• 한국신뢰성학회지:신뢰성응용연구
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• 제15권2호
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• pp.90-100
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• 2015

Reliability growth rate (or reliability growth curve slope) have the two cases of trend as a constant or changing one during the reliability growth testing. The changing case is very common situation. The reasons of reliability growth rate changing are that the failures to follow the NHPP (None-Homogeneous Poisson Process), and the solutions implemented during test to break out other problems or not to take out all of the root cause permanently. If the changing were big, the "Goodness of Fit (GOF)" of reliability growth curve to test data would be very low and then reduce the accuracy of assessing result with test data. In this research, we are using Duane model and AMSAA model for assessing test data and projecting the reliability level of complex and repairable system as like construction equipment and vehicle. In case of no changing in reliability growth rate, it is reasonable for reliability engineer to implement the original Duane model (1964) and Crow-AMSAA model (1975) for the assessment and projection activity. However, in case of reliability growth rate changing, it is necessary to find the method to increase the "GOF" of reliability growth curves to test data. To increase GOF of reliability growth curves, it is necessary to find the proper parameter calculation method of interesting reliability growth models that are applicable to the situation of reliability growth rate changing. Since the Duane and AMSAA models have a characteristic to get more strong influence from the initial test (or failure) data than the latest one, the both models have a limitation to contain the latest test data information that is more important and better to assess test data in view of accuracy, especially when the reliability growth rate changing. The main objective of this research is to find the parameter calculation method to reflect the latest test data in the case of reliability growth rate changing. According to my experience in vehicle and construction equipment developments over 18 years, over the 90% in the total development cases are with such changing during the developing test. The objective of this research was to develop the newly assessing method and the process for GOF level increasing in case of reliability growth rate changing that would contribute to achieve more accurate assessing and projecting result. We also developed the new evaluation method for GOF that are applicable to the both models as Duane and AMSAA, so it is possible to compare it between models and check the effectiveness of new parameter calculation methods in any interesting situation. These research results can reduce the decision error for development process and business control with the accurately assessing and projecting result.

• 한국구조물진단유지관리공학회 논문집
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• 제23권6호
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• pp.38-44
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• 2019

본 연구에서는 아스팔트 콘크리트 도로포장의 표면균열 검출을 위해 합성곱 신경망을 이용하였다. 합성곱 신경망의 학습에 사용되는 표면균열 이미지 데이터의 양에 따른 합성곱 신경망의 성능향상 정도를 평가하였다. 사용된 합성곱 신경망의 구조는 5개의 층으로 구성되어있으며, 3×3 크기의 convolution filter와 2×2 크기의 pooling kernel을 사용하였다. 합성곱 신경망의 학습을 위해서 도로노면 조사 장비를 통해 구축된 국내 도로포장 표면균열 이미지를 활용하였다. 표면균열 이미지 데이터를 학습한 합성곱 신경망 모델의 표면균열 검출 정확도, 정밀도, 재현율, 미검출율, 과검출율을 평가하였다. 가장 많은 양의 데이터를 학습한 합성곱 신경망 모델의 표면균열 검출 정확도, 정밀도, 재현율은 96.6% 이상, 미검출율, 과검출율은 3.4% 이하의 성능을 나타내었다.

• 한국정보시스템학회지:정보시스템연구
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• 제28권4호
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• pp.333-358
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• 2019

Purpose The study aims to develop a data-based decision model for private bankers when recommending hedge funds to their customers in financial institutions. Design/methodology/approach The independent variables are set in two groups. The independent variables of the first group are aggressive investors, active investors, and risk-neutral type investors. In the second group, variables considered by private bankers include customer propensity to invest, reliability, product subscription experience, professionalism, intimacy, and product understanding. A decision-making variable for a private banker is in recommending a first-rate general private fund composed of foreign and domestic FinTech products. These contain dependent variables that include target return rate(%), fund period (months), safeguard existence, underlying asset, and hedge fund name. Findings Based on the research results, there is a 94.4% accuracy in decision-making when the independent variables (customer rating, reliability, intimacy, product subscription experience, professionalism and product understanding) are used according to the following order of relevant dependent variables: step 1 on safeguard existence, step 2 on target return rate, step 3 on fund period, and step 4 on hedge fund name. Next, a 93.7% accuracy is expected when decision-making uses the following order of dependent variables: step 1 on safeguard existence, step 2 on target return rate, step 3 on underlying asset, and step 4 on fund period. In conclusion, a private banker conducts a decision making stage when recommending hedge funds to their customers. When examining a private banker's recommendations of hedge funds to a customer, independent variables influencing dependent variables are intimacy, product comprehension, and product subscription experience according to a categorical regression model and artificial neural network analysis model.

• 대한기계학회논문집A
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• 제41권5호
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• pp.391-399
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• 2017

절삭속도 및 이송속도가 평엔드밀로 하향절삭 가공된 측벽 형상에 미치는 영향을 실험을 통하여 알아보고자 한다. 실험은 절삭속도 및 공구 직경, 절삭날 당 이송거리를 변수로 하여 수행하며, 실험 결과로서 배분력과 축방향 형상을 측정한다. 연구 결과, 이송속도를 절삭속도로 나눈 값에 비례하는 날 당 이송거리가 작을수록 축방향 형상정밀도가 높아지는 것이 확인되었다. 아울러, 축방향 형상은 서로 다른 기울기를 갖는 두 직선이 특이점에서 만나는 형태로 단순화 할 수 있다. 그러므로 운전 중 작업자에 의한 형상정밀도의 추정 및 날 당 이송거리 조정에 의한 개선이 용이할 것으로 판단된다.

