• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.941-950
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• 2001

A study of counter-current two-phase flow in narrow rectangular channels has been performed. Two-phase flow regimes were experimentally investigated in a 760mm long and 100mm wide test section with 2.0 and 5.0mm gap widths. The resulting flow regime maps were compared with the existing transition criteria. The experimental data and the transition criteria of the models showed relatively good agreement. However, the discrepancies between the experimental data and the model predictions of the flow regime transition become pronounced as the gap width increased. As the gap width increased the transition gas superficial velocities increased. The critical void fraction for the bubbly-to-slug transition was observed to be about 0.25. The two-phase distribution parameter for the slug flow was larger for the narrower channel. The uncertainties in the distribution parameter could lead to a disagreement in slug-to-churn transition between the experimental findings and the transition criteria. For the transition from churn to annular flow the effect of liquid superficial velocity was found to be insignificant.

• IE interfaces
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• v.18 no.3
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• pp.277-287
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• 2005

In this paper, we deal with a new techno-economic modeling and analysis for optical access networks. In deploying the fiber-to-the-home (FTTH) architecture, network planner needs to consider the following techno-economic issues: when do we need to upgrade existing local access network to FTTH network? how much do we invest to maximize profit? In order to answer these techno-economic questions, we need to consider the impact of emerging technologies and business environment. Toward this end, we develop a new techno-economic modeling to deal with the inherent complexity of technology evolution and cost economics. In particular, the new modeling approach provides us with an techno-economic analysis of technology alternatives such as ethernet passive optical network (E-PON) and wavelength division multiplex passive optical network (WDM-PON). In this analysis, we focus on the impact of critical factors such as the cost characteristic of proposed architecture and digital subscriber line (DSL) subscriber's churn-in to FTTH service and churn-out. We develop mixed integer-programming models for finding the evolution path of local access networks to broadband network architectures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.27-30
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• 2003

The introduction of reseller In Australian Fixed Telecommunication Market resulted in the reduction of Telstra's local call market share by 13 percent and average fall rate was reduced. Thus, Telstra increased basic rate at 14.5 percent to compensate loss revenue in the local call market. With the deployment of carrier pre-selection of long distance and international calls, it reduced long distance rate at 23.5 percent and international tall rate at 53 percent, and increased the Churn rate. Therefore, the deployment of service-based competition brought efficient results for long distance and international call market. However, LM market created 13.4 percent reduction in call rates, complications in charge system, technical barriers and the preference of one-bill by customers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.490-508
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• 2013

One of the most important scalability issues facing the current Internet is the rapidly increasing rate of BGP updates (BGP churn), to which route flap and path exploration are the two major contributors. Current countermeasures would either cause severe reachability loss or delay BGP convergence, and are becoming less attractive for the rising concern about routing convergence as the prevalence of Internet-based real time applications. Based on the observation that highly active prefixes usually repeatedly explore very few as-paths during path exploration, we propose a router-level mechanism, Path Exploration Aggregation (PEA), to scale BGP without either causing prefix unreachable or slowing routing convergence. PEA performs aggregation on the transient paths explored by a highly active prefix, and propagates the aggregated path instead to reduce the updates caused by as-path changes. Moreover, in order to avoid the use of unstable routes, PEA purposely prolongs the aggregated path via as-path prepending to make it less preferred in the perspective of downstream routers. With the BGP traces obtained from RouteViews and RIPE-RIS projects, PEA can reduce BGP updates by up to 63.1%, shorten path exploration duration by up to 53.3%, and accelerate the convergence 7.39 seconds on average per routing event.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.47-58
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• 2018

Game bots distribute a large amount of goods or items used in a game, thereby lowering the value of game goods and items. Also, a large number of game bots hunt monsters and collect items, which hinders ordinary users from enjoying content normally. However, no research has been done on the type of user and the type of activity that the increase in bots specifically affects. Therefore, this study provides a practical implication to encourage users to use games by classifying types based on the game users' access data and analyzing the correlation with user departure due to the increase of bots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.78-79
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• 2010

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.12a
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• pp.119-129
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• 2005

현재의 이동통신시장은 시장의 포화상태로 인해 신규 고객의 확보보다는 기존 고객의 유지에 마케팅 활동을 강화하고 있다. 본 연구에서는 이탈고객관리(churn management)를 위한 방안으로 데이터마이닝 기법에 기반하여 고객을 등급별로 세분화하였다. 이동통신 고객데이터를 활용하여 로짓모형, 인공신경망, SVM 등을 이탈고객 예측모형을 개발하였고, 각 모형별 성과를 통계적으로 비교하였다. 이탈고객 예측모형을 통해 고객의 이탈가능성을 등급화하여 등급별 이탈확률과 점유율, 적중률을 산출하였다. 제안된 고객등급화 방법을 통해 이동통신사들은 고객의 이탈확률에 따른 차별화된 마케팅 전락을 수행할 수 있을 것으로 기대된다.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1007-1018
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• 2008

The purpose of this case study is to demonstrate database-marketing management. First, we explore original variables for insurance customer's data, modify them if necessary, and go through variable selection process before analysis. Then, we develop churn prediction models using logistic regression, neural network and SVM analysis. We also compare these three data mining models in terms of misclassification rate.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.