• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.823-832
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• 2011

In this paper, an efficient blind carrier frequency offset (CFO) estimation method in orthogonal frequency division multiplexing (OFDM) systems is proposed. In the proposed method, we obtain two time different received OFDM symbols by using both the cyclic prefix and oversampling technique, and a cost function is defined by using the two OFDM symbols. We show that the cost function can be approximately expressed as a cosine function. Using a property of the cosine function, a formular for estimating the CFO is derived. The estimator of the CFO requires three independent cost function values calculated at three different points of frequency offset. The proposed method is very efficient in computational complexity since no searching operation for the minimum cost value is required. The proposed method reduces 97% of the amount of FFT computation, compared with the ML method. Unlike the conventional methods such as the ML method and the MUSIC] method, the accuracy of the proposed method is independent of the searching resolution since the closed form solution exists. The computer simulation shows that the performance of the proposed method is superior to those of the MUSIC and the ML method.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.99-110
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• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.441-452
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• 2013

This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1207-1212
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• 2018

The signal intelligence (SIGINT) technology is actively used for collecting various data, in a number of fields, including a military industry. In order to collect the signal information and data and to transmit/receive the collected data efficiently, the accurate angle-of-arrival (AOA) information is required and communication disturbance from the interference or jamming signal should be minimized. In this paper, we present the structure of an adaptive beam-forming satellite system based on the planar array antenna, for collecting and transmitting/receiving the signal information and data efficiently. The presented adaptive beam-forming system consists of an antenna in the form of a planar array, an AOA estimator based on the Multiple Signal Classification (MUSIC) algorithm, an adaptive Minimum Variance Distortionless Response (MVDR) interference canceler, a signal processing and D/B unit, and a transmission beamformer based on Minimum mean Square Error (MMSE). In addition, through the computer simulation, we evaluate and analyze the performance of the proposed system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.39-57
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• 2001

Categorical data has sometimes misclassification errors. If this data will be analyzed, then estimated cell probabilities could be biased and the standard Pearson X2 tests may have inflated true type I error rates. On the other hand, if we regard wellclassified data with misclassified one, then we might spend lots of cost and time on adjustment of misclassification. It is a necessary and important step to ask whether categorical data is misclassified before analyzing data. In this paper, when data is misclassified at one of two variables for two-dimensional contingency table and marginal sums of a well-classified variable are fixed. We explore to partition marginal sums into each cells via the concepts of Bound and Collapse of Sebastiani and Ramoni (1997). The double sampling scheme (Tenenbein 1970) is used to obtain informations of misclassification. We propose test statistics in order to solve misclassification problems and examine behaviors of the statistics by simulation studies.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.451-466
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• 2020

CCC-r chart is effective for high-quality processes with a very low fraction nonconforming. The values of process parameters should be estimated from the Phase I sample since they are often not known. However, if the Phase I sample size is not sufficiently large, an estimation error may occur when the parameter is estimated and the practitioner may not achieve the desired in-control performance. Therefore, we adjust the control limits of CCC-r charts using the bootstrap algorithm to improve the in-control performance of charts with smaller sample sizes. The simulation results show that the adjustment with the bootstrap algorithm improves the in-control performance of CCC-r charts by controlling the probability that the in-control average number of observations to signal (ANOS) has a value greater than the desired one.

• Journal of The Korean Astronomical Society
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• v.42 no.3
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• pp.61-69
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• 2009

Transverse velocity vectors can be determined from a pair of images successively taken with a time interval using an optical flow technique. We have tested the performance of the new technique called NAVE (non-linear affine velocity estimator) recently implemented by Chae & Sakurai using real image data taken by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite. We have developed two methods of estimating the errors in the determination of velocity vectors, one resulting from the non-linear fitting ${\sigma}_{\upsilon}$ and the other ${\epsilon}_u$ resulting from the statistics of the determined velocity vectors. The real error is expected to be somewhere between ${\sigma}_{\upsilon}$ and ${\epsilon}_u$. We have investigated the dependence of the determined velocity vectors and their errors on the different parameters such as the critical speed for the subsonic filtering, the width of the localizing window, the time interval between two successive images, and the signal-to-noise ratio of the feature. With the choice of $v_{crit}$ = 2 pixel/step for the subsonic filtering, and the window FWHM of 16 pixels, and the time interval of one step (2 minutes), we find that the errors of velocity vectors determined using the NAVE range from around 0.04 pixel/step in high signal-to-noise ratio features (S/N $\sim$ 10), to 0.1 pixel/step in low signa-to-noise ratio features (S/N $\sim$ 3) with the mean of about 0.06 pixel/step where 1 pixel/step corresponds roughly to 1 km/s in our case.