• The Korean Journal of Applied Statistics
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• v.37 no.4
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• pp.467-484
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• 2024

Longitudinal data are measured repeatedly over time from the same subject, so there is a correlation from the repeated outcomes. Therefore, when analyzing this correlation, both serial correlation and between-subject variation must be considered in longitudinal data analysis. In this paper, we will focus on the marginalized models to estimate the population average effect of covariates among models for analyzing longitudinal binary data. Marginalized models for longitudinal binary data include marginalized random effects models, marginalized transition models, and marginalized transition random effect models, and in this paper, these models are first reviewed, and simulations are conducted using complete data and missing data to compare the performance of the models. When there were missing values in the data, there is a difference in performance depending on the model in which the data was generated. We analyze Korea Health Panel data using marginalized models. The Korean Medical Panel data considers subjective unhealthy responses as response variables as binary variables, compares models with several explanatory variables, and presents the most suitable model.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.500-506
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• 2019

In this study, we present a decision function of optimal smoothing parameter for baseline correction using Asymmetrically reweighted penalized least squares (arPLS). Baseline correction is very important due to influence on performance of spectral analysis in application of spectroscopy. Baseline is often estimated by parameter selection using visual inspection on analyte spectrum. It is a highly subjective procedure and can be tedious work especially with a large number of data. For these reasons, an objective procedure is necessary to determine optimal parameter value for baseline correction. The proposed function is defined by modeling the median value of possible parameter range as the length and order of the background signal. The median value increases as the length of the signal increases and decreases as the degree of the signal increases. The simulated data produced a total of 112 signals combined for the 7 lengths of the signal, adding analytic signals and linear and quadratic, cubic and 4th order curve baseline respectively. According to the experimental results using simulated data with linear, quadratic, cubic and 4th order curved baseline, and real Raman spectra, we confirmed that the proposed function can be effectively applied to optimal parameter selection for baseline correction using arPLS.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1169-1177
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• 2003

The blocking effect results from independent coding of each image block and becomes highly visible, especially coded at very low bit rates. In this paper, a blocking effect reduction circuit is designed which is composed of a memory, arithmetic and logic unit, and control block. The circuit is based on a rational open uniform B-spline curve that uses to produce a smooth curve through a set of control points. The weight values and the modified pixel values in a rational open uniform B-spline curve are calculated using arithmetic and logic circuits. The simulation results show that the circuit has excellent performance for ail pattern of the blocking effects.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.171-177
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• 2004

In this paper, we propose a cognition-based navigational algorithm for mobile robots in dynamic environments. The proposed algorithm consists of two main stages: (i) the fuzzy logic-based perception stage that constructs knowledge from the sensory data for subsequent usage in reasoning, and (ii) the planning stage that identifies the path between a starting and a goal position within its environment on the basis of the knowledge base on the environment and information from the perception stage. A mobile robot reasons places and moves to goal using ambiguous information and ambiguous knowledge through ‘perception’ and ‘planning’. We provide computer simulation results for a mobile robot in order to show the validity of the proposed algorithm.

• Korean Journal of Optics and Photonics
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• v.17 no.1
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• pp.31-37
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• 2006

We propose a digital watermarking technique using a computer generated hologram. The proposed method uses two random patterns separated from the computer generated hologram (CGH). One of those is embedded into the original image as hidden watermark information and then the reconstructed image can be obtained by an optical decoding algorithm with the other one as a decoding key. We analyze an occlusion of the watermarked image that is the original image containing the hidden pattern. The embedding process is performed digitally and reconstruction optically Computer simulation and an optical experiment are shown in support of the proposed technique.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.533-541
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• 2013

In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.507-511
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• 2010

Digital Radio Frequency Memory (below, DRFM) performs RF signal data store, delay and re-transmission. DRFM is wildly used as core module of Jammer, EW simulator, Target Echo Generator etc. This paper suggests a hardware design of DRFM which is composed RF section(RF Input/Output Module, Local Oscillator Module) and Digital section(ADC module, memory, DAC module), and confirm the validity of the propose by the test result.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.573-586
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• 2012

Point clouds acquired by a LIDAR(Light Detection And Ranging, also LADAR) system often contain erroneous points called outliers seeming not to be on physical surfaces, which should be carefully detected and eliminated before further processing for applications. Particularly in case of LIDAR systems employing with a Gieger-mode array detector (GmFPA) of high sensitivity, the outlier ratio is significantly high, which makes existing algorithms often fail to detect the outliers from such a data set. In this paper, we propose a method to discriminate outliers from a point cloud with high outlier ratio acquired by a GmFPA LIDAR system. The underlying assumption of this method is that a meaningful targe surface occupy at least two adjacent pixels and the ranges from these pixels are similar. We applied the proposed method to simulated LIDAR data of different point density and outlier ratio and analyzed the performance according to different thresholds and data properties. Consequently, we found that the outlier detection probabilities are about 99% in most cases. We also confirmed that the proposed method is robust to data properties and less sensitive to the thresholds. The method will be effectively utilized for on-line realtime processing and post-processing of GmFPA LIDAR data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.72-77
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• 2009

In this paper, a 3.2Gb/s clock and data recovery (CDR) circuit for a high-speed serial data communication without the reference clock is described This CDR circuit consists of 5 parts as Phase and frequency detector(PD and FD), multi-phase Voltage Controlled-Oscillator(VCO), Charge-pumps (CP) and external Loop-Filter(KF). It is adapted the PD and FD, which incorporates a half-rate bang-bang type oversampling PD and a half-rate FD that can improve pull-in range. The VCO consists of four fully differential delay cells with rail-to-rail current bias scheme that can increase the tuning range and tuning linearity. Each delay cell has output buffers as a full-swing generator and a duty-cycle mismatch compensation. This materialized CDR can achieve wide pull-in range without an extra reference clock and it can be also reduced chip area and power consumption effectively because there is no additional Phase Locked- Loop(PLL) for generating reference clock. The CDR circuit was designed for fabrication using 0.18um 1P6M CMOS process and total chip area excepted LF is $1{\times}1mm^2$. The pk-pk jitter of recovered clock is 26ps at 3.2Gb/s input data rate and total power consumes 63mW from 1.8V supply voltage according to simulation results. According to test result, the pk-pk jitter of recovered clock is 55ps at the same input data-rate and the reliable range of input data-rate is about from 2.4Gb/s to 3.4Gb/s.