• International Journal of Contents
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• v.18 no.1
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• pp.1-16
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• 2022

With the advent of the 5G, networks and information and communication technologies have been continuously developed. In the fields of art galleries, virtual reality (VR) exhibitions that can be visited online have emerged, innovating the way of human-computer interaction and creating new artistic experiences for users. This study explores the three-dimensionality, clarity, and innovative interactions that users experience when viewing a VR exhibit, which affects the exhibit's presence. Besides, in terms of research method, the research sets spatial participation and immersion as dependent variables, with three-dimensionality (high versus low), clarity (high versus low), and innovation (high versus low) in a 2×2×2 design as the base, and explores their interaction effects. The results show that three-dimensionality and innovative interactions affect spatial participation. First of all, in groups with high innovation and low three-dimensionality, spatial participation presents a higher positive factor. Secondly, with regard to immersion, three-dimensionality, clarity and innovation present a tripartite interaction. Groups with low three-dimensionality and high clarity have a higher positive effect on immersion when the level of innovation is low. When the degree of innovation is high, the positive effect on immersion is higher in groups with high three-dimensionality and low clarity. The above results show that in the production of VR exhibitions, it is necessary to increase the three-dimensionality and clarity of exhibited image contents, while taking into account the user's perception and innovativeness. On the other hand, this study puts forward suggestions for the design, content and future development of VR exhibitions, which has important reference significance for the improvement and innovation of future VR exhibitions.

• ETRI Journal
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• v.38 no.1
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• pp.206-215
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• 2016

The detection of gene-gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the "curse of dimensionality" -that is, it works well for two- or three-way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10-way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre-evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.31-36
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• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.5
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• pp.746-754
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• 1987

A method for measuring three-dimensional turbulent flows by the hot-wire anemometer is introduced. Mojolla's method using the X-type probe is adopted and modified for the slantwire probe without the linearizer. The probe is aligned with specified angles to the given uniform flow and the shear layer to verify the measuring errors due to the three-dimensionality and the turbulence level. Errors in the measurements of mean velocities and Reynolds stresses increase with the degree of three dimensionality in the flow. The incoming flow angle of 20 degree seems to be the limit of reasonable flow measurements. But there still appear large data scatterings in Reynolds shear stresses.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.468-488
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• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.501-517
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• 2020

Three-dimensional numerical simulations were performed to study the effects of flow direction and flow velocity on the flow regime behind a curved pipe represented by a curved circular cylinder. The cylinder is based on a previous study and consists of a quarter segment of a ring and a horizontal part at the end of the ring. The cylinder was rotated in the computational domain to examine five incident flow angles of 0-180° with 45° intervals at Reynolds numbers of 100 and 500. The detailed wake topologies represented by λ2 criterion were captured using a Large Eddy Simulation (LES). The curved cylinder leads to different flow regimes along the span, which shows the three-dimensionality of the wake field. At a Reynolds number of 100, the shedding was suppressed after flow angle of 135°, and oblique flow was observed at 90°. At a Reynolds number of 500, vortex dislocation was detected at 90° and 135°. These observations are in good agreement with the three-dimensionality of the wake field that arose due to the curved shape.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.2
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• pp.215-231
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• 2023

This study classifies short front long back skirts from the Joseon Dynasty by style type, identifies their formative characteristics based on their external morphological properties and internal composition, and examines their correlation with Korean thought. A literature review and empirical research were conducted for this study. The style of short front long back skirts is classified as inverted "b"-shaped, lower lip, wavy, trapezoid with a raised center hem, or half-circle. As such, this skirt possesses the formative properties of imbalance, variability of shape, intentional three-dimensionality, and confluence. In other words, with an imbalance resulting from the difference in length between the front and back, these skirts are characterized by variability in shape created by intentional three-dimensionality expressed as intentional three-dimensional beauty, the confluence of planes and dimensions, as well as of materials and colors. These properties are correlated with Korean ways of viewing the world. This study contributes to the development of Korean designs.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• 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.