• 한국도서관정보학회지
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• 제48권3호
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• pp.45-61
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• 2017

본 연구의 목적은 DDC가 당시 도서관 장서의 급속한 증가 문제를 해결하기 위해 도서관 최초로 상관식 배가법을 도입하고 세분화된 분류체계를 적용한 것이 도서관계에 어떤 영향을 미쳤는지 분석하는데 있다. 이를 위해 DDC가 상관식 배가법을 도입하고, 분류체계를 세분화하여, 도서관과 타 분류법에 미친 영향 등을 비교 분석하였다. 분석 결과 첫째, DDC는 이전에는 존재하지 않았던 상관식 배가법이라는 혁신적인 방법을 적용하여, 세분화된 분류체계를 도입하면서 당시 도서관이 처해있던 급속한 장서 증가 문제를 해결하였다. 둘째, 세부적인 분류를 위해 형식 구분을 분류기준으로 적용하여 실질적으로 도서관의 도서 분류에 도움을 주었다. 셋째, 분류체계에 십진법을 도입함으로써 분류체계의 무한정 세분화가 가능하여, 경제성과 실용성을 획득하였다. 넷째, 전개분류법이나 주제분류법을 비롯한 현대 도서관 분류법 발전에 큰 영향을 미쳤다. 이처럼 상관식 배가법을 적용하고 세분화된 분류체계를 가진 DDC는 시대적 요구에 적합한 분류법이었고, 개별 도서관에서 실용적으로 사용할 수 있는 분류법이었다.

• 한국의류산업학회지
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• 제6권3호
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• pp.285-296
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• 2004

Fashion business must reflect the seasonable fashion trend because fashion has change always, and therefore fashion business has a big risk at the attribute. Careful consideration should be given to the selection of a particular color code to meet the purpose of marketing research in various color products. It must be designed to grasp systematically and comprehensively the current trend of colors. The most suitable color code for meeting this proposition would be one based on the designation by color ranges. The ISCC-NBS method of designating colors, published in 1955, was established by dividing the color solid into 267 color name blocks. The detailed classification like the ISCC-NBS system is very appropriate to serve the purpose of giving all color names according to color ranges. But it is somewhat too complicated to answer the purpose of surveying the trend of colors and of comparing and evaluating the ups and downs in the popularity of the range of each individual color. I have worked out the most convenient method of designating colors in accordance with the type of investigation needed. It is the classification which involves four classification system in itself, fundamental, gross, medium, and minute. The fundamental classification system classifies hues and neutrals into 16ranges. The gross classification system divides the above 16 ranges into 30. The medium classification divides the above 30 ranges into 103 in terms of tones. The minute classification divides the above 103 ranges into 207 in terms of specipic hues.

• Journal of Korean Biological Nursing Science
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• 제16권4호
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• pp.342-348
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• 2014

Purpose: The purpose of this study was to examine the effects of pressure ulcer classification system education on hospital nurses' knowledge and visual discrimination ability of pressure ulcer classification system and incontinence-associated dermatitis. Methods: One group pre- and post-test was used. A convenience sample of 96 nurses participating in pressure ulcer classification system education, were enrolled in single institute. The education program was composed of a 50-minute lecture on pressure ulcer classification system and case-studies. The pressure ulcer classification system and incontinence-associated dermatitis knowledge test and visual discrimination tool, consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 18.0. Results: The overall mean difference of pressure ulcer classification system knowledge (t=4.67, p<.001) and visual discrimination ability (t=10.58, p<.001) were statistically and significantly increased after pressure ulcer classification system education. Conclusion: Overall understanding of pressure ulcer classification system and incontinence-associated dermatitis after pressure ulcer classification system education was increased, but tended to have lack of visual discrimination ability regarding stage III, suspected deep tissue injury. Differentiated continuing education based on clinical practice is needed to improve knowledge and visual discrimination ability for pressure ulcer classification system, and comparison experiment research is required to evaluate its effects.

• 산업경영시스템학회지
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• 제43권2호
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.668-670
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• 1999

We have developed a automatic surface inspection system for cold Rolled strips in steel making process for several years. We have experienced the various kinds of surface inspection systems, including linear CCD camera type and the laser type inspection system which was installed in cold rolled strips production lines. But, we did not satisfied with these inspection systems owing to insufficient detection and classification rate, real time processing performance and limited line speed of real production lines. In order to increase detection and computing power, we have used the Dark Field illumination with Infra_Red LED, Bright Field illumination with Xenon Lamp, Parallel Computing Processor with Area typed CCD camera and full software based image processing technique for the ease up_grading and maintenance. In this paper, we introduced the automatic inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms. As a result of experiment, under the situation of the high speed processed line(max 1000 meter per minute) defect detection is above 90% for all occurred defects in real line, defect name classification rate is about 80% for most frequently occurred 8 defect, and defect grade classification rate is 84% for name classified defect.

