• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.566-579
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• 2012

Ubiquitous computing called pervasive one is based on the thought of pervading ability of computation in daily life applications. In other words, it aims to include computation in devices such as electronic equipment and automobiles. This has led to disengagement of computers from desktop form. Accordingly, the notice in ubiquitous computing being taken of a world steeped in remote and wireless computer-based-services. Handheld and wearable programmed devices such as sense and control appliances are such devices. This advancement is rapidly moving domestic tasks and life from device-and-human communication to the device-and-device model. This model called Machine to Machine (M2M) has led to acceleration of developments in sciences such as nano-science, bio-science, and information science. As a result, M2M led to appearance of applications in various fields such as, environment monitoring, agricultural, health care, logistics, and business. Since it is envisaged that M2M communications will play a big role in the future in all wireless applications and will be emerged as a progressive linkage for next-generation communications, this paper aims to consider how much M2M architectures can realize ubiquitous computing in daily life applications. This is carried out after acquainting and initiating readers with M2M architectures and arguments for M2M. Some of the applications was not achievable before but are becoming viable owing to emergence of M2M communications.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.354-364
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• 2017

This study tries to evaluate the effect of ITT introduction in Busan New Port. The study used the estimation model of the number of vehicles required in accordance with the backhaul rate. The model used big data, COPINO e-document for one year in 2015. COPINO recorded the event such as truck ID, container ID, ATA, damage etc when truck arrived at gate. The study finds important information to estimate the required number of trucks for handling current ITT containers in Busan New Port: Daily throughput in Busan New Port is 1650 vans, especially night throughput recorded peak level in 1800 hours to 2400 hours, the throughput between adjacent terminals recorded high, i.e PNIT to HPNT. The transportation capability for 6 hours between terminals is from 4 vans to 7 vans. The required trucks are estimated 89 currently without considering peak level. If we change the back haul rate from current 20% to 40%, 60% and 80%, how much would the cost drop? It was discovered that, if it is raised to 40%, 60% and 80%, the number of vehicle required will be reduced from 89 (current) to 76, 65 and 59. It was also discovered that the total savings will reduce down to 12%, 25% and 34%.

• Korean System Dynamics Review
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• v.6 no.1
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• pp.99-123
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• 2005

Although the generation market is competitive, the power market is easily exercised the market power by one generator due to its special futures such as a limited supplier, large investment cost, transmission constraints and loss. Specially, as Korea Electric industry restructuring is similar US competitive wholesale electricity market structure which discovered the several evidences of market power abuse, when restructuring is completed the possibility that market power will be exercised is big. Market power interferes with market competitions and efficiency of system. The goal of this study is to investigate the market price effects of the potential market power and the proposed market power mitigation strategy in Korean market using the forecasting wholesale electricity market model. This modeling is developed based on the system dynamics approach. it can analyze the dynamic behaviors of wholesale prices in Korean market. And then it is expanded to include the effect of market condition changed by 'strategic behavior' and 'real time pricing.' This model can generate the overall insights regarding the dynamic impact of output withholding by old gas fire power plant bon as a marginal plant in Korean market at the macro level. Also it will give the energy planner the opportunity to create different scenarios for the future for deregulated wholesales market in Korea.

• Journal of the Korean Dietetic Association
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• v.25 no.1
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• pp.44-58
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• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.1-6
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• 2021

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Smart Structures and Systems
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• v.17 no.6
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• pp.957-980
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• 2016

Structural damage detection (SDD) is a challenging task in the field of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into the SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters, such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.611-620
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• 2020

Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.63-81
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• 2021

Joint financial network of Korea Financial Telecommunications and Clearings Institute, which is an essential facility with a natural monopoly, maintained its closedness as monopoly/public utility model, but it has evolved in the form of open banking in order to obtain domestic fintech competitiveness in the rapidly changing digital financial ecosystem such as the acceleration of Big Blur. In accordance with digital transformation strategy of financial institutions, various ICT companies are actively participating in the financial industries, which has been exclusive to banks, through the link technology called Open API. For this reason, there has been a significant change in the financial service supply chain in which ICT companies participate as users. The level of security in the financial service supply chain is determined based on the weakest part of the individual components according to the law of minimum. In addition, there is a perceived risk of personal information and financial information leakage among the main factors that affect users' intention to accept services, and appropriate protective measures against perceived security risks can be a catalyst, which increases the acceptance of open banking. Therefore, this is a study on factors affecting the introduction of open banking to achieve financial innovation by developing an open banking security control model for financial institutions, as a protective measures to user organizations, from the perspectives of cyber financial security and customer information protection, respectively, and surveying financial security experts. It is expected, from this study, that effective information protection measures will be derived to protect the rights and interests of financial customers and will help promote open banking.