• The Journal of the Korea Contents Association
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• v.2 no.1
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• pp.68-76
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• 2002

Clustering is representative method of components classification. But, previous clustering method that use cohesion and coupling can not be effective, because design pattern has consisted by relation between classes. In this paper, we classified design patterns with special quality of pattern structure. Classification by clustering had expressed higher correctness degree than classification by facet. Therefore, can do that it is effective that classify design patterns using clustering algorithms that is automatic classification method. When we are searching design patterns, classification of design patterns can compare and analyze similar patterns because similar patterns is saved to same category. Also we can manage repository efficiently because of using and storing link information of patterns.

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.315-333
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• 2016

This study aims to perform damage identification for Da-Sheng-Guan (DSG) high-speed railway truss arch bridge using fuzzy clustering analysis. Firstly, structural health monitoring (SHM) system is established for the DSG Bridge. Long-term field monitoring strain data in 8 different cases caused by high-speed trains are taken as classification reference for other unknown cases. And finite element model (FEM) of DSG Bridge is established to simulate damage cases of the bridge. Then, effectiveness of one fuzzy clustering analysis method named transitive closure method and FEM results are verified using the monitoring strain data. Three standardization methods at the first step of fuzzy clustering transitive closure method are compared: extreme difference method, maximum method and non-standard method. At last, the fuzzy clustering method is taken to identify damage with different degrees and different locations. The results show that: non-standard method is the best for the data with the same dimension at the first step of fuzzy clustering analysis. Clustering result is the best when 8 carriage and 16 carriage train in the same line are in a category. For DSG Bridge, the damage is identified when the strain mode change caused by damage is more significant than it caused by different carriages. The corresponding critical damage degree called damage threshold varies with damage location and reduces with the increase of damage locations.

• Journal of Distribution Science
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• v.18 no.3
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• pp.25-36
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• 2020

Purpose: Based on previous researches on social factors of digital item purchase in digital contents distribution platforms such as SNS, we aim to develop the integrated model that accounts for the dynamic and interactive relationship between social structure indicators and digital item purchase. Research design, data and methodology: A PVAR model was used to capture endogenous and dynamic relationships between digital item purchase and network indicators. Results: We find that there exist considerable endogenous and dynamic relationships between digital item purchase and network structure variables. Not only lagged in-degree and out-degree but also in-closeness and out-closeness centrality have significant and positive impacts on digital item purchase. Lagged clustering has a significant and negative effect on digital item purchase. Lagged purchase has a significant and positive impact just on the present in-closeness and out-closeness centrality; but there is no significant effect of lagged purchase on the other two degree variables and clustering coefficient. We also find that both closeness centralities have much higher carryover effect on digital item purchase and that the elasticity of both closeness centralities on the purchase of digital items is even higher than that of other network structure variables. Conclusions: In-closeness and out-closeness are the most influential factors among social structure variables of this study on digital item purchase.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.751-754
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• 2000

In this paper, we propose an efficient method to detect abrupt shot changes in compressed MPEG video data by using reference ratios among video frames. The reference ratios among video frames imply the degree of similarities among adjacent frames by prediction coded type of each frames. A shot change is detected if the similarity degrees of a frame and its adjacent frames are low. This paper proposes an efficient shot change detection algorithm by using Fuzzy c-means(FCM) clustering algorithm. The FCM clustering uses the shot change probabilities evaluated in the mask matching of reference ratios and difference measure values based on frame reference ratios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1696-1702
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• 2008

A MANET(Mobile Ad-hoc Network) is a multi-hop routing protocol formed by a collection without the intervention of infrastructure. So the MANET also depended on the property as like variable energy, high degree of mobility, location environments of nodes etc. Generally the various clustering technique and routing algorithm would have proposed for improving the energy efficiency. One of the popular approach methods is a cluster-based routing algorithm using in MANET. In this paper, we propose an algorithm techniques which is TICC (Time Interval Clustering Control) based on energy value in property of each node for solving cluster problem. It provides improving cluster energy efficiency how can being node manage to order each node's energy level. TICC could be able to manage the clustering, re-configuration, maintenance and detection of Node in MANET. Furthermore, the results of modeling shown that Node's energy efficiency and lifetime are improved in MANET.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.205-215
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• 2024

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.438-447
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• 2006

Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.275-280
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• 2021

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

• Journal of Distribution Science
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• v.16 no.5
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• pp.45-59
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• 2018

Purpose - This paper aims to analyze the distinct pattern of clustering of imported automobile distributors and provide evidence for the phenomenon using Korean data. Research design, data, and methodology - In this paper, we use data from Korea Automobile Importers & Distributors Association of 23 foreign automobile brands to evaluate the degree of concentration of showrooms using locational Gini index. We identify possible causes for the high level of clustering from two perspectives; 1) on the distributors' side and 2) on the customers' side. Results - We find a very strong locational concentration of imported automobile showrooms within close vicinity in the major cities and districts in Korea. Locational Gini coefficients are 0.1024 at the national level, 0.1836~0.3763 at city level, and 0.3941~0.4311 at district level on a [0,0.5] scale. Conclusions - Luxury foreign automobile customers tend to shop extensively around multiple brands prior to their ideal model selection. Accordingly, the imported automobile distributors cluster together close to their direct competitors in order to give a good comparison opportunity for the potential customers. This will maximize the probability of the visits of potential customers and lead to successful sales performance.

• Journal of Korean Society for Quality Management
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• v.39 no.1
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• pp.98-108
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• 2011

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.