• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.1
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• pp.53-60
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• 2018

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.9-20
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• 2010

In order to visualize participating media in 3D space, they usually calculate the incoming radiance by subdividing the ray path into small subintervals, and accumulating their respective light energy due to direct illumination, scattering, absorption, and emission. Among these light phenomena, scattering behaves in very complicated manner in 3D space, often requiring a great deal of simulation efforts. To effectively simulate the light scattering effect, several approximation techniques have been proposed. Volume photon mapping takes a simple approach where the light scattering phenomenon is represented in volume photon map through a stochastic simulation, and the stored information is explored in the rendering stage. While effective, this method has a problem that the number of necessary photons increases very fast when a higher variance reduction is needed. In an attempt to resolve such problem, we propose a different approach for rendering particle-based volume data where kernel smoothing, one of several density estimation methods, is explored to represent and reconstruct the light in-scattering effect. The effectiveness of the presented technique is demonstrated with several examples of volume data.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of Distribution Science
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• v.12 no.2
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• pp.49-57
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• 2014

Purpose - Theorists and researchers in the area of customer satisfaction generally agree that product satisfaction intervenes between expectancy disconfirmation and various post-purchase cognitive states including attitude and behavioral intention. Studies in a variety of settings have supported the effect of expectation and its disconfirmation on satisfaction, but only a small number of studies address the cognitive consequences of satisfaction decisions and none report data on choice processes such as brand selection. This study examines the influence of satisfaction and its determinants on behavioral intention and product preference in eight coffee shops across the country. Generally it was found in both overall and summed attribute analyses that satisfaction was a function of expectation and disconfirmation, that intention was a function of satisfaction, and that preference was influenced by satisfaction and disconfirmation, the latter having the greater effect. Research design, data, and methodology - The main objective of this study was to assess the dimensions of consumer selection and satisfaction in choosing a coffee shop. In order to achieve this objective, a study of coffee shops across the country was conducted. This study comprised in-depth questionnaires distributed to coffee shop customers. A survey was conducted from September 1, 2011 to September 30, 2011, involving franchise coffee shop, independently owned coffee shop, and roastery coffee shop customers. Results - Hypothesis 1-1, which states that coffee shop choice attributes differ based on the type of coffee shop, is accepted. It has a significance level of 0.05, according to choosing properties of coffee shop by convenience of transportation, varieties of beans, residence of the owner (manager), information, and relationships. Hypothesis 1-2, which states that satisfaction with the choice factor differs depending on the type of coffee shops, is accepted. The P-values for cleanliness and varieties of beans were 0.04 and 0.00, respectively, and have a significance level of 0.05, according to the satisfaction with the chosen coffee shop. Hypothesis 2-1, which states that the importance of the choice attributes in coffee shop selections differs based on the demographic characteristics of the customers, is accepted. According to the t-test result, convenience of parking and residence of the owner (manager) are significant. Hypothesis 2-2, which states that satisfaction with the choice factor will differ depending of the type of coffee shop, is accepted. According to the t-test result, convenience of parking and residence of the owner (manager) are significant. Conclusions - This study has shown that intention to revisit a certain shop is most likely correlated to satisfaction in all cases. In order to offer subsequent developments for coffee shops, this study also identifies relations between customer satisfaction and selection by finding significant factors. In order to maximize customers' satisfaction, coffee shops should analyze and satisfy customers' needs and wants in terms of coffee service. While the findings do not generalize beyond the mall sampling procedure used here, we have hopefully identified a close approximation of the process of satisfaction decisions used by consumers generally.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.468-469
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• 2018

We design CNN(convolutional neural network) which is applicable to steering control system of autonomous vehicle. CNN has been widely used in many fields, especially in image classifications. But CNN has not been applied much to the regression problem such as function approximation. This is because the input of CNN has a multidimensional data structure such as image data, which makes it is not applicable to general control systems. Recently, autonomous vehicles have been actively studied, and many techniques are required to implement autonomous vehicles. For this purpose, many researches have been studied to detect the lane by using the image through the black box mounted on the vehicle, and to get the vanishing point according to the detected lane for control the autonomous vehicle. However, in detecting the vanishing point, it is difficult to detect the vanishing point with stability due to various factors such as the external environment of the image, disappearance of the instant lane and detection of the opposite lane. In this study, we apply CNN for steering control of an autonomous vehicle using a black box image of a car.

