• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.25-32
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• 1998

This paper is based on the design and analysis computing model of oxygen saturation with the pulse oximeter using the integral ratio of pulsating components. In our proposed algorithm. we modeled the transmitted optical signal in fingertip or earlobe to DC component $A_{dc}$ pulsating component $A_a\;Sinwt$, noise component $A_{noise}$ and etc.. To separate the pulsating components and DC components efficiently, we defined the signal average to DC components. Also we presented the way to eliminate the noise using integral ratio. To acquire a linearity of correlation graph for pulsating components ratios and non invasive oxygen saturation. we intensively observed on the oxygen saturations in the range of 75-100% in consideration of the error range of simulator. Also, for real time processing we experimented on changing the period of area calculating cycle from 1 to 6. The functional evaluation of the algorithm is compared with the method using the amplitude ratio of pulsating components frequently seen with pulse oximeter. The result was that our algorithm with 4 cycles of area calculating cycle which considered to be best fit by 1% to the existing method. Moreover r , the decision coefficient showing the correlation of regression graph with real data, proved better result of 0.985 than 0.970.

• Journal of Korean Society of Forest Science
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• v.68 no.1
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• pp.11-17
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• 1985

Height growth curve to be required for estimating site index was formulated with 10 models based on the average tree height and tree age. Among them, the model of $H=K-ab^A$ was evaluated to be best fit for estimating average tree height(H) with tree age (A). Equations, $H=28.364-26.125(0.818)^A$ and $H=26.331-25.125(0.886)^A$, were situated from the model for estimating average tree height of Larix leptolepis and Pinus koraiensis, respectively (in this case the tree age was categorized into 0 for 5 -year- old tree, 1 for 10 -year- old tree and 2 for 15 -year- old tree ect.). Result of comparing the site indices calculated by the Bryant method, it was proved that the site index of Larix leptolepis was estimated higher than that of Pinus koraiensis within the limits of site index class 6 to 18. On the contrary the site index of Pinus koraiensis turned out to surpass that of Larix leptolepis at the site index class 20 or over.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1797-1807
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• 2013

Dams always have the possibility of failure due to unexpected natural phenomena. In particular, dam failure can cause huge damage including damage for humans and properties when dam downstream regions are densely populated or have important national facilities. Although many studies have been conducted on the analysis of flood waves about single dam failure thus far, studies on the analysis of flood waves about the sequential failure of dams are lacking. Therefore, the purpose of this study was to calculate the peak discharge of sequential failure of dams through flood wave analysis of sequential failure of dams and this analysis techniques to predict flood wave propagation situation in downstream regions. To this end, failure flood wave analysis were conducted for Lawn Lake Dam which is a case of sequential failure of dams among actual failure cases using DAMBRK to test the suitability of the dam failure flood wave analysis model. Based on the results, flood wave analysis of sequential failure of dams were conducted for A dam in Korea assuming a virtual extreme flood to predict flood wave propagation situations and 2-dimensional flood wave analysis were conducted for major flooding points. Then, the 1, 2-dimensional flood wave analysis were compared and analyzed. The results showed goodness-of-fit values exceeding 90% and thus the accuracy of the 1-dimensional sequential failure of dams simulation could be identified. The results of this study are considered to be able to contribute to the provision of basic data for the establishment of disaster prevention measures for rivers related to sequential failure of dams.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2349-2359
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• 2013

The purpose of this study was to predict the factors which are related to the intention and behaviour for breast self-examination (BSE) of middle aged women using the theory of planned behavior (TPB). A survey using a structured questionnaire was conducted wih 217 middle aged women. BSE behaviour was assessed at 1-month follow-up. The overall fit of the structural model to the date was acceptable(${\chi}^2$=1246.6(p<.001), ${\chi}^2$/df=2.72, CFI=.831, TLI=.817, RMSEA=.089). The BSE behavior rate within one month was 56.2%. The TPB explained 43.9% of the variance in BSE intentions and 10.9% of the variance in BSE behavior. The subjective norm(${\beta}$=.364, p<.001) and the perceived behavioral control(${\beta}$=.553, p<.001) both positively influenced the behavioral intention, and the behavioral intention(${\beta}$=.768, p<.01) positively influenced the behavior. This study shows the model's applicability in explaining BSE behavior of middle aged women, and suggests that health intervention programs should focus on strengthening the intention for the promotion of BSE behavior.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.9-18
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• 2015

Software Reliability Growth Models (SRGMs) are useful for determining the software release date or additional testing efforts by using software failure data. It is not appropriate for a SRGM to apply to all software. And besides a large number of SRGMs have already been proposed to estimate software reliability measures. Therefore selection of an optimal SRGM for use in a particular case has been an important issue. The existing methods for selecting a SRGM use the entire collected failure data. However, initial failure data may not affect the future failure occurrence and, in some cases, it results in the distorted result when evaluating the future failure. In this paper, we suggest a method for selecting a SRGM based on the evaluation goodness-of-fit using partial data. Our approach uses partial data except for inordinately unstable failure data in the entire failure data. We will find a portion of data used to select a SRGM through the comparison between the entire failure data and the partial failure data excluded the initial failure data with respect to the predictive ability of future failures. To justify our approach this paper shows that the predictive ability of future failures using partial data is more accurate than using the entire failure data with the real collected failure data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.364-372
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• 2016

