• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.979-984
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• 2004

The ball bearing having faults generally shows, nonlinear vibration characteristics. For the effective method of fault diagnosis on bail bearing, non-linear diagnostic methods can be used. In this paper, the correlation dimension analysis based on nonlinear timeseries was applied to diagnose the faults of ball bearing. The correlation dimension analysis shows some Intrinsic information of underlying dynamical systems, and clear the classification of the fault of ball bearing.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.749-753
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• 2007

In this paper, a generalized version of the Motion Silhouette Image(MSI) called the Generic Motion Silhouette Image (GMSI) is proposed for gait recognition. The GMSI is a gray-level image and involves the spatiotemporal information of individual motion. The GMSI not only generalizes the MSI but also reflects a flexible feature of a gait sequence. Along with the GMSI, we use the Principal Component Analysis(PCA) to reduce the dimensionality of the GMSI and the Nearest Neighbor(NN) for classification. We apply the proposed feature to NLPR database and compare it with the conventional MSI. Experimental results show the effectiveness of the GMSI.

• Journal of Integrative Natural Science
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• v.5 no.2
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• pp.120-126
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• 2012

This paper describes the algorithm that lowers the dimension, maintains the gesture recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

• Bulletin of the Korean Chemical Society
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• v.17 no.2
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• pp.192-198
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• 1996

We present a theoretical formulation of diffusion process on solid surface based on multidimensional transition state theory (TST). Surface diffusion of single adatom results from hopping processes on corrugated potential surface and is affected by surface vibrations of surface atoms. The rate of rare events such as hopping between lattice sites can be calculated by transition state theory. In order to include the interactions of the adatom with surface vibrations, it is assumed that the coordinates of adatom are coupled to the bath of harmonic oscillators whose frequencies are those of surface phonon modes. When nearest neighbor surface atoms are considered, we can construct Hamiltonians which contain terms for interactions of adatom with surface vibrations for the well minimum and the saddle point configurations, respectively. The escape rate constants, thus the surface diffusion parameters, are obtained by normal mode analysis of the force constant matrix based on the Hamiltonian. The analysis is applied to the diffusion coefficients of W, Ir, Pt and Ta atoms on the bcc(110) plane of W in the zero-coverage limit. The results of the calculations are encouraging considering the limitations of the model considered in the study.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.331-341
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• 2023

Handling missing values in data analysis is essential in constructing a good prediction model. The easiest way to handle missing values is to use complete case data, but this can lead to information loss within the data and invalid conclusions in data analysis. Imputation is a technique that replaces missing data with alternative values obtained from information in a dataset. Conventional imputation methods include K-nearest-neighbor imputation and multiple imputations. Recent methods include missForest, missRanger, and mixgb ,all which use machine learning algorithms. This paper compares the imputation techniques for datasets with mixed datatypes in various situations, such as data size, missing ratios, and missing mechanisms. To evaluate the performance of each method in mixed datasets, we propose a new imputation performance measure (IPM) that is a unified measurement applicable to numerical and categorical variables. We believe this metric can help find the best imputation method. Finally, we summarize the comparison results with imputation performances and computational times.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.99-105
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• 2014

Since traffic accidents account for the highest proportion of the artificial disasters which occur in urban areas along with fire, more scientific an analysis on the causes of traffic accidents and various prevention measures against traffic accidents are needed. In this study, the research selected Jinju-si, which belongs to local small and medium-sized cities as a research target to analyze the characteristics of temporal and spacial distribution of traffic accidents by associating the data of traffic accidents, occurred in 2013 with the causes of traffic accidents and location information that includes occurrence time and seasonal features. It subsequently examines the spatial correlation between traffic accidents and the characteristics of urban space development according to the plans of land using. As a result, the characteristics of accident distribution according to the types of accidents reveal that side right-angle collisions (car versus car) and pedestrian-crossing accident (car versus man) showed the highest clustering in the density analysis and average nearest neighbor analysis. In particular, traffic accidents occurred the most on roads which connect urban central commercial areas, high-density residential areas, and industrial areas. In addition, human damage in damage conditions, clear day in weather condition, dry condition in the road condition, and three-way intersection in the road way showed the highest clustering.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.547-557
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• 2012

This study purposed to estimate the forest biomass using k-Nearest Neighbor (k-NN) algorithm. Multiple data sources were used for the analysis such as forest type map, field survey data and Landsat TM data. The accuracy of forest biomass was evaluated with the forest stratification, horizontal reference area (HRA) and spatial filtering. Forests were divided into 3 types such as conifers, broadleaved, and Korean pine (Pinus koriansis) forests. The applied radii of HRA were 4 km, 5 km and 10 km, respectively. The estimated biomass and mean bias for conifers forest was 222 t/ha and 1.8 t/ha when the value of k=8, the radius of HRA was 4 km, and $5{\times}5$ modal was filtered. The estimated forest biomass of Korean pine was 245 t/ha when the value of k=8, the radius of HRA was 4km. The estimated mean biomass and mean bias for broadleaved forests were 251 t/ha and -1.6 t/ha, respectively, when the value of k=6, the radius of HRA was 10 km. The estimated total forest biomass by k-NN method was 799,000t and 237 t/ha. The estimated mean biomass by ${\kappa}NN$method was about 1t/ha more than that of filed survey data.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.47-65
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• 2021

This study aims to identify specific topics, trends, and structural characteristics of scholarly communication research, based on 1,435 articles published from 1970 to 2018 in the Scopus database through Latent Dirichlet Allocation topic modeling, serial analysis, and network analysis. Topic modeling, time series analysis, and network analysis were used to analyze specific topics, trends, and structures, respectively. The results were summarized into three sets as follows. First, the specific topics of scholarly communication research were nineteen in number, including research resource management and research data, and their research proportion is even. Second, as a result of the time series analysis, there are three upward trending topics: Topic 6: Open Access Publishing, Topic 7: Green Open Access, Topic 19: Informal Communication, and two downward trending topics: Topic 11: Researcher Network and Topic 12: Electronic Journal. Third, the network analysis results indicated that high mean profile association topics were related to the institution, and topics with high triangle betweenness centrality, such as Topic 14: Research Resource Management, shared the citation context. Also, through cluster analysis using parallel nearest neighbor clustering, six clusters connected with different concepts were identified.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1189-1192
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• 2005

본 논문에서는 스케치 연산자를 적용하여 견실한 얼굴인식 방법을 제안한다. 제안된 방법은 인식 대상의 중요한 특성인 에지(edge), 벨리(valley) 및 질감(texture) 성분을 효과적으로 표현하기 위한 방법으로써, BDIP(block difference of inverse probabilities)를 사용하여 얼굴의 특징을 스케치 영상과 같이 나타내는 얼굴 영상을 획득한다. 그리고, BDIP 처리된 얼굴 영상은 입력 데이터의 차원 축소 및 얼굴 특징 벡터의 추출을 위해 PCA(Principal Component Analysis)를 수행한 후, Nearest Neighbor 분류기를 통해 인식을 수행한다. 제안된 방법의 성능을 평가하기 위하여, 일반적으로 많이 사용되는 HE(Histogram equalization)을 사용한 얼굴 인식 방법과의 비교를 수행한다. 실험결과, 본 논문에서 제안한 방법이 고유값이 적은 경우에 가장 높은 인식률을 나타내는 것을 알 수 있었다.