• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.1
• /
• pp.49-57
• /
• 2008

In this paper, we propose a novel hierarchical distributed intrusion detection system, named HNHDIDS(Home Network Hierarchical Distributed Intrusion Detection System), which is not only based on the structure of distributed intrusion detection system, but also fully consider the environment of secure home networks service. The proposed system is hierarchically composed of the one-class support vector machine(support vector data description) and local agents, in which it is designed for optimizing for the environment of secure home networks service. We support our findings with computer experiments and analysis.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.1
• /
• pp.1-5
• /
• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Geomechanics and Engineering
• /
• v.13 no.3
• /
• pp.447-458
• /
• 2017

This study proposes a sensitivity analysis method for slope stability based on the least squares support vector machine (LS-SVM) to examine the influencing factors of slope stability. The method uses LS-SVM as an algorithm for machine learning. An appropriate training dataset is established according to the slope characteristics, and a testing dataset is designed orthogonally. Results of the testing data in the experiment design are calculated after training using the LS-SVM model. The sensitivity of the slope stability of each factor is examined via gray correlation analysis. The results are consistent with those of the traditional Bishop analysis and can be used as a reference for optimizing slope design.

• The KIPS Transactions:PartB
• /
• v.15B no.4
• /
• pp.285-292
• /
• 2008

Color histograms have been used as feature vectors to characterize the color features of given images, but they have a limitation in efficiency by generating high-dimensional feature vectors. In this paper, we present a method to reduce the dimension of the feature vectors by applying PCA (principal components analysis) to the color histogram of a given vehicle image. With SVM (support vector machine) method, the dimension-reduced feature vectors are used to recognize the colors of vehicles. After reducing the dimension of the feature vector by a factor of 32, the successful recognition rate is reduced only 1.42% compared to the case when we use original feature vectors. Moreover, the computation time for the color recognition is reduced by a factor of 31, so we could recognize the colors efficiently.

• Smart Structures and Systems
• /
• v.6 no.4
• /
• pp.405-421
• /
• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.115-125
• /
• 2017

For many stroke patients undergoing rehabilitation therapy, there is a need for indicator for evaluating the body function in paretic and non-paretic regions of stroke patients quantitatively. In this paper, the function of muscles and cells in paretic and non-paretic regions of severe and mild hemiplegic stroke patients was evaluated using multi-channel bioelectrical impedance spectroscopy. The paretic and non-paretic regions of severe and mild stroke patients were quantitatively assessed by using bioelectrical impedance parameters such as prediction marker (PM), phase angle (${\theta}$), characteristic frequency ($f_c$), and bioelectrical impedance vector analysis (BIVA). The mean values of impedance vector were significantly discriminated in all comparisons (severe-paretic, severe-non-paretic, mild-paretic, and mild-non-paretic). The bioelectrical impedance parameters were proved to be a very valuable tool for quantitatively evaluating the paretic and non-paretic regions of hemiplegic stroke patients.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.37 no.12
• /
• pp.839-846
• /
• 1988

If we use the 2-dimensional analyzing method with the magnetic vector potential for the analysis of eddy current distribution in electric machinery, we can obtain the magnitude of eddy current but can't have the characteristic of eddy current distribution. For the settlement of this problem, we have induced the governing equation with the current vector potential and attemptted 2-dimensional analysis of eddy current distribution by finite element method. And the time domain weighted residual method is used in treatment of time differential term and we have developed the algorithm by it. And then, we analyze eddy current distributions of analytic model and aluminium disk in singlephase watt hour meter. Consequently we have verified the propriety and utility of above mentioned method.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.2
• /
• pp.304-315
• /
• 2013

In this paper, analysis of cascaded H-bridge multilevel inverter in DTC-SVM (Direct Torque Control-Space Vector Modulation) based induction motor drive for FCEV (Fuel Cell Electric Vehicle) is presented. Cascaded H-bridge multilevel inverter uses multiple series units of H-bridge power cells to achieve medium-voltage operation and low harmonic distortion. In FCEV, a fuel cell stack is used as the major source of electric power moreover the battery and/or ultra-capacitor is used to assist the fuel cell. These sources are suitable for utilizing in cascaded H-bridge multilevel inverter. The drive control strategy is based on DTC-SVM technique. In this scheme, first, stator voltage vector is calculated and then realized by SVM method. Contribution of multilevel inverter to the DTC-SVM scheme is led to achieve high performance motor drive. Simulations are carried out in Matlab-Simulink. Five-level and nine-level inverters are applied in 3hp FCEV induction motor drive for analysis the multilevel inverter. Each H-bridge is implemented using one fuel cell and battery. Good dynamic control and low ripple in the torque and the flux as well as distortion decrease in voltage and current profiles, demonstrate the great performance of multilevel inverter in DTC-SVM induction motor drive for vehicle application.

• Geophysics and Geophysical Exploration
• /
• v.5 no.2
• /
• pp.99-107
• /
• 2002

An analytical forward modeling algorithm was developed for the efficient application to the geotechnical engineering in SASW (Spectral Analysis of Surface Waves) method. for the theoretical dispersion curve, the finite difference method using motion stress vector, which was proposed by Aki and Richards, was employed and verified with two earth models. For the stable and fast calculation, it was found that the model size depending on the frequency range is suitable $1.5\~2$ times bigger than the wavelength.

• Structural Engineering and Mechanics
• /
• v.72 no.1
• /
• pp.31-41
• /
• 2019

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.