• Journal of Astronomy and Space Sciences
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• v.25 no.2
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• pp.139-148
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• 2008

It has been known that high-speed solar wind streams associated with coronal holes lead to quasi-periodic substorms that occur approximately every $2{\sim}4$ hours. In this paper we examined 222 repetitive substorms that occurred during high-speed stream periods in July through December in 2003 to quantitatively determine a range of energy input from the solar wind into the magnetosphere between two consecutive substorms. For this study, we have used the Akasofu ${\varepsilon}$-parameter to time-integrate it for the interval between two consecutive substorms, and have applied this method to the 222 substorms. We find that the average amount of solar wind input energy between two adjacent substorms is $1.28{\times}10^{14}J$ and about 85% out of the 222 substorms occur after an energy input of $2{\times}10^{13}{\sim}2.3{\times}10^{14}J$. Based on these results, we suggest that it is not practical to predict when a sub storm will occur after a previous one occurs purely based on the solar wind-magnetosphere energy coupling. We provide discussion on several possible factors that may affect determining substorm onset times during high-speed streams.

• The KIPS Transactions:PartD
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• v.19D no.3
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• pp.237-246
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• 2012

Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.

• Journal of the Korea Concrete Institute
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• v.26 no.1
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• pp.23-34
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• 2014

This study investigates the relationship between the performance of fresh and hardened Ultra-High Performance Concrete (UHPC) without heat treatment. The performance of fresh UHPC is determined by the slump flow test related to the fluidity of concrete mixtures, and the air content test. The variables of these tests are the water to binder ratio, superplasticizer dosages and volume fractions of steel fiber. Generally, insufficient fluidity and excessive air contents in concrete mixtures lead to the insufficient packing density related to the performance of harden concrete. The performance of hardened UHPC is determined by the compressive and flexural tensile tests. The results of the fresh UHPC tests show that there is the linear correlation between each variable and the slump flow diameter, and that the slump flow diameter is linearly decreased as the air content ratio increase. Using these results, the formula is developed to predict the fresh performance before mixing UHPC. The results of the hardened UHPC tests show that the hardened performance is not influenced by the air content ratio in the range of 3.2 to 4.2 per cent. However, the flexural tensile strength dominantly influenced by the volume fractions of steel fiber.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.949-959
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• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.155-161
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• 2020

In general, optimization problems are difficult to solve simply. The reason is that the given problem is solved as soon as it is simple, but the more complex it is, the very large number of cases. This study is about the optimization of AI neural network. What we are dealing with here is the relief method for constructing AI network. The main topics deal with non-deterministic issues such as the stability and unstability of the overall network state, cost down and energy down. For this one, we discuss associative memory models, that is, a method in which local minimum memory information does not select fake information. The simulated annealing, this is a method of estimating the direction with the lowest possible value and combining it with the previous one to modify it to a lower value. And nonlinear planning problems, it is a method of checking and correcting the input / output by applying the appropriate gradient descent method to minimize the very large number of objective functions. This research suggests a useful approach to relief method as a theoretical approach to solving optimization problems. Therefore, this research will be a good proposal to apply efficiently when constructing a new AI neural network.

• Journal of the Mineralogical Society of Korea
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• v.28 no.1
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• pp.51-60
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• 2015

We applied gemological analytical approaches on Aquamarine from the Gilgit-Baltistan of Northern Areas, Pakistan. The standard gemological testing indicates that they are consistent with general characteristics of natural aquamarines. We have identified the inclusions of Tantalite-Mn by Raman analysis. It indicates that they occurs in association with the veins of Be-rich coarse pegmatite. And the results of chemical analyses, infrared absorption spectroscopy and Raman spectroscopy indicate that $H_2O$ molecules in channel mostly exist in Type-I and a little Type-II with low alkali ion. The comparison of relative peak intensity of FT-IR analysis can be used for prediction of $Na_2O$ content within not only emerald but also aquamarine.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.71-79
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• 2020

