• 한국지반공학회지:지반
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• 제4권2호
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• pp.5-14
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• 1988

균질토 사면에서 진행성 파괴에 대한 확를론적 모델이 제시되었다. 파괴면 위의 어떤 절편에 대한 국부적인 Safety Margin은 정규분포차 가정하였다. 파괴면을 따라 존재하는 전단강도의 불확실성은 1차원 Random Field Models로 표현되었다. 이 연구에서는 파괴가 Toe에서 시작되어 사면 정상까지 진행되는 경우만을 고려하였다. 파괴면위의 어느 두 인접 절편의 Safety Margin의 Joint Distribution은 Bivariate Normal로 가정하였다. 활동파괴의 전체적인 파괴확률은 일련의 Conditional events의 급으로 표현되었다. 최종적으로 개발된 절차가 절취사면의 신뢰도를 얻기 위하여 한 예에 적용되었다.

• 대한토목학회논문집
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• 제8권3호
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• pp.85-90
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• 1988

균질토 사면에서 파괴에 대한 확률론적모델이 제시되었다. 사면의 안전은 관례적인 안전을 보다는 파괴확률로써 측정된다. 사면파괴의 Safety Margin은 정규분포라 가정하였다. 어떤 균질한 흙층에 있어서 흙의 특성에 영향을 주는 불확실성의 원인은 본래의 공간적인 가변성, 불충분한 시료에서 오는 판단오차 그리고 실험오차등이 었다. 파괴면을 따라 존재하는 전단강도의 불확실성은 1차원 Random Field Madels로 표현되었다. 파괴aus의 양상은 대수나선 곡선을 따른다고 가정하였다. 파괴면과 그것을 따라 작용하는 힘의 통계적 특성을 유도하여 사면의 파괴확률을 계산하였다. 마지막으로 개발된 절차가 사면의 신뢰성 해석에 대한 하나의 예제 연구에 적용되었다.

• 한국농공학회논문집
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• 제59권3호
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Structural Engineering and Mechanics
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• 제15권6호
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• pp.669-683
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• 2003

The stochastic optimal nonlinear control of coupled adjacent building structures is studied based on the stochastic dynamical programming principle and the stochastic averaging method. The coupled structures with control devices under random seismic excitation are first condensed to form a reduced-order structural model for the control analysis. The stochastic averaging method is applied to the reduced model to yield stochastic differential equations for structural modal energies as controlled diffusion processes. Then a dynamical programming equation for the energy processes is established based on the stochastic dynamical programming principle, and solved to determine the optimal nonlinear control law. The seismic response mitigation of the coupled structures is achieved through the structural energy control and the dimension of the optimal control problem is reduced. The seismic excitation spectrum is taken into account according to the stochastic dynamical programming principle. Finally, the nonlinear controlled structural response is predicted by using the stochastic averaging method and compared with the uncontrolled structural response to evaluate the control efficacy. Numerical results are given to demonstrate the response mitigation capabilities of the proposed stochastic optimal control method for coupled adjacent building structures.

• Journal of Mechanical Science and Technology
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• 제19권6호
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• pp.1280-1289
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• 2005

This paper presents the effects of corrosion environments of failure pressure model for buried pipelines on failure prediction by using a failure probability. The FORM (first order reliability method) is used in order to estimate the failure probability in the buried pipelines with corrosion defects. The effects of varying distribution types of random variables such as normal, lognormal and Weibull distributions on the failure probability of buried pipelines are systematically investigated. It is found that the failure probability for the MB31G model is larger than that for the B31G model. And the failure probability is estimated as the largest for the Weibull distribution and the smallest for the normal distribution. The effect of data scattering in corrosion environments on failure probability is also investigated and it is recognized that the scattering of wall thickness and yield strength of pipeline affects the failure probability significantly. The normalized margin is defined and estimated. Furthermore, the normalized margin is used to predict the failure probability using the fitting lines between failure probability and normalized margin.

