• KIEAE Journal
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• v.13 no.2
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• pp.77-84
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• 2013

Schools are one of favorable public buildings for Renewable Energy(RE) systems due to site conditions and their energy demand profiles(e.g. daytime-based use of hot water and heating/cooling). Although the government encourages schools to be equipped with RE systems, the adoption of RE systems in existing energy supply systems faces technical and financial barriers. For example, when installing a RE-based combination system(RECS) to meet the energy demand at various school scales, identifying cost effective combination of capacities of the RECS is not trivial since it usually requires technically intensive work including detailed simulation and demand/supply analysis with extensive data. This kind of simulation-based approaches is hardly implementable in practice. To address this, a simpler and applicable decision-supporting method is suggested in this study. This paper presents a simplified model in support of decision-making for optimal capacities of RECS within given budget scales and schools sizes. The proposed model was derived from detailed simulation results and statistical data. Using this model, the optimal capacities of RECS can be induced from the number of classes in a school.

• The Journal of Internal Korean Medicine
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• v.38 no.6
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• pp.891-901
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• 2017

Objectives: The aim of the study was to evaluate the anti-asthmatic effect of alismatis rhizoma and alisol acetate B combination therapy in a murine asthma model. Methods: C57BL/6 mice were sensitized to and challenged with a mixture of ragweed, dust mite, and aspergillus to induce an asthma animal model. Alismatis rhizoma extract and alisol acetate B combination therapy was co-administered only in the experimental group. To evaluate the anti-asthmatic effect of the combination therapy, inflammatory cell counts in bronchoalveolar lavage (BAL) fluid were determined, and tissue was examined histologically with hematoxylin and eosin (H & E) and periodic acid-Schiff (PAS) stains, by enzyme-linked immunosorbent assay (ELISA) of IgE, IL-4, and IL-5, and with reverse transcription polymerase chain reaction (RT-PCR) of IL-5, IL-33, MUC5AC. Results: Alismatis rhizoma and alisol acetate B combination therapy reduced the number of inflammatory cells, alleviated histologic features, and down-regulated all the investigated asthma mediators, IgE, IL-4, IL-5, IL-33, and MUC5AC. Conclusions: According to the above results, alismatis rhizoma and alisol acetate B combination therapy may have therapeutic potential for asthma.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.265-271
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• 2016

Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.299-308
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• 2004

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a Protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as teaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.252-261
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• 2016

A spar-type floating substructure that is being widely used for offshore wind power generation is vulnerable to resonance in the heave direction because of its small water plane area. For this reason, the stable dynamic response of this floating structure should be ensured by accurately identifying the resonance characteristics. The purpose of this study is to analyze the characteristics of the combination resonance between the excitation frequency of a regular wave and natural frequencies of the floating substructure. First, the nonlinear equations of motion with two degrees of freedom are derived by assuming that the floating substructure is a rigid body, where the heaving motion and pitching motions are coupled. Moreover, to identify the characteristics of the combination resonance, the nonlinear term in the nonlinear equations is approximated up to the second order using the Taylor series expansion. Furthermore, the validity of the approximate model is confirmed through a comparison with the results of a numerical analysis which is made by applying the commercial software ANSYS AQWA to the full model. The result indicates that the combination resonance occurs at the frequencies of ${\omega}{\pm}{\omega}_5$ and $2{\omega}_{n5}$ between the excitation frequency (${\omega}$) of a regular wave and the natural frequency of the pitching motion (${\omega}_{n5}$) of the floating substructure.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.165-175
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• 2016

In order to examine the applicability, the evaporation estimation approaches based on solar radiation are classified into 3 different model groups (Model groups A, B, and C) in this study. Each group is tested in the 6 study stations (Seoul, Daejeon, Jeonju, Busan, Mokpo, and Jeju). The model parameters of each model group are estimated and verified with measured pan evaporation data. The applicability of verified model groups are compared with results of Penman (1948) combination approach. Nash-Sutcliffe (N-S) efficiency coefficients greater than 0.663 in all study stations indicate satisfactory estimates of evaporation. On the other hand, in the model verification process, N-S efficiency coefficients greater than 0.526 in all study stations indicate also satisfactory estimates of evaporation. However, N-S efficiency coefficients in all study cases except Model groups B and C in Busan are less than those of Penman (1948) combination approach. Therefore, it is concluded in this study that the evaporation estimation approaches based on solar radiation have capability to replace Penman (1948) combination approach for the estimation of evaporation in case that some meteorological data (wind speed, relative humidity) are missing or not measured.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.871-877
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• 2012

An error analysis of PPP (Precise Point Positioning) using IF (Ionosphere Free) combination is given in this paper. It is shown that the performance of the ordinary model with positions, clock bias, integer ambiguities and ionosphere delay as unknowns is equivalent to that of an ionosphere difference combination where ionosphere delay is cancelled out. Furthermore, it is shown that IF combination is an ionosphere difference combination but not unique. It is also proved that all difference models show same performances. The error analysis evaluated with a hardware simulator and real measurements show that the ionosphere delay is effectively eliminated by IF combination or equivalently by the ionosphere difference combination. However, if bias errors such as troposphere, clock bias or multipath are included in the measurements, the performance of the IF combination is degraded because the bias errors are amplified by the ionosphere difference operation.

• Journal of Oriental Neuropsychiatry
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• v.30 no.4
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• pp.327-340
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• 2019

Objectives: Alzheimer's disease (AD) is a complex disease accompanied by slow impairment of memory and coordination leading to behavioral changes. To date, the only treatment option is to delay the progress of the disease. The purpose of this study was to investigate the synergistic effects of combination treatment with donepezil and three herbal extracts SIP-3 in the AD mouse model induced by amyloid-β (Aβ). Methods: We tested SIP-3 extracts for the cytotoxicity on Aβ-treated SH-SY5Y cells. Then the synergistic effects of SIP-3 and donepezil were evaluated in the AD mouse model using animal experiments and the next generation sequencing (NGS) study. Results: We found that co-treatment with SIP-3 extracts and donepezil increased the viability in Aβ-treated SH-SY5Y cells. The beneficial effects of the co-treatment were also observed in the Aβ-induced AD mouse model. The NGS study was performed to show that the co-treatment of SIP-3 and donepezil restored the disease phenotype closely to the normal level in the AD mouse model in terms of mRNA expression. However, the phenotypes were only partially restored. Conclusions: This study suggests that the combination treatment has a potential to be used for the treatment of AD. However, longer periods of treatment may be required.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.265-274
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• 2003

Time series data are influenced by the external events such as holiday, strike, oil shock, and political change, so the external events cause a sudden change to the time series data. We regard the observation as outlier that occurred as a result of external events. In general, it is called intervention if we know the period and the reason of external events, and it makes an analyst difficult to establish a time series model. Therefore, it is important that we analyze the styles and effects of intervention. In this paper, we considered the linear time series model with invention and compared with nonlinear time series models such as ARCH, GARCH model and also we compared with the combination prediction method that Tong(1990) introduced. In the practical case study, we compared prediction power with RMSE among linear, nonlinear time series model with intervention and combination prediction method.