• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.684-690
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• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.643-652
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• 2021

Accidents or faults in the transmission and distribution system are never completely avoidable, and short-circuit and earth faults are occurs despite the efforts of the TSO and DSO. Recently, the connection to the transmission and distribution system of large-capacity new and renewable distributed power has increased rapidly and has various effects on the operation of the system. In order to minimize this, connection standards such as FRT (Fault-Ride-Through) have been established to provide wind turbines or solar inverters. In the event of a major faults of the power system, the operation support shall be provided so that the operator can stably operate the system by smoothly performing connection maintenance or rapid system separation. In this paper, in order to appropriately determine whether the FRT condition, which is the grid connection criterion for a representative DERs, is sufficient, a detection system using a PMU (Phasor Measurement Unit) that measures a synchro-phasors was designed and deployment and a system accident due to a generator step-out to analyze and evaluate the proposed system based on the case.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.657-665
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• 2021

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

• Journal of Information Technology Services
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• v.20 no.2
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• pp.127-146
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• 2021

Donation-based crowdfunding(DBC) is conducted from voluntary participation by project operators. It requires a different operational strategy than general crowdfunding. However, there is a limited amount of research on the operational strategy of DBC. This study explores the value co-creation(VCC) strategy of DBC and analyzes the operation of DonorsChoose.org. The research questions encompass the following: First, we identify the VCC activities of DBC. Second, we uncover activities of the platform operators that facilitate the participation of project operators in VCC activities. Third, we explore how VCC activities affect project operational performance and platform performance in DBC. By adopting a single case study method on DonorsChoose.org, this study provides meaningful insight and detailed understanding into the VCC in DBC. First, VCC processes of DBC are identified(Co-ideation, Co-design, Co-funding, Co-operation, and Co-evaluation). Also, interactions between platform operators and project operators were defined at each stage. Second, this study confirmed that standardization and simplification of platform operators, support for pre- and post-activity, and high-quality information delivery activities were critical. Third, we confirm that these VCC activities improve VCC operational performance and platform performance. The theoretical significance of this study is that the concept of VCC, previously concentrated on participants with economic drivers(consumers and investors), has been applied to the context of DBC, a form of participation by participants with non-economic drivers(supporters and donors). In addition, this study practically contributes to the practice of VCC strategy among various platform operating strategies in DBC.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Korean Journal of Organic Agriculture
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• v.30 no.2
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• pp.207-230
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• 2022

A survey on the understanding of environment-friendly certification system was conducted for certified operators. The survey included the necessity of certification system, difficulties in producing certified products, and general evaluation of the current certification system. Certified operators were generally satisfied with the certification system in terms of the certification standards, the public subsidy programs, and the farm management costs. Individuals with parallel production farms were relatively less satisfied than the group with full-time organic farmers. Analysis using the ranking probit model indicated that the longer the certification experience, the more highly aware the need for the certification system was. Our results indicated that policy strategies are needed to enlarge the marketability of environment-friendly agricultural products since economic factors of organic products were the most important factor for maintaining and expanding certification in overseas as well as in Korea. It seems to be necessary to implement economic triggers for certified operators to continue their certification programs by promoting the transition period certification for individual farms in parallel with conventional agriculture. Analysis of the variables correlated with the expansion of environment-friendly agriculture by the logit model implied that certified operators with the younger age and higher annual incomes were more likely to expand environment-friendly agriculture. Therefore, it might also be important to provide financial support and incentives for new entry farmers to participate in environment-friendly agriculture and establish a system to share the know-how of successful certified organic farmers.

• Journal of Korean Library and Information Science Society
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• v.54 no.1
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• pp.69-90
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• 2023

The purpose of this study is to analyze the current status and bibliographic characteristics of large-print books produced and distributed by the Korea Library Association, which participates as an auxiliary operator in the support of the large-print books distribution project promoted by the Ministry of Culture, Sports and Tourism. As a result of analyzing the list of production books from 2009 to 2022, the average type was 20.5 books and the number of books was 21.7. The subject field of the selected books was 'literature (39.5%)', the proportion of translated books was 19.9%, and the author of the most books was a Beopryun monk with a total of six books. In addition, an average annual number of public libraries where large-print books were distributed was 454, and based on the research results, appropriate measures were sought and proposed in terms of policy, selection, production, and guidance so that the project could continue stably and achieve higher results in the future.

• Journal of Korean Society of Rural Planning
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• v.29 no.3
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• pp.53-67
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• 2023

In this study, the problem of the idleness of public buildings in rural areas is seriously emerging, but there are no clear standards for assessing the utilization of these buildings. Therefore, this study endeavors to investigate the actual state of idleness of buildings created by rural area development projects based on the criteria for each type. As part of this study, rural facilities were selected in two locations in Sangju City and Miryang City among the target areas of the Rural Agreement. Utilization evaluation criteria were developed to identify the conditions of underutilization. In order to determine the utilization pattern for each facility type, an in-depth interview with the operator and an inspection of the facility were conducted. Based on the analysis, the degree of utilization (e.g., low use, non-use, etc.) was different for each facility type, and among the four facility types (rural tourism, culture and welfare, exercise and recreation, income-based), rural tourism was rated as the least efficient. It has been found that the operating entity's capacity has a significant influence on the utilization of facilities. Socioeconomic factors, such as a decrease in utilization rates, are greatly influenced by the operating entity's capacity. Therefore, support from local governments as well as the national level is required to recycle idle facilities. Lastly, this study suggests the need for different standards for utilization, depending on the type of facility. This moves beyond checking the degree of idleness by the same standard that was previously implemented.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of Korea Trade
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• v.27 no.1
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• pp.101-118
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• 2023

Purpose - Korea became a trillion-dollar trading country in 2011. With the exponential increase in Korea's trade volume over the past decades, trade-related administrative burdens per capita for Korea Customs became enormous, for which the government established the Single Window, a trade-facilitating system, in 2004 to enhance the efficiency of customs-clearing procedures for traders. This paper focuses on finding whether the Korean Single Window system affects the country's trade facilitation positively through an empirical methodology. Design/methodology - To find empirical evidence that Single Window affects trade facilitation for the customs-clearing procedure, this study assumes that a time-efficient environment enables the handling of the increase in trade volume, under which four independent variables related to import customs-clearing procedures and two dependent variables to import were adopted for empirical analysis. The import customs procedures are classified into four steps from port entry to declaration acceptance. To understand the relationship between variables, scattered plots and correlation coefficients were calculated. Eight hypotheses were set and underwent simple linear regression. The data for analysis were collected by Korea Customs, and were about the lead time of import, the volume of imports in million USD, and the number of import declarations reported to customs offices on a monthly basis from 2005 to 2013. Findings - Six of the eight hypotheses showed the statistically significant result that lead time in the import customs-clearing procedure positively affects the number of import declaration reports and import volume. Specifically, Hypothesis 1, Hypothesis 2, and Hypothesis 3 strongly support the assumption lead time in import customs declaration has an inverse relationship with the number of import declarations, which means that the shorter the import lead time, the more import declaration increases. Research Limitations/Implications - With limited data accessibility to the government's custom-sclearing procedures, only the import lead time for customs clearance were adopted as independent variables. This paper, however, successfully found that the Single Window system contributed to trade facilitation. Originality/value - This study found that the time-saving Single Window system of Korea Customs enables itself to manage an exponentially-increasing trade volume by creating a trade-facilitating environment for customs personnel and traders, which may be a unique implication found through quantitative methodology.