• Journal of Korean Society on Water Environment
• /
• v.34 no.5
• /
• pp.537-547
• /
• 2018

Sediment discharge by rainfall runoff affects water quality in rivers such as turbid water, eutrophication. In order to solve various problems caused by soil loss, it is important to establish a sediment management plan for watersheds and rivers in advance. However, there is a lack of sediment data available for estimating sediment discharge in ungauged basins.. Thus, reasonable research is very important to evaluate and predict the sediment discharge quantitatively. In this study, cluster analysis was conducted to classify gauged watersheds into hydrologically homogeneous groups based on the watershed characteristics. Also, this study suggests a method to efficiently predict the sediment discharge for ungauged basins by developing and evaluating the SDR equations based on the PA-SDR module. As the result, the SDR equations for the classified watersheds were derived to predict the most reasonable sediment discharge of ungauged basins with 0.24 % ~ 10.89 % errors. It was found that the optimal parameters for the gauged basins reflect well characteristic of sediment movement. SDR equations proposed in this study will be available for estimating sediment discharge on ungauged basins. Also it is possible to utilize establishing the appropriate sediment management plan for integrated management of watershed and river in Korea.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.1019-1032
• /
• 2013

The accurate prediction of future mortality is an important issue due to recent rapid increases in life expectancy. An accurate estimation and prediction of mortality is important to future welfare policies. The optimal selection of a mortality model is important to estimate and predict mortality; however, the period of time series data used is also an important issue. It is essential to understand that the time series data for mortality is short in Korea and the data before 1982 is incomplete. This paper divides the time series of Korean mortality into two sets to compare the parameter estimates of the LC model and LC model with a cohort effect by the period of data used. A modeling and prediction of the mortality index and cohort effect index as well as the evaluation of future life expectancy is conducted. Finally, some suggestions are proposed for the future prediction of mortality.

• Journal of Korea Water Resources Association
• /
• v.50 no.12
• /
• pp.849-862
• /
• 2017

The increased frequency of meteorological disasters has been observed due to increased extreme events such as heavy rainfalls and flash floods. Numerous studies using high-resolution weather radar rainfall data have been carried out on the hydrological effects. In this study, a conditional merging technique is employed, which makes use of geostatistical methods to extract the optimal information from the observed data. In this context, three different techniques such as kriging, inverse distance weighting and spline interpolation methods are applied to conditionally merge radar and ground rainfall data. The results show that the estimated rainfall not only reproduce the spatial pattern of sub-hourly rainfall with a relatively small error, but also provide reliable temporal estimates of radar rainfall. The proposed modeling framework provides feasibility of using conditionally merged rainfall estimation at high spatio-temporal resolution in ungauged areas.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.2
• /
• pp.349-356
• /
• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.993-1000
• /
• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.2
• /
• pp.183-188
• /
• 2014

In this paper, the propagation effect of power supply uncertainty on the output of class-F power amplifier has been estimated. Also, a 1.9 GHz, 10 watt class-F power amplifier was measured to verify the estimation and to find the optimal biasing point. By approximating the propagation theory of uncertainties, the propagation effect of bias uncertainty was mathmatically calculated. As a result, the DC biases have propagated uncertainties of 15~70 mW. However, at the optimized bias point, the uncertainty in the output power could be dropped less than 15 mW while the output power has dropped by 0.37 dB.

