• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.85-94
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• 2010

The objective of this study is to estimate the flood runoff for three guaged stations within Namgang-Dam watershed which are operated by KWATER. For a flood runoff simulation, HEC-HMS was applied and the simulated runoff was compared with observed from 2004 to 2008. The watershed area of Sancheong, Shinan, and Changchon were 693.6 $km^2$, 413.4 $km^2$, and 346.48 $km^2$, respectively. The average runoff ratio of observed runoff for three watersheds were 0.725, 0.418, and 0.586, respectively. The dominant land cover of three watersheds are forest with the value of 71.6 %, 73.1 %, and 82.0 %. Three different cases according to the potential maximum retention of forest areas for calculating the curve number were applied to decrease the error between the simulated and observed. The simulated peak runoff of case 3 which applied the 90 % of potential maximum retention of curve number which is equivalent to AMCI for all the AMCI, AMCII, and AMCIII conditions showed least root mean square error (RMSE). The case 1, which was suggested by previous study, showed high discrepancy between the simulated and observed. Since the forest area consists of more than 70 % for all three watersheds, the application of curve number for forest is critical to improve the estimation errors. Further research is required to estimate the more accurate curve number for forest area.

• 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 Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3146-3152
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• 2014

This paper investigates the determinants for the controlling power and the concentration of the subcontracting trade between the downstream producer and the upstream supplier using a survey data for the IT industry in Gyeonggi-Do. The estimation results of the ordered logit and least square analyses are as follows. First, a firm. s controlling power across the downstream producer and the upstream supplier in the subcontracting trade would grow bigger when the company is bigger, more manufacture-oriented, and has higher ratio of export in sales. Second, the analysis for the upstream suppliers indicates that the higher dependent ratio of the subcontracting trade in the sales, the lower the concentration ratio of the R&D in the sales. Lastly, the analysis of the downstream producers indicates that the higher the dependent ratio of the subcontracting trade in the sales, the higher the concentration ratio of the R&D in the sales, which is distinctively contrast with the analysis result of the upstream suppliers. The overall estimation results are, hence, unsupporting to the transaction cost theory which predicts the increase of R&D investments in both downstream producer and upstream supplier.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.471-479
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• 2022

Recently, piezoelectric devices, such as ultrasonic surgery, ultrasonic atomizer, and ultrasonic speaker, are analyzed and designed by finite element simulation methods. However, the discrepancy between the design and the experiment results of the device typically occurs due to the inaccuracy of the piezoelectric material properties. To improve the simulation accuracy, the material properties of the PZT ceramics were better refined using parameter estimation method. The material parameters are elastic stiffness c^E_ij and piezoelectric constant e_ij of PZT ceramics. The impedance curve characteristics for the LTE mode of PZT ceramics were calculated. The mismatch between the simulation and the experimental data were compared and minimized by a least square method. Finally, the simulated impedance data were compared with the experimental data for the various vibration modes of PZT ceramics and the optimized material properties of PZT ceramics were verified. To further verify the accuracy, this method was also applied to piezoelectric PMN-PT single crystals.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.917-925
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• 2006

Experimental tests were performed to estimate velocities of the acoustic signals through prestressed concrete beam and source locations using acoustic emission (AE) techniques. Seven AE sensors are mounted on the surface of 5m length test beam with equal spacing and using Schmidt Hammer AE events are made at 18 locations. The velocities of AE signals are estimated using the time differences of arrival times and the distances between the source locations and the AE sensor locations. In addition, using the Least Square Method, the AE source locations are re-evaluated reversely using both of the arrival times and the velocities of AE signals. Test results show the average velocity of the AE signals is about 4,000 m/sec and the velocity decreased with the increase of the distance from source locations to AE sensors due to the effect of attenuation. Based on the estimation of the source locations, it is observed that the errors of source locations are decreased when the velocities of each AE sensor are used rather than the average velocity.

• Management & Information Systems Review
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• v.37 no.2
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• pp.209-233
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• 2018

This study investigates the question of how political and economic factors may affect the export of renewable energy technologies. The relationships are tested using panel data for 19 OECD member countries over the period 1992-2012. Before establishing the empirical model, the current study checks the characteristics of the panel data, which includes various panel framework analyses, such as tests for the presence of normality, structural breaks, first-order autocorrelation, heteroscedasticity, cross-sectional dependence, panel unit-root. From the panel framework analyses, a dynamic panel model is established to test the relationship between the variables examined in this study. In order to reduce the bias of the estimation of the dynamic panel model and obtain efficient parameters, this study uses the bias-corrected least square dummy variable(LSDVC) estimator to estimate the empirical model. The results of this study show that governmental policies expressed as coercive pressure and market size positively affect the export growth of renewable energy technologies. However, public pressure and traditional energy industry have no significant effects on export performance. Policy implications are presented based on the results of this study.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.1-10
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• 2019

