• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.1-18
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• 2007

Non-linear elastic wavefield inversion is a powerful method for estimating elastic parameters for physical constraints that determine subsurface rock and properties. Here, I introduce six elastic-wave velocity models by reconstructing elastic-wave velocity variations from real data and a 2D elastic-wave velocity model. Reflection seismic data information is often decoupled into short and long wavelength components. The local search method has difficulty in estimating the longer wavelength velocity if the starting model is far from the true model, and source frequencies are then changed from lower to higher bands (as in the 'frequency-cascade scheme') to estimate model elastic parameters. Elastic parameters are inverted at each inversion step ('simultaneous mode') with a starting model of linear P- and S-wave velocity trends with depth. Elastic parameters are also derived by inversion in three other modes - using a P- and S-wave velocity basis $('V_P\;V_S\;mode')$; P-impedance and Poisson's ratio basis $('I_P\;Poisson\;mode')$; and P- and S-impedance $('I_P\;I_S\;mode')$. Density values are updated at each elastic inversion step under three assumptions in each mode. By evaluating the accuracy of the inversion for each parameter set for elastic models, it can be concluded that there is no specific difference between the inversion results for the $V_P\;V_S$ mode and the $I_P$ Poisson mode. The same conclusion is expected for the $I_P\;I_S$ mode, too. This gives us a sound basis for full wavelength elastic wavefield inversion.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.822-831
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• 2021

Although programs have been developed to evaluate the risk of dragging anchors, it is practically difficult for VTS(vessel traffic service) operators to calculate and evaluate these risks by obtaining input factors from anchored ships. Therefore, in this study, the gross tonnage (GT) that could be easily obtained from the ship by the VTS operators was set as an independent variable, and linear and nonlinear regression analyses were performed using the input factors as the dependent variables. From comparing the fit of the polynomial model (linear) and power series model (nonlinear), the power series model was evaluated to be more suitable for all input factors in the case of container ships and bulk carriers. However, in the case of tanker ships, the power supply model was suitable for the LBP(length between perpendiculars), width, and draft, and the polynomial model was evaluated to be more suitable for the front wind pressure area, weight of the anchor, equipment number, and height of the hawse pipe from the bottom of the ship. In addition, all other dependent variables, except for the front wind pressure area factor of the tanker ship, showed high degrees of fit with a coefficient of determination (R-squared value) of 0.7 or more. Therefore, among the input factors of the dragging anchor risk assessment program, all factors except the external force, seabed quality, water depth, and amount of anchor chain let out are automatically applied by the regression analysis model formula when only the GT of the ship is provided.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.779-783
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• 2021

The amount of sunlight (irradiation) acts as a very important factor for paprika (Capsicum annuum L.) productivity, but there are difficulties in developing a standard model for estimating paprika productivity using irradiation factors. This study was conducted to investigate how to increase the suitability of using irradiation as an independent variable when developing a standard model. In the linear regression analysis using the independent variable (cumulative irradiation) and the dependent variable (cumulative productivity) were classified as the average value of the total farm productivity (MTFP), and above and below (MHFP, MLFP) based on the average value, respectively. The RMSE value of the estimated linear regression model was 0.9418 kg·m^-2 in the MHFP, which was significantly lower than 1.5468 kg·m^-2 in the MTFP and 1.3812 kg·m^-2 in the MLFP. And in due course of time (month), RMSE value was also the lowest in MHFP, below 1.0 kg·m^-2 in all months. Therefore, when developing a regression model for estimating paprika productivity using irradiation, it is judged that it will improve the suitability of the estimation model by classifying and analyzing the difference in productivity of farms with an appropriate method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.5 s.51
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• pp.73-83
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• 2006

The purpose of this study is to investigate the seismic behavior of reinforced concrete pier walls under cyclic out-of-plane loading and to develop improved seismic design criteria. The accuracy and objectivity of the assessment process can be enhanced by using a sophisticated nonlinear finite element analysis program. A computer program, named RCAHEST (Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. A 4-node flat shell element with drilling rotational stiffness is used for spatial discretization. The layered approach is used to discretize the behavior of concrete and reinforcement through the thickness. Material nonlinearity is taken into account by comprising tensile, compressive and shear models of cracked concrete and a model of reinforcing steel. The method is verified a useful tool to assess the seismic performance of reinforced concrete pier walls subjected to cyclic out-of-plane load through comparing with reliable experimental results.

