• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1262-1262
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• 2001

The rice plant is one of the important staple crops in Korea. The high yield with low cost in rice is required the soil fertility and the development of new precise method of fertilizer application by nutritional diagnosis. Now, in Korea, the nitrogen application system for the rice plant is composed of the basal fertilization, fertilization at tillering stage and fertilization at panicle stage, which the nitrogen fertilization at panicle stage amount to about 30 percent in the total amount. Thus, this experiment carried out to the development of the system that can measure the nitrogen content in the rice plant at panicle stage rapidly with the near infrared spectroscopy, and to predict the appropriate quantity of the nitrogen fertilization at panicle stage based on calibration model for test of nitrogen content in rice plant. The samples were collected from 48 varieties in 4 regions which are mainly cultivated in the southern part of Korea. And then, it collected by classifying into the leaf, the whole plant and the stem since 7 days before the nitrogen fertilization at panicle stage. The ranges of the nitrogen contents were 1.6∼4.0%, 1.7∼3.0% and 1.4∼2.7% in the leaf, the whole plant and the stem, respectively. In the calibration models created by each part of the plant under the Multiple Linear Regression(MLR) method, the calibration model for the leaf recorded the relatively high accuracy. The mutual crossing test on unknown samples were carried out using Partial Least Square(PLS) calibration model. That is, the nitrogen content in the stem was tested by calibration model made by the leaf model and that of stem was tested by calibration model made by whole plant sample. When unknown leaf sample was tested by calibration model made by all sample that collected from each part in rice plant such as leaf, stem and whole plant, it recorded the highest accuracy. As a result, to test the nitrogen content in the rice plant at panicle stage, the nitrogen content in the leaf shall be tested by the calibration model composed of the leaf, the stem and the whole plant. In future, to estimated the amount of nitrogen fertilization at panicle stage for rice plant , it will be calculated based on regression model between rice yield and nitrogen content of leaf measured by calibration model made by mixed sample including leaf, stem and whole plant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7403-7415
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• 2014

This study points out a problem that the vehicle rotation area provided in a dead-end parking lot for apartment blocks is misused as unreasonable parking places but accordingly, the edge parking spaces are rarely used for parking. Therefore, this study aims to establish a parking design standard to improve the parking convenience and land-use efficiency by investigating the real parking behaviors and problems identified in the study area, multiple apartment blocks in Haeundae-gu, Busan. This study calculated two simple linear regression models for two mutually exclusive factors, such as the parking convenience and land-use efficiency, respectively, and specified a trade-off point that optimizes both factors. The study results found that parking convenience and land-use efficiency can be improved by not only changing the misused vehicle rotation area to normal parking spaces depending on the usage pattern, but also by increasing the width of the edge parking spaces from 2.3m to 2.6m. Finally, this study suggests two parking design cases for more realistic design applications by considering the parking environment in the dead-end parking lot for apartment blocks.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.3
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• pp.434-442
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• 2008

The objective of this study was to determine the optimum ratio of methionine to methionine plus cystine for growing pigs. A nitrogen balance trial was conducted using a total of 21 barrows (Large WhiteLandrace) over two replicates. The initial body weight was $20.36{\pm}1.22kg$ (mean${\pm}$SD) in the first replicate and $23.54{\pm}1.02kg$ (mean${\pm}$SD) in the second. For each replicate, the 21 pigs were randomly assigned to one of seven dietary treatments with three observations per treatment. The diets included a methionine and cystine-deficient basal diet with all other essential nutrients meeting nutrient requirements and six diets formulated with graded levels of DL-methionine (0.00, 0.03, 0.06, 0.10, 0.13, 0.16%) and $L-Cystine{\cdot}HCl{\cdot}H_2O$ (0.19, 0.15, 0.11, 0.07, 0.04, 0.00%). This resulted in ratios of methionine to methionine plus cystine of 41.3, 29.6, 35.3, 41.2, 46.0, 51.6 and 57.5%. Each experimental period lasted 12 days consisting of a seven-day adaptation period followed by a five-day total collection of urine and feces. During the collection period, pigs were fed 900 g/day for the first replicate and 1,200 g/day for the second replicate. The feed was provided in three equal portions at 0800, 1500, and 2200 h daily. Pigs had ad libitum access to water after feeding. There was a linear (p<0.01) and quadratic (p<0.01) effect on daily gain and feed conversion as the ratio of methionine to methionine plus cystine increased. Pigs receiving the diets providing a methionine to methionine plus cystine ratio of 51.6% had the best daily gain and feed conversion. Plasma urea nitrogen was also lowest for this treatment. Nitrogen retention increased (p<0.01) as the relative proportion of methionine increased up to 51.6% and then a downward trend occurred at 57.5%. The quadratic regression model, as well as one- and two- slope regression line models, were used to determine the optimum ratio of methionine to methionine plus cystine. Eliminating the 35.3% methionine to methionine plus cystine treatment resulted in $R^2$ values in excess of 0.92. The optimal ratio of methionine to methionine plus cystine was estimated to be 54.15% for nitrogen retention and 56.72% for plasma urea nitrogen.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.809-825
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• 2017

The number of TV entertainment shows is increasing. Competition among programs in the entertainment market is intensifying since cable channels air many entertainment TV shows. There is now a need for research on program ratings and the number of episodes. This study presents predictive models for entertainment TV show ratings and number of episodes. We use various data mining techniques such as linear regression, logistic regression, LASSO, random forests, gradient boosting, and support vector machine. The analysis results show that the average program ratings before the first broadcast is affected by broadcasting company, average ratings of the previous season, starting year and number of articles. The average program ratings after the first broadcast is influenced by the rating of the first broadcast, broadcasting company and program type. We also found that the predicted average ratings, starting year, type and broadcasting company are important variables in predicting of the number of episodes.

