• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.11
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• pp.999-1011
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• 2015

In modern electronic warfare, RADAR is under constant threat of ECM(Electronic Counter Measures) signals from nearby jammers. The conventional linear frequency modulated(Linear-FM) waveform is easy to be intercepted to estimate its signal parameters due to its periodical phase transition. Recently, APCN(Advanced Pulse Compression Noise) waveform using random amplitude and phase transition was proposed for LPI(Low probability of Intercept). But random phase code signals such as APCN waveform tend to be sensitive to Doppler frequency shift and result in performance degradation during moving target detection. In this paper, random phase and code rate transition based radar waveform(RPCR) is proposed for Doppler tolerance improvement. Time frequency analysis is carried out through ambiguity analysis to validate the improved Doppler tolerance of RPCR waveform. As a means to measure the vulnerability of the proposed RPCR waveform against LPI, WHT(Wigner-Hough Transform) is adopted to analyze and estimate signal parameters for ECCM(Electronic Counter Counter Measures) application.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.111-116
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• 2015

우리는 딥펜나노리소그래피에서 일어나는 박막 성장에 관한 잉크 확산 모형을 랜덤 워크 방법을 사용해 구현하였다. 분자동역학 연구를 바탕으로 제안된 hopping down, serial pushing, 단일 밀림 길이를 고려한 모형에 따른 박막 성장 특성을 비교하였다. 모형에 따라 그 박막 성장 특성에 확연한 차이가 있음을 발견하였으며, 잉크 분자와 기판 사이의 결합력이 중요한 변수임을 확인할 수 있었다. 그리고 원자힘현미경 탐침에서 떨어지는 잉크 분자의 속도와 단일 밀림 길이에 따른 박막 성장 차이를 알아보았다. 단일 밀림 길이가 커질수록, 탐침에서 떨어지는 잉크 분자의 속도가 빨라질수록 가지 모양의 박막이 형성됨을 알 수 있었다.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.208-216
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• 2009

A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2953-2966
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• 2018

Housing prices are closely related to various variables that indicate macroeconomic conditions. In this paper, empirical analysis based on data is performed referring to previous studies. Focusing on the policy interest rate among the factors affecting the housing price, the non-linear impulse responses of other variables to the interest rate shock are analyzed. Using the random forest algorithm, the variable importance scores of the macroeconomic variables presented in the previous studies are calculated. After selecting the variables through this process, the impulse responses are calculated using a model that can capture non-linearity. According to the model, the responses of housing prices to the policy rate is only significant when the rate is raised. Especially, the impulse response is amplified when the shock increases due to the non-linear characteristics that can not be captured by the traditional VAR methodology. The analysis results suggest that the interest rate as a policy instrument should be approached from a more cautious perspective.

• Journal of KIISE:Software and Applications
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• v.28 no.1
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• pp.46-51
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• 2001

2D로 확장한 HMM은 다수 제안되었지만 엄밀한 의미에 있어서 2D HMM이라고 하기에 부족한 점이 많다. 본 논문에서는 기존의 랜덤 필드 모형이 아닌 새로운 2D HMM을 제안한다. 상하 및 좌우 방향의 causal chain 관계를 가정하고 완전한 격자 형성 조건을 두어 2D HMM의 평가, 매개 변수를 추정하는 알고리즘을 제시하였다. 각각의 알고리즘은 동적 프로그래밍과 최우 추정법에 근거한 것이다. 변수 추정 알고리즘은 반복적으로 이루어지며 국소 최적치에 수렴함을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.8
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• pp.76-85
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• 2000

Effects of the random fluctuation in grating half-period have been studied by an effective index transfer matrix method in DFB lasers. The laser facets are assumed to be perfectly antireflection coated, and the period fluctuation is modeled as a Gaussian random variable. The random fluctuation breaks spectral symmetry in both uniform-grating and quarter-wavelength -shifted(QWS) DFB lasers, and decreases the effective coupling coefficient. This leads to increased average mirror loss of ${\pm}$1 modes and reduced stopband width in uniform grating DFB lasers, and degradation in the wavelength accuracy and the single mode stability in QWS-DFB lasers. Threshold gain difference decreases with increasing period fluctuation irrespective of grating coupling coefficient in QWS-DFB lasers, while spatial hole-burning effect is exacerbated or alleviated when the normalized coupling coefficient is lower and higher than 1.5, respectively.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1341-1348
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• 2013

To solve the classification problems, various data mining techniques have been applied to database marketing, credit scoring and market forecasting. In this paper, we compare various techniques such as bagging, boosting, LASSO, random forest and support vector machine with the daily lens transaction data. The classical techniques-decision tree, logistic regression-are used too. The experiment shows that the random forest has a little smaller misclassification rate and standard error than those of other methods. The performance of the SVM is good in the sense of misclassfication rate and bad in the sense of standard error. Taking the model interpretation and computing time into consideration, we conclude that the LASSO gives the best result.