• Journal of Preventive Medicine and Public Health
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• v.46 no.5
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• pp.271-281
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• 2013

Objectives: Emerging evidence indicates that sleep duration is associated with health outcomes. However, the relationship of sleep duration with long-term health is unclear. This study was designed to determine the relationship of sleep duration with mortality as a parameter for long-term health in a large prospective cohort study in Korea. Methods: The study population included 13 164 participants aged over 20 years from the Korean Multi-center Cancer Cohort study. Information on sleep duration was obtained through a structured questionnaire interview. The hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality were estimated using a Cox regression model. The non-linear relationship between sleep duration and mortality was examined non-parametrically using restricted cubic splines. Results: The HRs for all-cause mortality showed a U-shape, with the lowest point at sleep duration of 7 to 8 hours. There was an increased risk of death among persons with sleep duration of ${\leq}5$ hours (HR, 1.21; 95% CI, 1.03 to 1.41) and of ${\geq}10$ hours (HR, 1.36; 95% CI, 1.07 to 1.72). In stratified analysis, this relationship of HR was seen in women and in participants aged ${\geq}60$ years. Risk of cardiovascular disease-specific mortality was associated with a sleep duration of ${\leq}5$ hours (HR, 1.40; 95% CI, 1.02 to 1.93). Risk of death from respiratory disease was associated with sleep duration at both extremes (${\leq}5$ and ${\geq}10$ hours). Conclusions: Sleep durations of 7 to 8 hours may be recommended to the public for a general healthy lifestyle in Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.1 s.13
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• pp.53-63
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• 1999

For the close range digital photogrammetry, multi-step procedures should be embodied in an integrated system. However, it is hard to construct an Integrated system through conventional procedural processing. Using Object Oriented Programming(OOP), photogrammetric processings can be classified with corresponding subjects and it is easy to construct an integrated system lot digital photogrammetry as well as to add the newly developed classes. In this study, the equation of 3-dimensional mathematic model is developed to make an immediate calibration of the CCD camera, the focus distance of which varies according to the distance of the object. Classes for the input and output of images are also generated to carry out the close range digital photogrammetric procedures by OOP. Image matching, coordinate transformation, dirct linear transformation and bundle adjustment are performed by producing classes corresponding to each part of data processing. The bundle adjustment, which adds the principle coordinate and focal length term to the non-photogrammetric CCD camera, is found to increase usability of the CCD camera and the accuracy of object positioning. In conclusion, classes and their hierarchies in the digital photogrammetry are designed to manage multi-step procedures using OOP and close range digital photogrammetric process is implemented using CCD camera in an integrated System.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2027-2035
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• 2001

In this paper, we present an audio stream delivery using the AMR (Adaptive Multi-Rate) coder that was adopted by ETSI and 3GPP as a standard vocoder for next generation IMT-2000 service in which includes combined sender (FEC) and receiver reconstruction technique in the Internet. By use of the media-specific FEC scheme, the possibility to recover lost packets can be much increased due to the addition of repair data to a main data stream, by which the contents of lost packets can be recovered. The AMR codec is based on the code-excited linear predictive (CELP) coding model. So we use a frame erasure concealment for CELP-based coders. The proposed scheme is evaluated with ITU-T G.729 (CS-ACELP) coder and AMR - 12.2 kbit/s through the SNR (Signal to Noise Ratio) and the MOS (Mean Opinion Score) test. The proposed scheme provides 1.1 higher in Mean Opinion Score value and 5.61 dB higher than AMR - 12.2 kbit/s in terms of SNR in 10% packet loss, and maintains the communicab1e quality speech at frame erasure rates lop to 20%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.361-366
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• 2017

As the recent development of computing architecture and application software technology, real world simulation, which is the ultimate destination of computer simulation, is emerging as a practical issue in several research sectors. In this paper, metal plate motion in a square shock tube for small time interval was calculated using a supercomputing-based fluid-structure-combustion multi-physics simulation tool called Illinois Rocstar, developed in a US national R amp; D program at the University of Illinois. Afterwards, the simulation results were compared with those from experiments. The coupled solvers for unsteady compressible fluid dynamics and for structural analysis were based on the finite volume structured grid system and the large deformation linear elastic model, respectively. In addition, a strong correlation between calculation and experiment was shown, probably because of the predictor-corrector time-integration scheme framework. In the future, additional validation studies and code improvements for higher accuracy will be conducted to obtain a reliable open-source software research tool.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.384-392
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• 2022

The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R²: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.

