• Title/Summary/Keyword: Dynamic Network

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A Dynamic Routing Algorithm for Management of the IMS Nodes Using SNMP (SNMP를 이용한 IMS 노드의 동적 라우팅 알고리즘)

  • Cho, Jae-Hyoung;Lee, Jae-Oh
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.3B
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    • pp.214-219
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    • 2011
  • The IMS (IP Multimedia Subsystem) is used for providing multimedia services to network provider. The IMS is composed of many nodes, depending on their function. According to the status (fault, performance, etc.) of each node, we need to provide dynamic routing algorithm. In this paper, we propose a dynamic routing algorithm for management of the IMS nodes using SNMP based on FCAPS (Fault, Configuration, Accounting, Performance and Security) in the IMS network.

Dynamic Characteristics Analysis of Linear DC Motor Using 3D Equivalent Magnetic Circuit Network Method by Voltage Driven (전압구동 3차원 등가자기회로망법을 이용한 선형 직류전동기의 동특성 해석)

  • Yeom, Sang-Bu;Ha, Kyung-Ho;Hong, Jung-Pyo;Hur, Jin
    • Proceedings of the KIEE Conference
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    • 2000.11b
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    • pp.271-273
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    • 2000
  • This paper presents the dynamic characteristics Linear DC Motor(LDM) using 3 Dimensional Equivalent Magnetic Circuit Network Method(3-D EMCN) by voltage driven. The movement of mover substitutes for the movement of magnetization in permanent magnet expressed by Fourier series. The Dynamic characteristics are carried out from coupling the electrical circuit equation and mechanical kinetic equation.

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Indirect adaptive control of nonlinear systems using Genetic Algorithm based Dynamic neural network (GA 학습 방법 기반 동적 신경 회로망을 이용한 비선형 시스템의 간접 적응 제어)

  • Cho, Hyun-Seob;Oh, Myoung-Kwan
    • Proceedings of the KAIS Fall Conference
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    • 2007.11a
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    • pp.81-84
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    • 2007
  • In this thesis, we have designed the indirect adaptive controller using Dynamic Neural Units(DNU) for unknown nonlinear systems. Proposed indirect adaptive controller using Dynamic Neural Unit based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

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A Tier-Based Duty-Cycling Scheme for Forest Monitoring

  • Zhang, Fuquan;Gao, Deming;Joe, In-Whee
    • Journal of Information Processing Systems
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    • v.13 no.5
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    • pp.1320-1330
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    • 2017
  • Wireless sensor networks for forest monitoring are typically deployed in fields in which manual intervention cannot be easily accessed. An interesting approach to extending the lifetime of sensor nodes is the use of energy harvested from the environment. Design constraints are application-dependent and based on the monitored environment in which the energy harvesting takes place. To reduce energy consumption, we designed a power management scheme that combines dynamic duty cycle scheduling at the network layer to plan node duty time. The dynamic duty cycle scheduling is realized based on a tier structure in which the network is concentrically organized around the sink node. In addition, the multi-paths preserved in the tier structure can be used to deliver residual packets when a path failure occurs. Experimental results show that the proposed method has a better performance.

End-to-End Delay Analysis of a Dynamic Mobile Data Traffic Offload Scheme using Small-cells in HetNets

  • Kim, Se-Jin
    • Journal of Internet Computing and Services
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    • v.22 no.5
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    • pp.9-16
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    • 2021
  • Recently, the traffic volume of mobile communications increases rapidly and the small-cell is one of the solutions using two offload schemes, i.e., local IP access (LIPA) and selected IP traffic offload (SIPTO), to reduce the end-to-end delay and amount of mobile data traffic in the core network (CN). However, 3GPP describes the concept of LIPA and SIPTO and there is no decision algorithm to decide the path from source nodes (SNs) to destination nodes (DNs). Therefore, this paper proposes a dynamic mobile data traffic offload scheme using small-cells to decide the path based on the SN and DN, i.e., macro user equipment, small-cell user equipment (SUE), and multimedia server, and type of the mobile data traffic for the real-time and non-real-time. Through analytical models, it is shown that the proposed offload scheme outperforms the conventional small-cell network in terms of the delay of end-to-end mobile data communications and probability of the mobile data traffic in the CN for the heterogeneous networks.

