• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.201-217
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• 2013

This paper analyzes the effect of network neutrality regulation on social welfare using the two-sided market under the network congestion. This paper deals with zero price rule regulation which means the price regulation on the side of content. The results are as follows : First, under the monopoly platform, internet use price, contents price, and the number of internet user and content provider are all decreasing as the network congestion increases. Second, under the monopoly platform internet use price, contents price, and the number of internet user and content provider are all increasing as the network capacity increases. Third, the price of internet use and contents internet use which maximize social welfare are increasing and the number of internet user and content provider are decreasing as the network congestion increases. Fourth, optimal network capacity for monopoly platform provider is less than socially optimal network capacity. Fifth, if network neutrality regulation is enforced, the price of internet use is higher than monopoly platform provider and the price of contents is lower than monopoly platform provider. Also, the number of internet user is less than monopoly platform provider and the number of content provider is more than that. By the way, when network congestion increases, internet use price, the number of internet user, and the number of content provider are decreasing. Sixth, network neutrality regulation is more effective for internet user side than contents provider when network congestion is considered. This means that network neutrality regulation is not effective for contents market side when network congestion is seriously large.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• The KIPS Transactions:PartC
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• v.16C no.4
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• pp.511-520
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• 2009

Grid technology enables high performance computation and mass data processing by connecting geographically distributed computing resources to high-speed network. To carry on grid tasks requiring guaranteed quality of service, we need to build converged infrastructure which controls and manages network resources as well as computing resources. In this paper, we defined grid network service interface and information model to support grid network path, resource creation and reservation. In addition to resource creation and reservation messages of the existing GLIF, grid network path and notification messages were defined in the grid network service interface. We also defined related statuses and messages for two-phase resource reservation mechanism. We designed and implemented network resource management system based on the grid network information model and service interface. We experimented in resource reservation, allocation and release of network resources in grid environment with GMPLS control and transport network.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.83-102
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• 2018

The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.

• The Journal of the Korea Contents Association
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• v.4 no.2
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• pp.61-67
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• 2004

This paper describes the importance of the network equipment markets and the necessity of the testing and the verification for the network equipments and the effective system for network equipment test. With the rapid internet service development, the demands of the network equipments(for example, router, switching equipment etc.) are expanded. The network equipments request the high love technologies, so the foreign network equipments are prevailed in the domestic market. But the development of the network equipments in korea is in the beginning stage. To deploy the network equipments into the real network the network equipments should be tested and verified by the certified test labs. To get the verification from the foreign test lab, it takes a lot of expenses and times. For the competitiveness of our products in the international markets, the technique of the test and verification on the network equipments should be level-up. This paper investigates the network equipments market condition and the network equipments test condition in Korea. For the competitiveness of our commodities in the international markets, this paper proposes the testing strategy for network equipments.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.121-128
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• 2013

Existing sensor network studies have only emphasized energy aspects that sensors themselves had. But when an actual sensor network is established, biased use of a specific sensor may cause a partial disconnection of the sensor network. It becomes an disadvantage to fail efficient operation of the sensor network for a long time and energy efficiency of specific sensor energy causes to drop the efficiency of the sensor network. When a sensor network is composed of many clusters or made up of a large network, sensor network's disconnection cannot be avoided because they emphasize sensor's energy efficiency. Therefore, it was tried to lengthen the lifespan of the sensor network by making sensors in the sensor network avoid disconnection through even use of all the sensors composing of the sensor network. This article proposes a protocol to maintain a sensor network for a long time by preventing a sensor networks' disconnection through efficient management of sensor network energy composed of the protocols composing of the sensor network in ubiquitous environments.

• KNOM Review
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• v.21 no.2
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• pp.1-9
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• 2018

Network virtualization refers to a technology creating independent virtual network environment on a physical network. Network virtualization technology can share the physical network resources to reduce the cost of establishing the network for each user and enables the network administrator to dynamically change the network configuration according to the purpose. Although the network management can be handled dynamically, the management is manual, and it does not maximize the profit of network virtualization. In this paper, we propose Machine-Learning technology to allow the network to learn by itself and manage its management dynamically. The proposed approach is to dynamically allocate appropriate resources by predicting resource demand of VNF in service function chaining, which is a core and essential problem in virtual network management. Our goal is to predict the resource demand of the VNF and dynamically allocate the appropriate resources to reduce the cost of network operation while preventing service interruption.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.101-112
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• 2022

In this paper, we analyze 5G network slicing and propose an efficient network slice configuration method in 5G mobile networks. Network slicing can be identified and performed based on the network slice instance information in 5G mobile networks. In case of discordance between the UE's network slice instance information and the network's one, the unnecessary signalling overhead occurs, when the UE's PDU Session Establishment request to the network fails. To solve this problem, this paper proposes two efficient network slice configuration methods, the UE-based ENSC(Efficient Network Slice Configuration) method and the Network-based ENSC method. The proposed schemes perform the prompt the configuration and provision of the updated network slice instance information between the UE and network and improve battery and resource efficiency and minimize unnecessary signalling overhead compared to existing methods in 5G mobile networks.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.7-14
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• 2006

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.269-272
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• 2005

무선 네트워크 이동성 기술에 대한 연구가 수년간 진행되어 오면서 Mobile Network에 PAN(Personal Area Network)과 유사한 형태의 Nested Mobile Network에 대한 관심이 높아지고 있으며, 이러한 Nested Mobile Network에서의 경로최적화(Route Optimization : RO) 기술에 대한 연구가 활발하게 진행되고 있다. NEMO(NEtwork MObility)의 RO를 위해 제안된 논문 중에 ORC(Optimized Route Cache Protocol)에 대한 제안이 있었다.[1] NEMO Basic Support가 표준안으로 채택되면서 연구 대상에서 거론되지 않고 있지만, 복잡한 이동성 기술인 Nested Mobile Network상의 RO를 위해 다시 검토해 볼 수 있을 것이다. 또한 동일 저자에 의해 제안된 Nested Mobile Network 내부에 Ad-hoc Routing 알고리즘인 OLSR(Optimized Link State Routing Protocol)을 적용한 제안이 발표되었다.[2] 본 논문에서는 ORC와 Nested Mobile Network상의 OLSR Scheme을 적용하여 RO를 위한 방안을 제안하고자 한다.