• Journal of the Korean earth science society
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• v.22 no.6
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• pp.512-527
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• 2001

Separated flows passed complex geometries are modeled by discrete vortex techniques. The flows are assumed to be rotational and inviscid, and a new techlnique is described to determine the stream functions for linear shear profiles. The geometries considered are the snow cornice and the backward-facing step, whose edges allow for the separation of the flow and reattachment downstream of the recirculation regions. A point vortex has been added to the flows in order to constrain the separation points to be located at the edges, while the conformal mappings have been modified in order to smooth the sharp edges and to let the separation points free to oscillate around the points of maximum curvature. Unsteadiness is imposed to the flow by perturbing the vortex location, either by displacing the vortex from the equilibrium, or by imposing a random perturbation with zero mean to the vortex in equilibrium. The trajectories of passive scalars continuously released upwind of the separation point and trapped by the recirculating bubble are numerically integrated, and concentration time series are calculated at fixed locations downwind of the reattachment points. This model proves to be capable of reproducing the trapping and intermittent release of scalars, in agreement with the simulation of the flow passed a snow cornice performed by a discrete multi-vortex model, as well as with direct numerical simulations of the flow passed a backward-facing step. The results of simulation indicate that for flows undergoing separation and reattachment the unsteadiness of the recirculating bubble is the main mechanism responsible for the intense large-scale concentration fluctuations downstream.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.54.1-54.1
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• 2016

Multi-wavelength variability is a staple of active galactic nuclei (AGN). Optical variability probes the nature of the central engine of AGN at smaller linear scales than conventional imaging and spectroscopic techniques. Previous studies have shown that optical variability is more prevalent at longer timescales and at shorter wavelengths. Intra-night variability can be explained through the damped random walk model but small samples and inhomogeneous data have made constraining this model hard. To understand the properties and physical mechanism of intra-night optical variability, we are performing the KMTNet Active Nuclei Variability Survey (KANVaS). Using KMTNet, we aim to study the intra-night variability of ~1000 AGN at a magnitude depth of ~19mag in R band over a total area of ${\sim}24deg^2$ on the sky. Test data in the COSMOS, XMM-LSS, and S82-2 fields was obtained over 4, 6, and 8 nights respectively during 2015, in B, V, R, and I bands. Each night was composed of 5-13 epoch with ~30 min cadence and 80-120 sec exposure times. As a pilot study, we analyzed data in the COSMOS field where we reach a magnitude depth of ~19.5 in R band (at S/N~100) with seeing varying between 1.5-2.0 arcsec. We used the Chandra-COSMOS catalog to identify 166 AGNs among 549 AGNs at B<23. We performed differential photometry between the selected AGN and nearby stars, achieving photometric uncertainty ~0.01mag. We employ various standard time-series analysis tools to identify variable AGN, including the chi-square test. Preliminarily results indicate that intra-night variability is found for ~17%, 17%, 8% and 7% of all X-ray selected AGN in the B, V, R, and I band, respectively. The majority of the identified variable AGN are classified as Type 1 AGN, with only a handful of Type 2 AGN showing evidence for variability. The work done so far confirms there are more variable AGN at shorter wavelengths and that intra-night variability most likely originates in the accretion disk of these objects. We will briefly discuss the quality of the data, challenges we encountered, solutions we employed for this work, and our updated future plans.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.45-59
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• 2009

