• The Pure and Applied Mathematics
• /
• v.31 no.2
• /
• pp.131-158
• /
• 2024

This study investigates the impact of precautionary measures, such as isolating exposed individuals, wearing masks, and maintaining physical distance, on preventing infectious disease. A deterministic SEIQHRV epidemic model is employed for this purpose. The model's positivity, boundedness, disease-free, and endemic equilibrium points are identified. A sensitivity test assesses the impact of preventive measures on infected classes. Results show that a basic reproduction number less than unity drives disease eradiction, while a higher unity value encourages the adoption of preventive measures.

• Journal of the Korean association of regional geographers
• /
• v.7 no.3
• /
• pp.107-120
• /
• 2001

This study is concerned with providing a reliable procedure of calibrating a set of places specific distance parameters and with applying it to U.S. inter-State migration flows between 1985 and 1900. It attempts to conform to recent advances in quantitative geography that are characterized by an integration of ESDA(exploratory spatial data analysis) and local statistics. ESDA aims to detect the spatial clustering and heterogeneity by visualizing and exploring spatial patterns. A local statistic is defined as a statistically processed value given to each location as opposed to a global statistic that only captures an average trend across a whole study region. Whereas a global distance parameter estimates an averaged level of the friction of distance, place-specific distance parameters calibrate spatially varying effects of distance. It is presented that a poisson regression with an adequately specified design matrix yields a set of either origin-or destination-specific distance parameters. A case study demonstrates that the proposed model is a reliable device of measuring a spatial dimension of migration, and that place-specific distance parameters are spatially heterogeneous as well as spatially clustered.

• Journal of KIISE:Software and Applications
• /
• v.35 no.9
• /
• pp.572-587
• /
• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• The Journal of the Korea Contents Association
• /
• v.7 no.2
• /
• pp.117-124
• /
• 2007

This paper describes methodology that enables animators to create the facial expression animations and to control the facial expressions in real-time by reusing motion capture datas. In order to achieve this, we fix a facial expression state expression method to express facial states based on facial motion data. In addition, by distributing facial expressions into intuitive space using LLE algorithm, it is possible to create the animations or to control the expressions in real-time from facial expression space using user interface. In this paper, approximately 2400 facial expression frames are used to generate facial expression space. In addition, by navigating facial expression space projected on the 2-dimensional plane, it is possible to create the animations or to control the expressions of 3-dimensional avatars in real-time by selecting a series of expressions from facial expression space. In order to distribute approximately 2400 facial expression data into intuitional space, there is need to represents the state of each expressions from facial expression frames. In order to achieve this, the distance matrix that presents the distances between pairs of feature points on the faces, is used. In order to distribute this datas, LLE algorithm is used for visualization in 2-dimensional plane. Animators are told to control facial expressions or to create animations when using the user interface of this system. This paper evaluates the results of the experiment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.3
• /
• pp.246-252
• /
• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.

• The Journal of the Korea Contents Association
• /
• v.22 no.5
• /
• pp.15-25
• /
• 2022

This study aims to identify the family and lineage of a part of a "Fish Tribe Conference" in the section Tokkijeon by utilizing computer literature analysis techniques. First of all, we encode the classification for a pairwise comparison's type of each paragraph to build a corpus, and based on this, we use the Hamming distance to calculate the distance matrix between each classification for a pairwise comparison's. We visualized classification for a pairwise comparison's clustering pattern by applying multidimensional scale method, and hierarchical clustering to explore the characteristics of the 'fish family' line and lineage compared to the existing cluster analysis study on entire paragraphs of "Tokkijeon". As a result, unlike the cluster analysis of the entire paragraph of "Tokkijeon", which consists of six categories, the "Fish Tribe Conference" section has five categories and some classification for a pairwise comparison's accesses. The results of this study are that the relative distance between Yibon was measured and systematic classification was performed in an objective and empirical way by calculation, and the characteristics of the line of the fish family were revealed compared to the analysis of the entire rabbit exhibition.

• Proceedings of the Korea Association of Crystal Growth Conference
• /
• 2000.06a
• /
• pp.195-214
• /
• 2000

The high-speed steel (shorten as HSS) consists of Fe and several kinds of transition metal carbides. The cutting tools or wear-resistant materials made from HSS experience relatively high thermal shock because a coolant such as water or oil is flowed over the surface of heated HSS. The purpose of this research is to increase the hardness, strength, fracture toughness and thermal shock resistance of HSS. A possible strategy is to incorporate a hard ceramic material with high strength in HSS matrix. This paper describes the processing, microstructure and mechanical properties of the oriented unidirectional mullite fiber/HSS composite. The unidirectional mullite fibers of 10${\mu}{\textrm}{m}$ diameter were dispersed by the ultrasonic irradiation of 38 kHz in an ethylenglycol suspension containing HSS powder of 11${\mu}{\textrm}{m}$ median size. The dried green composites with 4-68 vol% fibers were hot-pressed for 2h at 100$0^{\circ}C$ in Ar atmosphere under a pressure of 39 MPa. The higher density was achieved in the composite with a lower content of fibers. The oriented unidirectional fibers were well dispersed in the HSS matrix. The average distance between the center of fibers in the cross section was close to the value calculated from the fiber fraction. No reaction occurred at the interfaces between HSS and mullite fibers in the composites. The composite with 13.6 vol% fibers showed 100 MPa of four point flexural strength at room temperature. The thermal expansion of composite with heating was influenced by the orientation of mullite fibers.

• The Journal of Society for e-Business Studies
• /
• v.20 no.4
• /
• pp.41-59
• /
• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

• Science of Emotion and Sensibility
• /
• v.7 no.4
• /
• pp.57-65
• /
• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.5
• /
• pp.1033-1039
• /
• 2012

In this paper, a simple and robust algorithm is proposed for detecting each steel plate from a image which contains several steel plates. Steel plate is characterized by line edge, so line detection is a fundamental task for analyzing and understanding of steel plate images. To detect the line edge, the proposed algorithm uses the small eigenvalue analysis. The proposed approach scans an input edge image from the top left corner to the bottom right corner with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Using the detected line edges, each plate is determined based on the directional information and the distance information of the line edges. The results of the experiments emphasize that the proposed algorithm detects each steel plate from a image effectively.