• Korean Journal of Construction Engineering and Management
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• v.7 no.1 s.29
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• pp.138-147
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• 2006

Construction companies have been interested in the construction duration which importantly affects the performance and the success of the construction projects in accordance with the systemic changes such as five days per week system, introduction of construction duration reduction bidding system and post sale system nowadays. It is also very important to estimate and forecast properly the construction duration as the construction companies compete for the projects in the situation of construction market reduction and the lowest bidding system. Recognizing the importance about the construction duration, the researches about comparing and analyzing or estiamting the construction duration have been performed. However, comparing studies about the construction duraion have been limited to the apartment and office building in domestic area. Many studies about forecasting construction duration have been performed through stochastic analysis and simulations. Little research has been addressed the comparison analysis of the real construction duration about the mix-use building and officetel building which occured according to the changes of the building requirements. Therefore, the objective of this study is to compare and analyze the real construction duration and the hypothetical construction duration about the mix-use building and officetel building of the domestic companies. Moreover, we select the most competitve construction company to get the strengths and analyze the competitive advatages of the construction companies about construction duration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.37-45
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• 2011

We studied noise influence on nonlinear chaotic system by using Logistic data series which is known as a typical nonlinear chaotic system. We regenerated Logistic data series by the method of adding noise according to noise level. And, we performed some analyses such as phase space reconstruction, correlation dimension, BDS statistics, and DVS Algorithms which are known as the methods of nonlinear deterministic or chaotic analysis. If we see the results of analysis, the characteristics of data series are gradually changed from nonlinear chaotic data series to random stochastic data series according to increasing noise level. We applied Low Pass Filter (LPF) and Kalman Filter techniques for the investigation of removing effect of the added noise to data series. Typical nonparametric method cannot distinguish nonlinear random series but the BDS statistic can distinguish the nonlinear randomness of the time series. Therefore this study used the BDS statistic which is well known as nonlinear statistical method for the investigation of randomness of time series for the effect of removing noise of data series. We found that Kalman filter is better method to remove the noise of chaotic data series even for high noise level.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.613-619
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• 2005

Indigenous Yakutian cattle' adaptation to the hardest subarctic conditions makes them a valuable genetic resource for cattle breeding in the Siberian area. Since early last century, crossbreeding between native Yakutian cattle and imported Simmental and Kholmogory breeds has been widely adopted. In this study, variations at 22 polymorphic microsatellite loci in 5 populations of Yakutian, Kholmogory, Simmental, Yakutian-Kholmogory and Yakutian-Simmental cattle were analysed to estimate the genetic contribution of Yakutian cattle to the two hybrid populations. Three statistical approaches were used: the weighted least-squares (WLS) method which considers all allele frequencies; a recently developed implementation of a Markov chain Monte Carlo (MCMC) method called likelihood-based estimation of admixture (LEA); and a model-based Bayesian admixture analysis method (STRUCTURE). At population-level admixture analyses, the estimate based on the LEA was consistent with that obtained by the WLS method. Both methods showed that the genetic contribution of the indigenous Yakutian cattle in Yakutian-Kholmogory was small (9.6% by the LEA and 14.2% by the WLS method). In the Yakutian-Simmental population, the genetic contribution of the indigenous Yakutian cattle was considerably higher (62.8% by the LEA and 56.9% by the WLS method). Individual-level admixture analyses using STRUCTURE proved to be more informative than the multidimensional scaling analysis (MDSA) based on individual-based genetic distances. Of the 9 Yakutian-Simmental animals studied, 8 showed admixed origin, whereas of the 14 studied Yakutian-Kholmogory animals only 2 showed Yakutian ancestry (>5%). The mean posterior distributions of individual admixture coefficient (q) varied greatly among the samples in both hybrid populations. This study revealed a minor existing contribution of the Yakutian cattle in the Yakutian-Kholmogory hybrid population, but in the Yakutian-Simmental hybrid population, a major genetic contribution of the Yakutian cattle was seen. The results reflect the different crossbreeding patterns used in the development of the two hybrid populations. Additionally, molecular evidence for differences among individual admixture proportions was seen in both hybrid populations, resulting from the stochastic process in crossing over generations.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.273-277
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• 2010

This research was performed to examine changes in the timing of the growth of crops along with changes in temperatures due tochanges and to analyze the change of water-supply-reliability by adding an analysis of the change of agricultural water supply patterns in the basin area of Miryang dam in Korea. Had-CM3 model from U.K. was the tool adopted for the GCM model, a stochastic, daily-meteorology-generation-model called LARS-WG was alsoused for downscaling and for the climate change scenario (A1B) which represents Korea's circumstances best. First of all, to calculate changes in the timing of the growth of crops during this period, the theory of GDD was applied. Except for the period of transplanting and irrigation, there was no choice but to find the proper accumulated temperature by comparing actual temperature data and the supply pattern of agricultural use due to limited temperature data. As a result, proper temperatures were found for each period. $400^{\circ}C$ for the preparation period of a nursery bed, $704^{\circ}C$ for a nursery bed's period, $1,295^{\circ}C$ for the rice-transplanting period, $1,744^{\circ}C$ for starting irrigation, and $3,972^{\circ}C$ for finishing irrigation. To analyze future agricultural supply patter changes, the A1B scenario of Had-CM3 model was adopted, and then Downscaling was conducted adopting LARS-WG. To conduct a stochastical analysis of LARS-WG, climate scenarios were generated for the periods 2011~2030, 2046~2065, 2080~2099 using the data of precipitation andMax/Min temperatures collected from the Miryang gauging station. Upon reviewing the result of the analysis of accumulated temperatures from 2011~2030, the supply of agricultural water was 10 days earlier, and in the next periods-2046~2065, 2080~2099 it also was 10 days earlier. With these results, it is assumed that the supply of agricultural water should be about 1 month ahead of the existing schedule to meet the proper growth conditions of crops. From the results of the agricultural water supply patterns should be altered, but the reliability of water supply becomes more favorable, which is caused from the high precipitation change. Furthermore, since the unique characteristics of precipitation in Korea, which has high precipitation in the summer, water-supply-reliability has a pattern that the precipitation in September could significantly affect the chances of drought the following winter and spring. It could be more risky to make changes to the constant supply pattern under these conditions due to the high uncertainty of future precipitation. Although, several researches have been conducted concerning climate changes, in the field of water-industry, those researches have been solely dependent on precipitation. Even so, with the high uncertainty of precipitation, it is difficult for it to be reflected in government policy. Therefore, research in the field of water-supply-patterns or evapotranspiration according to the temperature or other diverse effects, which has higher reliability on anticipation, could obtain more reliable results in the future and that could result in water-resource maintenance to be safer and a more advantageous environment.

