• 수산경영론집
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• 제49권3호
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• pp.85-99
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• 2018

This study is to compare and evaluate the estimating method of WTP(willingness to pay) for the valuation of oceans and fisheries resources with non-market goods characteristics using contingent valuation method. In general, when estimating parameters of the WTP function, we should take into account the assumption of probability distribution, inclusion of covariates, method of inducement of payment, and the treatment of 0 payment intention and resistance responses. This study utilizes survey data that was used to estimate the value of fisheries resource protection zones, with a total of 1,200 samples. The main results of this study are summarized as follows: First, the final willness to pay amount is estimated at a statistical significance of less than 1 percent, and the distribution of the final willness to pay amount is from \6,926 of the double bounded dichotomous model to \10,721 of the spike model. Second, the willness to pay amount based on assumptions about the normal and logistic probability distributions are estimated to be \9,429 and \9,370 respectively, so there was no significant difference. Third, the willness to pay amount of the single bounded dichotomous model and the double bounded dichotomous model are estimated to be \8,951 and \6,926 respectively, making a relatively large difference. Fourth, the willness to pay amount of the model without covariates and the model with covariates are estimated to be \9,429 and \8,951, respectively, so the willness to pay amount is underestimated when the covariates are included. Fifth, the Spike model that considers zero payment intention and resistance response estimates \10,405 as the highest payment in this study. Finally, the CVM analysis guidelines proposed by the Korea Development Institute (KDI) are estimated to be \9,749 and \10,405 respectively, depending on including no covariates and with covariates. Compared to other models, the final willness to pay amount is not estimated underestimated. Therefore this study suggests the use of KDI's guidance under government public policy projects. In view of these results, the estimating model for willness to pay amount model will be selected by considering the sample size, the suitability of the model, the sign of the estimated coefficient, the statistical significance, the ratio of the zero payment intention and the payment rejection. And, for CVMs on government public policy projects, it is desirable to estimate by the method proposed by the KDI.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 춘계 학술발표회
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• pp.1466-1472
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• 2010

In order to construct extremely large scale of sea dike like Saemanguem dike, extremely large amount of mass of rock are needed. In this case, it is general methods to estimate required amount of rock mass based on characteristics of consolidation settlement and bearing capacity of seabed, because it is impossible to estimate exact amount of rock material based on varied seabed condition.. Even in this general methods, it is very few case to manage rock mass amount by estimation of actual input rock mass but the main point is focused on the final section formation considering of designed section and reserve embankment, so excessive or underestimating result of rock mass would be occurred surely. This general methods is not resonable in the points of economic and stable. In this study, optimum construction management method of rubble mound in the 3rd section construction of Saemanguem sea dike is suggested based on comparing required rock mass estimating from consolidation settlement theory with actual input rock mass. It is found out that the optimum input quantity of rock mass is about $1,900{\sim}2,000m^3$/day.

• Journal of Mechanical Science and Technology
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• 제18권10호
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• pp.1722-1729
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• 2004

This paper presents a design methodology for determining configurations of slider air bearings considering the randomness of the air-bearing surface (ABS) geometry by using the iSIGHT. A reliability-based design optimization (RBDO) problem is formulated to minimize the variations in the mean values of the flying heights from a target value while satisfying the desired probabilistic constraints keeping the pitch and roll angles within a suitable range. The reliability analysis is employed to estimate how the fabrication tolerances of individual slider parameters affect the final flying attitude tolerances. The proposed approach first solves the deterministic optimization problem. Then, beginning with this solution, the RBDO is continued with the reliability constraints affected by the random variables. Reliability constraints overriding the constraints of the deterministic optimization attempt to drive the design to a reliability solution with minimum increase in the objective. The simulation results of the RBDO are listed in comparison with the values of the initial design and the results of the deterministic optimization, respectively. To show the effectiveness of the proposed approach, the reliability analyses are simply carried out by using the mean value first-order second-moment (MVFO) method. The Monte Carlo simulation of the RBDO's results is also performed to estimate the efficiency of the proposed approach. Those results are demonstrated to satisfy all the desired probabilistic constraints, where the target reliability level for constraints is defined as 0.8.

• 대한기계학회논문집B
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• 제22권12호
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• pp.1715-1725
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• 1998

A two-dimensional transient inverse heat conduction problem involving the estimation of the unknown location, ($X^*$, $Y^*$), and timewise varying unknown strength, $G({\tau})$, of a line heat source embedded inside a rectangular bar with insulated boundaries has been solved simultaneously. The regular conjugate gradient method, RCGM and the modified conjugate gradient method, MCGM with adjoint equation, are used alternately to estimate the unknown strength $G({\tau})$ of the source term, while the parameter estimation approach is used to estimate the unknown location ($X^*$, $Y^*$) of the line heat source. The alternate use of the regular and the modified conjugate gradient methods alleviates the convergence difficulties encountered at the initial and final times (i.e ${\tau}=0$ and ${\tau}={\tau}_f$), hence stabilizes the computation and fastens the convergence of the solution. In order to examine the effectiveness of this approach under severe test conditions, the unknown strength $G({\tau})$ is chosen in the form of rectangular, triangular and sinusoidal functions.

