• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.1
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• pp.1-11
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• 1999

The three-dimensional laminar mixed convection flow between the conductive printed circuit boards. on which the heat generating rectangular blocks are uniformly distributed, has been examined in the present study. The flow and heat-transfer characteristics are assumed to be pseudo periodic in the streamwise direction and symmetric in the cross-stream direction. Using an algorithm of SIMPLER, the continuity equation. the Navier-Stokes equations and the energy equation are solved numerically in the three-dimensional domain Inside the channel. The convective derivative terms are discretized by the QUICK scheme to accurately capture the flow field. The flow and the heat transfer characteristics are thoroughly examined for various Re and Gr.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.389-395
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• 2009

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.275-282
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• 2014

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.705-711
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• 2007

This Paper has adapted SERVPERVAL scale so that hotel manager can use it to determine how customers perceive the service Quality in hotel. A considerable amount of research has focused on the dimensionality of service quality construct. As later found with the generic SERVQUAL, tangible was the most important out of the 5 dimensions. Listed in descending order of importance to hotel customers, the orders of the other 4 dimensions are assurance, reliability, responsiveness, and empathy. But there was order structure in SERVPERVAL's 5 dimensions. Order structure of 5 dimensions is divided by 3 order groups because there was same order among the 5 dimensions.

• International Journal of Contents
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• v.9 no.3
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.231-237
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• 2008

This research proposes a modified version of single pass algorithm specialized for text clustering. Encoding documents into numerical vectors for using the traditional version of single pass algorithm causes the two main problems: huge dimensionality and sparse distribution. Therefore, in order to address the two problems, this research modifies the single pass algorithm into its version where documents are encoded into not numerical vectors but other forms. In the proposed version, documents are mapped into tables and the operation on two tables is defined for using the single pass algorithm. The goal of this research is to improve the performance of single pass algorithm for text clustering by modifying it into the specialized version.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.31.2-31.2
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• 2011

We have investigated frequency dependant conductivity (or permittivity) of low dimensional oxide structures represented by [($Sr_{0.75}$, $La_{0.25}$)$TiO_3$]$_1$/1$[SrTiO_3]_n$ superlattices. The low dimensional oxide superlattice was made by cumulative stacking of one unit cell thick La doped $SrTiO_3$ and $SrTiO_3$ with variable thickness from 1 to 6 unit cell, i,e, [($Sr_{0.75}$, $La_{0.25}$)$TiO_3$]$_1$/$[SrTiO_3]_n$ (n=1, 2, 3, 4, 5, 6). We found two kinds of relaxation when n is 3 and 4, while, inductance component was observed at n=1. This behavior can be explained by electron modulation in ($Sr_{0.75}$, $La_{0.25}$)$TiO_3/SrTiO_3$ superlattices. When n is 1, electrons by La doping well extend to un-doped layer. Therefore, the transport of superlattices follows bulk-like behavior. On the other hand, as n increased, the doped electrons became two types of carrier: one localized and the other extended. These results in two kinds of transport phase. At further increase of n, most of doped electrons are localized at the doped layer. This result shows that dimensionality of the oxide structure significantly affect the transport of oxide nanostructures.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.231-234
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• 2001

This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and Fisherface algorithm. EGM as one of dynamic lint architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional method, the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds. Especially, we could get maximum recognition rate of 99.3% by leaving-one-out method for the experiments with the Yale Face Databases.

• Journal of the Korean Institute of Landscape Architecture
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• v.22 no.2
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• pp.25-38
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• 1994

Service quality is defined as the perceived difference between performed service and expected service. In this paper, theme park service quality is conceptualized in relation to consumer's total satisfaction on theme park visitation. A 20-item scale was constructed to measure Theme Park Service Quality. The following four steps were employed in developing the service quality measure: 1)identification of service quality dimensions, 2)development of scales from a set of items describing the dimensions, 3)empirical verification of the scale's construct validity which refers to dimensionality, convergent validity, and nomological validity, and 4)confirmation of the utility. The scale was found to be an empirically valid and reliable evaluation tool for service quality enhancement. In addition, the scale would be an useful criterion for market segmentation and positioning.