• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.9
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• pp.673-684
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• 2003

A degenerated differential pair has been widely used as a standard topology for digitally programmable CMOS VGAs. A variable degeneration resistor has been implemented using a resistor string or R-2R ladder with MOSFET switches. However, in the VGAs using these conventional methods, low-voltage and high-speed operation is very hard to achieve due to the dc voltage drop over the degeneration resistor. To overcome this problem a new variable degeneration resistor is proposed where the dc voltage drop is almost removed. Using the proposed gain control scheme, a low-voltage and high-speed CMOS VGA is designed. HSPICE simulation results using a 0.25${\mu}{\textrm}{m}$ CMOS process parameters show that the designed VGA provides a 3dB bandwidth of 360MHz and a 80dB gain control range in 2dB step. Gain errors are less than 0.4dB at 200MHz and less than l.4dB at 300MHz. The designed circuit consumes 10.8mA from a 2.5V supply and its die area is 1190${\mu}{\textrm}{m}$${\times}$360${\mu}{\textrm}{m}$.

• Korean Journal of Environmental Agriculture
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• v.31 no.3
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• pp.205-211
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• 2012

BACKGROUND: Generally, nitrogen (N) fertilization higher than the recommended dose is applied during vegetable cultivation to increase productivity. But higher N fertilization also increases the concentrations of nitrate ions and nitrous oxide in soil. In this experiment, the impact of N fertilization was studied on nitrous oxide ($N_2O$) emission to standardize the optimum fertilization level for minimizing $N_2O$ emission as well as increasing crop productivity. Herein, we developed $N_2O$ emission inventory for upland soil region during red pepper and Chinese milk vetch cultivation. METHODS AND RESULTS: Nitrogen fertilizers were applied at different rates to study their effect on $N_2O$ emission during red pepper and Chinese milk vetch cultivation. The gas samples were collected by static closed chamber method and $N_2O$ concentration was measured by gas chromatography. The total $N_2O$ flux was steadily increased due to increasing N fertilization level, though the overall pattern of $N_2O$ emission dynamics was same. Application of N fertilization higher than the recommended dose increased the values of both seasonal $N_2O$ flux (94.5% for Chinese cabbage and 30.7% for red pepper) and $N_2O$ emission per unit crop yield (77.9% for Chinese cabbage and 23.2% for red pepper). Nitrous oxide inventory revealed that the $N_2O$ emission due to unit amount of N application from short-duration vegetable field in fall (autumn) season (6.36 kg/ha) was almost 70% higher than that during summer season. CONCLUSION: Application of excess N-fertilizers increased seasonal $N_2O$ flux especially the $N_2O$ flux per unit yield during both Chinese cabbage and red pepper cultivation. This suggested that the higher N fertilization than the recommended dose actually facilitates $N_2O$ emission than boosting plant productivity. The $N_2O$ inventory for upland farming in temperate region like Korea revealed that $N_2O$ flux due to unit amount of N-fertilizer application for Chinese cabbage in fall (autumn) season was comparatively higher than that of summer vegetables like red pepper. Therefore, the judicious N fertilization following recommended dose is required to suppress $N_2O$ emission with high vegetable productivity in upland soils.

• Journal of Korean society of Dental Hygiene
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• v.11 no.5
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• pp.811-822
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• 2011

Objectives : The purpose of this study was to examine the relationship of the self-efficacy of dental hygiene students to their stressors and ways of stress-adaptation patterns. Methods : The subjects in this study were dental hygiene juniors in four selected colleges located in the metropolitan area. Results : 1. They got a mean of 3.22 in self-efficacy. They gave the highest mark(3.50) to an item 'I can attain it if I set a primary goal.' 2. They got a mean of 3.18 in stressors. Among the stressors, task assignments(3.74) were identified as the greatest stressor, followed by the curriculum(3.25), learning environments(3.16), prospects of employment (3.07) and test anxiety(2.95). 3. They got a mean of 2.02 in stress-adaptation method. They got 2.31 and 1.72 in long-term and short-term adaptation respectively, which showed that long-term stress adaptation method were more prevailing than short-term ones. 4. As a result of analyzing whether there would be any gaps in self-efficacy according to general characteristics, statistically significant gaps were found in that regard according to experience of preparing for college admission after leaving high school, academic standing, satisfaction with the department of dental hygiene and prospects of employment(p<.05). 5. As a result of checking the relationship of their self-efficacy to their stressors and ways of stress adaptation method, there were statistically significant differences in that aspect according to examinations and prospect of employment(p<0.05). As for ways of stress adaptation method, there were statistically significant differences in long-term adaptation method (p<0.05). Conclusions : As it's found that the level of the self-efficacy of the dental hygiene students was linked to the efficiency of their ways of stress adaptation method, the development and implementation of programs geared toward boosting the self-efficacy of dental hygiene students are required to teach them to successfully cope with various kinds of stress that they are likely to face after getting a job.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.25-39
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• 2017

Over 5 million people participate in cycle racing betting and its revenue is more than 2 trillion won. This study predicts the ranking of cycle racing using various statistical analyses and identifies important variables which have influence on ranking. We propose competitive ranking prediction models using various classification and regression methods. Our model can predict rankings with low misclassification rates most of the time. We found that the ranking increases as the grade of a racer decreases and as overall scores increase. Inversely, we can observe that the ranking decreases when the grade of a racer increases, race number four is given, and the ranking of the last race of a racer decreases. We also found that prediction accuracy can be improved when we use centered data per race instead of raw data. However, the real profit from the future data was not high when we applied our prediction model because our model can predict only low-return events well.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Animal Bioscience
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• v.34 no.2
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• pp.223-232
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• 2021