As the urbanization trend in modern society continues, the concentration of the population induces the urban problems in the residential area. One of the well-known issues among various urban problems is the garbage problem, which causes deterioration of the residential environment of citizens and directly affects the satisfaction of municipal administration. Such garbage problem cannot be accurately predicted by analyzing the amount of waste emitted from residential areas, but it is necessary to analyze the lifestyle and characteristics of residents living in residential areas. In this study, we propose an agent-based residential modeling and simulation environment using discrete event system formalism to analyze the garbage problem and satisfaction level according to the distribution of residents in the residential area. To model the behavior of the residents, we utilized the Atomic Model to capture the temporal behavior. Also, we used the Coupled Model to model the multi-family and the building to enhance the reusability of the simulation model. Also, this study carried out simulation modeling and simulation for a multi-family residential area. The simulation results of the multi-family housing area show that considering the characteristics of the residents gives better results compared to the simulation results without considering the characteristics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.32-45
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• 2018

This study aims to compare the mutual perception gap on dangerous driving behavior between male and female drivers in multiple aspects, analyze them, and identify factors that trigger this different perception. To understand the mutual perception gap on dangerous driving behavior, DBQ(Driving Behavior Questionnaire) was applied as a rating scale. By applying results into the Co-oreintation model, this study compared the mutual perception gap between male drivers and female drivers and analyze results. In addition, factors that generate the perception gap between both genders were drawn by analyzing factors. This study suggested that objective consistency identified the perception gap that driving behaviors of others were more dangerous between two genders. In addition, subjective consistency was different as both genders assumed that the counterpart's driving behavior takes more risks than their own actual driving behaviors. In regard to the accuracy, men were aware that female driving behaviors are more dangerous than their behaviors. However, female driving behavior assumed by women was consistent with male perception in all factors, which indicated that women perceive men precisely. In addition, results were compared and analyzed in both perspectives of male drivers and female drivers by combining predictive models. Based on these results, both genders perceived that counterpart's driving behavior is more dangerous among both genders.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.509-521
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• 2016

A novel Samples Balanced Genetic Algorithm combined with Extreme Learning Machine (SBGA-ELM) for Parkinson's Disease diagnosis and detecting bio-markers is presented in this paper. Proposed approach uses genes' expression data of 22,283 genes from open source ParkDB data base for accurate PD diagnosis and detecting bio-markers. Proposed SBGA-ELM includes two major steps: feature (genes) selection and classification. Feature selection procedure is based on proposed Samples Balanced Genetic Algorithm designed specifically for genes expression data from ParkDB. Proposed SBGA searches a robust subset of genes among 22,283 genes available in ParkDB for further analysis. In the "classification" step chosen set of genes is used to train an Extreme Learning Machine (ELM) classifier for an accurate PD diagnosis. Discovered robust subset of genes creates ELM classifier with stable generalization performance for PD diagnosis. In this research the robust subset of genes is also used to discover 24 bio-markers probably responsible for Parkinson's Disease. Discovered robust subset of genes was verified by using existing PD diagnosis approaches such as SVM and PBL-McRBFN. Both tested methods caused maximum generalization performance.

• Applied Biological Chemistry
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• v.50 no.3
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• pp.147-153
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• 2007

To search for a molecular design of a new breast cancerous inhibitory active compound, 2D-QSAR and HQSAR between the substituents of flavopiridol analogues as substrates and their breast cancerous inhibitory activities against MCF-7 cell were analyzed and discussed quantitatively. It was found that the dispersion with molecule and steric hindrance with substituents will have a tremendous impact on the inhibitory activities from the 2D-QSAR model (1). Also, MR constant is better than that of MS constant as animportant factor. The inhibitory activities from 2D-QSAR model (2) were dependent upon the optimum MR constant (MR = 126 $Cm^3/mol$). Optimized HQSAR model (V) exhibited the best predictability of the inhibitory activities based on the cross-validated $r^2_{cv}$($q^2$= 0.583) and non-cross-validated conventional coefficient ($r^2_{ncv}$= 0.982). From the contribution maps, the inhibitory activity by the imino group on $C_9$ atom was higher than that of the hydroxyl group of $C_8$ atom on the A ring in molecule. Therefore, we can confirm that the dispersion by substituents in molecule is the most important factor in inhibitory activities against MCF-7 cell.