• International Journal of Contents
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• 제18권1호
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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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• 제38권1호
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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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• 제21권3호
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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.

• 대한기계학회논문집
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• 제11권5호
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• pp.746-754
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• 1987

본 연구에서는 X형 프로우브에 Mojolla의 방법을 적용하였으며, 경사프로우브 에는 프로우브의 경사각도와 회전각도에 따른 속도성분과 출력전압과의 관계를 유도하 여 적용하였으며, 이들 방법에 의한 3차원 유동계측의 정확성과 적용법위를 조사하였 다.

• Journal of Information Processing Systems
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• 제12권3호
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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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• 제10권8호
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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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• 제12권1호
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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.

• 한국의류학회지
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• 제47권2호
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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.

• 한국전자통신학회논문지
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• 제13권6호
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• pp.1333-1342
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• 2018

지금까지 뇌파(Electroencephalography - EEG)는 뇌전증 진단 및 치료를 위한 가장 중요하고 편리한 방법이었다. 그러나 뇌전증 뇌파 신호의 파형 특성은 매우 약하고 비 정지 상태이며 배경 노이즈가 강하기 때문에 식별하기가 어렵다. 이 논문에서는 간질 뇌파의 특징 선택을 통한 차원 감소를 통한 분류 방법의 효과를 분석한다. 우리는 차원 감소를 위해 주 요소 분석, 커널 요소 분석, 선형 판별 분석 방법을 사용하였다. 차원 감소방법의 성능 분석을 위해 Support Vector Machine: SVM), Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR), Random Forest(: RF) 분류 방법들을 사용해 평가하였다. 실험 결과에 따르면, PCA는 SVM, LR 및 K-NN에서 75% 정확도를 나타냈다. KPCA는 SVM과 K-KNN에서 85%의 성능을 보였으며 LDA는 K-NN를 이용했을 때 100 %의 정확도 보여주었다. 따라서 LDA를 이용한 차원 감소가 뇌전증 EEG 신호에 대한 최고의 분류 결과 보여주었다.

• 융합보안논문지
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• 제23권1호
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• pp.117-123
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• 2023

점점 더 고도화되고 있는 랜섬웨어 공격을 기계학습 기반 모델로 탐지하기 위해서는, 분류 모델이 고차원의 특성을 가지는 학습데이터를 훈련해야 한다. 그리고 이 경우 '차원의 저주' 현상이 발생하기 쉽다. 따라서 차원의 저주 현상을 회피하면서 학습모델의 정확성을 높이고 실행 속도를 향상하기 위해 특성의 차원 축소가 반드시 선행되어야 한다. 본 논문에서는 특성의 차원이 극단적으로 다른 2종의 데이터세트를 대상으로 3종의 기계학습 모델과 2종의 특성 추출기법을 적용하여 랜섬웨어 분류를 수행하였다. 실험 결과, 이진 분류에서는 특성 차원 축소기법이 성능 향상에 큰 영향을 미치지 않았으며, 다중 분류에서도 데이터세트의 특성 차원이 작을 경우에는 동일하였다. 그러나 학습데이터가 고차원의 특성을 가지는 상황에서 다중 분류를 시도했을 경우 LDA(Linear Discriminant Analysis)가 우수한 성능을 나타냈다.