• Journal of Information Technology Applications and Management
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• v.18 no.4
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• pp.21-40
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• 2011

Emoticons perform an important role as an enhancement to written communication, in areas such as Windows Live Messenger instant messaging, e-mails, mobile Short Message Services (SMS), and others. Emoticons are graphic images used in communications to indicate the feelings of people exchanging messages via mobile technology. In this research, the perceived usefulness of the emoticon in mobile phone text messages is verified with consumers using the five construct model of Huang. A K-means clustering technique for separating three groups based on levels of perceived usefulness of mobile emoticons is used with a structural equation model test using Smart PLS 2.0, and the bootstrap re-sampling procedure. We analyzed relationships among use of emoticons, enjoyment, interaction, information richness, and perceived usefulness. The results show there are relationships among use of emoticons, enjoyment, interaction, perceived usefulness, and information richness, however enjoyment of emoticons did not significantly affect the perceived usefulness of messages with emoticons alone. The results suggest emoticons have different affects on emotion in both mobile, and Messenger contexts. Our study did not consider more detailed media properties, and thus more studies are needed. Our research results contribute to mobile communication activation, provides companies with an understanding of key characteristics of consumers who use emoticons, and provides useful implications for improving management and marketing strategies.

• Journal of Information Technology Applications and Management
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• v.18 no.4
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• pp.81-93
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• 2011

By using GOSST theory, this paper models SSCFLP taking FLP, capacity of the facilities, single source capacitated limitation level and service enhancement issues into consideration. GOSST theory is strongly suggested as the solution procedure for these issues. We have used clustering of Center of Gravity method using the case study of the company S and then, took a heuristic GOSST measure in the alternative selection process. As a result, the research finds an alternative solution that both meets the satisfactory level of service and achieves consistent distribution capacity. When using this modeling, especially, to select the location of the logistics distribution center, the efficiency of current facilities is maximized while offering the minimum geometric distance for the alternative. Also, we can expect that the illustrated model and alternative solution can be applied to architecture of distribution system, to selection of telecommunication system locations for wireless network and to relocation of related facilities due to their sensitivities to location and weight.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.1-6
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• 2020

The purpose of this study is to theoretically understand hope theory suggested by Snyder and confirm its utility in the school settings. We analyzed the survey data responded by general high school students to find clustering types of hope and mean difference of learning strategies by each type through ANOVA. Results are as follows. First, hope by cluster analysis resulted in four types. Second, hope and learning strategy showed statistically positive correlation. Especially two sub-variables of hope and meta-cognition had highest correlation. Researchers suggested the direction of a future study to investigate structural relation among hope profile, student achievement, adjustment, and etc.

• Journal of Contemporary Eastern Asia
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• v.13 no.1
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• pp.69-81
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• 2014

While much in the field of public management has emphasized the importance of nonprofit advocacy activities in policy and decision-making procedures, few have considered the relevance and impact of leading actors on structuring diverse patterns of information sharing and communication through social media. Building nonprofit advocacy is a complicated process for a single organization to undertake, but social media applications such as Facebook and Twitter have facilitated nonprofit organizations and stakeholders to effectively share information and communicate with each other for identifying their mission as it relates to environmental issues. By analyzing the Australian Marine Conservation Society's (AMCS) Twitter network data from the period 1 April to 20 April, 2013, this research discovered diverse patterns in nonprofit advocacy by leading actors in building advocacy. Based on the webometrics approach, analysis results show that nonprofit advocacy through social media is structured by dynamic information flows and intercommunications among participants and followers of the AMCS. Also, the findings indicate that the news media and international and domestic nonprofit organizations have a leading role in building nonprofit advocacy by clustering with their followers.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.620-622
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• 2004

Mobility of an indoor wheeled robot is affected by adhesion force that is related to various floor conditions. When the adhesion force between driving wheels and the floor decreases suddenly, the robot has a slip state. In order to overcome this slip problem, optimal slip velocity must be decided for stable movement of wheeled robot. First of all, this paper shows that conventional PI control can not be applied to a wheeled robot of the light weigh. Secondly, reposed fuzzy logic applied by the Takagi-Sugeno model for the configuration of fuzzy sets. For the design of Takaki-Sugeno model and fuzzy rule, proposed algorithm uses FCM(Fuzzy c-mean clustering method) algorithm. In additionally, this algorithm controls recovered driving torque for the restrain the re-slip. The proposed fuzzy logic controller(FLC) is pretty useful with prevention of the slip phenomena through that compare fuzzy with PI control for the controller performance in the re-adhesion control strategy. These procedures are implemented using a Pioneer 2-DXE wheeled robot parameter.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products me classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem far disposal products. In this paper, a heuristic approach fuzzy ART neural network is suggested. The modified fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its ai is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.

• Smart Structures and Systems
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• v.7 no.2
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2B
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• pp.269-278
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• 2010

In the clustering protocol, lifetime of the cluster members radically decrease because frequency interference between clusters make every cluster member consume a lot of energy to maintain or increase its transmission rate. In this paper, we analyze the frequency interference among the clusters with the game theory which deals with resource bargaining problems between players, and present a rational power allocation strategy. Both the cases that each cluster tries to selfishly occupy and cooperatively share the resource are analyzed in terms of non-cooperative and cooperative games. In simulation, we compare the cooperative game with non-cooperative game in terms of the node lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.43-59
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korean new year addresses, one of most important and authoritative document publicly announced by North Korean government. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. We propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and co-occurrence networks. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of Kim Jung Un of the North Korea from 2017 to 2019. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. We found that uncovered semantic structures of North Korean new year addresses closely follow major changes in North Korean government's positions toward their own people as well as outside audience such as USA and South Korea.