• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.175-191
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• 2017

The basis of economic statistics for evaluating the security industry's growth and inter-industry impacts is to create a standardized industry classification along with the scope of the security industry. The industrial classification should be written in such a way that it complies with and complies with the standards of the international and domestic standardized standard industrial classifications. Representative classifications of information security, physical security, and convergence security as well as classification of products and services related to security at present are not in line with the criteria of industrial classification based on the characteristics of production activities for products. The results of the convergence security industrial classification study are also consumer-oriented classification, which differs from the supplier-centric classification officially used in statistics, law, and policy enforcement in the present country. In this study, we first summarized the criteria of Korean and international industrial classification, and then examined whether the current classification of security meets these criteria. Next, to examine the classification directions of newly formed industries such as security industry, we reviewed some cases of domestic industrial special classification and types, and proposed the industrial classification criteria and direction of the security industry on the basis of them.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.3
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• pp.1-18
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• 2017

This study constructed a high-resolution bioclimatic classification map of South Korea which classifies land into homogeneous zones by similar environment properties using advanced statistical techniques compared to existing ecological area classification studies. The climate data provided by WorldClim(1960-1990) were used to generate 27 bioclimatic variables affecting biological habitats, and key environmental variables were derived from Correlation Analysis and Principal Component Analysis. Clustering Analysis was performed using the ISODATA method to construct a 30'(~1km) resolution bioclimatic classification map. South Korea was divided into 21 regions and the results of classification were verified by correlation analysis with the Gross Primary Production(GPP), Actual Vegetation map made by the Ministry of Environment. Each zones' were described and named by its environmental characteristics and major vegetation distribution. This study could provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.4
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• pp.141-157
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• 2008

This study suggests some ideas for the improvements of the classes of Language(700) and Literature(800) of the 4th Edition of Korean Decimal Classification(KDC). They includes some recommendations for the followings: introducing some new entries of the time table for the Korean Literature and English and American Literatures; relocating some entries for the improvements including language policy and administration; using new terminologies: adding new and revised notes for the appropriate entries; changing some specific classifying methods including the classification of bilingual dictionaries; introducing some options including those for the subdivisions of modern novels and those for American Literature; and discontinuing some entries not used, especially those in other Languages.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.6
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• pp.520-529
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• 2017

In Korea, the largest agricultural lands are paddy fields which have poor infiltration and drainage properties. Recently, Korean government pursuits cultivating upland crops in paddy fields to reduce overproduced rice in Korea. In order to succeed this policy, it is critical to set criteria suitability classification for upland crops cultivating in paddy field soils. The objective of this study was developing guideline of suitability classification for sesame cultivation in paddy field soils. Yields of sesame cultivated in paddy field soils and soil properties were investigated at 40 locations at nationwide scale. Soil properties such as topography, soil texture, soil moisture contents, slope, and drainage level were investigated. The guideline of suitability classification for sesame was determined by multi-regression method. As a result, sesame yields had the greatest correlation with topography, soil moisture content, and slope. Since sesame is sensitive to excessive soil moisture content, paddy fields with well drained, slope of 7-15% and mountain foot or hill were best suit for cultivating sesame. Sesame yields were greater with less soil moisture contents. Based on these results, area of best suitable paddy field land for sesame was 161,400 ha, suitable land was 62,600 ha, possible land was 331,600 ha, and low productive land was 1,075,500 ha. Compared to existing suitability classification, the new guideline of classification recommended smaller area of best or suitable areas to cultivate sesame. This result may suggest that sesame cultivation in paddy field can be very susceptible to soil moisture contents.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.41-52
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• 2023

With the international change in the medical device market owing to the development of innovative medical engineering and the use of various raw materials, a systematic and rational medical device classification system is needed to safely manage newly developed medical devices. This study aims to improve the domestic medical device classification system by proposing product establishment and segmentation. It is based on medical device products from the United States and Europe that are only available in foreign systems and are more subdivided than domestic products. This study analyzes and compares the domestic and foreign medical device classification systems by examining laws, guidelines, and analysis reports in Korea, the United States, and Europe. In accordance with product establishment and segmentation criteria, products subject to improvement are presented. This study contributes to safely managing medical devices that do not fit with the current classification system and to solving the confusion caused by the lack of international harmony in product classification systems.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• The Korean Journal of Emergency Medical Services
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• v.24 no.1
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• pp.25-36
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• 2020

Purpose: We analyzed the characteristics and differences in patients' medical benefits and health insurance based on disease severity classification. Methods: We examined 29,139 patients who visited the emergency medical center of K Hospital from January 1,2016 to December 31, 2016. Survey items included the Korean Triage and Acuity Scale (KTAS) classification of emergency and non-emergency situations ratio and type of insurance. Results: According to KTAS classification, 76.2% of patients exhibited an emergency condition and 23.8% exhibited a non-emergency condition. Emergency patients exhibited more trauma than non-emergency patients. According to the type of insurance coverage, the duration of stay in the emergency room was longer for patients with medical care than for patients with health insurance. Additionally, 119 ambulances use was significantly higher among patients with medical care. Conclusion: Policy discussions should address alternative ways to replace the 119 ambulances used by patients in this study. Additionally, health care administrators should identify alternative care agencies as potential alternatives to emergency room visits.

• Journal of Technology Innovation
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• v.3 no.1
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• pp.213-236
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• 1995

Advanced Manufacturing Technology(AMT), a comprehensive collection of new technologies for the efficiency and flexibility of manufacturing systems has received a growing attention recently, AMT consists of various industrial and technological components, homogeneous in some aspects while heterogeneous in others. Thus, it is difficult but necessary task to construct a classification framework in which the relationship among individual technologies are depicted in a meaningful fashion. In this, paper, we propose a hierarchical framework in which the objective and criteria of classification are decomposed into three level: industrialization, development and application of AMT. At the first and highest level, the main interest is to "industrialize" AMT. The major actors at this level are policy makers(public sector) and top management(private sector) and the primary classification criterion is the interrelationship between industry and technology. At the middle level exist system engineers whose main objective is to "develop" new technologies and/or systematize individual technologies. At the final and bottom level, shop floor managers need to "apply" AMT in order to enhance the efficiency and flexibility of manufacturing process. It should be stressed that, as a whole, the above three levels should be interactively linked to that each level contributes to the balanced development of AMT.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.