurrence of accidents varies by characteristic of each sea area, and it was proved that DESD can be used as a leading indicator of accidents.

• Journal of Korean Library and Information Science Society
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• v.50 no.4
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• pp.33-55
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• 2019

A marketing platform based on the legal deposit system was designed. It is an e-book distribution platform that systematically binds library networks nationwide by linking with the National Library's deposit system to complete the existing paper that the library should become a publishing platform. The purpose is to bundle e-books into a distribution space and naturally assemble readers to serve as virtual platforms for domestic publishers, authors, bookstores, and platforms running various publishing / subscribing services. The premise is not a sale of e-books, but a rental concept, but the platform is valid regardless. In addition, the platform takes the form of social cooperatives to represent the interests of all members involved in publishing services. The lead-based distribution platform is the most ideal business model to compromise with reality. Conceptually, the service of the publishing content-related industry is a model in which all content is supplied from the lead-bone system, which is a collaborative space, and technically, the central e-book database controls the flow of all content, but the publishing content-related industry takes the form of controlling its flow through virtual.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.67-77
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• 2018

Currently, there are many problems due to the decline in school-age population. Moreover, Korea has the largest number of universities compared to the population, and the university enrollment rate is also the highest in the world. As a result, the minimum student retention rate required for the survival of each university is becoming increasingly important. The purpose of this study was to examine the effects of reducing the number of graduates of education and the social climate that prioritizes employment. And to determine what the basic direction is for students to manage the student retention rate, which can be maintained from admission to graduation, to determine the optimal input variables, Based on the input parameters, we will make associative analysis using apriori algorithm to collect training data that is most suitable for maintenance rate management and make base data for development of the most efficient Deep Learning module based on it. The accuracy of Deep Learning was 75%, which is a measure of graduation using decision trees. In decision tree, factors that determine whether to graduate are graduated from general high school and students who are female and high in residence in urban area have high probability of graduation. As a result, the Deep Learning module developed rather than the decision tree was identified as a model for evaluating the graduation of students more efficiently.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.65-75
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• 2009

The semiconductor manufacturing process normally includes a great number of complex sequential steps those are related with various types of equipment. Such equipments are installed with the mixed patterns of serial or parallel structures while considering a number of engineering or environmental factors at the same time. It is thus extremely difficult to change the layout after installation due to expensive costs and other related factors. Because of these reasons, a new investment or layout change, which is usually caused by the production policy such as product mix or production quantity, must be carefully considered. This case study introduces a simulation conducted in a semiconductor parts production company which produces the Board on Chip (BOC)-type of packaging substrate and has plans to change the facility layout. For this study, we used $QUEST^{(R)}$ for simulation modeling and evaluated various strategies which may cause layout changes. Further, the Analytic Hierarchy Process (AHP) is applied to select the best strategy from several alternatives with multiple decision criteria.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.13-35
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• 2011

The advancement of the information technology provides a wide range of corporate to cope with new business environments that are different in size, life and operation conditions. The Research methodology used in this study is Technology Acceptance Model. The Study Method are mainly survey and questionnaire. The major result of article can be summarized. Its are as the follows: This paper ware investigated the determinants of 'port service quality', 'perceived usefulness', 'perceived ease of use', 'utilization intention', 'practice use'. There are 400 sended samples and 150 returns, 173 of them are analyzed on a port utilization using TAM model. 1. The Port service quality are found to have a positive effect to 'perceived usefulness', 'perceived ease of use', 'utilization intention' 2. The perceived ease of use are found to have a positive effect to 'perceived usefulness', 'utilization intention' 3. The perceived usefulness is found to be positively related to 'utilization intention' 4. The utilization intention is found to have a positive effect to ''practice use' we hove to provide useful contribution to increase the Korea ports' competitiveness in introduction of port information system. In addition, in order to port development offer some insight in further research.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.411-422
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• 2009

The study seeks to explore essential factors that influence tourists' continual usage intentions to mobile tour information services. The variables such as characteristics of tour information service, accuracy, interactivity, context, ubiquitous connectivity, perceived usefulness, perceived ease of use were adopted from previous research and the hypotheses were developed on the basis of Davis's Technology Acceptance Mode(TAM). The survey was conducted by users who have previously experienced mobile tourism information service. Structural equation modeling was used to Investigate the relationships between the factors. The results showed that interactivity, context, perceived usefulness and perceived ease of use were found to have a positive impact on satisfaction. In particular, interactivity and context were found to be the most significant factors that influence reuse intentions. It suggests that increasing context and interactivity to make tourist trust about accuracy, ubiquitous connectivity is better than increasing perceived usefulness and perceived easiness. The identified factors that influence continual usage intentions on mobile services can be useful for analyzing the market trends and suggesting industrial guidelines of mobile services.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.15-30
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• 2007

The purpose of this study is to explore essential factors of influencing user's usage intentions on telematics services and to find relationships among factors. This study derives several variables and hypotheses through literature reviews on the extended research model based on Davis's Technology Acceptance Model. With the objective of statistical verification, derived variables were sorted into meaningful categories through various statistical packages. These were included multiple regression analysis to improve the reliability of survey data. The demand analysis was conducted by targeting on users who have previously experienced telematics services. Indicated analyzed results which 'suitability', 'localization', and 'instant connectivity' variables were positively related to the user's usage intentions, especially accuracy of information variable. The factors of influencing the usage intention on telematics services can be useful empirical data in order to analyze the market trends and to suggest industrial or political guideline of telematics services.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.