This research presents the effect of Asian dust on the derived sea surface temperature (SST) from measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument flown onboard NOAA polar orbiting satellites. To analyze the effect, A VHRR infrared brightness temperature (TB) is estimated from simulated radiance calculated from radiative transfer model on various atmospheric conditions. Vertical profiles of temperature, pressure, and humidity from radiosonde observation are used to build up the East Asian atmospheric conditions in spring. Aerosol optical thickness (AOT) and size distribution are derived from skyradiation measurements to be used as inputs to the radiative transfer model. The simulation results show that single channel TB at window region is depressed under the Asian dust condition. The magnitude of depression is about 2K at nadir under moderate aerosol loading, but the magnitude reaches up to 4K at slant path. The dual channel difference (DCD) in spilt window region is also reduced under the Asian dust condition, but the reduction of DCD is much smaller than that shown in single channel TB simulation. Owing to the depression of TB, SST has cold bias. In addition, the effect of AOT on SST is amplified at large satellite zenith angle (SZA), resulting in high variance in derived SSTs. The SST depression due to the presence of Asian dust can be expressed as a linear function of AOT and SZA. On the basis of this relationship, the effect of Asian dust on the SST retrieval from the conventional daytime multi-channel SST algorithm can be derived as a function of AOT and SZA.

• 한국사회복지학회:학술대회논문집
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• 2003.10a
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• pp.185-216
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• 2003

This study was aimed at investigating important factors influencing career success among regular workers. The current researcher scrutinized the degree to which variables and factors affect the career success and occupational turnover rates of the research participants. At the same tune, two hypothetical path models established by the researcher were examined using linear multiple regression methods and the LISREL. After examining the differences among the factors of career success, a comparison was made between the disabled worker group and the non-disabled worker group. A questionnaire using the 5-point Likert scale was distributed to a group of 374 workers with disabilities and 463 workers without disabilities. For the data analysis purpose, the structural equation model, factor analysis, correlation analysis, and multiple regression analysis were carried out. The results of this study ran be summarized as follows. First, the results of factor analysis showed important categories of conceptual themes of career success. The initial conceptual factor model did not accord with the empirical one. A three-factorial model revealed categories of personal, family, and organizational factor respectively. The personal factor was composed of the self-esteem and self-efficiency. The family factor was consisted of the multi-roles stress and the number of children. Finally, the organizational factor was composed of the capacity for utilizing resources, networking, and the frequency of mentoring. In addition, the total 10 sub areas of career success were divided by two important aspects; the subjective career success and the objective career success. Second, both research participant groups seemed to be influenced by their occupational types. However, all predictive variables excluding the wage rate and the average length of work years had significant impact on job success for the disabled work group, while all the variables excluding the frequency of advice and length of working years had significant impact on job success for the non-disabled worker group. Third, the turnover rate was significantly influenced by the age and the experience of turnover of the research participants. However, the number of co-workers was the strongest predictive variable for the worker group with disabilities, but the occupation choice variable for the worker group without disabilities. For the disabled worker group, the turnover rate was differently influenced by the type of occupation, the length of working years, while multi-role stress and the average working years at the time of turnover for the worker group without disabilities. Fifth, as a result of verifying the hypothetical path model, it showed that the first model was somewhat proper and could predict the career success on both research participant groups. In the second model, the Chi-square, the degree of freedom (($x^2=64.950$, df=61, P=0.341), and the adjusted Goodness of Fit Index (AGFI) were .964, and the Comparative Fit Index (CFI) were .997, and the Root Mean Squared Residual (RMR) was respectively. .038. The model was best fitted and could predict the career success more highly because the goodness of fit index in the whole models was within the allowed range. In conclusion, the following research implications can be suggested. First, the occupational type of research participants was one of the most important variables to predict the career success for both research participant groups. It means that people with disabilities require human development services including education. They need to improve themselves in this knowledge-based society. Furthermore, for maintaining the career success, people with disabilities should be approached by considering the subjective career success aspects including wages and the promotion opportunities than the objective career success aspects.