• Journal of the Korean earth science society
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• v.27 no.4
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• pp.374-389
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• 2006

The purpose of this study was to investigate the alternative models of seasonal changes that preservice secondary teachers presented using the Lakatosian methodology. Participants included 74 undergraduate students who majored in science education within the college of education. Their responses to these questions revealed students' alternative models were inconsistent with scientific models. A great deal of this apparent inconsistency could be explained by assuming that the students used, in a consistent fashion, a alternative core belief on seasonal changes. This study also discussed the core beliefs and the possible sources held by preservice teachers in order to overcome their alternative models. The sources of alternative models may lie in the contents used in textbooks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• Journal of The Korean Astronomical Society
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• v.46 no.6
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• pp.235-242
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• 2013

We compare mass-loss rates of OH/IR stars obtained from radio observations with those derived from the dust radiative transfer models and IR observations. We collect radio observational data of OH maser and CO line emission sources for a sample of 1533 OH/IR stars listed in Suh & Kwon (2011). For 1259 OH maser, 76 CO(J=1-0), and 55 CO(J=2-1) emission sources, we compile data of the expansion velocity and mass-loss rate. We use a dust radiative transfer model for the dust shell to calculate the mass-loss rate as well as the IR color indices. The observed mass-loss rates are in the range predicted by the theoretical dust shell models corresponding to $\dot{M}=10^{-8}M_{\odot}/yr-10^{-4}M_{\odot}/yr$. We find that the dust model using a simple mixture of amorphous silicate and amorphous $Al_2O_3$ (20% by mass) grains can explain the observations fairly well. The results indicate that the dust radiative transfer models for IR observations generally agree with the radio observations. For high mass-loss rate OH/IR stars, the mass-loss rates obtained from radio observations are underestimated compared to the mass-loss rates derived from the dust shell models. This could be because photon momentum transfer to the gas shell is not possible for the physical condition of high mass-loss rates. Alternative explanations could be the effects of different dust-to-gas ratios and/or a superwind.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.422-430
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• 2008

Data integration is techniques to combine heterogeneous data from different sources, and to allow users to transparently access all data from multiple sources via a single view. The difficulty with data integration is data heterogeneity (i.e. schema heterogeneity, semantic heterogeneity). Richer semantics of data is a major factor in resolving conflicts among heterogeneous data sources. As UML class model represents only schema-based semantics of data, alternative methods such as ontology is useful for representing additional semantics. This paper proposes a method for integrating two data sources with UML class models by using an analysis of their ontologies. In our framework, ontology will be applied to describe semantics of data in each source. Then the ontologies are analysed and compared to determine their similarities and differences. The result of the comparison is used to devise an integrated ontology that will enable querying on the integrated information.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.1
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• pp.15-25
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• 2009

Donghae city is one of the most representative cement industrial city in Korea. The area is faced with the East Sea to the East and with high montane region of Tae-Back mountain range to the West. Many pollutant sources of air pollution are located near the coast, but the largest point sources of the region are located at the bottom of the mountain area in Donghae city. The local wind is highly affected by local topography and plays an important role in transport and dispersion of contaminants from the pollution sources. This study was designed to evaluate enhancement of MM5 predictions by using Four Dimensional Data Assimilation (FDDA), the SONDE data and the national meteorological station, data only. The alternative meteorological fields predicted with and without FDDA were used to simulate spatial and temporal variations of NOx in combined with Atmospheric Dispersion Models (CALPUFF). For the modeling domain, the alternative meteorological fields with 1.1 km spatial resolution were interpolated to the CALMET with 0.5 km resolution. The vertical layers set to have 35 and 12 layers for MM5 and CALPUFF, respectively. MM5 with the FDDA did not resulted in significant improvement of meteorological field prediction in Donghae region, which is primarily because of complex geography and wind scheme. The result of CALPUFF, however, showed reduction of uncertainty errors by using the interpolation scheme of the actual measurement data.

