• Journal of the Korean earth science society
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• v.25 no.7
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• pp.629-636
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• 2004

In order to study developments of the marine atmosphere boundary layer in cloud incoming, important parameters like heat advection, surface layer heat flux, and radiation energy were estimated using the rawinsonde, AWS data, satellite images, and buoy data which was installed at the East Sea. We explained the variation and the development of mixed layer in terms of surface layer heat flux and long wave radiation under the cloudy sky. The heat flux was obtained by means of the bulk method. Conservation of heat was analysed by heat budget equation, which was consist of buoy data in the East sea, and sounding data at Ulleungdo and at Pohang. During the inflow of cloud, radiative cooling at the surface after was suppressed and long wave radiation from cloud played a role of warming. The surface layer temperature was also remained warm by influence of warm advection from south-easterly direction. The air temperature in night was increased, as a result, mixed layer was not destroyed and The nocturnal boundary layer was composed of the mixed layer and the residual layer.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1460-1463
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• 2003

In this study, plastic detection technique was developed, applying remote sensing technology as a method to extract plastic wastes, which is one of the big causes of concern contributing to environmental destruction. It is possible to extract areas where plastic (including polypropylene and polyethylene) wastes are prominent, using ASTER data by taking advantage of its absorptive characteristics of ASTER/SWIR bands. The algorithm is applicable to define large industrial wastes disposal sites and areas where plastic greenhouses are concentrated. However, the detection technique with ASTER/SWIR data has some research tasks to be tackled, which includes a partial secretion of reference spectral, depending on some conditions of plastic wastes and a detection error in a region mixed with vegetations and waters. Following results were obtained after making comparisons between several detection methods and plastic wastes in different conditions; (a)'spectral extraction method' was suitable for areas where plastic wastes exist separated from other objects, such as coastal areas where plastic wastes drifted ashore. (single plastic spectral was used as a reference for the 'spectral extraction method') (b)On the other hand, the 'spectral extraction method' was not suitable for sites where plastic wastes are mixed with vegetation and soil. After making comparison of the processing results of a mixed area, it was found that applying both 'separation method' using un-mixing and ‘spectral extraction method’ with NDVI masked is the most appropriate method to extract plastic wastes. Also, we have investigated the possibility of reducing the influence of vegetation and water, using ASTER/TIR, and successfully extracted some places with plastics. As a conclusion, we have summarized the relationship between detection techniques and conditions of plastic wastes and propose the practical application of remote sensing technology to the extraction of plastic wastes.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.441-447
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• 2006

See-through HMD type 3D display can provide an advantage of us seeing virtual 3D data used stereoscopic display simultaneously with real object(MR-Mixed Reality). But, when user sees stereoscopic display for a long time, not only eye fatigue phenomenon happens but also de-focus phenomenon of data happens by fixed focal point of virtual data. Dissatisfaction of focus adjustment of eye can be considered as the important reason of this phenomenon. In this paper, We proposed an application of multi-focus in see-through HMD as a solution of this problem. As a result, we confirmed that the focus adjustment coincide between the object of real world and the virtual data by multi-focus in monocular condition.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.671-677
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• 2011

A diversity of railway network function enhancement projects such as the double tracking, electrification, and direct operation have been actively executed to improve the railway service. When the new rapid transit is provided, how many people will use it instead of other transports? How will the railway choice behavior be changed? Accordingly, in this paper, the applicability of diverted travel demand forecast methods, by Revealed Preference(RP) and Stated Preference(SP) data was reviewed for Daegu metropolitan rail rapid transit service. As the result of combining RP and SP data, including the sequential and simultaneous approach, the total travel time and travel cost parameters are of the right sign and are highly significant. The simultaneous approach is more efficient in terms of the estimation of coefficients. In particular, methods to improve validity of the Mixed RP/SP models, when RP data is used proportionally, the diverted travel demand can be easily identified by railway fare and travel time service level. Therefore, it is considered that this will practically apply even in other regions as well as Daegu metropolitan railway.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.401-404
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• 2008

This study exploited the design of mixture proportion for the high strength concrete to establish the method of the quality control and high strength ready-mixed concrete for the application to the construction filed systematically how to output the estimated formula which could forecast mixture proportion for the high strength concrete classed 40${\sim}$60MPa through a experiment. It might contribute for systematic establishment of the method of the quality control and high strength ready-mixed concrete because it was possessed of the function of common data though a server, preservation and output of data, and estimation for the design of mixture proportion for the high strength concrete due to the experimental result, and Visual Basic, MS-SQL were used. Simply, it was produced corresponding to the condition of a laboratory, so it could be fundamental data for the design of mixture proportion for the high strength concrete. If upgrade is enforced with mixture proportion data of the each factory after then, it may contribute to the stability on quality and manufacture of high strength ready-mixed concrete to agree with the properties of each factory.

• Journal of Korean Biological Nursing Science
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• v.23 no.1
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• pp.72-82
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• 2021

Purpose: This study used a mixed methods research design in an attempt to verify the effects of robot-assisted rehabilitation on the gait ability of stroke patients with hemiplegia, and thereby further understand the benefits and challenges of stroke patients' experiences relying on robot-assisted rehabilitation. Methods: An exploratory sequential mixed methods study design was used in order to combine both quantitative and qualitative data. For the quantitative data collection, a total of 30 stroke patients with hemiplegia were recruited from one rehabilitation hospital. Qualitative data were collected through individual interviews using semi-structured questionnaires for a group of 15 patients who were currently undergoing robot-assisted rehabilitation. The data were analyzed through qualitative content analysis. Results: As a result of the quantitative analysis, there were significant differences between the two groups in terms of daily living activity patterns, total number of steps, and average walking speed. As a result of the qualitative analysis, the four main themes derived consisted of, 'curiosity about the usage of robot-assisted rehabilitation,' 'pleasure experienced while using the robots,' 'insufficient information about robots,' and 'a lack of education about robot-assisted rehabilitation.' Conclusions: Robot-assisted rehabilitation had a significant effect on the walking ability of stroke patients with hemiplegia. Additionally, stroke patients with hemiplegia experienced difficulty during the course of their robot-assisted rehabilitation, due to a lack of sufficient information on correct usage techniques. These quantitative and qualitative findings could provide the basic foundation for the development of an educational program on robot-assisted rehabilitation.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.1
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• pp.53-60
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• 2018

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.589-598
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• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.189-197
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• 2001

The estimation of mean lifetimes in presence of interval censoring with mixed replacement procedure is examined when the distributions of lifetimes are exponential. It is assumed that, due to physical restrictions and/or economic constraints, the number of failures is investigated only at several inspection times during the lifetime test; thus there is interval censoring. The maximum likelihood estimator is found in an implicit form. The Cramor-Rao lower bound, which is the asymptotic variance of the estimator, is derived. The estimation of mean lifetimes for competing failures model has been expanded.