• Journal of Korean Society of Steel Construction
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• v.13 no.5
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• pp.535-545
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• 2001

A refined method of analysis which is called the Advanced Analysis has been introduced This method is to consider the intial member imperfection residual stress and second-order effects so as to estimate the overall behavior of steel frame accurately Based on the refined plastic hinge method that is more suitable and practical in design practice. the program RPH-2DF is coded using the log model which represents the moment-rotation relationship of connection. The validity of this program is examined by frame test data. Finally to investigate the difference between behaviors of rigid and semi-rigid frame. the 10-story frame analysis results designed by MIDAS-GEN v4.2.2 are compared with the results by RPH-2DF.

• The Journal of the Acoustical Society of Korea
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• v.17 no.4
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• pp.82-87
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• 1998

여러 음원들에 의해 형성된 파동장내에서 각 신호음의 주파수 특성과 시간 지연 (time delay)을 추정할 수 있는 알고리즘을 개발하였다. 이 알고리즘의 관련 수식은 두 개의 상호 간섭하는 신호가 입사하고 여기에 주변 환경에 의한 랜덤 잡음이 첨가된다고 가정하여 유도되었으며 두 개 이상의 신호음이 있는 상황에 대해 확장이 가능하다. 본 논문에서 시간 지연이 일정한 수신 신호 영역에 등간격으로 놓여진 수신기로부터 각 센서에 수신된 신호의 스펙트럼은 M개의 센서에 대해 K개의 음원 스펙트럼과 K개의 조정 벡터(steering vector) 의 선형 조합(linear combination)으로 주파수에서 모델된다. 각 음원의 주파수 특성과 음원 으로 들어오는 신호의 입사각을 결정하기 위하여 본 알고리즘은 전달 행렬(transfer matrix) 을 계산하고 그 전달 행렬의 고유값(eigenvalue)과 고유벡터(eigenvector)를 분석한다. 이 고 유값들은 복소수이며 그 크기는 진폭 변환 계수를 결정한다. 위상은 수신기의 간격으로부터 시간 지연을 결정하는 기울기를 갖는 주파수의 선형 함수이다. 전달 행렬에의 입력 자료들 은 동일 간격 소자간의 cross-power spectra이다.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.102-107
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• 2016

본 연구에서는 동역학적 격자기반 대정준 Monte Carlo (Kinetic Lattice Grand Canonical Monte Carlo, KLGCMC) 모의실험 방법을 이용하여 비정상 몰분율 효과 (Anomalous mole fraction effect)에 대해서 알아보고자 하였다. 이를 위해 양이온 선택성을 가진 이온채널 모델에서 $NH_4{^+}$와 $Rb^+$의 혼합물에 대하여 몰분율의 변화에 따른 이온전도도를 KLGCMC 모의실험을 이용하여 계산하고, 이를 평균장 이론인 Poisson-Nernst-Planck (PNP)의 결과와 비교해 봄으로써 비정상 몰분율 효과에 대하여 심도 있게 이해하고자 하였다. 본 연구 결과로부터 비정상 몰분율 효과는 이온채널의 이온 선택성에 의해서 발생함을 확인할 수 있었다. 즉, 두 종류 이상의 이온들이 채널 내부로 이동할 때, 이온채널의 이온 선택성에 의해서 각 이온들과 채널 간에 서로 상이한 상호작용을 하게 되고, 이로 인해서 이온 혼합물 조성의 변화, 즉 몰분율의 변화에 대해서 이온 전류가 선형적이 아닌 비선형적으로 변하게 됨을 알 수 있었다.