• 한국군사과학기술학회지
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• 제16권5호
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• pp.675-682
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• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 하계학술대회 논문집
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• pp.319-322
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• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• International Journal of Naval Architecture and Ocean Engineering
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• 제9권6호
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• pp.613-623
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• 2017

A semi-empirical model to predict ship resistance in level ice based on Lindqvist's model is presented. This model assumes that contact between the ship and the ice is a case of symmetrical collision, and two contact cases are considered. Submersion force is calculated via Lindqvist's formula, and the crushing and breaking forces are determined by a concept of energy consideration during ship and ice impact. The effect of the contact coefficient is analyzed in the ice resistance prediction. To validate this model, the predicted results are compared with model test data of USCGC Healy and icebreaker Araon, and full-scale data of the icebreaker KV Svalbard. A relatively good agreement is achieved. As a result, the presented model is recommended for preliminary total resistance prediction in advance of the evaluation of the icebreaking performance of vessels.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 춘계 학술논문 발표대회
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• pp.164-165
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• 2014

In the case of U.S, various damage prediction models of buildings from natural disasters have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model of hurricane have been conducted, the scope has been only limited to consider the property of hurricane. However, it is necessary to consider various factors such as socio-economic, physical, geographical, and built environmental factors to predict the damages. Therefore, as a basic study on a damage prediction model development of buildings, this study is to analyze the correlation between various factors related to hurricane. The findings of study can be utilized to develop for predicting the damage of hurricane on buildings.

• 대한기계학회논문집
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• 제18권6호
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• pp.1465-1473
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• 1994

This paper describes the development strategy and contents of a fatigue life evaluation system, FLEVA. The system is composed of 4 parts; material properties, load histories, cycle counting and life prediction. The cycle counting is based on the rain-flow counting method and peak counting method, and the life prediction is performed based on the linear damage rule. Material properties(static, fatigue) are also provided as a database obtained by a computer aided test system. Case study is performed to verify the developed program.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.19-22
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• 1996

A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.

• 전기학회논문지
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• 제66권4호
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• pp.715-720
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• 2017

This study tries to analyze morphology and formant frequencies of linear prediction spectra of stethoscope sounds for heart diseased children. For this object, heart diseased stethoscope sounds were collected in the pediatrics of an university hospital. The collected signals were preprocessed and analyzed by the Burg algorithm, a kind of linear prediction analysis. The linear prediction spectra and the formant frequencies of the spectra for the stethoscope sounds for the normal and the diseased children are estimated and compared. The spectra showed outstanding differences in morphology and formant frequencies between the normal and the diseased children. Normal children showed relatively low frequency of F1(the first formant) and small negative slope from F1. VSD children revealed stiff slope change around F1 to F3. Spectra of ASD children is similar with the normal case, but have negative values of F3. F1-F2 difference of the functional murmur children were relatively large.

• 응용통계연구
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• 제16권2호
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• pp.293-303
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• 2003

개입효과가 포함된 시계열 자료에 대한 여러 시계열 모형에 의한 예측 방법들이 비교 분석된다. 개입이 있는 선형 ARIMA 모형, 비선형 ARCH 모형 및 개입이 있는 비선형 ARCH 모형 그리고 TONG 이 제안한 결합예측방법들이 소개되고, 실증분석으로 개입이 있다고 생각되는 한국건축허가면적 자료로부터 그 예측 수월성이 비교된다.

• 한국농공학회지
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• 제42권5호
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• pp.144-150
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• 2000

This study dealt with the comparison of lateral baring capacity for vertical PHC pile between predicted and measured values driven in weathered granite soils to build world cup gymnasium in Kwangju area. Recently, the calculation of horizontal bearing capacity of piles foundation has been considered very important for earthquake or wind resistant design in Korea. During this study , Matlock & Reese, Broms and Chang's methods were selected in prediction of lateral resistant of PHC piles. As for case study, the prediction values were compared with 5 measured ones based on ASTM. The result showed that prediction values proposed by Matlock & Reese , Chang and Broms were smaller that real values. Three proposed methods by Matlock & Reese and Chang based on lateral deflection and Broms by ultimated lateral resistance turned out valid in view of engineering practice.

• Journal of the Korean Statistical Society
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• 제17권1호
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• pp.9-23
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• 1988

The classicla theory of statistical calibration assumes that the standard measurement is exact. From a realistic point of view, however, this assumption needs to be relaxed so that more meaningful calibration procedures may be developed. This paper presents a model which explicitly considers errors in both standard and nonstandard measurements. Under the assumption that replicated observations are available in the calibration experiment, three estimation techniques (ordinary least squares, grouping least squares, and maximum likelihood estimation) combined with two prediction methods (direct and inverse prediction) are compared in terms of the asymptotic mean square error of prediction.