• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.407-415
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• 2003

As services requesting data transfer from a sender to a multiple number of receivers become popular, efficient group communication mechanisms like multicast get much attention. Since multicast is more efficient than unicast in terms of bandwidth usage and group management for group communication, many multicast protocols providing scalability and reliability have been proposed. Recently, router-supported reliable multicast protocols have been proposed because routers have the knowledge of the physical multicast tree structure and, in this scheme, repliers which retransmit lost packets are selected by routers. Repliers are selected dynamically based on the network situation, therefore, any receiver within a multicast group can become a replier Hence, all receivers within a group maintains a buffer for loss recovery within which received packets are stored. It is an overhead for all group receivers to store unnecessary packets. Therefore, in this paper, we propose a new scheme which reduces resource usage by discarding packets unnecessary for loss recovery from the receiver buffer. Our scheme performs the replier selection and the loss recovery of lost packets based on the LSM [1] model, and discards unnecessary packets determined by ACKs from erasers which represent local groups.

• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.119-128
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• 2005

Although the evaluation of the mechanical properties and behavior of jointed rock masses is very important for the design of tunnel and underground openings, it has always been considered the most difficult problem. One of the difficulties in describing the rock mass behavior is the selection of the appropriate constitutive model. This limitation may be overcome with the progress in discrete element software such as PFC, which does not need the user to prescribe a constitutive model for rock mass. In this paper, a 30\;m\;\times\;30\;m\;\times\;30\;m m jointed rock mass of road tunnel site was analyzed. h discrete fracture network was developed from the joint geometry obtained from core logging and surface survey. Using the discontinuities geometry from the DFN model, PFC simulations were carried out, starting with the intact rock and systematically adding the joints and the stress-strain response was recorded for each case. With the stress-strain response curves, the mechanical properties of jointed rock masses were determined. As expected, the presence of joints had a pronounced effect on mechanical properties of the rock mass. More importantly, getting the mechanical response of the PFC model doesn't require a user specified constitutive model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.163-172
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• 2007

The web as global network includes text document, video, sound, etc and connects each distributed information using link Through development of web, it accumulates abundant information and the main is text based documents. Most of user use the web to retrieve information what they want. So, numerous researches have progressed to retrieve the text documents using the many methods, such as probability, statistics, vector similarity, Bayesian, and so on. These researches however, could not consider both the subject and the semantics of documents. As a result user have to find by their hand again. Especially, it is more hard to find the korean document because the researches of korean document classification is insufficient. So, to overcome the previous problems, we propose the korean document classification method for semantic retrieval. This method firstly, extracts TF value and RV value of concepts that is included in document, and maps into U-WIN that is korean vocabulary dictionary to select the topic of document. This method is possible to classify the document semantically and showed the efficiency through experiment.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.539-546
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• 2016

It is over 12 years since the launch of Korea Train eXpress (KTX) services. Demand for the KTX has been on the increase continuously but few studies have been produced related to this phenomenon. KTX passenger demand has been constantly increasing due to influencing factors such as the expansion of network, rise of oil prices, etc. In this study, our main focus is to verify that there are other types of elements that are causing an increase in KTX demand; our approach looks at changes in social and psychological aspect that have occurred due to the reduction of travel time and cost, as well as the imposition of a five-day workweek. In other words, we considered diffusion theory in the marketing area, which affects product selection and purchasing attitudes, as a key factor that is causing passenger demand to increase. That is to say that it is hypothesized that the demand for travel on the KTX has increased due to the train's utility, which is spread by the diffusion effect Therefore, the Bass diffusion model was applied to explain the dramatic increase in KTX passenger demand. Based on this foundation, it was also discussed how certain marketing strategies that incorporate the diffusion effect should be considered variously for sustainable management of rail transportation, while considering a steady passenger demand.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.229-235
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• 2011