Objective: To investigate the improvement in utilization efficiency of total mixed ration (TMR) on Tibetan plateau, TMR were ensiled with different additives. Methods: A total of 150 experimental silos were prepared in a completely randomized design to evaluate the six treatments: i) control (without additive), ii) Lactobacillus buchneri (L. buchneri), iii) acetic acid, iv) propionic acid, v) 1,2-propanediol; and vi) 1-propanol. After 90 days of ensiling, silos were opened for fermentation quality and in vitro analysis, and then subjected to an aerobic stability test for 14 days. Results: Treating with L. buchneri, acetic acid, 1,2-propanediol and 1-propanol decreased propionic acid contents and yeast number, whereas increased (p<0.05) pH, acetic acid and ethanol contents in the fermented TMR. Despite increased dry matter (DM) loss in the TMRs treated with 1,2-propanediol and 1-pronanol, additives did not affect (p>0.05) all in vitro parameters including gas production at 24 h (GP24), GP rate constant, potential GP, in vitro DM digestibility and in vitro neutral detergent fibre digestibility. All additives improved the aerobic stability of ensiled TMR to different extents. Specially, aerobic stability of the ensiled TMR were substantially improved by L. buchneri, acetic acid, 1,2-propanediol, and 1-propanol, indicated by stable pH and lactic acid content during the aerobic stability test. Conclusion: L. buchneri, acetic acid, 1,2-propanediol, and 1-propanol had no adverse effect on in vitro digestibility, while ensiling TMR with the additives produced more acetic acid and ethanol, subsequently resulting in improvement of aerobic stability. There is a potential for some fermentation boosting additives to enhance aerobic stability of fermented TMR on Tibetan plateau.

• Journal of Veterinary Science
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• v.24 no.6
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• pp.83.1-83.12
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• 2023

Background: Ellipticine (Ellip.) was recently reported to have beneficial effects on the differentiation of adipose-derived stem cells into mature chondrocyte-like cells. On the other hand, no practical results have been derived from the transplantation of bone marrow stem cells (BMSCs) in a rabbit osteoarthritis (OA) model. Objectives: This study examined whether autologous BMSCs incubated with ellipticine (Ellip.+BMSCs) could regenerate articular cartilage in rabbit OA, a model similar to degenerative arthritis in human beings. Methods: A portion of rabbit articular cartilage was surgically removed, and Ellip.+BMSCs were transplanted into the lesion area. After two and four weeks of treatment, the serum levels of proinflammatory cytokines, i.e., tumor necrosis factor α (TNF-α) and prostaglandin E2 (PGE2), were analyzed, while macroscopic and micro-computed tomography (CT) evaluations were conducted to determine the intensity of cartilage degeneration. Furthermore, immuno-blotting was performed to evaluate the mitogen-activated protein kinases, PI3K/Akt, and nuclear factor-κB (NF-κB) signaling in rabbit OA models. Histological staining was used to confirm the change in the pattern of collagen and proteoglycan in the articular cartilage matrix. Results: The transplantation of Ellip.+BMSCs elicited a chondroprotective effect by reducing the inflammatory factors (TNF-α, PGE2) in a time-dependent manner. Macroscopic observations, micro-CT, and histological staining revealed articular cartilage regeneration with the downregulation of matrix-metallo proteinases (MMPs), preventing articular cartilage degradation. Furthermore, histological observations confirmed a significant boost in the production of chondrocytes, collagen, and proteoglycan compared to the control group. Western blotting data revealed the downregulation of the p38, PI3K-Akt, and NF-κB inflammatory pathways to attenuate inflammation. Conclusions: The transplantation of Ellip.+BMSCs normalized the OA condition by boosting the recovery of degenerated articular cartilage and inhibiting the catabolic signaling pathway.

• Korean Journal of Radiology
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• v.24 no.6
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• pp.498-511
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• 2023

Objective: To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. Materials and Methods: This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Results: Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. Conclusion: CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Pediatric Infection and Vaccine
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• v.4 no.1
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• pp.116-125
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• 1997

Purpose : Studies on the duration of immune response against Japanese encephalitis virus from recipients with JE vaccine (Nakayama-NIH strain) in Korea. Methods : To determinate the immune response and the duration of antibody against JE vaccine, 213 students were examined since 1994 using hemmaglutination inhibition test and plaque reduction neutralization test (PRNT). Results : 24 months after the first vaccination, haemmaglutination inhibition and neutralizing antibody maintained from the recipients 63.4% (>1:20) and 100% (>1:20), respectively. In April 1996, one dose booster to the same recipients those who were vaccinated in 1994, the GMT antibody for HI and PRNT titer were both increased from 1:11.6 to 1:13.2 and 1:275.7 to 1:348.1, respectively, after 6 months booster (after 30 months from the initial vaccination). This results showed that the antibody from the active immunity could be maintained more than 12 months after the initial vaccination. On the basis of these results, inactivated killed JE vaccine (Nakayama-NIH strain) using for preventing against JE purpose seems to produce antibody enough to protect against JE at present. Conclusions : Along with the results of this study demonstrating duration of antibody, the active immunization could be maintained as long as by initial vaccination of 2 doses, a single dose of booster vaccination made during a period of 1 month to 12 months and the successive booster vaccination by 2 or 3 year intervals. However, the immunization schedule should be concerned with both epidemiology of disease and the immune response of vaccinated individuals.