• 한국환경과학회지
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• 제28권1호
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• pp.19-35
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• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.25-32
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• 2022

IR-UWB radar has been regarded as the most promising technology for non-contact respiration and heartbeat monitoring because of its ability of detecting slight motion even in submillimeter range. Measuring heart rate is most challenging since the chest movement by heartbeat is quite subtle and easily interfered with by a random body motion or background noise. Additionally, periodic sampling can be limited by the performance of computer that handles the radar signals. In this paper, we deploy Lomb-Scargle periodogram method that estimates heart rate even with irregularly sampled data and uneven signal amplitude. Lomb-Scargle periodogram is known as a method for finding periodicity in irregularly-sampled and noisy data set. We also implement a motion detection scheme in order to make the heart rate estimation pause when a random motion is detected. Our scheme is implemented using Novelda's X4M03 radar development kit and its corresponding drivers and Python packages. Experimental results show that the estimation with Lomb-Scargle periodogram yield more accurate heart rate than the method of measuring peak-to-peak distance.

• 한국기계가공학회지
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• 제21권1호
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• pp.73-80
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• 2022

Sensors are applied to small aviation platforms for various purposes. Radio frequency (RF) antennas, which are representative sensors, are available in many forms but require the application of slot array RF antennas to ensure high performance and designation. Slot RF array antennas are applied to dip brazing techniques, but the yield and production time are determined by the proficiency of production personnel in a labor-intensive form. Unmanned aerial vehicles or drones, which are representative small aviation platforms, are continuously exposed to various random vibrations because propellers and multiple power sources are used in them. In this study, the fatigue life of slot array RF antennas applied with additive manufacturing was evaluated through the cumulative damage method (Miner's rule) in a vibration environment with a small aviation platform. For the evaluation, an S N curve obtained from a fatigue strength test was used.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Asian-Australasian Journal of Animal Sciences
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• 제23권6호
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• pp.814-824
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• 2010

The objective of this research was to assess the effect of experience, education, record keeping, labor, and decision making on monthly milk yield per farm (MYF), monthly milk yield per cow (MYC), monthly milk revenue per farm (MRF), and monthly revenue per cow (MRC) of dairy farms supported by a private organization in Central Thailand. The dataset contained 34,082 monthly milk yield and revenue records collected from January 2004 to December 2008 on 497 farms, and information on individual farmer experience and education, record keeping, and decision making obtained with a questionnaire. Farmer experience categories were i) no experience, ii) one year, iii) two to five years, iv) six to ten years, v) eleven to fifteen years, vi) sixteen to twenty years, and vii) more than twenty years. Farmer education categories were i) no education or primary school, ii) high school, and iii) bachelor or higher degree. Record keeping categories were: i) no records and ii) kept records. Labor categories were: i) family, ii) hired people, and iii) family and hired people. Decision making categories were: i) decisions made by farmers themselves, ii) decisions made with help from government officials, and iii) decisions made with help from organization staff. The mixed linear model contained the fixed effects of year-season, farm location-farm size subclass, experience, education, record keeping, labor, and decision making on sire selection, and the random effects of farm and residual. Results showed that longer experience increased (p<0.05) monthly milk yield (MYF and MYC) and revenue (MRF and MRC). Farms that hired people produced the highest (p<0.05) monthly milk yield (MYF and MYC) and revenue (MRF and MRC), followed by farms that used family, and the lowest values were for farms that used both family and hired people. Better educated farmers produced more MYC and MRC (p<0.05) than lower educated farmers. Farms that kept records had higher MYF and MRF (p<0.05) than those without records. Although differences among farms were non-significant, farms that received help from the organization staff had higher monthly milk yield (MYF and MYC) and revenue (MRF and MRC) than those that decided by themselves or with help from government officials. These findings suggested that dairy farmers needed systematic training and continuous support to improve farm milk production and revenues in a sustainable manner.