• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2010년도 춘계 학술대회 제22권1호
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• pp.405-406
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• 2010

본 연구에서는 Bayesian 확률법을 이용한 성능기반형 배합설계방법(Perfomance Based Mixture Design, 이하 PBMD)을 고강도 콘크리트 배합설계에 활용하여 요구성능을 만족하는 고강도 콘크리트 배합비를 찾는 것을 목표로 하고 있다. 고강도 콘크리트의 여러 가지 재료 성능 변수들을 구하기 위해 수행한 여러 가지 실험들의 결과를 바탕으로 고강도 콘크리트 배합설계 시 PBMD 방법의 적용가능성에 대해 검토하였으며 지역에 따른 환경조건, 사용 가능한 재료, 적용 가능한 콘크리트생산기술 등을 고려하여 목표성능을 만족시키는 최적의 콘크리트 배합비를 구하는 과정을 PBMD 방법을 적용한 예제를 통해 나타내었다.

• Structural Engineering and Mechanics
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• 제84권5호
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• pp.651-664
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• 2022

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1991년도 춘계학술대회
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• 품질경영학회지
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• 제42권3호
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• pp.311-324
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• 2014

Purpose: It can deduce improvement plan that recognizes any risk factors in initial production and mass production by using FMEA and through this process, the appropriate criteria for defence items can be established. Methods: It proposes two methodology - Apply DT/OT data achieved from the beginning mass production stage based on FMECA data of the design stage, to risk management, and risk management plan that reflected line and field faliure data in case of is offered. Results: It proposes the risk management plan through Bayesian method and the risk identification that considered MTTF estimated value in case of initial production process. In case of mass production process, both risk identification by using fault occurrence frequency scores and Byaesian method, In case of the Initial production and mass production, it proposes use both two methods. Conclusion: A more realistic risk identification method can be applied, and by this method the quality improvement effect is expected.

• Asian Pacific Journal of Cancer Prevention
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• 제17권2호
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• pp.867-872
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• 2016

Background: Esophageal cancer is one of the most serious malignancies. Due to the aggressive nature of this cancer, the prognosis is poor. A network meta-analysis with simultaneous comparison of multiple treatments can help determine better treatment options that have higher effects on overall survival of patients with lower adverse events. The aim of this review is to simultaneously compare efficacy and adverse events of treatment interventions for esophageal cancer. Materials and Methods: In this review, only randomized control trials (RCT) will be considered for network meta-analysis. All international electronic databases including Medline, Web of Sciences, Scopus, Cochran's library, EMBASE and Cancerlit will be searched to find randomized control trials which compared two or more treatment interventions for esophageal cancer. A network plot will be drawn for visual representation of all available treatment interventions. Bayesian approach will be used to combine the direct and indirect evidence. Treatment effects (e.g. hazard ratio for time to event outcomes, risk ratio for binary outcomes, and rate ratio for count outcomes with 95% credible interval) will be reported. Moreover, cumulative probability of the treatment ranks will be reported using the surface under the cumulative ranking (SUCRA) graphs. Consistency assumption will be assessed by the loop-specific and design-by-treatment interaction approaches. Conclusions: The results of this study may be helpful for the patients, clinicians and health policy makers in selecting treatments that have the best effect on survival and lowest adverse events.

• 전자공학회논문지
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• 제52권3호
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• pp.89-95
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• 2015

무선 헬스케어 서비스에서 생체신호 모니터링 시스템의 전력소모를 효과적으로 감소시킬 수 있는 압축센싱 기법을 다양한 생체신호에 적용하여 압축률을 비교하였다. 압축센싱 기법을 이용하여 일반적인 심전도, 근전도, 뇌전도 신호의 압축과 복원을 수행하였고, 이를 통해 복원된 신호와 원신호를 비교함으로써, 압축센싱의 유효성을 판단하였다. 유사랜덤 행렬을 사용하여 실제 생체신호를 압축하였으며, 압축된 신호는 Block Sparse Bayesian Learning(BSBL) 알고리즘을 사용하여 복원하였다. 가장 산제된 특성을 가지는 근전도 신호의 최대 압축률이 10배로 확인되어 가장 높았으며, 심전도 신호의 최대 압축률은 5배였다. 가장 산제된 특성이 작은 뇌전도 신호의 최대 압축률은 4배였다. 연구된 심전도, 근전도, 뇌전도 신호의 압축률은 향후 압축센싱을 적용한 무선 생체신호 모니터링 회로 및 시스템 개발시 유용한 기초자료로 활용될 수 있다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.1434-1438
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• 2010

