• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.790-792
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• 2017

As the demand of digital image processing devices has been rapidly increased recently, the excellent image quality is required. However, degradation can be occurred with multiple causes during transmission and processing process. Therefore, the needs to eliminate the noise are increased and the noise elimination technology became the major study area. Therefore, image restoration algorithm was suggested to apply the filter differently by edge and non-edge areas, using modified edge detection with preprocessing process so as to relieve the effect of additive white Gaussian noise(AWGN) which is added in the image, in this article. In addition, it was compared with the existing methods using peak signal to noise ratio(PSNR) as the objective determination standard of the improvement effect.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.679-685
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• 2011

This paper presents an algorithm identifying devices that generate observed mixed signals that are collected at main power-supply line. The proposed algorithm, which is necessary for low cost electric power monitoring system at appliance-level, that is non-intrusive load monitoring system, divides incoming mixed signal into multiple time intervals, calculating difference-signals between consecutive time interval, and identifies which device is operating at the time interval by analysing the difference-signals. Since the features of one device can remain when the time interval is short enough and the features are independent and additive, well-known classification algorithms can be used to classify the difference-signals with features of N individual devices, otherwise $2^N$ features might be necessary. The proposed algorithm was verified using data mixed in a laboratory with individual devices's data collected from field. When maximum 4 devices operate or stop sequentially and when features satisfy the requirements of proposed algorithm, the proposed algorithm resulted nearly 100% success rate under the constrained test condition. In order to apply the proposed algorithm in real world, the number devices shall increase, the time interval shall be smaller and the pattern of mixture shall be more diverse. However we can expect, if features used follow guidelines of proposed algorithm, future system could have certain level of performance without the guideline.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.201-217
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• 2016

We accessed the climate change effects on the distributions of warm-evergreen broad-leaved trees (shorten to warm-evergreens below) in the Korean Peninsula (KP). For this, we first selected nine warm-evergreens with the northern distribution limits at mid-coastal areas of KP and climate variables, coldest month mean temperature and coldest quarter precipitation, known to be important for warm-evergreens growth and survival. Next, species distribution models (SDMs) were constructed with generalized additive model (GAM) algorithm for each warm-evergreen. SDMs projected the potential geographical distributions of warm evergreens under current and future climate conditions in associations with land uses. The nine species were categorized into three groups (mid-coastal, southwest-coastal, and southeast-inland) based on their current spatial patterns. The effects of climate change and land uses on the distributions depend on the current spatial patterns. As considering land uses, the potential current habitats of all warm-evergreens decrease over 60%, showing the highest reduction rate for the Kyungsang-inland group. SDMs forecasted the expansion of potential habitats for all warm-evergreens under climate changes projected for 2050 and 2070. However, the expansion patterns were different among three groups. The spatial patterns of projected coldest quarter precipitation in 2050 and 2070 could account for such differences.

• Journal of Embryo Transfer
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• v.29 no.3
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• pp.289-296
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• 2014

The pedigree information and race records of 1,000 m finishing time of Jeju race horses at KRA were used to study the effect of amount of pedigree information and parental misidentification on the estimates of genetic parameters. The modified data sets were made at the range of 2.5 to 25% parental misidentifications or loss of parental information of individuals with an increment of 2.5 percent. For each incremental level, 20 randomly replicated data sets were obtained and analyzed by single-trait analysis with a DF-REML(AI) algorithm. As the rate of misidentification increased or the amount of pedigree information decreased, the estimates of fraction of additive genetics variance component gradually decreased almost linearly (p<0.05), while the estimated fractions of error variance and permanent environmental variance components gradually increased for the finishing time. Regression coefficients of the percentage amount of both parents' information loss and incorrect pedigree information on additive genetic variances were -0.079 and -0.114, respectively (p<0.01). The estimate of heritability decreased by 0.92% for one percent loss of both parents' information and 1.39% for one percent increase of both parental misidentifications of progeny (p<0.01). For the consideration of probable incorrect and missing parent information of progeny in this early population of Jeju horses, the estimates of additive genetic parameters would be biased downward about ten percent. This results indicate that the amount of pedigree information loss and misidentification of progeny would severely affect estimates of genetic parameters and would reduce genetic gains for selection in Jeju horse population.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.3-8
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• 2016

