• Geomechanics and Engineering
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• 제11권1호
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• pp.77-94
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• 2016

A forked tunnel, as a special complicated underground structure, is composed of big-arch tunnel, multi-arch tunnel, neighborhood tunnels and separate tunnels according to the different distances between two separate tunnels. Due to the complicated process of design and construction, surrounding jointed rock mass stability of the big-arch tunnel which belongs to the forked tunnel during excavation is a hot issue that needs special attentions. In this paper, an elasto-plastic damage constitutive model for jointed rock mass is proposed based on the coupling method considering elasto-plastic and damage theories, and the irreversible thermodynamics theory. Based on this elasto-plastic damage constitutive model, a three dimensional elasto-plastic damage finite element code (D-FEM) is implemented using Visual Fortran language, which can numerically simulate the whole excavation process of underground project and perform the structural stability of the surrounding rock mass. Comparing with a popular commercial computer code, three dimensional fast Lagrangian analysis of continua (FLAC3D), this D-FEM has advantages in terms of rapid computing process, element grouping function and providing more material models. After that, FLAC3D and D-FEM are simultaneously used to perform the structural stability analysis of the surrounding rock mass in the forked tunnel considering three different computing schemes. The final numerical results behave almost consistent using both FLAC3D and D-FEM. But from the point of numerically obtained damage softening areas, the numerical results obtained by D-FEM more closely approach the practical behaviors of in-situ surrounding rock mass.

• 대한원격탐사학회지
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• 제40권1호
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• Structural Engineering and Mechanics
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• 제15권3호
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• pp.299-314
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• 2003

This investigation presents an efficient method for identifying modal characteristics from the measured displacement, velocity and acceleration signals of multiple channels on structural systems. A Vector Backward Auto-Regressive model (VBAR) that describes the relationship between the output information in different time steps is used to establish a backward state equation. Generally, the accuracy of the identified dynamic characteristics can be improved by increasing the order of the Auto-Regressive model (AR) in cases of measurement of data under noisy circumstances. However, a higher-order AR model also induces more numerical modes, only some of which are the system modes. The proposed VBAR model provides a clear characteristic boundary to separate the system modes from the spurious modes. A numerical example of a lumped-mass model with three DOFs was established to verify the applicability and effectiveness of the proposed method. Finally, an offshore platform model was experimentally employed as an application case to confirm the proposed VBAR method can be applied to real-world structures.

• Nuclear Engineering and Technology
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• 제51권2호
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• pp.424-431
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• 2019

The aim of the present study was to develop model for detonation cell sizes prediction based on a deep artificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussion about the currently available algorithms compared existing solutions and resulted in a conclusion that there is a need for a new model, free from uncertainty of the effective activation energy and the reaction length definitions. The model offers a better and more feasible alternative to the existing ones. Resulting predictions were validated against experimental data obtained during the investigation of detonation parameters, as well as with data collected from the literature. Additionally, separate models for individual mixtures were created and compared with the main model. The comparison showed no drawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the model may be easily extended by including more independent variables. As an example, dependency on pressure was examined. The preparation of experimental data for deep neural network training was described in detail to allow reproducing the results obtained and extending the model to different mixtures and initial conditions. The source code of ready to use models is also provided.

• 대한의용생체공학회:의공학회지
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• 제23권5호
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• pp.385-390
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• 2002

오늘날 의료기기의 국산화 연구가 활발히 진행되고 있는 가운데 본 연구에서는 적추용 fusion device를 새롭게 개발하고자 시도하였다. 일반적으로 사용되고 있는 삽입형과 나사형을 기본 대상으로 하되 수술 시 뼈를 보다 적게 제거하여 삽입할 수 있게 하는 데 .) 주안점을 두고 형상 개발을 시도하였다. 아직 그 실효성을 논하기는 어려우나 고안된 척추용 fusion device의 형상을 중심으로 소개하고자 한다. 본 연구에서 경첩형과 분리삽입형을 고안하게 되었다. 그러나 경첩형은 기존 형상의 절반 크기로 삽입되는 장점은 있으나 핀의 강도해석과 안정된 잠금 장치의 개발에 더 연구가 요구되어 일단 본 연구에서는 분리삽입형을 중심으로 시제품과 3차인 유한요소 모델을 제작하여 그 역학적 해석을 행하였다. 그 결과 척추에 작용하는 기본 하중에 대한 역학적 안전성은 인정되나 아직 실용화되기까지는 보조 수술장비의 개발이 필요하다고 사료된다.