This study examined the multi-phrased effects and outcomes of adult female learners who participated in lifelong learning activities, as well as the proposed structural relationships among the five latent variables. Questions established to achieve the purpose of the study are as follow: What effects do the learner's characteristics, lifelong education institutions, learning flow, and learning satisfaction have on the learning come? The participants of the survey numbered 632, but 54 respondents who were unreliable or did not complete their survey were excluded. A total of 578 cases were analyzed for this research. The structural relationships among the five latent variables-learner's characteristics, lifelong education institutions, learning flow and learning satisfaction, and learning outcome of the adult female learners-AMOS 18.0 program were also used for structural analysis. The major findings of this research are as follows. First, the model fitness showed that the hypothetical model provided a reasonable fit to the data ${\chi}^2=224.267$ (df=69, p<.001), RMSEA=.062, TLI=.943, RFI=.920, CFI=.957, IFI=.957, NFI=.939. Second, the learner's characteristics ( =.218, p<.001) and lifelong education institutions ( =.301, p<.001) have a direct effect on the learning outcomes. The learning flow ( =-.149 p=.541) does not have a direct effect on the learning outcome. Learning satisfaction ( =.405 p<.001) have a direct effect on the learning outcome. To put findings above together, in respect to adult female learners' performances, the learning outcomes are influenced directly by the learner characteristics, conditions of the lifelong education institutions, and learning satisfaction, whereas satisfaction indirectly affects the learners' learning outcome.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.23-34
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• 2009

This paper is described as an experimental analysis for the probabilistic directional design hour volume estimation. The main objective of this paper is to derive acceptable design rankings, PK factors, and PD factors. In order to determine an appropriate distribution for acceptable design rankings, 12 probability distribution functions were employed. The parameters were estimated based on the method of maximum likelihood. The goodness of fit test was performed with a Kolmogorov-Smirnov test. The Beta General distribution among the probability distributions was selected as an appropriate model for 2 lane roadways. On the other hand, the Weibull distribution is superior for 4 lanes. The method of the inverse cumulative distribution function came up with an acceptable design ranking of design for LOS D. An acceptable design ranking of 2 lanes is 190, while an acceptable design ranking for 4 lanes is 164. The PK factor and PD factor of 2 lanes was elicited for 0.119 (0.100-0.139) and 0.568 (0.545-0.590), respectively. On the other hand, the PK factor and PD factor for 4 lanes was elicited as 0.106 (0.097-0.114) and 0.571 (0.544-0.598), respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.40-49
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• 2013

Current service system of the Korea Meteorological Administration (KMA) for blooming date forecasting in spring depends on regression equations derived from long term observations in both temperature and phenology at a given station. This regression based system does not allow a timely correction or update of forecasts that are highly sensitive to fluctuating weather conditions. Furthermore, the system cannot afford plant responses to climate extremes which were not observed before. Most of all, this method may not be applicable to locations other than that which the regression equations were derived from. This note suggests a way to replace the location restricted regression equations with a thermal time based phenology model to complement the KMA blooming forecast system. Necessary parameters such as reference temperature, chilling requirement and heating requirement were derived from phenology data for forsythia, azaleas and Japanese cherry at 29 KMA stations for the 1951-1980 period to optimize spring phenology prediction model for each species. Best fit models for each species were used to predict blooming dates and the results were compared with the observed dates to produce a correction grid across the whole nation. The models were driven by the KMA's daily temperature data at a 5km grid spacing and subsequently adjusted by the correction grid to produce the blooming date maps. Validation with the 1971-2012 period data showed the RMSE of 2-3 days for Japanese cherry, showing a feasibility of operational service; whereas higher RMSE values were observed with forsythia and azaleas.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of The Korean Society of Grassland and Forage Science
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• v.8 no.2
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• pp.110-116
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• 1988

The response of forage yield was studied with various growth indices to develop yield model and to determine optimum cutting time in three cultivars of orchardgrass. 1. Number of tiller per plant was the highest at 3rd cutting stage. But, it was decreased rapidly at 4th cutting stage. Leaf Area Index (LAI) was the highest at 3rd cutting stage. LAI was increased slowly during 15 days to 20 days after cutting and thereafter increased rapidly. 2. In dry matter yield over cutting stages, 1st cutting and 3rd cutting stages were higher yield than others. Change of dry matter yield was similar to that of LAI in all cutting stages. 3. Leaf Elongation Rate (LER) and Specific Leaf Weight (SLW) were reached to maximum at 20 to 25 days and 25 to 30 days after cutting, respectively. 4. Dry matte yield was highly correlated with LAI (r-0.905)and with CGR (r-0.962) over three cultivars. Also, LAI was significantly with LER. The best-fit yield model was obtained in multiple regression equation which included both dependent variables of LAI and CGR. 5. Optimum cutting times which were determined by the relationships between D.M. yield and LAI, and between D.M. yield and CGR, were ranged from 32 days to 36 days depend on each cutting stages.