• 대한기계학회논문집A
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• 제31권8호
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• pp.833-838
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• 2007

In tube hydroforming process, several defective products could be obtained such as bursting, wrinkling, folding, buckling. Because, especially, bursting is most frequently occurred failure among the well known failures, it is mostly important to predict the onset of bursting failure on tube hydroforming process. For most sheet metal forming processes, strain based forming limit diagram(FLD) is used often as a criteria to estimate the possibility of onset of the failures proposed above. However, FLD has a shortcoming that it is dependent on strain path while stress based diagram is independent on strain history. Generally, tube hydroforming consists of three main processes such as pre-bending, pre-forming, and hydroforming and it means that the strain histories of final products are nonlinear. Therefore, forming limit stress diagram(FLSD) is more suitable to predict forming limit for hydroforming parts. In this study, FLSD is applied to estimate bursting failure for an engine cradle of an automobile part. Consequently, it is proved that application of FLSD to predict forming limit is available for tube hydroforming parts.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2016년도 춘계 종합학술대회 논문집
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• pp.241-242
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• 2016

Recent abnormal climate change induces localized heavy rainfall and extreme disasters such as debris flow near urban area. Thus many researches have been conducted to estimate and prevent, especially in focus of physical behavior of debris flow. Even though it is hardly to consider overall related parameters to estimate the extent and degree of directly or indirectly damages due to debris flow. Those analytic restraint would be caused by the diversity and complexity of regional topographic and hydrodynamic characteristics of debris flow inside. We have utilized the Bayesian method to compensate the uncertainty due to the complex characteristics of it after analyzing the numerical results from FLO-2D and field measurement data. Revised values by field measurements will enhance the numerical results and the missing parameters during numerical simulation will be supplemented with this methodology. As a final outcome in this study, the risk index of debris flow damage will be suggested to provide quantitative estimation in terms of hazard protection including the impact on buildings, especially in inner and outer of urban area.

• 한국안전학회지
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• 제36권3호
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• pp.66-73
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• 2021

This report documents the results of an at-power internal events Level 1 Probabilistic Safety Assessment (PSA) for a Korea research reactor (KRR). The aim of the study is to determine the accident sequences, construct an internal level 1 PSA model, and estimate the core damage frequency (CDF). The accident quantification is performed using the AIMS-PSA software version 1.2c along with a fault tree reliability evaluation expert (FTREX) quantification engine. The KRR PSA model is quantified using a cut-off value of 1.0E-15/yr to eliminate the non-effective minimal cut sets (MCSs). The final result indicates a point estimate of 4.55E-06/yr for the overall CDF attributable to internal initiating events in the core damage state for the KRR. Loss of Electric Power (LOEP) is the predominant contributor to the total CDF via a single initiating event (3.68E-6/yr), providing 80.9% of the CDF. The second largest contributor is the beam tube loss of coolant accident (LOCA), which accounts for 9.9% (4.49E-07/yr) of the CDF.

• 방송공학회논문지
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• 제26권1호
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• pp.3-13
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• 2021

본 논문은 다중 도메인 학습을 이용하여 화면 촬영 영상 내 모아레 무늬를 효과적으로 제거하는 기법을 제안한다. 제안하는 기법은 먼저 화소값 영역과 주파수 영역에서 입력 영상의 모아레 무늬를 각각 제거한다. 다음으로 모아레 영상에서 clean edge map을 추정하고, 추정된 clean edge map을 가이드 정보로 사용하여 화소값 영역과 주파수 영역에서 얻은 결과 영상의 품질을 향상시킨다. 마지막으로, 독립적으로 향상된 두 결과 영상을 적응적으로 결합하며 모아레 무늬가 제거된 최종 결과 영상을 생성한다. 컴퓨터 모의 실험결과를 통해 제안하는 기법이 기존의 알고리즘보다 모아레 무늬를 더욱 효과적으로 제거할 수 있음을 확인한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2483-2504
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• 2023

Most existing low-light enhancement algorithms either use a large number of training parameters or lack generalization to real-world scenarios. This paper presents a novel lightweight and robust pixel-wise polynomial approximation-based deep network for low-light image enhancement. For mapping the low-light image to the enhanced image, pixel-wise higher-order polynomials are employed. A deep convolution network is used to estimate the coefficients of these higher-order polynomials. The proposed network uses multiple branches to estimate pixel values based on different receptive fields. With a smaller receptive field, the first branch enhanced local features, the second and third branches focused on medium-level features, and the last branch enhanced global features. The low-light image is downsampled by the factor of 2b-1 (b is the branch number) and fed as input to each branch. After combining the outputs of each branch, the final enhanced image is obtained. A comprehensive evaluation of our proposed network on six publicly available no-reference test datasets shows that it outperforms state-of-the-art methods on both quantitative and qualitative measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.