• Journal of the Korean Geosynthetics Society
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• v.16 no.4
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• pp.191-210
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• 2017

The concern study, present the results of experimental study on the performance of deep excavation by using image processing technique particle image velocimetry (PIV). The purpose of present study is to check the application of PIV for the successive ground deformation during deep excavation. To meet the objectives of concern study, a series of reduce scale model test box experiments are performed by considering the wall stiffness, ground water table effect and ground relative density. The results are presented in form of contour and vector plots and further based on PIV analysis wall and ground displacement profile are drawn. The results of present study, indicate that, the PIV technique is useful to demonstrate the ground deformation zone during the successive ground excavation as the degree of accuracy in PIV analysis and measured results with LVDT are within 1%. Further the vector and contours plot effectively demonstrate the ground behavior under different conditions and the PIV analysis results fully support the measured results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.254-259
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• 2015

In this paper, The wireless robot control system was proposed using Brain-computer interface(BCI) systems based on the steady-state visual evoked potential(SSVEP). Cross Power Spectral Density(CPSD) was used for analysis of electroencephalogram(EEG) and extraction of feature data. And Linear Discriminant Analysis(LDA) and Support Vector Machine(SVM) was used for patterns classification. We obtained the average classification rates of about 70% of each subject. Robot control was implemented using the results of classification of EEG and commanded using bluetooth communication for robot moving.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.156-158
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• 2003

Analysis of high-resolution satellite image becomes important for cartography, surveillance, and remote sensing. However, there are lots of problems to be solved for automatic analysis of high-resolution satellite image especially in urban area. The problems are originated from the increased complexity due to the unnecessary details and shadows, and time-varying illuminations. Because of such obstacles, it seems impossible to make automatic analysis. This paper proposes a way of change detection of buildings in urban area by using digital vector map. The proposed way makes the buildings on the vector map parameterized, and searches them in the preprocessed high-resolution image by using generalized Hough transform. The searched building objects are overlaid on the satellite image. The overlaid image can help to detect the change of building rapidly.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.4
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• pp.377-383
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• 2002

To obtain a high performance in a direct vector controlled induction machine, it is essential to obtain the current rotor flux. The accuracy of flux observers for induction machines inherently depends on parameter sensitivity. This paper proposes an analysis method for conventional flux observers using "parameter sensitivity". The "parameter sensitivity" is defined as the ratio of the percentage change in the system transfer function to the percentage change of the parameter variation. We define the ratio between real flux and estimated no as the transfer function, and analyze a parameter sensitivity of this transfer function. Practical verification is presented to conform the capabilities of the proposed analysis method.sed analysis method.

• Journal of KIISE
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• v.42 no.11
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• pp.1452-1458
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• 2015

Korean Morphological Analysis is a difficult process. Because Korean is an agglutinative language, one of the most important processes in Morphological Analysis is Morpheme Recovery. There are some methods using Heuristic rules and Pre-Analyzed Partial Words that were examined for this process. These methods have performance limits as a result of not using contextual information. In this study, we built a Korean morpheme recovery system using deep learning, and this system used word embedding for the utilization of contextual information. In '들/VV' and '듣/VV' morpheme recovery, the system showed 97.97% accuracy, a better performance than with SVM(Support Vector Machine) which showed 96.22% accuracy.

• Reproductive and Developmental Biology
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• v.30 no.4
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• pp.229-234
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• 2006

We have generated transgenic mice that expressed mouse extracellular superoxide dismutase (EC-SOD) in their skin. In particular, the expression plasmid DNA containing human keratin K14 promoter was used to direct the keratinocyte-specific transcription of the transgene. To compare intron-dependent and intron-independent gene expression, we constructed two vectors. The vector B, which contains the rabbit -globin intron 2, was not effective for mouse EC-SOD overexpression. The EC-SOD transcript was detected in the skin, as determined by Northern blot analysis. Furthermore, EC-SOD protein was detected in the skin tissue, as demonstrated by Western blot analysis. To evaluate the expression levels of EC-SOD in various tissues, we purified EC-SOD from the skin, lungs, brain, kidneys, livers, and spleen of transgenic mice and measured its activities. EC-SOD activities in the transgenic mice skin were approximately 7 fold higher than in wild-type mice. These results suggest that the mouse overexpressing vector not only induces keratinocyte-specific expression of EC-SOD, but also expresses successfully functional EC-SOD. Thus, these transgenic mice appeared to be useful for the expression of the EC-SOD gene and subsequent analysis of various skin changes, such as erythema, inflamation, photoaging, and skin tumors.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.1
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• pp.39-45
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• 2009

An Analysis of wind is essential for planning runway direction. As a general rule, the principal traffic runway at an airport should be oriented as closely as practicable in the direction of the prevailing wind. Aircraft are able to maneuver on a runway as long as the wind component at right angles to the direction of landing and taking-off, the cross wind component, is not excessive. ICAO recommends that runway be oriented so that aircraft may be landed at least 95% of the time with allowable cross wind components not exceeding specified limits based upon the airport reference field length. Based on the recommendation, the direction of the runway or runways at an airport can be determined through graphical vector analysis on wind rose. This study is to develop the wind-rose software for planning the optimum runway direction at an airport with the raw wind data based on reliable wind distribution statistics that extend over as long as a period as possible, preferably of not less than 5 years.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.