• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3408-3416
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• 2000

This paper proposes an efficient method for improving the performance of a general regression neural network by using the feature to the independent variables as the center for partern-layer neurons. The adaptive principal component analysis is applied for extracting, efficiently the fcarures by reducing the dimension of given independent variables. In can acluevc a supertor property of the principal component analysis that converts input data into set of statistically independent features and the general regression neuralnetwork, espedtively. The proposed general regression neural network has been applied to regress the Solow's economy(2-independent variable set) and the wie elephone(1-independent vanable set). The simulation results show that the proposed meural networks have better performances of the regressionfor the lest data, in comparison with those using the means or the weighted means of independent variables. Also,it is affected less by the number of neurons and the scope of the smoothing factor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.4
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• pp.327-335
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• 2001

This paper present to how to make VGIS(Virtual Geographic Information System) using GIS and digital map. Because the development of the GIS has been 2-dimension in the last few years, viewpoint of the high-resolution image estimate was difficult. The geo-spatial information system has lots of errors in the policy decision and the principal decision making because it was displayed as 2 dimension map that the digital map and topographical map, geological map. agronomical map, cadastral map and underground facility map are expressed as a symbol practically in spite that it is spatial topography data. It is utilized as effective administration analyzing, all kinds of discussion, transportation and environmental effect evaluation, various kinds of building discussion and policy decision thorough researching the present condition of region as 3 dimension map using digital map and GIS when drafting and deciding all kinds of urban plaining so it is considered that errors of policy decision will be minimized.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.7
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• pp.907-913
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• 2011

Swirling flows are related to the spiral motion in the tangential direction in addition to the axial and radial direction using several swirl generators. These type of flows are used in combustion chambers to improve flame stability, heat exchanger to enhance heat transfer coefficients, agricultural spraying machines and some vertical pipes to move slurries or transport of materials. However, only a few studies three dimensional velocity profiles in a vertical pipe have been reported. In this present paper, 3 dimension particle image velocimetry(PIV) technique was employed to measure the velocity profiles in water along a vertical circular pipe with Reynolds number from 6000 to 13,000. A tangential inlet condition was used as the swirl generator to produce the required flow. The velocities were measured with swirling flow in the water along the test section using the PIV technique.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.598-601
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• 2004

In this paper, we suggest method that change odors to digital data. For this, we selected emotional adjective of odors as olfactory receptor This emotional adjective(expressional receptor) is about 40. Each odors are expressed by adjective equivalent to oneself. Expressed odors as emotional receptor is encoded as proposed method for transmission, and after transmission, It should be decoded for expression again. The applied decoding method is fuzzy c-means clustering algorithm(FCMA). But, because odor data is expressed to 40 dimensions, FCMA uses a lot of computing times and memories. To solve this problem, after we reduce dimension through principal component analysis(PCA), we use FCMA algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.305-309
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• 2000

In this paper, we presents a new methodology for face recognition after analysing conventional ICA(Independent Component Analysis) based approach. In the literature we found that ICA based methods have followed the same procedure without any exception, first PCA(Principal Component Analysis) has been used for feature extraction, next ICA learning method has been applied for feature enhancement in the reduced dimension. However, it is contradiction that features are extracted using higher order moments depend on variance, the second order statistics. It is not considered that a necessary component can be located in the discarded feature space. In the new methodology, features are extracted using the magnitude of kurtosis(4-th order central moment or cumulant). This corresponds to the PCA based feature extraction using eigenvalue(2nd order central moment or variance). The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. ICA methodology is analysed using SVD(Singular Value Decomposition). PCA does whitening and noise reduction. ICA performs the feature extraction. Simulation results show the effectiveness of the methodology compared to the conventional ICA approach.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.

• Laser Solutions
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• v.4 no.1
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• pp.39-48
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• 2001

This study was performed to evaluate basic characteristics of electron beam weldability for high strength aluminum alloys. The aluminum alloys used were A5083 and A6N01, and A7N01. The principal welding process parameters, such as accelerating voltage, beam current, welding speed and chamber pressure were investigated. The dimension and microstructure of welds were evaluated with OLM, and SEM (EDAX). In addition, weldability variation(cracking) due to process parameters was also evaluated. The degree of cracking in the EB fusion zone appears to be affected mainly by aspect ratio, such that as aspect ratio increases the cracking tendency also increases. The alloying element itself may also affect the hot cracking resistance, but its role is considered to be indirect effect such that the relatively higher vaporization pressure elements of Zn and Mg give deeper weld penetration and thus results in greater cracking tendency.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.2
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• pp.115-126
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• 1996

In order to obtain the principal design data for developing the Autonomous Robot Vehicle(ARV), Sensitivity analysis on the trajectory error and friction force with respect to the dynamic parameters is performed. In the straight motion, the trajectory error has been proved to be much affected by the mass variance of the ARV while the lateral friction force is much affected by the location of the mass center. In the curved motion, the effect of mass and moment of inertia is considered importantly. In addition, the lateral offset gives more effect than the geometric dimension of the ARV on the trajectory errors and friction force.