• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.53-57
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• 2005

This paper presents development of a classification system for an electrical fire investigation. In order to reduce an electrical fires and establish detailed prevention plans, the collection of an electrical fire causes and base data are very important. Based on this data, a new classification system for an electrical fire investigation was developed and the direction to the classification system was suggested by fundamental analysis. All of the collected information is analyzed by bottom-up method. Criteria items which based on base data were categorized to classify items. The classification of items were found out as follows : basic condition fire scene condition, fire sign, fire cause. Particularly, the fire cause category is classified. A new developed classification system for an electrical fire investigation will be used to analyse electrical fires easily and efficiently.

• Fire Science and Engineering
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• v.23 no.2
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• pp.36-40
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• 2009

This paper presents research about the development of electrical fire cause classification in order to improve the reliability of electrical fire statistics and to collect electrical fires data efficiently. The incorrect and biased knowledge for electrical fires changed the classification of certain types of fires, from non-electrical to electrical. It is convenient and required to develop the standardized form that makes, in the assessment of the cause of electrical fires, the fire investigators directly ticking the appropriate box on the fire report form or making an assessment of a text description. In this study, newly developed electrical fire cause classification structure, which is well-defined hierarchical structure so that there are not any relationship or overlap between cause categories, is suggested. Also the suggested classification structure can be used for electrical fire investigation and statistics, which minimizes the mistake that diagnose non-electrical fires into electrical ones.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.466-471
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• 2007

Electrical fires form over 30 percent of fires, but the study on the reliability of electrical fire statistics is not performed. Electrical roe occupancy was very high due to investigating and classifying fires, which is not directly continuous with electrical cause, as electrical fire because insufficiency of cause classification method or system, and the problems of the reliability of electrical fire statistics were presented. So, the reliability of electrical fire statistics must be guaranteed by improvement of the existing cause classification method of electrical fire. This paper analyzed the problems of electrical rue statistics by the existing cause classification method of electrical fire and presented the new method to classify causes of electrical fire.

• International Journal of Safety
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• v.6 no.2
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• pp.17-21
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• 2007

This paper aims at the statistical analysis of electrical fire and classification of electrical fire causes to collect electrical fires data efficiently. Electrical fire statistics are produced to monitor the number and characteristics of fires attended by fire fighters, including the causes and effects of fire so that action can be taken to reduce the human and financial cost of fire. Electrical fires make up the majority of fires in Korea(including nearly 30% of total fires according to recent figures), The incorrect and biased knowledge for electrical fires changed the classification of certain types of fires, from non-electrical to electrical. It is convenient and required to develop the standardized form that makes, in the assessment of the cause of electrical fires, the fire fighters directly ticking the appropriate box on the fire report form or making an assessment of a text description. Therefore, it is highly recommended to develop electrical fire cause classification and electrical fire assessment on the fire statistics in order to categorize and assess electrical fires exactly. In this paper newly developed electrical fire cause classification structure, which is well-defined hierarchical structure so that there are not any relationship or overlap between cause categories, is suggested. Also fire statistics systems of foreign countries are introduced and compared.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.77-86
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• 2005

In this paper, we designed a learning algorithm of LVQ that extracts classification errors and learns ones and improves classification performance. The proposed LVQ learning algorithm is the learning Networks which is use SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of LVQ. To extract pattern vectors which cause classification errors, we proposed the error-cause condition, which uses that condition and constructed the pattern vector space which consists of the input pattern vectors that cause the classification errors and learned these pattern vectors , and improved performance of the pattern classification. To prove the performance of the proposed learning algorithm, the simulation is performed by using training vectors and test vectors that are Fisher' Iris data and EMG data, and classification performance of the proposed learning method is compared with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional classification.

• IE interfaces
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• v.25 no.2
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Journal of the Korean Society of Safety
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• v.32 no.5
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• pp.179-185
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• 2017

Accident analysis is an essential process to make basic data for accident prevention. Most researches depend on survey data and accident statistics to analyze accidents, but these kinds of data are not sufficient for systematic and detailed analysis. We, in this paper, propose an accident classification model that extracts task type, original cause materials, accident type, and the number of deaths from accident reports. The classification model is a support vector machine (SVM) with word occurrence features, and these features are selected based on mutual information. Experiment shows that the proposed model can extract task type, original cause materials, accident type, and the number of deaths with almost 100% accuracy. We also develop an accident ontology to express the information extracted by the classification model. Finally, we illustrate how the proposed classification model and ontology effectively works for the accident analysis. The classification model and ontology are expected to effectively analyze various accidents.

• Journal of Haehwa Medicine
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• v.9 no.1
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• pp.423-442
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• 2000

Objectives : The objectives of this study is to find out the classification methods of the cause of disease and the Acupuncture points on the cough(咳嗽, hae-soo) from the oriental medical literature. The results obtained as follows. Methods : We surveyed the oriental medical books from Hung-Ti-Nei-Ching $\ll$黃帝內經$\gg$ to recent books concerning the Acupuncture therapy for the cough(咳嗽, hae-soo). Results : 1. There are the classification methods of the cause of the cough(咳嗽, hae-soo) by affection by exopathogen and internal injury, by five zang-organs and six fu-organs, by time-belt, and by cold and heat. 2. The acupuncture points at P'yesu(肺兪, BL13), T'aeyon(太淵, LU9), Ch'okt'aek(尺澤, LU5), P'ungmun(風門, BL12), Yolgyol(列缺, LU7), Ch'ondol(天突, CV22), Taech'u(大椎, GV14), Hapkok(合谷, LI4), Kohwang(BL43), T'aegye(太谿, KI3), Chok-samni(足三里, ST36) are most frequently used on the acupuncture therapy for the cough(咳嗽, hae-soo). Conclusions : Among the classification methods of the cause of the cough(咳嗽, hae-soo), the classification methods by affection by exopathogen and internal injury may be most effective and the acupuncture points at Lung meridian(手太陰肺經, LU), Bladder mendian(足太陽膀胱經, BL) are most frequently used on the acupuncture therapy for the cough(咳嗽, hae-soo).

• Journal of Physiology & Pathology in Korean Medicine
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• v.16 no.2
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• pp.209-214
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• 2002

At this paper, I classified ‘tetanus’ in 『DongEuiBoGam』 and studied the concept, causes, symptoms, pathological mechanisms of that disease and then I had a new understanding that concept of tetanus in 『DongEuiBoGam』 is different with concept of tetanus in Western Medicine. In the mean time, I investigated the classification in 「Classification of Korean Standard Cause of Death(Oriental Medicine)」 (1995, The Korean Economic Planning Board), and concluded the concept of tetanus in "DongEuiBoGam".

• Journal of the Korean Society of Safety
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• v.34 no.3
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• pp.42-49
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• 2019

This study statistically analyzed the laboratory accidents by investigating 806 laboratory accident survey reports which were officially submitted to government from 2013 to June 2017. After comparing domestic and foreign accident classification criteria, the laboratory accidents were classified using KCD7(Korean Standard Classification of Diseases) and OIICS(Occupational Injury and Illness Classification System) criteria. For the type and part of injury, KCD7 classification criteria was adopted. And, for the cause and occurrence type of accidents, OIICS was adopted to analyze the laboratory accidents. Most of injuries happened to the wrist and hand caused by sharp materials or chemical materials. The analysis of accident cause showed that accidents resulted in medical practice and accidents from handtools and chemical materials such as acid and alkali frequently occurred. The major occurrence types of laboratory accidents was body exposure to the chemical materials such as hydrochloric acid and sulfuric acid. In addition, the accidents resulted in destroy of grasped object or falling object were frequently reported.