수문자료의 계절성은 수자원관리의 관점에서 매우 중요한 요소로서 계절성의 변동은 댐의 운영, 홍수조절, 관계용수 관리 등 다양한 분야와 밀접한 관계를 가지고 있다. 그러나 지금까지의 수문 자료의 계절성 평가는 주로 이수과점에서 이루어지고 있으며 치수관점에서 극치수문량의 계절성을 평가하는 연구는 미진한 실정이다. 이는 극치수문량을 해석하는 방법론으로서 연최대치계열(annual maxima) 즉, Block Maxima가 이용됨에 따라 나타나는 문제점이다. 그러나 부분기간치계열(partial duration series)을 활용하게 되면 자료의 확충뿐만 아니라 자연적으로 극치수문량의 계절성에 대한 평가 또한 가능하다. 이러한 분석과정을 POT(peak over threshold)분석이라 하며 일정 기준값(threshold) 이상의 자료를 모두 취하여 빈도해석에 이용하는 방법으로서 기존 방법의 경우 연최대값이 일반적으로 7월과 8월에만 존재하게 되지만 POT 분석의 경우 여러 달에 걸쳐 빈도해석을 위한 자료가 구성되게 된다. 이를 빈도해석으로 연계시키기 위해서는 계절성을 비정상성으로 고려하여 모형화 할 수 있는 방법론의 개발이 필요하다. 본 연구에서는 이러한 목적을 위해서 계절성을 고려할 수 있는 비정상성빈도해석 기법의 개념을 제시하고 모형으로 개발하고자 한다. GEV 또는 Gumbel 분포의 매개변수와 계절성을 연계시키기 위해서 Fourier 급수가 활용되며 매개변수는 Bayesian 기법을 통해 최적화 된다. 이를 통하여 설계강수량의 계절적 분포를 정량적으로 해석할 수 있으며 미래의 극치강수량에 대한 분포특성 또한 확률적으로 해석이 가능하다. 본 연구에서 제안된 방법은 국내외 시간강수량자료에 적용되어 적합성과 적용성이 평가된다.

• Nuclear Engineering and Technology
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• 제21권1호
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• pp.1-11
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• 1989

원자로 정지동안에도, 잔열제거계통은 그 기능이 계속 유지되어야 하나, 실제로 가압 경수로에서 냉각상실고가 많이 발생되어 있다. 본 논문은 원자로 정지중의 냉각기능상실을 예방하고, 또한 냉각기능상실로 인한 노심손상의 중대성을 완화시키기 위한 대책을 강구하기 위한 시도로서, 전형적인 가압경수로에 대한 사고/고장 수목과 운전원실수 확률을 위한 HCR 모델, 초기 사상의 빈도를 위한 2단계 bayesian 방법 및 고장난 계통의 회복 활률을 위한 계단함수 모델 등을 이용한 원자로 정지 위해도 모델을 개발하여, 잔열제거계통의 신뢰도를 분석하였다. 그 결과는 원자로가 정지 중일 때의 위해도가 운전중일 때 이것에 비해 별로 낮지 않은 것으로 나타났으며, 몇 가지의 설계개선을 통하여 냉각기능상실로 인한 노심 손상확률을 상당히 낮출 수 있는 것으로 나타났다.

• Journal of Korea Trade
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• 제25권6호
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• pp.78-104
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• 2021

Purpose - Semiconductors are a significant export item for Korea that is expected to continue to contribute significantly to the Korean economy in the future. Thus, the semiconductor industry is a critical component in the 4th Industrial Revolution and is expected to continue growing as the non-face-to-face economy expands as a result of the COVID-19 pandemic. In this context, this paper aims to empirically investigate how semiconductors are imported and exported in Korea from a global supply chain perspective by analysing import and export data at the micro-level. Design/methodology - This study conducts a multifaceted analysis of the global supply chain for semiconductors and related equipment in Korea by examining semiconductor imports and exports by semiconductor type, year, target country, mode of transportation, airport/port, and domestic region, using import/export micro-data. The visualisation, flow analysis, and Bayesian Network methodologies were used to compensate for the limitations of each method. Findings - Korea is a major exporter of semiconductor memory and has the world's highest competitiveness but is relatively weak in the field of system semiconductors. The trade deficit in 'semiconductor equipment and parts' is clearly growing. As a result, continued investment in 'system semiconductors' and 'semiconductor equipment and parts' technology development is necessary to boost exports and ensure a stable supply chain. Originality/value - Few papers on semiconductor trade in Korea have been published from the perspective of the global supply chain or value chain. This study contributes to the literature in this area by focusing on import and export data for the global supply chain of the Korean semiconductor industry using a variety of approaches. It is our hope that the insights gained from this study will aid in the advancement of SCM research.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.