• Journal of Astronomy and Space Sciences
• /
• v.33 no.4
• /
• pp.265-271
• /
• 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
• /
• v.10 no.4
• /
• pp.299-308
• /
• 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
• /
• 2003.10a
• /
• pp.7-16
• /
• 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
• /
• v.8 no.3
• /
• pp.252-261
• /
• 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
• /
• v.49 no.2
• /
• pp.165-175
• /
• 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
• /
• v.18 no.9
• /
• pp.871-877
• /
• 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 the Korean Data and Information Science Society
• /
• v.14 no.2
• /
• pp.265-274
• /
• 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.

• Korean Institute of Interior Design Journal
• /
• no.31
• /
• pp.64-70
• /
• 2002

One of the main motivations of this research process is to develop an adaptable VR model whose color can be changed according to the emotional information of user. This paper addresses how to define color scheme and combine colors with harmony. The adaptable color of the VR model consists of three processes: emotional keyword identification, the color combination and the VR model adaptation processes. We have used the biorhythm to derive the emotional keyword which is used to find the color harmony scheme. The color harmony scheme provides information for the color combination of the VR model. Finally, we have obtained the VR model which color has been changed using the identified color schema.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.5
• /
• pp.134-140
• /
• 1994

In this paper, an algorithm of CBAM(Combination Bidirectional Associative Memory) model proposes, analyzes and tests CBAM model `s performancess by simulating with recalls and recognitions of patterns. In learning-procedure each correlation matrix of training patterns is obtained. As each correlation matrix's some elements correspond to juxtaposition, all correlation matrices are merged into one matrix (Combination Correlation Matrix, CCM). In recall-procedure, CCM is decomposed into a number of correlation matrices by spiliting its elements into the number of elements corresponding to all training patterns. Recalled patterns are obtained by multiplying input pattern with all correlation matrices and selecting a pattern which has the smallest value of energy function. By using a CBAM model, we have some advantages. First, all pattern having less than 20% of noise can be recalled. Second, memory capacity of CBAM model, can be further increased to include English alphabets or patterns. Third, learning time of CBAM model can be reduced greatly because of operation to make CCM.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1722-1737
• /
• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.