• 한국산업융합학회 논문집
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• 제23권6_2호
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• pp.1103-1110
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• 2020

In this study, a segmented model with Upside-Down bathtub shaped failure intensity for a repairable system are proposed under the assumption that the occurrences of the failures of a repairable system follow the Non-Homogeneous Poisson Process. The proposed segmented model is the compound model of S-PLP and LIP (Segmented Power Law Process and Logistic Intensity Process), that fits the separate failure intensity functions on each segment of time interval. The maximum likelihood estimation is used for estimating the parameters of the S-PLP and LIP model. The case study of system A shows that the S-PLP and LIP model fits better than the other models when compared by AICc (Akaike Information Criterion corrected) and MSE (Mean Squared Error). And it also implies that the S-PLP and LIP model can be useful for explaining the failure intensities of similar systems.

• 한국항행학회논문지
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• 제23권6호
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• pp.597-604
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• 2019

인공신경망은 뇌의 뉴런들에서 상호 작용과 경험을 통해 학습해 나가는 것을 모사해 만든 알고리즘으로, 데이터의 특성이 반영된 학습을 통하여 정확한 결과를 산출하는데 사용할 수 있는 방법이다. 본 연구에서 기상 역학 모델에서 예측된 풍속 값의 개선을 위하여 심층신경망을 이용한 모델을 제시하였다. 연구에서 제시한 심층신경망을 이용한 풍속 예측 개선 모델은 기상 역학 모델의 예측 값을 재 보정하는 모델을 구축하고 이에 대한 검증과 시험 과정 후 별도의 데이터를 통한 예측의 정확도를 높일 수 있는 것을 확인하였다. 풍속 예측의 개선을 위하여 예측 시간, 온도, 기압, 습도, 대기상태변수, 풍속 등과 같은 일반적 기상 현상 자료의 예측 값을 활용한 심층신경망을 구축하였고, 전체 데이터 중 일부 데이터는 모델의 적정성 확인용 데이터로 구분하여, 모델 구축 및 학습에 사용하지 않고 별도의 정확도를 확인하여 연구에서 제시한 방법의 적합성을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제1권3호
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• pp.302-307
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• 2006

The purpose of this paper is to develop an improved three-phase transformer model for ATP and parameter estimation methods that can efficiently utilize the limited available information such as factory test reports. In this paper, improved topologically-correct duality-based models are developed for three-phase autotransformers having shell-form cores. The problem in the implementation of detailed models is the lack of complete and reliable data. Therefore, parameter estimation methods are developed to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, ${\lambda}-i$ saturation characteristic and core loss.

• 대한간호학회지
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• 제29권5호
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• pp.1030-1041
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• 1999

This study examined those problems noticed under the Asymptotically Generalized Least Squares estimator in evaluating a structural model of physical health. The problems were highly correlated parameter estimates and high standard errors of some parameter estimates. Separate analyses of the endogenous part of the model and of the metric of a latent factor revealed a highly skewed and kurtotic measurement indicator as the focal point of the manifested problems. Since the sample sizes are far below that needed to produce adequate AGLS estimates in the given modeling conditions, the adequacy of the Maximum Likelihood estimator is further examined with the robust statistics and the bootstrap method. These methods demonstrated that the ML methods were unbiased and statistical decisions based upon the ML standard errors remained almost the same. Suggestions are made for future studies adopting structural equation modeling technique in terms of selecting of a reference indicator and adopting those statistics corrected for nonormality.

• 소음진동
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• 제10권3호
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• pp.531-535
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• 2000

A three-parameter model of viscoelastic damper which has a non-linear spring as an element is incorporated into an oscillator. The behavior of the damper model shows non-linear hysteresis curves which is qualitatively similar to those of real viscoelastic materials. The motion is governed by get analytic solutions of the system. The frequency-response curves show that multiple solutions co-exist and that the jump phenomena can occur. In addition it is shown that separate solution branch exists and that it can merge with the primary response curve. Saddle-node bifurcation sets explain the occurences of such non-linear phenomena. A direct time intergration of the original equation of motion validifies the use of the harmonic balance method to this sort of problem.