Performance Evaluation of Pipelined Dynamic Bandwidth Algorithm for XG-PON (XG-PON의 Pipeline 방식의 동적대역할당 성능평가)

  • Gong, Byung Gu;Han, Man Soo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2014.10a
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    • pp.148-149
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    • 2014
  • This paper evaluates performance of a pipelined dynamic bandwidth allocation scheme for XG-PON (10-Gbps-capable passive optical network) system for power saving. Under self-similar traffic and balanced input loads, we evaluate performance of XG-PON systems using computer simulations.

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No-reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

  • Kim, Hyoung-Gook;Shin, Seung-Su;Kim, Sang-Wook;Lee, Gi Yong
    • ETRI Journal
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    • v.43 no.3
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    • pp.538-548
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    • 2021
  • This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

CADRAM - Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing

  • Abdullah, M.;Surputheen, M. Mohamed
    • International Journal of Computer Science & Network Security
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    • v.22 no.3
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    • pp.95-100
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    • 2022
  • Cloud computing platform is a shared pool of resources and services with various kind of models delivered to the customers through the Internet. The methods include an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem with this model is the allocation of resource in dynamic. In this paper, we have proposed a mechanism to optimize the resource provisioning task by reducing the job completion time while, minimizing the associated cost. We present the Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing CADRAM system, which includes more than one agent in order to manage and observe resource provided by the service provider while considering the Clients' quality of service (QoS) requirements as defined in the service-level agreement (SLA). Moreover, CADRAM contains a new Virtual Machine (VM) selection algorithm called the Node Failure Discovery (NFD) algorithm. The performance of the CADRAM system is evaluated using the CloudSim tool. The results illustrated that CADRAM system increases resource utilization and decreases power consumption while avoiding SLA violations.

Lightweight Network for Multi-exposure High Dynamic Range Imaging (다중 노출 High Dynamic Range 이미징을 위한 경량화 네트워크)

  • Lee, Keuntek;Cho, Nam Ik
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • fall
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    • pp.70-73
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    • 2021
  • 최근 영상 및 비디오 분야에 심층 신경망(DNN, Deep Neural Network)을 사용한 연구가 다양하게 진행됨에 따라 High Dynamic Range (HDR) 이미징 기술에서도 기존의 방법들 보다 우수한 성능을 보이는 심층 신경망 모델들이 등장하였다. 하지만, 심층 신경망을 사용한 방법은 큰 연산량과 많은 GPU 메모리를 사용한다는 문제점이 존재하며, 이는 심층 신경망 기반 기술들의 현실 적용 가능성에 제한이 되고 있다. 이에 본 논문에서는 제한된 연산량과 GPU 메모리 조건에서도 사용 가능한 다중 노출 HDR 경량화 심층 신경망을 제안한다. Kalantari Dataset에 대해 기존 HDR 모델들과의 성능 평가를 진행해 본 결과, PSNR-µ와 PSNR-l 수치에서 GPU 메모리 사용량 대비 우수한 성능을 보임을 확인하였다.

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Dynamic Caching Routing Strategy for LEO Satellite Nodes Based on Gradient Boosting Regression Tree

  • Yang Yang;Shengbo Hu;Guiju Lu
    • Journal of Information Processing Systems
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    • v.20 no.1
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    • pp.131-147
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    • 2024
  • A routing strategy based on traffic prediction and dynamic cache allocation for satellite nodes is proposed to address the issues of high propagation delay and overall delay of inter-satellite and satellite-to-ground links in low Earth orbit (LEO) satellite systems. The spatial and temporal correlations of satellite network traffic were analyzed, and the relevant traffic through the target satellite was extracted as raw input for traffic prediction. An improved gradient boosting regression tree algorithm was used for traffic prediction. Based on the traffic prediction results, a dynamic cache allocation routing strategy is proposed. The satellite nodes periodically monitor the traffic load on inter-satellite links (ISLs) and dynamically allocate cache resources for each ISL with neighboring nodes. Simulation results demonstrate that the proposed routing strategy effectively reduces packet loss rate and average end-to-end delay and improves the distribution of services across the entire network.