• Speech Sciences
• /
• v.5 no.1
• /
• pp.7-21
• /
• 1999

This paper proposes an improved training procedure in speech recognition based on the continuous density of the Hidden Markov Model (CDHMM). Of the three parameters (initial state distribution probability, state transition probability, output probability density function (p.d.f.) of state) governing the CDHMM model, we focus on the third parameter and propose an efficient algorithm that determines the p.d.f. of each state. It is known that the resulting CDHMM model converges to a local maximum point of parameter estimation via the iterative Expectation Maximization procedure. Specifically, we propose two independent algorithms that can be embedded in the segmental K -means training procedure by replacing relevant key steps; the adaptation of the number of mixture Gaussian p.d.f. and the initialization using the CDHMM parameters previously estimated. The proposed adaptation algorithm searches for the optimal number of mixture Gaussian humps to ensure that the p.d.f. is consistently re-estimated, enabling the model to converge toward the global maximum point. By applying an appropriate threshold value, which measures the amount of collective changes of weighted variances, the optimized number of mixture Gaussian branch is determined. The initialization algorithm essentially exploits the CDHMM parameters previously estimated and uses them as the basis for the current initial segmentation subroutine. It captures the trend of previous training history whereas the uniform segmentation decimates it. The recognition performance of the proposed adaptation procedures along with the suggested initialization is verified to be always better than that of existing training procedure using fixed number of mixture Gaussian p.d.f.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.9
• /
• pp.2433-2443
• /
• 1996

In this paper, a new technique is proposed for obtaining the error probabilities of the VSB(vestigial sideband modulation) signal in the presence of the cochannel interference and frequency-selective fading channel. For the receivers, a suboptimal matched filter receiver and the MLSE(maximum likelihood sequence estimation) receiver, which is known to be optimal on the fading channel, are considered. First, for the matched filter receiver, the distributions of the random variables, which determine the SER(symbol error rate) are obtained by decomposing the multi-path fading channel into Rayleigh distributed main path and Gaussian distributed remained path channels. the random variables mean the energy of the main path and subpath respecitively, and SER can be calculated from the distribution of them. Next, for the case of the MLSE receover, it is found that the random variables are expressed as a function of integrals. In order to obtain the distribution for the random variables, we expanded each element of integrals with the KL(Karhunen-Loeve) transformation. And it is derived that the distributions for the transformed random variables are given by a sum of chi-square distributions. Finally, we calculated the error rate derived formula on the two-ray fading channel, which is one of widely used models for the frequency-selective fading channel. From the numerical results, it is found that for the matched filer receiver, performance degradation is significant, while the performance degradation at the MLSE receiver is insignificant on the frequency-selective fading channel. However, in case of cochannel interference environment, the error rateis found to increase significantly both at the matched filter and at the MLSE receiver.

• The KIPS Transactions:PartB
• /
• v.18B no.4
• /
• pp.193-200
• /
• 2011

This paper proposes a low-cost visual analyzer algorithm of human body motion for real-time applications such as human-computer interfacing, virtual reality applications in medicine and telemonitoring of patients. To reduce cost of its use, we design the algorithm to use a single camera. To make the proposed system to be used more conveniently, we avoid from using optical markers. To make the proposed algorithm be convenient for real-time applications, we design it to have a closed-form with high accuracy. To design a closed-form algorithm, we propose an idea that formulates motion of a human body joint as a 2D universal joint model instead of a common 3D spherical joint model, without any kins of approximation. To make the closed-form algorithm has high accuracy, we formulates the estimation process to be an optimization problem. Thus-desined algorithm is applied to each joint of the human body one after another. Through experiments we show that human body motion capturing can be performed in an efficient and robust manner by using our algorithm.

• Journal of Digital Convergence
• /
• v.11 no.10
• /
• pp.305-311
• /
• 2013

The size of the market as the economy continues to evolve, in order to make the right decisions to accurately predict the economic problems the market has emerged as an important issues. To express the modern economic system, the largest of the various economic indicators, pillars stock indicators analysis and decision-making with a proper understanding of the problem for the application of the model is suitable for time-series data concealment HMM. Based on this time series model and the calculation of the time and cost savings dimension decrease techniques for the estimation and prediction of the model was applied to the problem was to verify the validity. As a result, the model predictions in both the short term rather than long-term predictions of the model estimates the optimal predictive value similar pattern very similar to both the actual data and was able to confirm that.