• Journal of Korean Society of Forest Science
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• v.79 no.1
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• pp.1-15
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• 1990

In this study the stem analysis program, which can be operated with personal computer was developed to reduce time and cost of calculation, and to increase accuracy of analysis. The stem analysis method used in this program was compared with other methods. The results obtained were as follows : The value, 1/100mm measured from the latest annual ring measurement machine (Jahrringme${\beta}$geraete Johan Type II) was automatically inputed to the computer and saved into given file name. Turbo Pascal program was written to do this. The measured data was analyzed by stem analysis calculation program written by Fortran-77. Volume and height increments were approximated by spline function, and diameter of the stem disk was calculated by quadratic mean method. The increment values calculated by the programs were printed annually and in every five-year. Stem analysis diagram and several increment graphs were also easily printed. The result compared between those analysis methods showed that quadratic mean could reduce the error caused from eccentric pith. When the stem taper curve method, approximated by spline function, was used in the calculation of tree height and volume, increments would be more exactly calculated.

• Journal of Navigation and Port Research
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• v.44 no.4
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• pp.275-282
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• 2020

Recently, the occurrence of a ship capsizing was analyzed as the main cause of the lack of stability or loss because of the improper management of the center of gravity, the movement of cargo or heavy weight when excessive steering occurs or when navigating during bad weather. Thus, to prevent a ship from capsizing, it is necessary to secure stability to enable the ship's return to its upright position, even if a dangerous heel occurs. The GM is a crucial evaluation factor regarding stability, which the navigation officer uses to preserve stability. In this study, based on the stability data collected from the operating of ships for five years, The GM by ship's type according to the operating status was analyzed specifically such as a ship's length, breadth, and gross tonnage. The feature of the GM distribution according to a ship's length was confirmed, and after performing the correlation analysis between the breadth and the GM, the ratio of the GM to breadth was calculated, and the result was compared with the previous ratio. Additionally, a simple approximation formula and minimum GM for the estimation of the GM by ship type were proposed by the regression analysis of the GM using the gross tonnage (GT)/breadth (B) to reflect the trend of larger ships being built. The results of this study are expected to be used as data for the review of securing a stable GM on ships.

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.133-144
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• 2003

Horizontal consolidation characteristics of Busan marine clay were investigated by computing coefficient of horizontal consolidation from Piezocone data and comparing their results with those of standard consolidation test. It is well known that current prediction models of $c_h$ for high plastic soils have large uncertainties, and show a great difference between the predicted and the measured values. However, the spherical models and expanding cavity theory of Torstensson(1977), and Burns & Mayne(1998) based on modified Cam-Clay model with critical limit state concepts have relative reliability in estimating $c_h$ and good applicability in highly plasticity soils. In this paper, a normalization technique was used to evaluate $c_h$ using the Burns and Mayne's method based on the dissipation test, and their normalized consolidation curves give 0.015 of time factor($T_{50}$) when 50% degree of consolidation is completed. Comparison study using Piezocone data obtained at other similar ground site shows 1.5 times less systematicality than that of standard consolidation test, which indicates considerable approximation with the measured values because standard consolidation test gives the difference of three to few times compared with the measured values. In addition, design chart for estimating $c_h$ based on the chart from Robertson et al.(1992) and using the other method of the direct prediction from the of dissipation test was newly proposed. It is judged that new proposed chart is very applicable to Korean marine soils, especially in very high plastic soils.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.31-39
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• 2011

In this paper, we propose a signal readout system with small area and low power consumption for CNT sensor arrays. The proposed system consists of signal readout circuitry, a digital controller, and UART I/O. The key components of the signal readout circuitry are 64 transimpedance amplifiers (TIA) and SAR-ADC with 11-bit resolution. The TIA adopts an active input current mirror (AICM) for voltage biasing and current amplification of a sensor. The proposed architecture can reduce area and power without sampling rate degradation because the 64 TIAs share a variable gain amplifier (VGA) which needs large area and high power due to resistive feedback. In addition, the SAR-ADC is designed for low power with modified algorithm where the operation of the lower bits can be skipped according to an input voltage level. The operation of ADC is controlled by a digital controller based on UART protocol. The data of ADC can be monitored on a computer terminal. The signal readout circuitry was designed with 0.13${\mu}m$ CMOS technology. It occupies the area of 0.173 $mm^2$ and consumes 77.06${\mu}W$ at the conversion rate of 640 samples/s. According to measurement, the linearity error is under 5.3% in the input sensing current range of 10nA - 10${\mu}A$. The UART I/O and the digital controller were designed with 0.18${\mu}m$ CMOS technology and their area is 0.251 $mm^2$.