• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.551-560
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• 2010

This study presents the export of constituent transport loads through a river system. The proposed constituent transport load estimating procedure can be operated with the on-going Korean TMDL monitoring system. This study firstly discusses the use of a hydrologic simulation model (TANK) to estimate stream-flow for the 40 sub-catchments. Model parameters are estimated from 8-days intervals flow data which has been monitored by NIER since 2004. Constituent transport loads are estimated with the 7-parameter log linear model whose parameters are estimated by the minimum variance unbiased estimator. Results from Nakdong river basin reveals that the proposed procedure provides satisfactory TN, TP and SS transport load estimates. As an application, a representative load duration curve is derived to represent the overall hydrologic flux of TN, TP and SS at Nakdong river basin.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.1-8
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• 2023

An Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication system has drawn wide attention for its high transmission rate and high spectrum efficiency in not only radio but also Underwater Acoustic (UWA) applications. Because of the narrow sub-carrier spacing of OFDM, orthogonality between sub-carriers is easily affected by Doppler effect caused by the movement of transmitter or receiver. Previously, Doppler compensation signal processing algorithm for Desired propagation path was proposed. However, other Doppler shifts caused by delayed Undesired signal arriving from different directions cannot be perfectly compensated. Then Receiver Bit Error Rate (BER) is degraded by Inter-Carrier-Interference (ICI) caused in the case of Multi-path Doppler channel. To mitigate the ICI effect, a modified Delay and Doppler Profiler (mDDP), which estimates not only attenuation, relative delay and Doppler shift but also sampling clock shift of each multi-path component, is proposed. Based on the outputs of mDDP, an ICI canceling multi-tap equalizer is also proposed. Computer simulated performances of one-tap equalizer with the conventional Time domain linear interpolated Channel Transfer Function (CTF) estimator, multi-tap equalizer based on mDDP are compared. According to the simulation results, BER improvement has been observed. Especially, in the condition of 16QAM modulation, transmitting vessel speed of 6m/s, two-path multipath channel with direct path and ocean surface reflection path; more than one order of magnitude BER reduction has been observed at CNR=30dB.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.386-393
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• 2008

This paper proposes a new approach for speech enhancement in highly nonstationary noisy environments. The spectral subtraction (SS) is a well known technique for speech enhancement in stationary noisy environments. However, in real world, noise is mostly nonstationary. The proposed method uses an auto control parameter for an adaptive threshold to work well in highly nonstationary noisy environments. Especially, the auto control parameter is affected by a linear function associated with an a posteriori signal to noise ratio (SNR) according to the increase or the decrease of the noise level. The proposed algorithm is combined with spectral subtraction (SS) using a hangover scheme (HO) for speech enhancement. The performances of the proposed method are evaluated ITU-T P.835 signal distortion (SIG) and the segment signal to-noise ratio (SNR) in various and highly nonstationary noisy environments and is superior to that of conventional spectral subtraction (SS) using a hangover (HO) and SS using a minimum statistics (MS) methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.1-10
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• 2007

Most of natural images have a special property, what is called self-similarity, which is the basis of fractal image coding. Even though an image has local stationarity in several homogeneous regions, it is generally non-stationarysignal, especially in edge region. This is the main reason that poor results are induced in linear techniques. In order to overcome the difficulty we propose a non-linear technique using self-similarity in the image. In our work, an image is classified into stationary and non-stationary region with respect to sample variance. In case of stationary region, do-noising is performed as simply averaging of its neighborhoods. However, if the region is non-stationary region, stationalization is conducted as make a set of center pixels by similarity matching with respect to bMSE(block Mean Square Error). And then do-nosing is performed by Gaussian weighted averaging of center pixels of similar blocks, because the set of center pixels of similar blocks can be regarded as nearly stationary. The true image value is estimated by weighted average of the elements of the set. The experimental results show that our method has better performance and smaller variance than other methods as estimator.

