• The Journal of Korean Academic Society of Nursing Education
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• v.13 no.2
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• pp.203-211
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• 2007

Purpose: The purpose of this study was to identify the factors influencing Health Promoting Behavior(HPB) of nursing students. Method: The sample consisted of 418 college nursing students in G city. data collection method was a structured questionnaire. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation, and stepwise multiple regression. Result: The mean score for HPB was 2.48. In the subcategories, the highest degree of performance was interpersonal relationship and the lowest degree was exercise. HPB was significantly different according to economic status of parents, health concern of parents, and body mass index. The most powerful predictor of HPB was self esteem(33%). A combination of self esteem, social support, self efficacy and perceived health status accounted for 43% of the variance in HPB of nursing students. Conclusion: This study suggests that self esteem, social support, self efficacy and perceived health status are significantly influencing factors in HPB of nursing students.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.172-176
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• 2008

FDG PET has been used as a diagnostic tool for localization of seizure focus for last 2-3 decades. In this article, the clinical usefulness of FDG PET in the management of patients with epilepsy has been reviewed, which provided the evidences to justify the medicare reimbursement for FDG PET in management of patients with epilepsy. Literature review demonstrated that FDG PET provides an important information in localization of seizure focus and determination whether a patients is a surgical candidate or not. FDG PET has been reported to have high diagnostic performance in localization of seizure focus in neocortical epilepsy as well as temporal lobe epilepsy regardless of the presence of structural lesion on MRI. Particularly, FDG PET can provide the additional information when the results from standard diagnositic modality such as interictal or video-monitored EEG, and MRI are inconclusive or discordant, and make to avoid invasive study. Furthermore, the presence of hypometabolism and extent of metabolic extent has been reported as an important predictor for seizure free outcome. However, studies suggested that more accurate localization and better surgical outcome could be expected with multimodal approach by combination of EEG, MRI, and functional studies using FDG PET or perfusion SPECT rather than using a single diagnostic modality in management of patients with epilepsy. Complementary use of FDG PET in management of epilepsy is worth for good surgical outcome in epilepsy patients.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.1-10
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• 2007

In this paper, we proposed development of improved model reduction and design of common controller using reduction model. The Algorithm of improved model reduction considered the transient response and the steady-state response in response curve. The generalized controller is designed not only to ensure specified phase margin and iso-damping property also optimized smith-predictor controller about real model using reduction model. Simulation examples are given to show the better performance of the proposed method than convention methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.2
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• pp.81-86
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• 2014

In the scalable video coding standard, inter-layer prediction based on the coding information of the base layer was adopted to increase the coding performance. This prediction tool results in new syntax elements called motion_prediction_flag (mPF) and residul_prediction_flag(rPF), which are carried to notify the motion vector predictor (MVP) and reference block required in the motion compensation of the decoder. In this paper, an efficient coding method for mPF is proposed to enhance coding efficiency of the salable video coding standard. Through an analysis on the transmission of mPF based on the relationship between the MVPs, we discover the conditions where mPF is unnecessary at the decoder and suggest a modified rate-distortion (RD) cost function to make RD optimization more effective. Simulation results show that the proposed method offers BD rate savings of approximately 1.4%, compared with the conventional SVC standard.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5417-5421
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• 2014

Objectives: The identification of cutaneous T cell lymphoma (CTCL) biomarkers may serve as a predictor of disease progression and treatment response. The aim of this study was to map potential biomarkers in CTCL plasma. Design and Methods: Plasma metabolic perturbations between CTCL cases and healthy individuals were investigated using metabolomics and ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Results: Principal component analysis (PCA) of the spectra showed clear metabolic changes between the two groups. Thirty six potential biomarkers associated with CTCL were found. Conclusions: Based on PCA, several biomarkers were determined and further identified by LC/MS/MS analysis. All of these could be potential early markers of CTCL. In addition, we established that heparin as a nticoagulant has better pre-treatment results than EDTA with the UHPLC-QTOF/MS appraoch.

• Atmosphere
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• v.24 no.4
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• pp.533-540
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• 2014

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

• Korean Journal of Adult Nursing
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• v.14 no.2
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• pp.184-193
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• 2002

Purpose: The purpose of this study was to assess the characteristics of the user of complementary alternative therapies(CAT) and to identify the important predictive factors associated with them. Method: This study included 142 patients attending outpatient rheumatology clinics of D Hospital in Busan between July and August in 2001. The multiple logistic regression model was developed to estimate the likelihood of user or nonuser of CAT. Result: The duration of illness and chance score of health locus of control were found to be significant factors through the estimated coefficients of using CAT. Duration of illness is longer and chance score of health locus of control is higher in patients who have used CAT in past than that of nonuser. When the model performance was evaluated by comparing the observed outcome with predicted outcome, the model correctly identified 95% of user of CAT and 31% of nonuser. Conclusion: In this survey, duration of illness and chance score of health locus of control are found to be significant factors in predicting utilization of CAT. Nurses who care for rheumatoid arthritis patients should take consideration into health locus of control in planning health education programs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1330-1336
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• 2004

This paper addresses an adaptive motion vector prediction algorithm to improve the performance of video encoder. The block-based motion vector is characterized by non-stationary local statistics so that the coefficients of LS (Least Squares) based linear motion can be optimized. However, it requires very expensive computational cost. The proposed algorithm using LS approach with spatially varying motion-directed property adaptively controls the coefficients of the motion predictor and reduces the computational cost as well as the motion prediction error. Experimental results show the capability of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.217-223
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• 2005

In this paper, we propose a predictive block constrained trellis coded quantization (BC-TCQ) to quantize cepstral coefficients for the distributed speech recognition. For Prediction of the cepstral coefficients. the 1st order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively. we use a BC-TCQ. The performance is compared to the split vector quantizers used in the ETSI standard, demonstrating reduction in the cepstral distance and computational complexity.