• 한국해안해양공학회지
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• 제5권4호
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• pp.307-317
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• 1993

파랑의 비선형성 및 분산을 고려한, 연안역에서의 파랑변형에 관한 연구를 수행하였다. 규칙파의 변형에 관한 수학적 모형은 비선형 ray모델에 기초하였으며, ray 및 파동장에 관한 방정식들을 수립하였다. 비선형 파동장은 수정 Korteweg-de Vries 식으로서 나타내었으며, 이에 대한 몇몇 해석 해들을 구하였다. 또한 Caustic 변형 및 감쇄효과를 수학적 모형에 포함하였다. Korteweg-de Vries 방정식에 대한 수치계산 알고리즘과 안정조건을 기술하였으며, 연안역에서의 비선헝 파랑변형 계산 결과를 제시하였다.

• Journal of the Optical Society of Korea
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• 제20권6호
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• pp.745-751
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• 2016

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn-Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• Geomechanics and Engineering
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• 제13권1호
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• pp.99-117
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• 2017

Rock physics modeling of sandstone reservoir from gas fields of Krishna-Godavari basin represents the link between reservoir parameters and seismic properties. The rock physics diagnostic models such as contact cement, constant cement and friable sand are chosen to characterize reservoir sands of two wells in this basin. Cementation is affected by the grain sorting and cement coating on the surface of the grain. The models show that the reservoir sands in two wells under examination have varying cementation from 2 to more than 6%. Distinct and separate velocity-porosity and elastic moduli-porosity trends are observed for reservoir zones of two wells. A methodology is adopted for generation of Rock Physics Template (RPT) based on fluid replacement modeling for Raghavapuram Shale and Gollapalli Sandstones of Early Cretaceous. The ratio of P-wave velocity to S-wave velocity (Vp/Vs) and P-impedance template, generated for this above formations is able to detect shale, brine sand and gas sand with varying water saturation and porosity from wells in the Endamuru and Suryaraopeta gas fields having same shallow marine depositional characters. This RPT predicted detection of water and gas sands are matched well with conventional neutron-density cross plot analysis.

• Applied Science and Convergence Technology
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• 제26권1호
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• pp.6-10
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• 2017

We investigated the stability of ionic configurations of the tip of the cantilever in non-contact AFM.; For this, we used a computational model that couples the ionic motion of the MgO surface and the oscillating cantilever. The motion of ions was connected to the oscillating cantilever using a coupling method that had been recently developed. The adhesive process on the ionic MgO surface leads to energy dissipation of the cantilever. It is shown that limited types of ionic configurations of the tip are stable during the adhesive process. Based on the present computational model, we discuss the adhesive mechanism leading to energy dissipation.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• 수산해양기술연구
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• 제59권2호
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• pp.99-109
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• 2023

In this study, a comparative test operation was conducted through the alternate haul method to examine the selectivity of the four mesh sizes (60 mm, 90 mm, 110 mm, and 130 mm) of the trawl codend. The selectivity was analyzed using the SELECT model considering the fishing efficiency (split parameter) of each fishing gear in the comparative test fishing operation in the trawl and the maximum likelihood method for parameter estimation. A selectivity master curve was estimated for several mesh sizes using the extended-SELECT model. As a result of analyzing the selectivity for silver croaker based on the results of three times hauls for each experimental gear, it was found that the size of the fish caught increased as the size of the mesh size increased. When the selectivity for each mesh size analyzed by the SELECT model considering the split ratio was evaluated based on the size of the AIC value, the estimated split model was superior to the equal split model. Based on the master curve, the 50% selection length value was 2.893, which was estimated to be 136 mm based on the mesh size of 60 mm. In some selectivity models, there was a large deviance between observed and theoretical values due to the non-uniformity of the distribution of fished length classes. As a result, it is considered that appropriate sea trials and selectivity evaluation methods with high reliability should be applied to present trawl fishery resource management methods.

• 지구물리와물리탐사
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• 제25권4호
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• pp.227-241
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• 2022

전파형 역산은 석유가스 탐사를 위한 탄성파 자료처리 분야에서 지층의 속도 모델을 추정하는데 사용되는 역산 기법이다. 최근 탄성파 자료처리에 딥러닝 기술의 활용이 급격하게 증가하고 있는데, 전파형 역산 기술도 마찬가지로 다양한 연구가 이루어지고 있다. 초기에는 머신러닝 기술을 활용한 자료처리 기법이 전파형 역산을 위한 입력자료의 전처리 목적으로 활용되는 수준이었으나, 딥러닝 기술을 통해 전파형 역산을 직접적으로 구현하는 연구가 등장하기 시작하였다. 딥러닝 기술을 활용한 전파형 역산은 순수 데이터 기반 접근법, 물리 기반 신경망 활용법, 인코더-디코더 구조 활용법, 신경망 재매개변수화를 이용한 구현법, 물리정보 기반 신경망 기법 등으로 구분할 수 있다. 이 논문에서는 딥러닝 기반 전파형 역산 기법을 발전 과정 순서로 체계화하여 각각의 접근법에 대한 이론과 특징을 설명하였다. 전파형 역산 기술에 딥러닝 기법을 도입한 초기에는 데이터 과학의 기본 원리에 충실하게 대량의 학습자료를 준비하고 순수 데이터 기반 예측 모델을 적용하여 속도 모델을 역산하는 연구로 시작하였다. 최근 연구 동향은 탄성파 자료의 잔차나 파동방정식 자체의 물리정보를 심층 신경망에 활용하여 순수 데이터 기반 접근법의 단점을 보완해 나가는 방향으로 진행되고 있다. 이러한 발전으로 대량의 학습자료가 필요하지 않고, 전파형 역산의 태생적 한계점인 주기 놓침 현상을 완화하며 계산 시간을 획기적으로 줄일 수 있는 딥러닝 기반 전파형 역산 기술이 등장하고 있다. 딥러닝 기술의 도입으로 전파형 역산 기술은 탄성파 자료처리 분야에서 가치가 더 높아질 것으로 생각된다.

• EDISON SW 활용 경진대회 논문집
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• 제5회(2016년)
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• pp.368-372
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• 2016

In this paper, the physics-based analytical model of transition metal dichalcogenide (TMD)-based double-gate (DG) tunneling field-effect transistors (TFETs) is proposed. The proposed model is derived by using the two-dimensional (2-D) Landauer formula and the Wentzel-Kramers-Brillouin (WKB) approximation. For improving the accuracy, nonlinear and continuous lateral energy band profile is applied to the model. 2-D density of states (DOS) and two-band effective Hamiltonian for TMD materials are also used in order to consider the 2-D nature of TMD-based TFETs. The model is validated by using the tight-binding non-equilibrium Green's function (NEGF)-based quantum transport simulation in the case of monolayer molybdenum disulfide ($MoS_2$)-based TFETs.