• Journal of Korean Medicine for Obesity Research
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• v.19 no.2
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• pp.97-105
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• 2019

Objectives: We investigated differences between the tracheostomized and the non-tracheostomized stroke patients through microbiological analysis for the purpose of preliminary explorations of full-scale clinical research in the future. Methods: We collected tracheal aspirates samples from 5 stroke patients with tracheostomy and expectorated sputum samples from 5 stroke patients without tracheostomy. Genomic DNA from sputum samples was isolated using QIAamp DNA mini kit. The sequences were processed using Quantitative Insights into Microbial Ecology 1.9.0. Alpha-diversity was calculated using the Chao1 estimator. Beta-diversity was analyzed by UniFrac-based principal coordinates analysis (PCoA). To confirm taxa with different abundance among the groups, linear discriminant analysis effect size analysis was performed. Results: Although alpha-diversity value of the tracheostomized group was higher than that of the non-tracheostomized group, there was no statistically significant difference. In PCoA, clear separation was seen between clusters of the tracheostomized group and that of the non-tracheostomized group. In both groups, Bacteroidetes, Proteobacteria, Fusobacteria, Firmicutes, Actinobacteria were identified as dominant in phylum level. In particular, relative richness of Proteobacteria was found to be 31% more in the tracheotomized group (36.6%) than the non-tracheostomized group (5.6%)(P<0.05). In genus level, Neisseria (24%), Prevotella (17%), Streptococcus (13%), Fusobacteria (11%), Porphyromonas (7%) were identified as dominant in the tracheostomized group. In the non-tracheostomized group, Prevotella (38%), Veillonella (20%), Neisseria (9%) were genera that found to be dominant. Conclusions: It is meaningful in that the tracheostomized group has been identified a higher rate of microbiotas known as pathogenic in respiratory diseases compared to the non-tracheostomized group, confirming the possibility that the risk of opportunity infection may be higher.

• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.237-244
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• 2011

In this paper, a feature-based registration technique is proposed for pre-contrast and post-contrast lung CT images. It utilizes three dimensional(3-D) features with their descriptors and estimates feature correspondences by nearest neighborhood matching in the feature space. We design a transformation model between the input image pairs using a free form deformation(FFD) which is based on B-splines. Registration is achieved by minimizing an energy function incorporating the smoothness of FFD and the correspondence information through a non-linear gradient conjugate method. To deal with outliers in feature matching, our energy model integrates a robust estimator which discards outliers effectively by iteratively reducing a radius of confidence in the minimization process. Performance evaluation was carried out in terms of accuracy and efficiency using seven pairs of lung CT images of clinical practice. For a quantitative assessment, a radiologist specialized in thorax manually placed landmarks on each CT image pair. In comparative evaluation to a conventional feature-based registration method, our algorithm showed improved performances in both accuracy and efficiency.

• Geotechnical Engineering
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• v.3 no.4
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• pp.41-54
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• 1987

A stochastic numerical analysis for predicting the groundswater fluctuations in hillside slopes is performed in this paper to account for the uncertainties associated with the rainfall and site characteristics. The effect of spatial variabilities of aquifer parameters and the effect of temporal variability of recharge on the groundwater fluctuations are studied in depth. The Kriging is used to account for the spatial tariabilities of aquifer parameters. This technique prolevides the best linear unbiased estimator of a parameter and its minimum variance from a litsitem number of measured data. A stochastic one-dimensional numerical model is delreloped b) combining the groundwater flow model, the Kriging, and the first-order second-moment analysis. In addition, a two dimensional detelministic groundwater model is developed to study the change of ground water surfas in the transverse direction as well as in the downslope direction. It is revealed that the undulations of the impervious bedrock in addition to the permeability and the specific yield have an important influence on the fluctuations of the groundwater surface. It is also found that th'e groundwater changes significantly in the transverse direction as well as in the downslope direction. The results obtained in this analysis may be used for evaluation of landslide risks due to high porewater pressure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.445-452
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• 2015

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the reliability model with log and power intensity function (log linear, log power and exponential power), which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, was employed. Analysis of failure, using real data set for the sake of proposing log and power intensity function, was employed. This analysis of failure data compared with log and power intensity function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the log type model is also efficient in terms of reliability because it (the coefficient of determination is 70% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.591-598
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• 2021

The digital phase-locked loops(DPLL) is a circuit used for phase synchronization and has been generally used in various fields such as communication and circuit fields. State estimators are used to design digital phase-locked loops, and infinite impulse response state estimators such as the well-known Kalman filter have been used. In general, the performance of the infinite impulse response state estimator-based digital phase-locked loop is excellent, but a sudden performance degradation may occur in unexpected situations such as inaccuracy of initial value, model error, and disturbance. In this paper, we propose a minimum variance finite impulse response filter with optimal horizon for designing a new digital phase-locked loop. A numerical method is introduced to obtain the measured value interval length, which is an important parameter of the proposed finite impulse response filter, and to obtain a gain, the covariance matrix of the error is set as a cost function, and a linear matrix inequality is used to minimize it. In order to verify the superiority and robustness of the proposed digital phase-locked loop, a simulation was performed for comparison and analysis with the existing method in a situation where noise information was inaccurate.