• Journal of Astronomy and Space Sciences
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• v.22 no.1
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• pp.69-88
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• 2005

This study was to understand the components that influence preservice secondary teachers' conceptions about 'seasonal changes' We selected 74 university science education students among whom 23 were in the second, 23 in the third, and 28 in the fourth year. The data collected from the paper-pencil test and individual interview with students. The results of this study show that the students had considerable apparent alternative conceptions, and that the 'distance theory' had most important effects on their alternative conceptions. It can be said that preservice secondary teachers' initial models of the seasonal change have their origin in their belief sets (specific theory) related to 'seasonal change', on the basis of which they can interpret their observations and cultural information with the constraints of a naive frame-work of physics. The structures and possible sources of their alternative conceptions for overcoming these alternative conceptions were also discussed. Implications for preservice science teacher education related to the results were discussed.

• Journal of Korean Society on Water Environment
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• v.33 no.3
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• pp.326-333
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• 2017

The lakes and reservoirs have been reported as important sources of carbon emissions to the atmosphere in many countries. Although field experiments and theoretical investigations based on the fundamental gas exchange theory have proposed the quantitative amounts of Net Atmospheric Flux (NAF) in various climate regions, there are still large uncertainties at the global scale estimation. Mechanistic models can be used for understanding and estimating the temporal and spatial variations of the NAFs considering complicated hydrodynamic and biogeochemical processes in a reservoir, but these models require extensive and expensive datasets and model parameters. On the other hand, data driven machine learning (ML) algorithms are likely to be alternative tools to estimate the NAFs in responding to independent environmental variables. The objective of this study was to develop random forest (RF) and multi-layer artificial neural network (ANN) models for the estimation of the daily $CO_2$ NAFs in Daecheong Reservoir located in Geum River of Korea, and compare the models performance against the multiple linear regression (MLR) model that proposed in the previous study (Chung et al., 2016). As a result, the RF and ANN models showed much enhanced performance in the estimation of the high NAF values, while MLR model significantly under estimated them. Across validation with 10-fold random samplings was applied to evaluate the performance of three models, and indicated that the ANN model is best, and followed by RF and MLR models.

• Journal of Veterinary Science
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• v.21 no.3
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• pp.42.1-42.22
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• 2020

Regenerative medicine using stem cells from various sources are emerging treatment modality in several refractory diseases in veterinary medicine. It is well-known that stem cells can differentiate into specific cell types, self-renew, and regenerate. In addition, the unique immunomodulatory effects of stem cells have made stem cell transplantation a promising option for treating a wide range of disease and injuries. Recently, the medical demands for companion animals have been rapidly increasing, and certain disease conditions require alternative treatment options. In this review, we focused on stem cell application research in companion animals including experimental models, case reports and clinical trials in dogs and cats. The clinical studies and therapeutic protocols were categorized, evaluated and summarized according to the organ systems involved. The results indicate that evidence for the effectiveness of cell-based treatment in specific diseases or organ systems is not yet conclusive. Nonetheless, stem cell therapy may be a realistic treatment option in the near future, therefore, considerable efforts are needed to find optimized cell sources, cell numbers and delivery methods in order to standardize treatment methods and evaluation processes.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.3 s.21
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• pp.71-77
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• 2002

DEMs(digital elevation models) are generally used to automatically map the channel network and to delineate subbasins. At present, most DEM data are derived from three alternative sources which are ground survey, pphotogrammetric data capture and digitized cartographic data sources. The accuracy of a DEM is dependent on the spatial resolution, quality of the source data, collection and processing procedures, and digitizing systems. weather conditions and nature environment.etc provide us satellite image of the highest quality. However, Match in error of the auto generation DEM was severely affected by physical and environmental conditions at shooting time. This paper shows that real-time operation analysis of applied hydrology after extracting DEM Using a pair of Kompsat images.

• Journal of Hydrogen and New Energy
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• v.32 no.6
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• pp.483-488
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• 2021

With continuous emission of environmental pollutants and an increase in greenhouse gases such as carbon dioxide, demand to seek other types of energy sources, alternative energy, was needed. Hydrogen, an eco-friendly energy, is attracting attention as the ultimate alternative energy medium. Hydrogen storage technology has been studied diversely to utilize hydrogen energy. In this study, the gas behavior of hydrogen in the storage tank was numerically examined under charge conditions for the Tpe III hydrogen tank. Numerical results were compared with the experimental results to verify the numerical implementation. In the results of pressure and temperature values under charge condition, the Realizable k-ε model and Reynold stress model were quantitatively matched with the smallest error between numerical and experimental results.