• Korean Journal of Ichthyology
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• v.18 no.1
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• pp.45-54
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• 2006

Age and growth of the small yellow croaker, Larimichthys polyactis were estimated using right sagittal otoliths of 506 fish specimens from March to December, 2002 and from January to February, 2005 in the South Sea, part of the East China Sea of Korea. Examination of outer margins of the otolith showed that the opaque zone was formed once a year. Marginal increment of the otolith formed annual rings from May and June at the beginning of spawning season. In the relationship between total length and body weight, a multiplicative error structure was assumed because variability in growth increased as a function of the length, and the estimated equation was $BW=0.0044TL^{3.2502}$ ($R^2=0.97$). The relative growth as body weight at total length has significant difference between females and males (P<0.05). For describing growth of the small yellow croaker, Larimichthys polyactis a von Bertalanffy growth model was adopted. The von Bertalanffy growth curve had an additive error structure and the growth parameters estimated from non-linear regression were $L_{\infty}=33.88cm$, K=0.20/year and $t_0=-2.39year$. Growth at age of males and females shows no significant difference (P>0.05). Most examined fish were 1, 2 and 3 years old, although the oldest fish were 7 old for males and 8 for females.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.171-183
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• 2016

Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with $5.33^{\circ}C$. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with $2.34^{\circ}C$ and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.524-530
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• 2002

This note suggests a stepwise fuzzy moving sliding surface using Sugeno-type fuzzy system and presents a sliding mode control scheme using it. The fuzzy system has the angle of state error vector and the distance from the origin in the phase plane as inputs and a first-order linear differential equation as output. The surface initially passes arbitrary initial states and subsequently moves towards a predetermined surface via rotating or shifting. This method reduces the reaching and tracking time and improves robustness. Conceptually the slope of the Proposed fuzzy moving sliding surface increases stepwise in the stable region of the phase plane. The surface, however, rotates continuously because the surface is a fuzzy system. The asymptotic stability of the fuzzy sliding surface is proved. The validity of the proposed control scheme is shown in computer simulation for a second-order nonlinear system.

• Journal of Radiation Protection and Research
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• v.32 no.4
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• pp.168-177
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• 2007

A neural network model to evaluate the neutron exposure on the reactor pressure vessel inner diameter was developed. By using the three dimensional synthesis method described in Regulatory Guide 1.190, a simple linear equation to calculate the neutron spectrum on the reactor pressure vessel was constructed. This model can be used in a quick estimation of fast neutron flux which is the most important parameter in the assessment of embrittlement of reactor pressure vessel. This model also used in the selection of an optimum core loading pattern without the neutron transport calculation. The maximum relative error of this model was less than 3.4% compared to the transport calculation for the calculations from cycle 1 to cycle 23 of Kori unit 1.

• Journal of Advanced Research in Ocean Engineering
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• v.2 no.1
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• pp.1-11
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• 2016

The objective of this paper is to present an analytical solution in deep water waves and verify the validity of the theory (Shin, 2015). Hence this is a follow-up to Shin (2015). Instead of a variational approach, another approach was considered for a more accurate assessment in this study. The products of two coefficients were not neglected in this study. The two wave profiles from the KFSBC and DFSBC were evaluated at N discrete points on the free-surface, and the combination coefficients were determined for when the two curves pass the discrete points. Thus, the solution satisfies the differential equation (DE), bottom boundary condition (BBC), and the kinematic free surface boundary condition (KFSBC) exactly. The error in the dynamic free surface boundary condition (DFSBC) is less than 0.003%. The wave theory was simplified based on the assumption tanh $D{\approx}1$ in this paper. Unlike the perturbation method, the results are possible for steep waves and can be calculated without iteration. The result is very simple compared to the 5th Stokes' theory. Stokes' breaking-wave criterion has been checked in this study.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.10
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• pp.1128-1136
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• 2012

In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1997.12a
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• pp.255-262
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• 1997

A low-cost and precise metering device, which is suitable to automatic mixing of nutrient-solution for hydroponic culture, was developed for small-scale growers. The metering device was composed of three parts those were supply pumps, metering cylinders and venturi tube. Those parts were controlled by personal computer with time-based odoff control method. To verify the performance of the developed metering device, the relationship between operating time and discharge was examined and the accuracy of the developed metering device was compared with commercial metering pumps. The results of this study are as follows. 1. The correlation coefficient between the flow rate and operating time was 0.9999, and the linear regression equation computed was y=21.759x, where y is the discharge(g) and x is the operating time(s). 2. The developed device has greater accuracy than commercial metering pumps in terms of the full-scale error. Calculated errors for the developed metering device and two commercial pump were $\pm$0.3 %, $\pm$2.45 % and $\pm$1.38 % respectively. 3. Above results show developed metering device is a useful tool for nutrient-solution control system.

• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.370-375
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• 2006

The purpose of this work is to predict the systolic blood pressure (BP) during exercise from pulse transit time (PTT) for warning of possible danger. PTT was calculated as the time between R-peak of ECG and the peak of differential photoplethysmograph (PPG). For the PTT-BP model, we used regress equations from previous studies and 3 kinds of new models combining linear and nonlinear regress equation. The model parameters were estimated with the data measured under low to middle intensity exercise, and then was tested with the data measured under high intensity exercise. Predicted BP values after high intensity exercise were compared with those measured by cuff-type sphygmomanometer. The results showed that the error between measured and predicted values were acceptable for the monitoring BP. We tested PTT-BP models 1 month after the identification without further calibration. Models could predict the BP and the errors between measured and predicted BP were about 5mmHg. The suggested system is expected to be helpful in recognizing any danger during exercise.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.