• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.22 no.1
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• pp.82-90
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• 2012

Objectives: This study was performed to provide workplaces with political guidelines that apply international CMRs (Carcinogens, Mutagens, Reproductive toxins) information to Public Notice of TLVs (Threshold Limit Values). We analyzed information supply status about CMRs of international agencies and compared substances for which TLVs are set in KMoEL (Ministry of Employment and Labor in Korea). Methods: We referred to the reliable literature about classification criteria of CMRs corresponding to UN GHS (Globally Harmonized System of classification and Labeling of chemicals) and Public Notice No. 2009-68 'Standard for Classification, Labeling of Chemical Substance and Material Safety Data Sheet' in KMoEL. The classification system of CMRs in professional organizations (IARC, NTP, ACGIH, EU ECHA, KMoEL, etc.) was investigated through the internet and literature. Conclusions: 191 chemical substances among total 650 substances with TLVs are classified as carcinogens. Also, 43 substances classified as mutagens, and 44 as reproductive toxicants. These results suggest that the information of CMRs in Public Notice of TLV will be reorganized to 191 carcinogens, 43 mutagens, and 44 reproductive toxicants.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.145-152
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• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.

• Journal of Korea Technology Innovation Society
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• v.9 no.3
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• pp.496-513
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• 2006

Ministry of Science and Technology(MOST) has a plan to establish the National Science and Technology Information System(NTIS). For successful NTIS, there are three pre-standardizations. Standard classification is the one of the three standardizations. In this paper, the validity of current National Standard Classification of Science and Technology(NSCST) is analyzed for three aspects. One is the duplication of NSCST, one another is the high ratio of incorrectness of information changed by mapping table and the last is the incorrectness of the mapping table itself. So for the successful NTIS a new Science and Technology Classicication shoud be considered.

• KIEAE Journal
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• v.11 no.2
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• pp.123-130
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• 2011

BIM based design methodology requires more information than traditional design methodology in order to insure efficiency throughout the project. BIM based design not only requires all building data in the form of 3D shapes, but also all other relevant data regarding building components. Information is typically grouped in a standard classification system such as by standardized material names. The development of a domestic BIM based standard classification system is yet to be created and deployed in the industry. Each designer is specifying their own building information classification systems which is causing inconsistency in the industry. Therefore BIM based designs, are causing confusion in the industry as each designer follow no guidelines for material standardization classification. The lack of information regarding this in the BIM template will continue to cause confusion about a projects building information data consistently. This study is that of preliminary research to develop a BIM template. First, overseas BIM templates were analyzed regarding BIM standards and documentation. Examination then followed regarding the element and characteristics needed for the development of a BIM template, a suggested hierarchy of elements required for a BIM template were then made. The result of this research is that it will be used to develop a "BIM template prototype", to support the generation of building information data regarding neighborhood facilities.

• The Journal of Korean Medical History
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• v.19 no.1
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• pp.65-76
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• 2006

The present study examined subject classification and the characteristics of old Oriental medicine literature focused on Web services of Oriental medicine knowledge and information resources. For this, we reviewed how subject classification is applied to Oriental medicine in the codified literature classification table and, based on the results, examined how the classification system is used in libraries. Second, subject classification and the characteristics of old Oriental medicine literature were studied focused on Web services of Oriental medicine knowledge and information resources, and related problems and solutions were suggested.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.204-207
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• 2013

Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.33-57
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• 2017

This study aims to introduce an emerging prescriptive analytics method and suggest its efficient application to a category-based service system. Prescriptive analytics method provides the whole process of analysis and available alternatives as well as the results of analysis. To simulate the process of optimization, large scale journal articles have been collected and categorized by classification scheme. In the process of applying the concept of prescriptive analytics to a real system, we have fused a dynamic automatic-categorization method for large scale documents and intellectual structure analysis method for scholarly subject fields. The test result shows that some optimized scenarios can be generated efficiently and utilized effectively for reorganizing the classification-based service system.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.102-108
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• 2005

This paper studies medical data classification methods, comparing decision tree and system reconstruction analysis as applied to heart disease medical data mining. The data we study is collected from patients with coronary heart disease. It has 1,723 records of 71 attributes each. We use the system-reconstruction method to weight it. We use decision tree algorithms, such as induction of decision trees (ID3), classification and regression tree (C4.5), classification and regression tree (CART), Chi-square automatic interaction detector (CHAID), and exhausted CHAID. We use the results to compare the correction rate, leaf number, and tree depth of different decision-tree algorithms. According to the experiments, we know that weighted data can improve the correction rate of coronary heart disease data but has little effect on the tree depth and leaf number.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.838-856
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• 2014

Enhanced network speed and the appearance of various applications have recently resulted in the rapid increase of Internet users and the explosive growth of network traffic. Under this circumstance, Internet users are eager to receive reliable and Quality of Service (QoS)-guaranteed services. To provide reliable network services, network managers need to perform control measures involving dropping or blocking each traffic type. To manage a traffic type, it is necessary to rapidly measure and correctly analyze Internet traffic as well as classify network traffic according to applications. Such traffic classification result provides basic information for ensuring service-specific QoS. Several traffic classification methodologies have been introduced; however, there has been no favorable method in achieving optimal performance in terms of accuracy, completeness, and applicability in a real network environment. In this paper, we propose a method to classify Internet traffic as the first step to provide stable network services. We integrate the existing methodologies to compensate their weaknesses and to improve the overall accuracy and completeness of the classification. We prioritize the existing methodologies, which complement each other, in our integrated classification system.