• 한국안전학회지
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• 제35권5호
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• pp.39-48
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• 2020

This study proposes a fast estimation method of dynamic reliability indices or failure probability for SDOF structure subjected to earthquake excitations. The proposed estimation method attempts to derive coefficient function for correcting dynamic effects from static reliability analysis in order to estimate the dynamic reliability analysis results. For this purpose, a total of 60 cases of structures with various characteristics of natural frequency and damping ratio under various allowable limits were taken into account, and various types of approximation coefficient functions were considered as potential candidate models for dynamic effect correction. Each reliability index was computed by directly performing static and dynamic reliability analyses for the given 60 cases, and nonlinear curve fittings for potential candidate models were performed from the computed reliability index data. Then, the optimal estimation model was determined by evaluating the accuracy of the dynamic reliability analysis results estimated from each candidate model. Additional static and dynamic reliability analyses were performed for new models with different characteristics of natural frequency, damping ratio and allowable limit. From these results, the accuracy and numerical efficiency of the optimal estimation model were compared with the dynamic reliability analysis results. As a result, it was confirmed that the proposed model can be a very efficient tool of the dynamic reliability estimation for seismically excited SDOF structure since it can provide very fast and accurate reliability analysis results.

• 한국산학기술학회논문지
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• 제16권5호
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• pp.3434-3439
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• 2015

정보이론적 학습의 한 성능기준인 두 오차확률분포간 유클리드거리(MEDE)는 비선형 (결정 궤환, DF) 등화 알고리듬에 채택되었고 심각한 채널 왜곡과 충격성 잡음이 있는 환경에서 탁월한 성능을 보였다. 그러나 이 MEDE-DF 알고리듬은 과중한 계산 복잡성이라는 문제를 지니고 있다. 이 논문에서는 MEDE-DF 알고리듬을 위한 반복적 ED를 먼저 유도하고 그 다음 전후방 영역에 대해 가중치 기울기를 반복적으로 추정하는 식을 유도하였다. MEDE-DF 알고리듬의 반복적 기울기 추정방식의 효과를 입증하기위해 곱셈 계산량을 비교하였고 충격성 잡음과 수중 통신 환경에서 모의 실험한 MSE 성능 결과를 비교하였다. 제안한 DF 방식과 기존의 MEDE-DF 알고리듬의 곱셈 계산량 비는 샘플사이즈 N 에 대해 $2(9N+4):2(3N^2+3N)$로 나타나면서도 충격성 잡음과 수중통신 채널환경에서 동일한 MSE 학습 성능을 유지하였다.

• Kyungpook Mathematical Journal
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• 제28권2호
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• pp.185-196
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• 1988

We study existence of polynomial integrating factors and solutions F(x, y)=c of first order nonlinear differential equations. We characterize the homogeneous case, and give algorithms for finding existence of and a basis for polynomial solutions of linear difference and differential equations and rational solutions or linear differential equations with polynomial coefficients. We relate singularities to nature of the solution. Solution of differential equations in closed form to some degree might be called more an art than a science: The investigator can try a number of methods and for a number of classes of equations these methods always work. In particular integrating factors are tricky to find. An analogous but simpler situation exists for integrating inclosed form, where for instance there exists a criterion for when an exponential integral can be found in closed form. In this paper we make a beginning in several directions on these problems, for 2 variable ordinary differential equations. The case of exact differentials reduces immediately to quadrature. The next step is perhaps that of a polynomial integrating factor, our main study. Here we are able to provide necessary conditions based on related homogeneous equations which probably suffice to decide existence in most cases. As part of our investigations we provide complete algorithms for existence of and finding a basis for polynomial solutions of linear differential and difference equations with polynomial coefficients, also rational solutions for such differential equations. Our goal would be a method for decidability of whether any differential equation Mdx+Mdy=0 with polynomial M, N has algebraic solutions(or an undecidability proof). We reduce the question of all solutions algebraic to singularities but have not yet found a definite procedure to find their type. We begin with general results on the set of all polynomial solutions and integrating factors. Consider a differential equation Mdx+Ndy where M, N are nonreal polynomials in x, y with no common factor. When does there exist an integrating factor u which is (i) polynomial (ii) rational? In case (i) the solution F(x, y)=c will be a polynomial. We assume all functions here are complex analytic polynomial in some open set.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• 한국시뮬레이션학회논문지
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• 제26권3호
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• pp.105-113
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• 2017