• 한국통신학회논문지
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• 제17권12호
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• pp.1391-1400
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• 1992

본 논문에서는 CCITT에서 ATM 망의 사용 감시 알고리즘에 대한 정확도의 평가 기준으로 고려하고 있는 type-I/II 에러의 평가방법을 제안하였다. 그리고, ON/OFF 트래픽 모델에 대한 LB(leaky bucket)방식의 type-I/II 에러 분석을 통하여 평균 셀률과 최대 셀률을 감시 제어하기 위한 LB파라 미터의 결정방법을 고찰하였다. LB파라미터인 계수기의 감소율 a와 한계치 M은 type-I 에러를 만족하는 a-M 특성곡선을 구한후, 이 중에서 type-II 에러를 만족하면서 반응시간이 최소가 되는 (a, M)의 쌍으로 결정할 수 있음을 제시하였다. 이때, ON/OFF 트래픽에 대한 LB방식의 a-M 특성곡선은 트래픽의 버스트성에 의해 결정되어 짐을 알 수 있었다. 그리고, 성능분석 결과로부터 LB 방식은 최대 셀률에는 비교적 좋은 제어 성능을 가지지만, 평균 셀률 제어에 있어서는 정확도와 반응시간과의 상반관계로 인해 문제가 있음을 알 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제37권2호
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• 응용통계연구
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• 제30권6호
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• pp.993-1004
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• 2017

표본조사에서는 추정의 정확성 및 정밀성 향상을 위해 흔히 층화추출법을 사용하며 층 내에서는 동일한 표본 가중치를 이용하여 표본을 추출한다. 그러나 실제 응답률은 관심변수 값에 영향을 받을 수 있기 때문에 주어진 동일한 가중치는 응답률을 반영하여 보정되어야 한다. 또한 관심변수가 연속형 보조변수와 선형 관계가 있고 보조변수를 기준으로 층이 나누어진 경우에는 층 내에서 동일한 가중치를 사용하는 것 보다 층을 세분화한 후 얻어진 가중치를 사용하는 것이 효과적일 수 있다. 본 연구에서는 응답률이 관심변수 자료 값의 지수함수이고, 관심변수가 보조변수와 선형 관계가 있을 때 정보적 표본설계 기법을 이용하여 추정의 정확성과 정밀성을 높이는 방법을 제안하였다. 또한 모의실험을 통하여 제안된 방법의 우수성을 확인하였다.

• 한국생산제조학회지
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• 제25권2호
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• pp.124-131
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• 2016

Several research works for finding and optimizing the methods of dispensing high viscosity phosphor used in the fabrication of white LED's are currently in progress. High viscosity phosphor dispensing with a high accuracy is crucial because the dispensing rate and uniformity directly affect parameters such as the CIE chromaticity diagram, color temperature and luminous flux of white LED's. This study presents a novel method of dispensing high viscosity phosphor using electrohydrodynamic printing. The dispensing rate was optimized less than 0.01 mg phosphor using experiments and optimizing the process parameters including the standoff distance from the nozzle to the substrate, ink supply pressure, and multi-step pulsed waveform magnitude ratio. The dispensing rate was measured by dispensing 20 dots using drop-on-demand with the optimized parameters, and the experiments were repeated 10 times to maximize the data accuracy. The average dispensing rate that can be reliably used for high viscosity phosphor dispensing was 0.0052 mg.

• Structural Engineering and Mechanics
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• 제73권4호
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• pp.427-436
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• 2020

The use of final IGS precise orbit and clock products for high-rate GNSS-PPP proved its effectiveness in capturing dynamic displacement of engineering structures caused by earthquakes. However, the main drawback of using the final products is that they are available after approximately two weeks of data collection, which is not suitable for timely measures after an event. In this study, the use of ultra-rapid products (observed part), which are available after a few hours of data collection, and rapid products, which are available in less than 24 hrs, are investigated and their results are compared to the more precise final products. The tests are designed such that harmonic oscillations with different frequencies and amplitudes and ground motion of a simulated real earthquake are generated using a single axis shake table and the PPP was used to capture these movements by monitoring time-change of the table positions. To evaluate the accuracy of PPP using ultra-rapid, rapid and final products, their results were compared with relative GNSS positioning and LVDT (Linear Variable Differential Transformer) data, treated as reference. The results show that the high-rate GNSS-PPP solutions based on the three products can capture frequencies of harmonic oscillations and dynamic displacement with good accuracy. There were slight differences between ultra-rapid, rapid and final products, where some of the tested events indicated that the latter two produced are more accurate and provide better results compared to the ultra-rapid product for monitoring short-term dynamic displacements.

• 해양환경안전학회지
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• 제25권4호
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• pp.476-481
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• 2019

화재의 초기 검출은 인명과 재화의 손실을 최소화하기 위한 중요한 요소이다. 불꽃과 연기를 신속하면서 동시에 검출해야 하며 이를 위해 영상 기반의 화재 검출에 관한 연구가 다양하게 진행되고 있다. 기존의 화재 검출은 불꽃과 연기의 특징을 추출하기 위해 여러 알고리즘을 거쳐서 화재의 검출 유무를 판단하므로 연산량이 많이 소모되었으나, 딥러닝 알고리즘인 합성곱 신경망을 이용하면 별도의 과정이 생략되므로 신속하게 검출할 수 있다. 본 논문에서는 선박 기관실에서 화재 영상을 녹화한 데이터로 실험을 수행하였다. 불꽃과 연기의 특징을 외각 상자로 추출한 후 합성곱 신경망 중 하나인 욜로(YOLO)를 이용하여 학습하고 결과를 테스트하였다. 실험 결과를 검출률, 오검출률, 정확도로 평가하였으며 불꽃은 0.994, 0.011, 0.998, 연기는 0.978, 0.021, 0.978을 나타내었고, 연산시간은 0.009s를 소모됨을 확인하였다.