This paper proposes a method to improve the detectable distance by reducing noise to perform a signal processing technique on the received signals. To increase the radar detection range, the noise component of the received signal has to be reduced. The proposed method reduces the noise component by employing two methods. First, the radar signals received with multiple pulses are accumulated. As the number of additions increases, the noise component gradually decreases due to noise randomness. On the other hand, the signal term gradually increases and thus signal to noise ratio increases. Secondly, after converting the accumulated signal into the frequency spectrum, a Least Mean Square (LMS) filter is applied. In the case of the radar received signal, desired signal exists in a specific part and most of the rest is a noise. Therefore, if the LMS filter is applied in the time domain, the noise increases. To prevent this, the LMS filter is applied after converting the received signal into the entire frequency spectrum. The LMS filter output is then transformed into the time domain and then range estimation algorithm is performed. Simulation results show that the proposed scheme reduces the noise component by about 25 dB. The experiment was conducted by comparing the proposed results with the conventional results of the radars held by the Korea Aerospace Research Institute for the international space station.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3B
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• pp.259-267
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• 2009

Generalized Extreme Value (GEV) distribution is recommended for flood frequency and extreme rainfall distribution in many country. L-moment method is the most common estimation procedure for the GEV distribution. In this study, the relationships between the cross-site correlations between extreme events and the cross-correlation of estimators of L-moment ratios (L-moment Coefficient of Variation (L-CV) and L-moment Coefficient of Skewness (L-CS)) for data generated from GEV distribution were derived by Monte Carlo simulation. Those relationships were fit to the simple power function. In this Monte Carlo simulation, GEV+ distribution were employed wherein unrealistic negative values were excluded. The simple power models provide accurate description of the relationships between cross-correlation of data and cross-correlation of L-moment ratios. Estimated parameters and accuracies of the power functions were reported for different GEV distribution parameters combinations. Moreover, this study provided a description about regional regression approach using Generalized Least Square (GLS) regression method which require the cross-site correlation among L-moment estimators. The relationships derived in this study allow regional GLS regression analyses of both L-CV and L-CS estimators that correctly incorporate the cross-correlation among GEV L-moment estimators.

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.271-280
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• 2017

This study presents the application of satellite laser ranging (SLR) to orbit determination (OD) of high-Earth-orbit (HEO) satellites. Two HEO satellites are considered: the Quasi-Zenith Satellite-1 (QZS-1), a Japanese elliptical-inclinedgeosynchronous-orbit (EIGSO) satellite, and the Compass-G1, a Chinese geostationary-orbit (GEO) satellite. One week of normal point (NP) data were collected for each satellite to perform the OD based on the batch least-square process. Five SLR tracking stations successfully obtained 374 NPs for QZS-1 in eight days, whereas only two ground tracking stations could track Compass-G1, yielding 68 NPs in ten days. Two types of station bias estimation and a station data weighting strategy were utilized for the OD of QZS-1. The post-fit root-mean-square (RMS) residuals of the two week-long arcs were 11.98 cm and 10.77 cm when estimating the biases once in an arc (MBIAS). These residuals were decreased significantly to 2.40 cm and 3.60 cm by estimating the biases every pass (PBIAS). Then, the resultant OD precision was evaluated by the orbit overlap method, yielding three-dimensional errors of 55.013 m with MBIAS and 1.962 m with PBIAS for the overlap period of six days. For the OD of Compass-G1, no station weighting strategy was applied, and only MBIAS was utilized due to the lack of NPs. The post-fit RMS residuals of OD were 8.81 cm and 12.00 cm with 49 NPs and 47 NPs, respectively, and the corresponding threedimensional orbit overlap error for four days was 160.564 m. These results indicate that the amount of SLR tracking data is critical for obtaining precise OD of HEO satellites using SLR because additional parameters, such as station bias, are available for estimation with sufficient tracking data. Furthermore, the stand-alone SLR-based orbit solution is consistently attainable for HEO satellites if a target satellite is continuously trackable for a specific period.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.10-18
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• 2010

This paper proposes an image stabilization algorithm for close watching UAV(Unmanned Aerial Vehicle) using motion separation and stabilization mode. The motion of UAV is composed of its actual navigating motion and unwanted vibrating motion so that image sequences obtained from UAV are shaken randomly. In order to stabilize these images we separate the vibrating motion component from UAV motion and remove the effect caused by it from image sequences. In the proposed algorithm the motion and global intensity change of two consecutive images are modeled with 6 motion parameters and 2 intensity change parameters respectively. These modeled parameters are estimated by non-linear least square method based on Gauss-Newton algorithm. The vibrating motion component is separated from the estimated motion using IIR filtering and the geometric deformation caused by it is removed from image sequences. In order to apply the proposed method to real aerial image sequences with many abrupt changes of camera view, we proposed a stabilizing method using two different modes named as stabilizing and non-stabilizing mode. Experimental results show that the accuracy of motion estimation is 99% and the efficiency of removing the vibrating motion component is 90%. We apply the proposed method to real aerial image sequences and verified its stabilizing performance.