본 연구는 대공위협에 대한 생존성 향상을 위한 다층 대공방어 체계의 최적 투자 계획 모델을 고려한다. 최적화 모델 수립을 위해 다층 대공방어 체계를 네트워크 모델로 표현하고, 가용 예산이 제한되어 있는 상황 하에서 대응실패 확률을 최소화하기 위해 각 방어무기에 대하여 투자여부를 결정하는 모델과 연속적인 투자가 가능한 모델을 각각 제시한다. 비선형 형태의 목적함수를 로그함수를 통해 선형화하였으며, 제시된 최종 모델의 해법으로서 동적계획법 알고리즘과 선형계획법을 제안한다. 가상의 다층 대공 방어 상황을 설정한 후, 두 가지의 최적화 모델에 대한 최적해를 도출하고 그 결과를 분석하였다. 이는 다층 대공방어 체계의 신뢰도 향상을 위한 효과적인 투자 계획 수립의 필요성 및 접근방법을 제시한다.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• 한국해양공학회지
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• 제33권3호
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• pp.222-228
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• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• 한국융합학회논문지
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• 제12권2호
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• pp.29-34
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• 2021

본 논문에서는 중심에 동공을 갖는 원통형태 광결정 도파로가 제안되어지고, 이 전송로의 도파 특성에 대한 분석이 수행되어진다. 여기서 동공은 일반적인 공기이거나 임의의 액체나 고체 물질들에 의한 저지수 유전체로써 형성되게 된다. 베셀 함수를 이용한 분석적 방법으로 전자장에 대한 엄밀한 해를 구하기 위하여, 행렬 기법이 고유치 방정식의 유도에 사용되고, 실효 굴절률, 분산, 전자장 분포 등의 기본 모드의 중요한 전송 성질들이 조사된다. 또한 분석 결과 정확도의 검증을 위하여 엄밀한 완전 벡터 유한 차분법을 적용해보고, 광결정 도파로의 설계와 제조 상의 문제를 해결하는데 용이하게 활용하고자 한다. 설계된 중심-동공 광도파관의 실효 모드 면적이 2.6056 ㎛2에서 5.9673 ㎛2까지 동작 파장에 따라 다양하게 변하며, 일반적으로 광도파로의 중심으로부터 바깥쪽으로 원통형의 층수가 적을수록 그리고 굴절률 n1이 약간 큰 저지수일수록 실효 면적은 작아지므로, 비선형 소자 응용의 관점에서 훨씬 더 최적화된 결과를 나타낸다.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Steel and Composite Structures
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• 제41권6호
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• pp.899-910
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• 2021

A 4-unknown shear deformation theory is applied to investigate the vibration of functionally graded plates under thermal environment. The plate is fabricated from a functionally graded material mixed of ceramic and metal with continuously varying material properties through the plate thickness. Three types of thermal loadings, uniform, linear and nonlinear temperature rises along the plate thickness are taken into account. The present theory contains four unknown functions as against five or more in other higher order shear deformation theories. The through-the-thickness distributions of transverse shear stresses of the plate are considered to vary parabolically and vanish at upper and lower surfaces. The present model does not require any problem dependent shear correction factor. Analytical solutions for the free vibration analysis are derived based on Fourier series that satisfy the boundary conditions (Navier's method). Benchmark solutions are firstly considered to evaluate the accuracy of the proposed model. Comparisons with the solutions available in literature revealed the good capabilities of the present model for the simulations of vibration responses of FG plates. Some parametric studies are carried out for the frequency analysis by varying the volume fraction profile and the temperature distribution across the plate thickness.