• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.10-15
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• 2012

This paper considers an economic design problem of a tree-based network which is a kind of computer network. This problem can be modeling to be an objective function to minimize installation costs, on the constraints of spanning tree and maximum traffic capacity of sub tree. This problem is known to be NP-hard. To efficiently solve the problem, a tabu list coupled genetic algorithm approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a genetic algorithm approach.

• Convergence Security Journal
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• v.19 no.1
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• pp.3-10
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• 2019

The 5G network is a next-generation converged network that combines various ICT technologies to realize the need for high speed, hyper connection and ultra low delay, and various efforts have been made to address the security vulnerabilities of the previous generation mobile networks. However, the standards released so far still have potential security vulnerabilities, such as USIM deception and replication attack, message re-transmission attack, and race-condition attack. In order to solve these security problems, this paper proposes a new 5G-AKA protocol with PUF technology, which is a physical unclonable function. The proposed PUF-based 5G-AKA improves the security vulnerabilities identified so far using the device-specific response for a specific challenge and hash function. This approach enables a strong white-list policy through the addition of inexpensive PUF circuits when utilizing 5G networks in areas where security is critical. In addition, since additional cryptographic algorithms are not applied to existing protocols, there is relatively little burden on increasing computational costs or increasing authentication parameter storage.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.91-95
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• 2010

Using artificial neural network to use a search preference based on the user's information, the ranking of search results that will enable flexible searches can be improved. After trained in several different queries by other users in the past, the actual search results in order to better reflect the use of artificial neural networks to neural network learning. In order to change the weights constantly moving backward in the network to change weights of backpropagation algorithm. In this study, however, the initial training, performance data, look for increasing the number of lessons that can be overfitted. In this paper, we have optimized a lot of objects that have a strong advantage to apply genetic algorithms to the relevant page of the search rankings flexible as an object to the URL list on a random selection method is proposed for the study.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.71-81
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• 2010

In grid system, Task scheduling based on list scheduling models has showed low complexity and high efficiency in fully connected processor set environment. However, earlier schemes did not consider sufficiently the communication cost among tasks and the composition process of lightpath for communication in optical gird environment. In this thesis, we propose LSOG (Leveling Selection in Optical Grid) which sets task priority after forming a hierarchical directed acyclic graph (DAG) that is optimized in optical grid environment. To determine priorities of task assignment in the same level, proposed algorithm executes the task with biggest communication cost between itself and its predecessor. Then, it considers the shortest route for communication between tasks. This process improves communication cost in scheduling process through optimizing link resource usage in optical grid environment. We compared LSOG algorithm with conventional ELSA (Extended List Scheduling Algorithm) and SCP (Scheduled Critical Path) algorithm. We could see the enhancement in overall scheduling performance through increment in CCR value and smoothing network environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2B
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• pp.131-140
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• 2011

As the number of Internet users and applications is increasing, the importance of application traffic classification is growing more and more for efficient network management. While a number of methods for traffic classification have been introduced, such as signature-based and machine learning-based methods, Skype application, which uses encrypted communication on its own P2P network, is known as one of the most difficult traffic to identify. In this paper we propose a novel method to identify Skype application traffic on the fly. The main idea is to setup a list of Skype host information {IP, port} by examining the packets generated in the Skype login process and utilizes the list to identify other Skype traffic. By implementing the identification system and deploying it on our campus network, we proved the performance and feasibility of the proposed method.

• Korean Journal of Construction Engineering and Management
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• v.24 no.1
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• pp.40-50
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• 2023

Quality control (QC) is an essential work for the successful construction project execution. Recently, robust application of ICT to the QC tasks leads to utilizing innovative technologies and equipment. However, overall planing of QC works needs to take place before applying new technologies to each and individual QC task. The objectives of this research involve developing a database and an algorithm that identifies QC tasks and related information upfront. In addition, the researchers developed a methodology to generate inspection tasks in conjunction with construction work tasks. The Korean Ministry of Land and Transportation provides standard supervision checklists. They were classified based on criteria of inspection items, methods, period and the scope. Reinforced concrete work was selected as a case study for validation of the method. This framework can function when planing construction tasks with any type of planning tools and innovative technologies. The researchers expect this framework may contribute to various construction projects when developing QC plans and tasks with applicable technologies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.8
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• pp.1847-1854
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• 2005

This paper proposes a approach to configure efficient video-on-demand system by introducing Multicast and Cache Video-on-Demand (MCVoD) system. As a key element or the MCVoD system, interchange agent provides this system with multicasting and switching functions. With the multicasting, the MCVoD system is able to reduce the load on the network as well as VoD servers by transmitting only one video request instead of sending multiple requests on a same video stream. The switching enables clients to receive the lust stream of requested video streams instantly without waiting time and also allows avoiding undesirable duplication of video streams in the system. With various experiment results through simulation about waiting tine and cache hit ratio, we show that the MCVoD system employing the interchange agent provides better performance than current uni-proxy based system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.17-34
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• 2022

This paper develops a model for dynamic station assignment to optimize the Demand Responsive Transit (DRT) operation. In the process of optimization, we use the bus travel time as a variable for DRT management. In addition, walking time, waiting time, and delay due to detour to take other passengers (detour time) are added as optimization variables and entered for each DRT passenger. Based on a network around Anaheim, California, reserved origins and destinations of passengers are assigned to each demand responsive bus, using K-means clustering. We create a model for selecting the dynamic station and bus route and use Non-dominated Sorting Genetic Algorithm-III to analyze seven scenarios composed combination of the variables. The result of the study concluded that if the DRT operation is optimized for the DRT management, then the bus travel time and waiting time should be considered in the optimization. Moreover, it was concluded that the bus travel time, walking time, and detour time are required for the passenger.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.