• 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.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.253-258
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• 2021

With the 4th Industrial Revolution and the development of technology, markets of the VR&AR have increased. Also due to COVID-19 pandemic, demands for a digital environment were required because of physical space constraints. Firms are trying to solve this problem by using Metaverse platforms. However, with markets such as Metaverse, VR, AR, and Digital Twins are expanding, prior research on Metaverse definition or classification system is insufficient. Based on understanding VR&AR, Digital Twin, this study established a Industry-Service classification for Metaverse by defining Case studies on Metaverse and through prior research. And by Industry-Service classification for Metaverse this paper propose Metaverse Industry-Service Matrix to analyze the trend and possibility of Metaverse Platform

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.215-225
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• 2024

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Journal of the Ergonomics Society of Korea
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• v.23 no.4
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• pp.23-34
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• 2004

The importance of the industrial safety has been growing gradually as well as the prevention activities on industrial accidents. Industrial accident rates have been decreasing by the prevention activities. However, the fatal industrial accidents such as the death tend to increase and risk per accident has increased. The previous studies on the industrial accidents focus on the entire accidents. However, these studies are lacking for the fatal industrial accidents such as the death. The purpose of this paper is to analyze the characteristics and trend of death which occurred by industrial accident, based on the real data of deaths collected last 5 years from 1999 to 2003 in korea. This paper suggests a analysis method using MDS(MultiDimensional Scaling) that considers accidents variables and properties simultaneously. We evaluate MDPREF (Multidimensional Analysis of Preference Data), one of the MDS analysis, to know the relations between the type of industry and region as well as the type of industry and occupation. This paper finds the type of industry which has high possibilities of death by regional groups. In addition, we find the type of occupation which has high possibilities of death by the type of industry. These findings indicate that industrial classification should be differently controled according to type of occupation and region.

• International Journal of Safety
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• v.5 no.1
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• pp.37-41
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• 2006

The major objective of the statistical analysis about industrial accidents is to determine the safety factors so that it is possible to prevent or decrease the number of future accidents by educating those who work in a given industrial field in safety management. So far, however, there exists no quantitative method for evaluating danger related to industrial accidents. Therefore, as a method for developing quantitative evaluation technique, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze features of industrial accidents, a retrospective analysis was performed on 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work was chosen from data related to manufacturing businesses during a three-year period ($2002{\sim}2004$) in Korea. This study used AnswerTree of SPSS and the analysis results enabled us to determine the most important variables that can affect injured people such as the occurrence type, the company size, and the time of occurrence. Also, it was found that the classification system adopted in the present study using QUEST algorithm is quite reliable.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.17 no.4
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• pp.322-334
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• 2007

This study covers the investigation of the actual condition in the workplace to produce plastics products using synthetic resins and the investigation on the trends amount of the domestic production of thermoplastic resins. To analyze the monomers included in thermoplastic resins frequently used in the workplace, we analyzed thermal characteristics for test compounds using thermogravimetric analysis and did the qualitative analysis using Pyrolyzer GC-MSD & TDS GC-MSD. And then we classified the health hazard of monomers based on GHS classification criteria using information toxicity & carcinogenicity. The number of the workplace to produce plastics products among all domestic manufacturers of 73,884 was 4,391 (5.94%). The number of workers to produce plastics products among all workers of 2,522,750 in all domestic manufacturers was 104,971 (4.16%). The amount of production per year for thermoplastic resins is in the order of PP, HDPE, LDPE, PVC, ABS, PS and such compounds was producing over 1 Million ton per year each. The classification result based on GHS classification criteria for 22 main compounds included thermoplastic resins says 2 compounds of acrylonitrile, naphthalene are in Acute oral category 3 and benzene is in Acute dermal category 1. The classification results of health hazard of carcinogenicity based on IARC & ACGIH carcinogen classification says 2 compounds of benzene, vinyl chloride are in category 1A (known to be human carcinogens).