• International Journal of Safety
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• v.1 no.1
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• pp.28-35
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• 2002

Kullback Discrimination Information (KDI) is one of the pattern recognition methods. KDI defined as a measure of the mutual dissimilarity computed between two time series was studied for detection and classification of bearing flaking on outer-race and inner-races. To model the damages, the bearings in normal condition, outer-race flaking condition and inner-races flaking condition were provided. The vibration sensor was attached by the bearing housing. This produced the total 25 pieces of data each condition, and we chose the standard data and measure of distance between standard and tested data. It is difficult to detect the flaking because similar pulses come out when balls pass the defection point. The detection and classification method for inner and outer races are defected by KDI and nearest neighbor classification rule is proposed and its high performance is also shown.

• 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.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.23-31
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• 2024

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

• The Journal of Korean Physical Therapy
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• v.26 no.5
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• pp.315-323
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• 2014

Purpose: This study was conducted in order to investigate the effect of motor ability on mastery motivation in children with cerebral palsy. Methods: Sixty children with cerebral palsy (5~12 years) and their parents participated in the study. Data on general characteristics and disability condition, Gross Motor Functional Classification System, Manual Ability Classification System, and The Dimensions of Mastery questionnaire were collected for this study. Independent t-test, and ANOVA were used for analysis of the effect of The Dimensions of Mastery questionnaire according to general and disability condition, Gross Motor Functional Classification System, and Manual Ability Classification System. Linear regression analysis was performed to determine the effects of Gross Motor Functional Classification System and Manual Ability Classification System on The Dimensions of Mastery questionnaire. SPSS win. 22.0 was used and Tukey was used for post hoc analysis, level of statistical significance was less than 0.05. Results: The Dimensions of Mastery questionnaire score showed statistically significant difference according to gender, region, type, disability rating, Gross Motor Functional Classification System, and Manual Ability Classification System (p<0.05). Gross Motor Functional Classification System and Manual Ability Classification System were the effect factor on The Dimensions of Mastery questionnaire significantly (p<0.05). Conclusion: These results suggest that motor ability of children with cerebral palsy was an important factor having an effect on The Dimensions of Mastery questionnaire.

• 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.

• Korean Security Journal
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• no.3
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• pp.33-60
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• 2000

I study on the classification of cyber terror & counterplan against cyber terror The paper, purporting to consider security counterplans, comprise five chapters. Chapter I which introduction is followed by chapter II, dealing largely with the general definition and classification of cyber terror. Chapter III concerns the domestic & foreign cases of damages by cyber terror. Chapter III consider the condition of world nations against cyber terror and its actual condition. It is followed by concluding observation made in chapter V.

• Korean Journal of Oriental Medicine
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• v.10 no.1
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• pp.49-61
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• 2004

The Standardization of terms in The Pulse studies(脈學) is a need for development of learning. This study, for the correction of existing misused terms in The Pulse studies, we study on modernly and objectively the terms in The Pulse studies. By a focus of ${\ulcorner}$The Pulse Studies of Bin-Ho(瀕湖脈學)${\lrcorner}$, we studies on the new classification of pulse condition. The error of a existing technical books on Pulse studies begin that the classification of pulse condition is not establish a Standardization. For the correction of existing misused terms in The Pulse studies, we study on the pulse condition is expressed objectively a blood vessel that it is a subject of pulse condition. The expression of blood vessel contain a depth of blood vessel, a speed of pulsation, a curve of blood vessel, thickness of blood vessel, a diameter of blood vessel in expand and contract of blood vessel, a interval in expand and contract of blood vessel, a distinctness on a boundary of blood vessel, a speed of blood flow in blood vessel, a volume of blood flow in blood vessel, a condition of blood in blood vessel, a propelling power of blood vessel. These is standard of the new classification of pulse condition.

• Journal of Power System Engineering
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• v.7 no.2
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.6
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• pp.1347-1353
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• 2008

Pulse condition is the essential division for conducting pulse diagnosis which is one of the most fundamental and important diagnostics in traditional Korean/Chinese medicine. We studied the pulse condition referred to classics of traditional medicine for a full understanding in present time and come to a conclusion like below. The reference to pulse condition was concluded to 'twenty four pulse conditions' which is the fundamental conception generally accepted in present age since it had first mentioned in "Huangdi Neijing" and after it had passed through "Nanjing", "pulse pattern identification-chapter of normal pulse"of Zhang Zhongjing and reached "Maijing"of Wang Shuhe. Although medical partitioners had different views to some extent about pulse condition, there were no significant differences in the main theoretical frame. Even though there had been a diversity of opinions on the classification of pulse-condition between various medical practitioners, the method of Dae-dae and the method of systematic endeavored by Zhou Xueting and Zhou Xuehai who were medical scholars in the Ch'ing dynasty have been a criterion for the classification of pulse-condition up to date. We were able to recognize that the change of pulse condition caused by pathological situation should be compared to physiological pulse condition for detecting the deficiency and excess by researching the analyzing methods of pulse condition mentioned in the "Lingshu", and the book of Hua Shou and Zhou Xuehai). To sum up, first normal pulse which is the physiological pulse condition should be a standard for detecting physiological pulse condition. Secondly, Zhou Xueting insisted that relaxed pulse should be a standard pulse condition for detecting normal pulse.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.149-155
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• 2023

In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.