• The korean journal of orthodontics
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• v.52 no.1
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• pp.3-19
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• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.3
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• pp.127-132
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• 2018

In this paper, a scheme for the evaluation of variability in the eigen-modes of functionally graded material(FGM) beams is proposed within the framework of perturbation-based stochastic analysis. As a random parameter, the spatially varying elastic modulus of FGM along the axial direction at the mid-surface of the beam is chosen, and the thru-thickness variation of the elastic modulus is assumed to follow the original form of exponential variation. In deriving the formulation, the first order Taylor expansion on the eigen-modes is employed. As an example, a simply supported FGM beam having symmetric elastic modulus with respect to the mid-surface is chosen. Monte Carlo analysis is also performed to check if the proposed scheme gives reasonable outcomes. From the analyses it is found that the two schemes give almost identical results of the mean and standard deviation of eigen-modes. With the propose scheme, the standard deviation shape of respective eigen-modes can be evaluated easily. The deviated mode shape is found to have one more zero-slope points than the mother modes shapes, irrespective of order of modes. The amount of deviation from the mean is found to have larger values for the higher modes than the lower modes.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.56-64
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• 1995

The least mean fourth (LMF) adaptive algorithm is a stochastic gradient method that minimizes the error in the mean fourth sense. Despite its potential advantages, the algorithm is much less popular than the conventional least mean square (LMS) algorithm in practice. This seems partly because the analysis of the LMF algorithm is much more difficult than that of the LMS algorithm, and thus not much still has been known about the algorithm. In this paper, we explore the statistical convergence behavior of the LMF algorithm when the input to the adaptive filter is zero-mean, wide-sense stationary, and Gaussian. Under a system idenrification mode, a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the algorithm is derived. A condition for the conbergence is then found, and it turns out that the conbergence of the LMF algorithm strongly depends on the choice of initial conditions. Performances of the LMF algorithm are compared with those of the LMS algorithm. It is observed that the mean convergence of the LMF algorithm is much faster than that of the LMS algorithm when the two algorithms are designed to achieve the same steady-state mean-squared estimation error.

• Analytical Science and Technology
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• v.29 no.2
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• pp.79-84
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• 2016

A small and inexpensive thermal cycler (PCR machine), known as the MiniPCR^TM Mini8 Thermal Cycler (Amplyus, Cambridge, MA, USA), was developed. In this study, the performance of this PCR machine was compared with the GeneAmp^® PCR system 9700 (Applied Biosystems) using four autosomal short tandem repeat (STR) kits, a Y-chromosome STR kit, and a mitochondrial DNA HV1/HV2 sequence analysis. The sensitivity and stochastic effects of the STR multiplex kits and the quality of the DNA sequence analysis were similar between the two PCR machines. The MiniPCR^TM Mini8 Thermal Cycler could be used for analyses at forensic DNA laboratories and crime scenes. The cost of the PCR is so economical that school laboratories and individuals could use the machines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.667-680
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• 2008

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.2
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• pp.49-56
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• 2010

In general, the seismic intensity deduced from instrumental data has been evaluated from the empirical relation between the intensity and the PGA. From the point of view that the degree of earthquake damage is more closely associated with the seismic intensity than with the observed PGA, JMA developed the instrumental seismic intensity (JMA instrumental intensity) meter that estimate the real-time seismic intensity from the observed strong motion data to obtain a more correct estimate of earthquake damage. The purpose of the present study is to propose a practical application of the JMA instrumental intensity in Korea. Since the occurrence of strong earthquakes is scarce in the Korean Peninsula, there is an insufficiency of strong motion data. As a result, strong motion data were synthesized by a stochastic procedure to satisfy the characteristics of a seismic source and crustal attenuation of the Peninsula. Six engineering ground motion parameters, including the JMA instrumental intensity, were determined from the synthesized strong motion data. The empirical relations between the ground motion parameters were then analyzed. Cluster analysis to classify the parameters into groups was also performed. The result showed that the JMA acceleration ($a_0$) could be classified into similar group with the spectrum intensity and the relatively distant group with the CAV (Cumulative Absolute Velocity). It is thought that the $a_0$ or JMA intensity can be used as an alternative criterion in the evaluation of seismic damage. On the other hand, attenuation relation equations for PGA and $a_0$ to be used in the prediction of seismic hazard were derived as functions of the moment magnitude and hypocentral distance.