• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.55-62
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• 2014

A method for estimating software reliability for nuclear safety software is proposed in this paper. This method is based on the software reliability growth model (SRGM), where the behavior of software failure is assumed to follow a non-homogeneous Poisson process. Two types of modeling schemes based on a particular underlying method are proposed in order to more precisely estimate and predict the number of software defects based on very rare software failure data. The Bayesian statistical inference is employed to estimate the model parameters by incorporating software test cases as a covariate into the model. It was identified that these models are capable of reasonably estimating the remaining number of software defects which directly affects the reactor trip functions. The software reliability might be estimated from these modeling equations, and one approach of obtaining software reliability value is proposed in this paper.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.148-157
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• 2012

Reliability-based design optimization (RBDO) problem is usually formulated as an optimization problem to minimize an objective function subjected to probabilistic constraint functions which may include deterministic design variables as well as random variables. The challenging task is that, because the probability models of the random variables are often assumed based on limited data, there exists a possibility of selecting inappropriate distribution models and/or model parameters for the random variables, which can often lead to disastrous consequences. In order to select the most appropriate distribution model from the limited observation data as well as model parameters, this study takes into account a set of possible candidate models for the random variables. The suitability of each model is then investigated by employing performance and risk functions. In this regard, this study enables structural design optimization and fitness assessment of the distribution models of the random variables at the same time. As the first paper of a two-part series, this paper describes a new design method considering probability model uncertainties. The robust performance of the proposed method is presented in Part 2. To demonstrate the effectiveness of the proposed method, an example of ten-bar truss structure is considered. The numerical results show that the proposed method can provide the optimal design variables while guaranteeing the most desirable distribution models for the random variables even in case the limited data are only available.

• Smart Structures and Systems
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• v.15 no.2
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• pp.315-334
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• 2015

A main goal of this study is to propose a damage detection technique to detect and localize damages of a top-tensioned riser. In this paper, the top-tensioned finite element (FE) model is considered as an analytical model of the riser, and a vibration-based damage detection method is proposed. The present method consists of a FE model updating and damage index method. In order to accomplish the goal of this study, first, a sensitivity-based FE model updating method using natural frequencies and zero frequencies is introduced. Second, natural frequencies and zero frequencies of the axial mode on the top-tensioned riser are estimated by eigenvalue analysis. Finally, the locations and severities of the damages are estimated from the damage index method. Three numerical examples are considered to verify the performance of the proposed method.

• Steel and Composite Structures
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• v.19 no.1
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• pp.191-208
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• 2015

The sensitivity-based finite element model updating method has received increasing attention in damage detection of structures based on measured modal parameters. Finding an optimization technique with high efficiency and fast convergence is one of the key issues for model updating-based damage detection. A new simple and computationally efficient optimization algorithm is proposed and applied to damage detection by using finite element model updating. The proposed method combines the Gauss-Newton method with region truncation of each iterative step, in which not only the constraints are introduced instead of penalty functions, but also the searching steps are restricted in a controlled region. The developed algorithm is illustrated by a numerically simulated 25-bar truss structure, and the results have been compared and verified with those obtained from the trust region method. In order to investigate the reliability of the proposed method in damage detection of structures, the influence of the uncertainties coming from measured modal parameters on the statistical characteristics of detection result is investigated by Monte-Carlo simulation, and the probability of damage detection is estimated using the probabilistic method.

• Smart Structures and Systems
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• v.32 no.2
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• pp.123-133
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• 2023

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.354-362
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• 2008

This paper presents the new method of power system transient stability simulation, which combines the desirable features of both the time domain technique based on OOP(Object-oriented Programming) and the direct method of transient stability analysis using detailed generator model. OOP is an alternative to overcome the problems associated with the development, maintenance and update of large software by electrical utilities. Several papers have already evaluated this approach for power system applications in areas such as load flow, security assessment and graphical interface. This paper applied the object-oriented approach to the problem of power system dynamics simulation. The modeling method is that each block of dynamic system block diagram is implemented as an object and connected each other. In the transient energy method, the detailed synchronous generator model is so-called two-axis model. For the excitation model, IEEE type1 model is used. The developed mothed was successfully applied to New England Test System.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.523-533
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• 2020

This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.1
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• pp.41-46
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• 2016

It has been proven that the dissolved gas analysis (DGA) is the most effective and convenient method to diagnose the transformers. The DGA is a simple, inexpensive, and non intrusive technique. Among the various diagnosis methods, IEC 60599 has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using IEC code based neuro-fuzzy model. The proposed method proceeds two steps. First, IEC 60599 method is applied to diagnosis. If IEC code can't determine the fault type, neuro-fuzzy model is applied to effectively classify the fault type. To demonstrate the validity of the proposed method, experiment is performed and its results are illustrated.

• Proceedings of the Technology Innovation Conference
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• 2005.06a
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• pp.5-23
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• 2005

Since the emergence of the Internet, electronic business (e-business) has become one of the most widely investigated issues. E-business is considered to have the potential of generating considerable new values and the capability to transform the rules of competition in unprecedented ways. This study aim to suggest a approach for new business idea creation. This is based on the analysis and manipulation of business method patents. For this end, our research is performed in the following ways. First, business keywords are extracted from business method patents. Second, business model framework which is used to structuralize the business is suggested based on the literature survey. Third, the business keywords are classified into the business model framework. Forth, existing business model is expressed based on the suggested framework. Finally, new business idea is created from the existing business model by adding, subtracting, or substituting the business keywords.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.