• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.1037-1040
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• 1988

In this study, we performed the recognition test on Korean vowel using peripheral auditory model. In addition, for the purpose of objective comparision, the recognition test is performed by extracting LPC cepstrum coefficients from the same data. And the same speech data are mixed with the Guaussian white noise quantitatively, then we repeated the same test, too. So we verified that this auditory model has a adaptability on noise.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.689-694
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• 2004

We investigate the test for model adequacy in nonlinear regression. We can expect the usual likelihood ratio statistic to be unaffected by any parametric- effect curvature; only the effect of intrinsic curvature needs to be considered. Multiplicative correction factor is derived for the limiting distribution of test statistic, which is a function of the intrinsic curvature arrays.

• Steel and Composite Structures
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• 제23권2호
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• pp.239-250
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• 2017

In the concept of two-parameter fatigue failure criterion, the material fatigue failure is determined by the damage degree and the current stress level. Based on this viewpoint, a residual strength degradation model for stud shear connectors under fatigue loads is proposed in this study. First, existing residual strength degradation models and test data are summarized. Next, three series of 11 push-out specimen tests according to the standard push-out test method in Eurocode-4 are performed: the static strength test, the fatigue endurance test and the residual strength test. By introducing the "two-parameter fatigue failure criterion," a residual strength calculation model after cyclic loading is derived, considering the nonlinear fatigue damage and the current stress condition. The parameters are achieved by fitting the data from this study and some literature data. Finally, through verification using several literature reports, the results show that the model can better describe the strength degradation law of stud connectors.

• 응용통계연구
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• 제26권3호
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• pp.521-529
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• 2013

패널 시계열자료 분석에서 모수축약의 원칙에 충실하기 위해서 동질성 검정을 수행한다. 본 논문에서는 독립적인 중선형 시계열 패널 자료의 동질성 검정을 수행하기 위하여 먼저 중선형 시계열 모형의 정상성 조건을 구하고 최우추정량과 동질성 검정통계량과 극한 분포를 이끌어내며, 실증분석으로 우리나라 8도의 Mumps 패널자료를 이용해 8개 지역의 발병 추이에 대한 동질성 검정을 수행한다.

• 대한조선학회논문집
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• 제42권2호
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• pp.105-112
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• 2005

IMO standards for ship manoeuvrability were applied from January 1, 2004. Though model test or sea trial in full load condition is needed, it is not always possible to get such data for every ships. Therefore it is required to study the effect of loading conditions on ship manoeuvrability. Approximate formulae to estimate the hydrodynamic forces acting on a ship and the 2nd overshoot angle of $10^{\circ}$/$10^{\circ}$ zig-zag test in certain loading condition are proposed in this study These were derived from the results of model test and sea trial data. Captive model tests for 7 ships with 15 different loading conditions and sea trial data including free running test of 6 cases were used. Compared with experiment data and prediction formulae already proposed by others, the approximate formulae in this study show good agreement with model test results.

• Computers and Concrete
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• 제24권6호
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• 한국분무공학회지
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• 제27권3호
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• pp.155-160
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• 2022

Recently, major developed countries have strengthened automobile fuel efficiency regulations and carbon dioxide emission allowance standards to curb climate change caused by global warming worldwide. Accordingly, research and manufacturing on electric vehicles that do not emit pollutants during actual driving on the road are being conducted. Several automobile companies are producing and testing electric vehicles to commercialize them, but it takes a lot of manpower and time to test and evaluate mass-produced electric vehicles with driving mileage of more than 300km on a per-charge. Therefore, in order to reduce this, a simulation model was developed in this study. This study used vehicle information and MCT speed profile of small electric vehicle as basic data. It was developed by applying Simulink, which models the system in a block diagram method using MATLAB software. Based on the vehicle dynamics, the simulation model consisted of major components of electric vehicles such as motor, battery, wheel/tire, brake, and acceleration. Through the development model, the amount of change in battery SOC and the mileage during driving were calculated. For verification, battery SOC data and vehicle speed data were compared and analyzed using CAN communication during the chassis dynamometer test. In addition, the reliability of the simulation model was confirmed through an analysis of the correlation between the result data and the data acquired through CAN communication.

• Geomechanics and Engineering
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• 제29권5호
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• pp.487-496
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• 2022

Most of the pile's vertical static load tests in construction sites are the proof load tests, which is difficult to accurately estimate the ultimate bearing capacity and analyze the reliability of piles. Therefore, a reliability analysis method based on the proof load-settlement (Q-s) data is proposed in this study. In this proposed method, a simple ultimate limit state function based on the hyperbolic model is established, where the random variables of reliability analysis include the model factor of the ultimate bearing capacity and the fitting parameters of the hyperbolic model. The model factor M = R_uR / R_uP is calculated based on the available destructive Q-s data, where the real value of the ultimate bearing capacity (R_uR) is obtained by the complete destructive Q-s data; the predicted value of the ultimate bearing capacity (R_uP) is obtained by the proof Q-s data, a part of the available destructive Q-s data, that before the predetermined load determined by the pile test report. The results demonstrate that the proposed method can easy and effectively perform the reliability analysis based on the proof Q-s data.

• 자동차안전학회지
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• 제13권4호
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.149-156
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• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.