• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.679-682
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• 2008

High interest for the wireless network is going on the research to apply the related technologies in one's real life. Among these wireless network technologies, local area wireless network, Binary CDMA(Code Division Multiple Access), is the method transferring the data by using RF band based on 2.4Ghz. Binary CDMA has longer transmission distance than Bluetooth. Also, it is of benefit to an inexpensive price because the circuit is simple as compared with being similar to the performance of the existing CDMA. Though Binary CDMA has these benefits, one problem is a frequency overlap, and anther problem is to generate the sections with the shorter distance. To solve these problems, We propose the clustering method that can cover wide area.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.637-646
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• 2005

There are various problems while applying classification or clustering algorithm in that document classification which requires post processing or classification after getting as a web search result due to my keyword. Among those, two problems are severe. The first problem is the need to categorize the document with the help of the expert. And, the second problem is the long processing time the document classification takes. Therefore we propose a new method of web document clustering which can dramatically decrease the number of times to calculate a document similarity using the Transitive Closure Tree(TCT) and which is able to speed up the processing without loosing the precision. We also compare the effectivity of the proposed method with those existing algorithms and present the experimental results.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.115-125
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• 2015

This paper presents a Cuckoo search algorithm to secure adversaries misdirecting multi-hop routing in Mobile ad hoc networks (MANETs) using a robust Trust Predicated Routing Framework with an optimized cluster head selection. The clustering technique designed in this framework leads to efficient routing in MANETs. The heavy work load in the node causes an energy drop in cluster head, which leads to re-clustering of the group, and another cluster head is selected to avoid packet loss during data transmission. The problem in the re-clustering process is that the overall efficiency of the routing process is reduced and the processing time is increased. A Cuckoo search based optimization algorithm is proposed to solve the problem of re-clustering by selecting the secondary cluster head within the initially formed cluster group and eliminating the reclustering process. The proposed framework enables a node to select a reliable and secure route for MANET and the performance can be evaluated by comparing the simulated results with the AODV routing protocol, which shows that the performance of the proposed routing protocol are improved significantly.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1259-1268
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• 2017

The alpha eigenvalue problem in multigroup neutron diffusion is studied with particular attention to the theoretical analysis of the model. Contrary to previous literature results, the existence of eigenvalue and eigenflux clustering is investigated here without the simplification of a unique fissile isotope or a single emission spectrum. A discussion about the negative decay constants of the neutron precursors concentrations as potential eigenvalues is provided. An in-hour equation is derived by a perturbation approach recurring to the steady state adjoint and direct eigenvalue problems of the effective multiplication factor and is used to suggest proper detection criteria of flux clustering. In spite of the prior work, the in-hour equation results give a necessary and sufficient condition for the existence of the eigenvalue-eigenvector pair. A simplified asymptotic analysis is used to predict bands of accumulation of eigenvalues close to the negative decay constants of the precursors concentrations. The resolution of the problem in one-dimensional heterogeneous problems shows numerical evidence of the predicted clustering occurrences and also confirms previous theoretical analysis and numerical results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.1-18
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• 2018

Emergency response has been considered as an important task in practice, because it is directly associated with the survival of patients. However, it is very difficult to increase the number of fire stations due to the budget and efficiency problem. Under this circumstances, it is critical to consider the suitability of current arrangement for 911 fire station. This is especially true in Gyeonggi-Do where the characteristics of each sub-area are different. In response, this study aims to identify types of areas that are vulnerable for emergency situations, and try to find relevant solutions for each type. For this purpose, we collected 151,463 data for emergency declaration data which exceeds 10 minutes for its response. Total 19 clustering variables which are used as input variables are selected, considering the characteristics of each area. As a result of clustering analysis, three clusters are identified and analyzed. Finally, areas whose emergence response time is in top 10% are selected and analyzed. This paper is expected to find current issues and problems of emergency response for each area, and help to understand and solve the problem for the local government.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.225-227
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• 2006

The transmission network clustering using a fuzzy entropy function are proposed in this paper. We can define a similarity measure through a fuzzy entropy. All node in the transmission network system has its own values indicating the physical characteristics of that system and the similarity measure in this paper is defined through the system-wide characteristic values at each node. However, to tackle the geometric mis-clustering problem, that is, to avoid the clustering of geometrically distant locations with similar measures, the locational informations are properly considered and incorporated in the proposed similarity measure. In this paper, a new regional clustering measure for the transmission network system is proposed and proved. The proposed measure is verified through IEEE 39 bus system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.36-41
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• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. we show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1042-1044
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• 1996

The use of fuzzy theory in task of pattern recognition may be applicable gases and odours classification and recognition. This paper reports results obtained from fuzzy c-means algorithms to patterns generated by odour sensing system using an array of conducting polymer sensors, for volatile chemicals. For the volatile chemicals clustering problem, the three unsupervise fuzzy c-means algorithms were applied. From among the pattern clustering methods, the FCMAW algorithm, which updated the cluster centres more frequently, consistently outperformed. It has been confirmed as an outstanding clustering algorithm throughout experimental trials.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.590-600
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• 2014

In the wireless sensor network environment, clustering scheme has a problem that a large amount of energy is unnecessarily consumed because of frequently occurred entire re-clustering process. Some of the studies were attempted to improve the network performance by getting rid of the entire network setup process. However, removing the setup process is not worthy. Because entire network setup relieves the burden of some sensor nodes. The primary aim of our scheme is to cut down the energy consumption through minimizing entire setup processes which occurred unnecessarily. Thus, we suggest a re-clustering scheme that considers event detection, transmitting energy, and the load on the nodes. According to the result of performance analysis, our scheme reduces energy consumption of nodes, prolongs the network lifetime, and shows higher data collection rate and higher data accuracy than the existing schemes.