• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.384-388
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• 1996

The closed-loop system modeling of an Active/semiactive suspension system has been accomplished through an artificial neural Networks. The 7DOF full model as the system equation of motion has been derived and the output feedback linear quadratic regulator has been designed for the control purpose. For the neural networks training set of a sample data has been obtained through the computer simulation. A 7DOF full model with LQR controller simulated under the several road conditions such as sinusoidal bumps and the rectangular bumps. A general multilayer perceptron neural network is used for the dynamic modeling and the target outputs are feedback to the input layer. The Backpropagation method is used as the training algorithm. The modeling of system and the model validation have been shown through computer simulations.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제27권2호
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• pp.71-81
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• 2020

Multicollinearity is a common problem in linear regression models when two or more regressors are highly correlated, which yields some serious problems for the ordinary least square estimates of the parameters as well as model validation and interpretation. In this paper, first the problem of multicollinearity and its subsequent effects on the linear regression along with some important measures for detecting multicollinearity is reviewed, then the role of eigenvalues and eigenvectors in detecting multicollinearity are bolded. At the end a real data set is evaluated for which the fitted linear regression models is investigated for multicollinearity diagnostics.

• 한국연소학회지
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• 제1권2호
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• pp.51-58
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• 1996

The coherent flamelet model(CFM) is applied to symmetric counterflow turbulent premixed flames. The flame source term is set proportional to the turbulence intensity to reproduce the experimental correlation of Abdel-Gayed et al. for the turbulent burning velocity. Flame quenching by the turbulent rate of strain is modeled by an additional multiplication factor to the flame source term. A modified form of CFM is employed to consider coexistence of burned and unburned premixture with ambient air. The predicted flame position and turbulent flow field coincide well with the experimental data of Kostiuk et al., although there is some discrepancy in the radial rms velocity component and integral length scale near the symmetric plane.

• 한국압력기기공학회 논문집
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• 제16권2호
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• pp.47-57
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• 2020

A new elastic-plastic-creep constraint parameter is proposed to quantify the effect of constraint on creep crack initiation times. It represents the difference between the transient elastic-plastic-creep crack-tip opening stress and the Riedel-Rice opening stress field in plane strain, which can be determined analytically. Application of the proposed parameter to a large set of creep crack growth test data using C(T) and SEN(B) specimens of Type 316H stainless steel at 550℃ shows that creep crack initiation times can be more accurately characterized by the C⁎-integral together with the proposed parameter.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.136-146
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• 2021

This paper aims to numerically estimate the dynamic ice load on a conical structure. The Discrete Element Method (DEM) is employed to model the level ice as the assembly of numerous spherical particles. To mimic the realistic fracture mechanism of ice, the parallel bonding method is introduced. Cases with four different ice drifting velocities are considered in time domain. For validation, the statistics of time-varying ice forces and their frequencies obtained by numerical simulations are extensively compared against the physical model-test results. Ice properties are directly adopted from the targeted experimental test set up. The additional parameters for DEM simulations are systematically determined by a numerical three-point bending test. The findings reveal that the numerical simulation estimates the dynamic ice force in a reasonably acceptable range and its results agree well with experimental data.

• International journal of advanced smart convergence
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• 제11권1호
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.144-147
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• 2004

Two different sensors (here, KOMPSAT and RADARSAT) are considered for ship detection, and are used to delineate the detection performance for their data. The experiments are set for coastal regions of Mokpo Port and Ulsan Port and field experiments on board pilot boat are conducted to collect in situ ship validation information such as ship type and length. This paper introduce mainly the experiment result of ship detection by both RADARSAT SAR imagery and landbased RADAR data, operated by the local Authority of South Korea, so called vessel traffic system (VTS) radar. Fine imagery of Ulsan Port was acquired on June 19, 2004 and in-situ data such as wind speed and direction, taking pictures of ships and natural features were obtained aboard a pilot ship. North winds, with a maximum speed of 3.1 m/s were recorded. Ship's position, size and shape and natural features of breakwaters, oil pipeline and alongside ship were compared using SAR and VTS. It is shown that KOMPSAT/EOC has a good performance in the detection of a moving ship at a speed of 7 kts or more an hour that ship and its wake can be imaged. The detection capability of RADARSAT doesn't matter how fast ship is running and depends on a ship itself, e.g. its material, length and type. Our results indicate that SAR can be applicable to automated ship detection for a VTS and SAR combination service.

• 디지털융복합연구
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• 제12권12호
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• pp.293-301
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• 2014

본 논문에서는 데이터 마이닝에 필요한 클러스터링과정에서 불필요한 정보를 감축하기 위하여 베이지언 사후확률의 신뢰도를 이용한 새로운 척도를 제안한다. 데이터 감축을 위한 속성의 중요도가 클러스터링의 결과에 지배적이기 때문에 많은 속성의 변별력을 향상시키기 위하여 사후확률의 신뢰도에 정보 엔트로피를 적용하였다. 제안된 사후확률을 기반으로 한 러프 엔트로피 척도에 의한 속성의 신뢰도의 중복성은 엔트로피의 자연로그에 의하여 상당히 줄어든다. 따라서 제안된 척도에 의하여 생성된 군집화 알고리즘은 속성값의 변별력을 향상시켜 기존의 리덕트를 최소화하였고, 이는 분할의 효율성을 향상시킬 수 있었다. 제안된 알고리즘의 검증을 위해 패턴분류 문제에 적용되는 ACME 데이터에 대하여 속성간의 변별력, 분할결과에 따른 분할의 순정도를 기존의 알고리즘과 비교 분석하였다.

• 한국측량학회지
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• 제34권4호
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
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• pp.226-228
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• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.