• Journal of Environmental Impact Assessment
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• v.21 no.2
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• pp.315-325
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• 2012

The characteristics of stormwater runoff and estimation of unit loads were examined in suburban industrial complex areas. During rainfall event, the peak concentrations occurred within the first 100 minutes after rainfall and then the highest concentration of NPS pollutants sharply decreased, showing strong first flush effect in suburban industrial complex. The cumulative load curves for NPS pollutants showed above the straight line, indicating that first flush effect occurred in suburban industrial complex. While the mean TSS, BOD, COD, TN and TP EMCs values were shown the highest values as 120.6 mg/L, 20.8 mg/L, 44.0 mg/L, 5.58 mg/L and 1.46 mg/L respectively. Unit loads estimated from the EMCs were TSS $43.86kg/km^2/day$, COD $52.45kg/km^2/day$, BOD $24.79kg/km^2/day$, T-N $6.65kg/km^2/day$, T-P $1.75kg/km^2/day$, and Pb $0.10kg/km^2/day$. Results of unit loads were compared with the unit pollutant loads from land-use in Korea and USA. The unit load of TSS was lower than that of USA. Estimated BOD and T-N and T-P unit loads were lower than that of Korea.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.495-508
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• 2005

Moving objects are spatiotemporal data that change their location or shape continuously over time. Their location coordinates are periodically measured and stored i3l the application systems. The linear function is mainly used to estimate the location information that is not in the system at the query time point. However, a new method is needed to improve uncertainties of the location representation, because the location estimation by linear function induces the estimation error. This paper proposes an application method of the cubic spline interpolation in order to reduce deviation of the location estimation by linear function. First, we define location information of the moving object on the two-dimensional space. Next, we apply the cubic spline interpolation to location estimation of the proposed data model and describe algorithm of the estimation operation. Finally, the precision of this estimation operation model is experimented. The experimentation comes out more accurate results than the method by linear function.

• Wind and Structures
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• v.23 no.5
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• pp.385-403
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• 2016

The across-wind dynamic loads on L-shaped tall buildings with various geometric dimensions were investigated through a series of wind tunnel testing. The lift coefficients, power spectral densities and vertical correlation coefficients of the across-wind loads were analyzed and discussed in details. Taking the side ratio and terrain category as key variables, empirical formulas for estimating the across-wind dynamic loads on L-shaped tall buildings were proposed on the basis of the wind tunnel testing results. Comparisons between the predictions by the empirical formulas and the wind tunnel test results were made to verify the accuracy and applicability of the proposed formulas. Moreover, a simplified procedure to evaluate the across-wind dynamic loads on L-shaped tall buildings was derived from the proposed formulas. This study aims to provide a simple and reliable way for the estimation of across-wind dynamic loads on L-shaped tall buildings.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1125-1145
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• 2010

For modeling(skewed) semicircular data, we derive a new exponential family of distributions. We extend it to the l-axial exponential family of distributions by a projection for modeling any arc of arbitrary length. It is straightforward to generate samples from the l-axial exponential family of distributions. Asymptotic result reveals that the linear exponential family of distributions can be used to approximate the l-axial exponential family of distributions. Some trigonometric moments are also derived in closed forms. The maximum likelihood estimation is adopted to estimate model parameters. Some hypotheses tests and confidence intervals are also developed. The Kolmogorov-Smirnov test is adopted for a goodness of t test of the l-axial exponential family of distributions. Samples of orientations are used to demonstrate the proposed model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.50-52
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• 2010

통신 시스템의 성능을 향상시키는 핵심 문제 중에 하나인 채널을 추정하는 문제는 다양한 분야에서 연구되고 있다. 채널의 sparse한 특징으로 인해 기존의 linear square나 minimum mean square error보다 발전된 $l_1$-norm minimization 방법 등이 많이 연구되고 있다. 이에 본 논문은 sparse한 채널의 특징과 천천히 변화하는 채널환경 특징을 이용하여 기존의 방법에 비해 더 높은 성능의 채널 추정 기법을 연구한다. 천천히 변화하는 채널환경의 특징으로 인해 이전 채널 정보를 현재 채널 추정에 사용할 수 있고 sparse한 채널의 특징으로 $l_1$-norm minimization을 사용할 수 있다. 이러한 두 가지의 정보를 이용하여 weighted $l_1$-norm minimization 이용한 support detection후 MMSE를 이용한 채널 추정기법을 연구한다.

• Economic and Environmental Geology
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• v.28 no.4
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• pp.433-438
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• 1995

The most popular minimization method is based on the least-squares criterion, which uses the $L_2$ norm to quantify the misfit between observed and synthetic data. The solution of the least-squares problem is the maximum likelihood point of a probability density containing data with Gaussian uncertainties. The distribution of errors in the geophysical data is, however, seldom Gaussian. Using the $L_2$ norm, large and sparsely distributed errors adversely affect the solution, and the estimated model parameters may even be completely unphysical. On the other hand, the least-absolute-deviation optimization, which is based on the $L_1$ norm, has much more robust statistical properties in the presence of noise. The solution of the $L_1$ problem is the maximum likelihood point of a probability density containing data with longer-tailed errors than the Gaussian distribution. Thus, the $L_1$ norm gives more reliable estimates when a small number of large errors contaminate the data. The effect of outliers is further reduced by M-fitting method with Cauchy error criterion, which can be performed by iteratively reweighted least-squares method.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.79-81
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• 1998

This paper presents analytic and numerical analysis of ultrasonic motor, specially linear motion ultrasonic machine. For rough estimation of characteristics of linear ultrasonic motor, the analytic method is used and a three-dimensional numerical analysis with experimental material data using ABAQUS, is performed. The validity of analysis is confirmed by comparing experimental results with numerical ones.

• Journal of Computing Science and Engineering
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• v.11 no.2
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• pp.58-68
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• 2017

While graphics processing units (GPUs) can be used to improve the performance of real-time embedded applications that require high throughput, it is challenging to estimate the worst-case execution time (WCET) of GPU programs, because modern GPUs are designed for improving the average-case performance rather than time predictability. In this paper, a reordering framework is proposed to regulate the access to the GPU data cache, which helps to improve the accuracy of the estimation of GPU L1 data cache miss rate with low performance overhead. Also, with the improved cache miss rate estimation, tighter WCET estimations can be achieved for GPU programs.

• Journal of Environmental Science International
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• v.5 no.3
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• pp.377-385
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• 1996

This study aims at the estimation of a river management flow in urban basin analyzing Sinchun basin to be the tributary of Kumho river basin. The river management flow has to satisfy a low flow as natural flow and an environmental preservation flow estimated by a dilution flow to satisfy a target water quality in drought flow. Therefore for the estimation of a river management flow in Sinchun in this study, first Tank model as a basin runoff model estimates a low flow, a drought flow from a flow duration curve in Sinchun, second QUAL2E model as water quality model simulates water quality in Sinchun and estimates environmental preservation flow to satisfy a target water qua%its, BOD 8 mg/l by a dilution flow derived from Kumho river, Nakdong river and around water. And the river management flow is estimated by addition of a use flow and a loss flow to more flow between a low-flow and an environmental preservation flow.