• Smart Media Journal
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• v.4 no.3
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• pp.9-15
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• 2015

A pub(lish)/sub(scribe) system is a data forwarding system which forwards only interesting data among the whole published data, which is related to the subscriptions registered by end users. Classical pub/sub systems are realized by constructing a network of brokers which are responsible for storing or forwarding data. Along with the substantial increase of the population mobile users, it is required that the pub/sub system handles the subscriptions of user locations which changes continuously and frequently. In this paper, a new broker network based pub/sub system which efficiently handles the frequent changes of subscriptions related to user locations is proposed. In consideration of moving patterns of users and geographical property, the proposed pub/sub system categorize the entire data space into Slow Moving Region and Normal Moving Region, and manages the brokers which are responsible for these regions by using Q+R tree in order to handle user requests more efficiently. Through the extensive simulation, it is presented that the proposed Q+R tree based pub/sub system can reduce unnecessary needs of brokers and network traffic and can support the dynamic subscription related to user location.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.783-792
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• 2022

Versatile Video Coding (VVC) is the latest video coding standard, which had been developed by the Joint Video Experts Team (JVET) of ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Moving Picture Experts Group (MPEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal block structures during the encoding process. In this paper, we propose a fast ternary tree decision method using two neural networks with 7 nodes as input vector based on the multi-layer perceptron structure, names STH-NN and STV-NN. As a training result of neural network, the STH-NN and STV-NN achieved accuracies of 85% and 91%, respectively. Experimental results show that the proposed method reduces the encoding complexity up to 25% with unnoticeable coding loss compared to the VVC test model (VTM).

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1213-1220
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• 2022

The Emergency Diesel Generator (EDG) is a critical and essential part of the Nuclear Power Plant (NPP). Due to past catastrophic disasters, critical systems of NPP like EDG are designed to meet high dependability requirements. Therefore, we propose a framework for the dynamic reliability assessment using the Fault Tree and the Dynamic Bayesian Network. In this framework, the information of the component's failure probability is updated based on observed data. The framework is powerful to perform qualitative as well as quantitative analysis of the system. The validity of the framework is done by applying it on several NPP systems.

• The Journal of Information Systems
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• v.8 no.1
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• pp.109-129
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• 1999

This study deals with the problem of information dissemination in a communication network, which is defined to be the process whereby a set of messages, generated by an originator, is transmitted to all the members within the network. Since this type of message generally includes control data to manage the network or global information that all members should know, it is to be required to transmit it to all the members as soon as possible. In this study, it is assumed that a member can either transmit or receive a message and an informed member can transmit it to only one of its neighbors at time. This type of transmission is called 'local broadcasting' Several schemes of call sequencing are designed for a general-type network with nonuniform edge transmission times, and then computer simulations are performed. Some heuristics for information dissemination are proposed and tested. For this, optimal call sequence in a tree-type network, sequencing theory and graph theory are applied. The result shows that call sequencing based on the shortest path tree is the most desirable.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.11
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• pp.1-11
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• 2009

A content based pub/sub (Publish/subscribe) services at the peer-to-peer network has the requirements about how to distribute contents information of subscriber and to delivery the events efficiently. For satisfying the requirements, a DHT(Distributed Hash Table) based pub/sub overlay networking and tree type topology based network construction using filter technique have been proposed. The DHT based technique is suitable for topic based pub/sub service but it's not good contents based service that has the variable requirements. And also filter based tree topology networking is not efficient at the environment where the user requirements are distributed. In this paper we propose the R-Tree algorithm based pub/sub overlay network construction method. The proposed scheme provides cost effective event delivery method by mapping user requirement to multi-dimension and hierarchical grouping of the requirements. It is verified by simulation at the variable environment of user requirements and events.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.346-353
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• 2011

Routing schemes that service applications with various delay times, maintaining the long network life time are required in wireless sensor & actor networks. However, it is known that network lifetime and hop count of trees used in routing methods have the tradeoff between them. In this paper, we propose a Pareto Ant Colony Optimization algorithm to find the Pareto tree set such that it optimizes these both tradeoff objectives. As it enables applications which have different delay times to select appropriate routing trees, not only satisfies the requirements of various multiple applications but also guarantees long network lifetime. We show that the Pareto tree set found by proposed algorithm consists of trees that are closer to the Pareto optimal points in terms of hop count and network lifetime than minimum spanning tree which is a representative routing tree.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.760-766
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• 2012

In this paper, we propose an efficient flooding process on link-state routing protocol. It is possible to exchange information using typical link-state routing protocol; for example, OSPF(Open Short Path First) or IS-IS(Intermediate system routing protocol) that floods LSA between nodes when the network topology change occurs. However, while the scale of network is getting bigger, it affects the network extensibility because of the unnecessary LSA that causes the increasing utilization of CPU, memory and bandwidth. An existing algorithm based on the Minimum spanning tree has both network instability and inefficient flooding problem. So, we propose algorithm for efficient flooding while maintaining network stability. The simulation results show that the flooding of proposed algorithm is more efficient than existing algorithm.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.693-712
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• 2012

The paper presents three machine learning based keyphrase extraction methods that respectively use Decision Trees, Na$\ddot{i}$ve Bayes, and Artificial Neural Networks for keyphrase extraction. We consider keyphrases as being phrases that consist of one or more words and as representing the important concepts in a text document. The three machine learning based keyphrase extraction methods that we use for experimentation have been compared with a publicly available keyphrase extraction system called KEA. The experimental results show that the Neural Network based keyphrase extraction method outperforms two other keyphrase extraction methods that use the Decision Tree and Na$\ddot{i}$ve Bayes. The results also show that the Neural Network based method performs better than KEA.

• Journal of the Ergonomics Society of Korea
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• v.21 no.1
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• pp.1-14
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• 2002

The main purpose of this paper is to present selection criteria for ROK Airforce pilot training candidates in order to save costs involved in sequential pilot training. We use classification models such Decision Tree, Logistic Regression and Neural Network based on aptitude test results of 288 ROK Air Force applicants in 1994-1996. Different models are compared in terms of classification accuracy, ROC and Lift-value. Neural network is evaluated as the best model for each sequential flight test result while Logistic regression model outperforms the rest of them for discriminating the last flight test result. Therefore we suggest a pilot selection criterion based on this logistic regression. Overall. we find that the factors such as Attention Sharing, Speed Tracking, Machine Comprehension and Instrument Reading Ability having significant effects on the flight results. We expect that the use of our criteria can increase the effectiveness of flight resources.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.397-399
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• 1994

In this paper a feature-based automatic neural network signal classifier is presented, where five neural network algorithms such as MLP, RBF, LVQ2, MLP-Tree and LVQ-Tree are combined in parallel to classifiy various signals from their features, based on the majority vote method. To demonstrate the performance and applicability of the proposed signal classifier, some test results for the classification of synthetic waveforms and power disturbances are provided.