We propose a modified form of the decision-directed least mean square (DD LMS) algorithm that is widely used in the optimization of self-adaptive equalizers, and show the modified version greatly improves the initial convergence properties of the conventional algorithm. Existing DD LMS regards the difference between a equalizer output and a quantization value for it as an error, and achieves an optimization of the equalizer based on minimizing the mean squared error cost function for the equalizer coefficients. This error generating method is useful for binary signal or a single-level signals, however, in the case of multi-level signals, it is not effective in the initialization of the equalizer. The modified DD LMS solves this problem by modifying the error generation. We verified the usefulness and performance of the modified DD LMS through experiments with multi-level signals under distortions due to intersymbol interference and additive noise.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.94-102
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• 1999

This paper is related with the compensation of communication channel characteristics using predistorter, and the considered characteristic is the additive noise, phase rotation and frequency selective fading which occurred in communication channel. Predistorter can minimize the effect of obstacle element which occured in channel at receiving side by transmitting the predistortion of signal after modulation, the coefficient of inverse electrical charateristic of communication channel is performed at transmitting side. For this purpose, the predistorter is designed by using Tricepstrum Equalization Algorithm which is adaptive equlizer algorithm, and the receiving side must transmit the probing signal to transmitting side. Using the probing signal, the transmitting side can obtain the inverse characteristic coefficient of communication channel, and this probing signal must be transmitted periodically. We assumed that the channel characteristic do not change during this one period. As a result of computer simulation, we confirmed that the performance of predistorter was fairly good as same as the adaptive equalizer, and this technique have a effectiveness that can be used in the forward channel of mobile communication in order to achieve high speed transmission.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.105-110
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• 2015

This paper relates with the eualization performance of Constellation Matching-MMA (CM-MMA) in order to the consists of optimum receiver for the minimization of intersymbol interference and additive noise effects that is occurs in the nonlinear communication channel. The error signal were obtained that combines the Constellation Matching technique that inserts the zero point between the signal point of equalizer for improving the residual isi and convergence speed compared to the currently used MMA algorithm. In the initial state of adaptive equalization, it depends on the MMA characteristics mainly. And in the steady state, it depends on the CM characteristics mainly. In order to analyzing the equalization performance, the output signal constellation, residual isi, maximum distortion, MSE and SER were applied, then it were compared with the present MMA algorithm. As a result of computer simulation, the CM-MMA has more better performance in the every performance index, and it was also confirmed that the constellation matching effect can be obtained in the greater than 20dB signal to noise ratio.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.15-22
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• 2006

This paper presents a novel signal tracking algorithm for GNSS receivers using a MLE technique. In order to perform a robust signal tracking in severe signal environments, e.g., high dynamics for navigation vehicles or weak signals for indoor positioning, the MLE based signal tracking approach is adopted in the paper. With assuming white Gaussian additive noise, the cost function of MLE is expanded to the cost function of NLSE. Efficient and practical approach for Doppler frequency tracking by the MLE is derived based on the assumption of code-free signals, i.e., the cost function of the MLE for carrier Doppler tracking is used to derive a discriminator function to create error signals from incoming and reference signals. The use of the MLE method for carrier tracking makes it possible to generalize the MLE equation for arbitrary codes and modulation schemes. This is ideally suited for various GNSS signals with same structure of tracking module. This paper proposes two different types of MLE based tracking method, i.e., an iterative batch processing method and a non-iterative feed-forward processing method. The first method is derived without any limitation on time consumption, while the second method is proposed for a time limited case by using a 1st derivative of cost function, which is proportional to error signal from discriminators of conventional tracking methods. The second method can be implemented by a block diagram approach for tracking carrier phase, Doppler frequency and code phase with assuming no correlation of signal parameters. Finally, a state space form of FLL/PLL/DLL is adopted to the designed MLE based tracking algorithm for reducing noise on the estimated signal parameters.