• Korean Journal of Social Welfare
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• v.66 no.1
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• pp.251-276
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• 2014

This study scrutinizes the common sense in the field of disability employment that the bigger the size of a firm, the lower the employment rate of people with disabilities. This common sense has been established by conventional cross-tabulation and multiple regression analyses without taking into account possible interactions between the sizes of firms and the industries in which they operate. This study shows that the distribution of the disability employment rate violates the linearity and homoscedasticity assumptions of the OLS. In an effort to find models that explain the data better, this study fits the OLS model, the weighted linear regression model, and the multinomial logit model as well as the path analysis which is meant to examine the relationships between firm size and other variables relevant to disability employment. The result shows that, when an interaction term between firm size and industry is added to the model, firm size does not have any significant effect on disability employment rate for those firms with 100 or more regular employees, to the contrary of the findings of prior studies. It also demonstrates that other factors such as job setting, the extent of helpfulness of disability employment employers perceive, employers' care for disability, and employers' awareness of disability policies may matter more than does firm size. This study proposes that future research and policy implementation for disability employment should pay no less attention to industry and other factors mentioned above than to firm size.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.85-100
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• 2022

Purpose: This study is aimed to examine the association between initial enteral nutrition (EN) and the clinical prognosis among patients with severe and multiple traumatic injuries, and to investigate whether this association is modified by the patients' catabolic status. Methods: This was a retrospective study of 302 adult patients with severe and multiple traumatic injuries admitted between January 2017 and September 2020 at Ajou University hospital in Suwon, Korea. The initial nutritional support by EN and parenteral nutrition were monitored up to day 7 after admission. Patients were classified into "No", "Low", and "High" EN groups according to the level of initial EN. Multivariable-adjusted logistic regression and linear regression models were used to estimate the association of the initial EN levels at hospital admission with the risk of mortality, morbidities, and levels of nutrition-associated biochemical markers. Results: High EN support was associated with reduced mortality (odds ratio, 0.07; 95% confidence interval [CI], 0.02, 0.32) and lower levels of C-reactive protein (β, -0.22; 95% CI, -8.66, 1.48), but longer stay in the intensive care unit (β, 0.19; 95% CI, 1.82, 11.32). In analyses stratified by catabolic status, there were fewer incidences of hospital-acquired infections with increasing EN levels in the moderate or higher nitrogen balance group than in the mild nitrogen balance group. Conclusion: Our observation of the inverse association between levels of initial EN administration with mortality risk and inflammatory markers may indicate the possible benefits of active EN administration to the recovery process of severe and multiple trauma patients. Further studies are warranted on whether the catabolic status modifies the association between the initial EN and prognosis.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.

• Safety and Health at Work
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• v.4 no.1
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• pp.27-36
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• 2013

Objectives: Mental health has been shown to be linked with certain underlying physiological mechanisms. The objective of this cross sectional study was to investigate the relationship between depressive symptoms and brachial artery reactivity (BAR) in an understudied population: police officers. Methods: Participants were 351 police officers who were clinically examined in the Buffalo Cardio-Metabolic Police Stress (BCOPS) study. BAR was performed using standard B-Mode ultrasound procedures. Depressive symptoms were measured using the Center for Epidemiological Studies Depression (CES-D) scale. Mean values of the difference between the baseline and maximum diameters of the brachial artery were determined across three categories of CES-D score using the analysis of variance and the analysis of covariance. p-values for linear trends were obtained from linear regression models. Results: The mean age (${\pm}$ standard deviation) of all officers was $40.9{\pm}7.2$ years. Women had a slightly higher mean CES-D score than men ($8.9{\pm}8.9$ vs. $7.4{\pm}6.4$) and a slightly higher percentage increase of BAR than men (6.90 vs. 5.26%). Smoking status significantly modified the associations between depressive symptoms and BAR. Among current smokers, mean absolute values of BAR significantly decreased as depressive symptoms increased after adjustment for age, gender, race/ethnicity, hypertension, and diabetes; the multivariate-adjusted p-values were 0.033 (absolute) and 0.040 (%). Associations between depressive symptoms and BAR were not statistically significant among former smokers or never smokers. Conclusion: Depressive symptoms were inversely associated with BAR among police officers who were current smokers and together may be considered a risk factor for cardiovascular disease among police officers. Further prospective research is warranted.