In order to manage a sewer system effectively, flow conditions such as flux, water quality, Infiltration and Inflow (I/I), Combined Sewer Overflows (CSOs), etc need to be monitored on a regular base. Therefore, in sewer networks, a monitoring is so important to prevent the river disaster. Monitoring all nodes of an entire sewer system is not necessary and cost-prohibitive. Water quality monitoring points that can represent a sewer system should be selected in a economical manner. There is no a standard for the selection of monitoring points and the quantitative analysis of the observed data has not been applied in sewer system. In this study, the entropy method was applied for a sewer network to evaluate and determine the optimal water quality monitoring points using genetic algorithm. The entropy method allows to analyze the observed data for the pattern and magnitude of temporal water quality change. Since water quality measurement usually accompanies with flow measurement, a set of installation locations of flowmeters was chosen as decision variables in this study.

• Annals of Surgical Treatment and Research
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• v.95 no.5
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• pp.278-285
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• 2018

Purpose: We investigated the clinical outcomes of deceased donor kidney transplantation (KT) using kidneys with terminal acute kidney injury (AKI). Methods: Between February 2000 and December 2013, we performed 202 deceased donor renal transplants from 159 brain dead donors. According to the expanded criteria donor (ECD) and AKI network criteria, we divided 202 recipients into 4 groups: Group I: Non-AKI & standard criteria donor (SCD) (n = 97); group II: Non-AKI & ECD (n = 15); group III: AKI & SCD (n = 52); and group IV: AKI & ECD (n = 38). Results: The incidence of delayed graft function (DFG) was significantly higher in patients with AKI than it was in the non-AKI group (P = 0.008). There were no significant differences among the 4 groups in graft survival (P = 0.074) or patient survival (P = 0.090). However, the long-term allograft survival rate was significantly lower in group IV than it was in other groups (P = 0.024). Conclusion: Allografts from deceased donors with terminal AKI had a higher incidence of DGF than did those from donors without AKI. However, there is no significant difference in graft and patient survival rates among the groups. So, the utilization of renal grafts from ECDs with terminal AKI is a feasible approach to address the critical organ shortage.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.325-339
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• 2021

The purpose of the study is to examine sports-related cognitive functions through a systematic review and to suggest effective instruments to measure the cognitive functions. The present study was conducted based on the systematic review and meta-analysis protocol-the PRISMA. Of 429 articles searched through keywords from 2008 to 2020, 45 articles that met the selection criteria were analyzed. It was revealed that athletes had better cognitive functions than non-athletes, that the higher the sports expertise was, the higher the cognitive functions, and that there were differences in cognitive functions according to the sport types. The primary cognitive functions related to sports performance summarized as executive functions (inhibition ability, cognitive flexibility), information processing speed, spatial ability, and attention. As tasks for measuring each cognitive function, a stop signal task for inhibition ability, a design flexibility task for cognitive flexibility, a simple and choice reaction time test for information processing, a mental rotation task for spatial ability, and an attention network test for attention are appropriate.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.848-863
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• 2021

Purpose: On August 16, 2021, the Taliban established the Taliban regime after conquering capital Kabul of the Afghan by using the strong alliance of international terrorist organizations. The Taliban carried out terrorism targeting the Korean people, including the kidnapping of Kim Seon-il in 2004, the abduction of a member of the Saemmul Church in 2007, and the attack on Korean Provincial Reconstruction Team in 2009. Therefore, this research has shown the possibility of Taliban terrorism in Korea. Method: Based on the statistical data on terrorism that occurred in Afghanistan, Taliban's various terrorist activities such as tactics, strategies, and weapons are examined. Consequently, the target facilities and the type of terrorist attacks are analyzed. Result: The Taliban are targeting the Afghan government as their main target of attack, and IS and the Taliban differ in their selection of targets for terrorism. Conclusion: From the result of this research, we recommend Korea need to reinforce the counter terrorism system in soft targets. Because If the Taliban, which has seized control of Afghanistan, and IS, which has established a worldwide terrorism network, cooperate to threaten domestic multi-use facilities with bombing, the Republic of Korea may face a terrorist crisis with insufficient resources and counter-terrorism related countermeasures.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.