• 대한인간공학회지
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• 제31권2호
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• 한국산림과학회지
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• 제85권3호
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• pp.396-408
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• 1996

본 연구는 우리나라 간벌림 벌채작업에서 작업강도를 구명하여 산림작업을 성력화하며, 아울러 작업방법의 개선과 생산성 향상을 도모하는데 그 목적이 있다. 이를 구명하기 위하여 침엽수 간벌림에서 벌채작업을 요소작업으로 구분하여 순수작업시간과 맥박수를 측정 분석하였으며, 그 결과는 다음과 같다. 1. 맥박수 측정 분석에서 전체 순수작업시간에서의 1분당 평균맥박수는 작업원 A의 경우 108로 나타났으며, 작업원 B의 경우 130, 작업원 C는 119, 그리고 작업원 D는 125로 나타났다. 2. 요소작업 구분별로 맥박수를 분석한 결과에서는 1분당 맥박수가 가장 높을 때는 작업원 A의 경우 주위정리에서 115였고, 작업원 B는 이동에서 131, 작업원 C는 지타작업에서 122, 작업원 D는 현목처리에서 128로 나타났다. 3. 작업원별로 기준맥박을 100%로 보았을 때 작업강도는 작업원 A(기본맥박 61=100%)가 전체 작업강도 160%, 요소작업 구분중에서는 주위정리가 188%로 가장 높게 나타났다. 작업원 B(기본맥박 57=100%)의 전체 작업강도는 220%, 요소작업중에서는 이동이 229%로 가장 높았으며, 작업원 C(기본맥박 73=100%)의 경우에는 전체 강도는 159%, 요소작업중에서는 지타작업이 168%로 가장 높았고, 작업원 D(기본맥박 70=100%)는 전체 작업강도 156%, 요소작업중에서는 현목처리가 182%로 가장 높게 나타났다. 4. 전체 작업강도를 나다내는 작업원에 따른 노동이행능력 한계점에서의 1분당 초과맥박수는 작업원 A의 경우 30, 작업원 B의 경우 207, 작업원 C는 14이며, 작업원 D는 67로 작업원 B가 가장 작업강도가 높아 신체적인 부담을 크게 받는 것으로 나타났다.

• 한국해안·해양공학회논문집
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• 제28권6호
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• pp.319-331
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• 2016

HF-Radar관측자료의 시간평균 간격에 따른 신호특성을 살펴보고, 국립해양조사원에서 운영하고 있는 HF-Radar관측소별로 수집률과 공분산을 분석하여 자료질이 높은 대표정점을 선점(選點)하였다. HF-Radar관측의 시간평균 간격이 짧아질수록, 취득률은 낮아지나 고주파 신호특성을 관측할 수 있었다. 그러나 조류예측에서는 현행 60분 간격의 평균자료와 20분 간격의 자료에서 취득되는 조류의 차이는 거의 없었다. 수집률 기준을 높이고 공분산을 고려한 자료는 기존에 수집률 50%만을 기준으로 한 정점에 비해 관측품질이 높아졌다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.

• 전기학회논문지
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• 제57권11호
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• pp.1910-1915
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• 2008

The load analysis for the distribution system and facilities has relied on measurement equipment. Moreover, load monitoring incurs huge costs in terms of installation and maintenance. This paper presents a new model to analyze wherein facilities load under a feeder every 15 minutes using meter reading data that can be obtained from a power consumer every 15 minute or a month even without setting up any measuring equipment. After the data warehouse is constructed by interfacing the legacy system required for the load calculation, the relationship between the distribution system and the power consumer is established. Once the load pattern is forecasted by applying clustering and classification algorithm of temporal data mining techniques for the power customer who is not involved in Automatic Meter Reading(AMR), a single-line diagram per feeder is created, and power flow calculation is executed. The calculation result is analyzed using various temporal and spatial analysis methods such as Internet Geographic Information System(GIS), single-line diagram, and Online Analytical Processing (OLAP).