• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.577-585
• /
• 2006

This paper presents a method for reliability evaluation of a telecommunication network with variable link-capacities when the simple paths of the network are known. The LP-EM, suggested by Lee and Park (2001), identifies a composite path as a subnetwork and adds only a minimal set of links at each step which gives maximal increase on the maximum capacity flow of the subnetwork. Thereby the LP-EM reduces the possible occurrence of redundant composite paths significantly over other existing methods. Based on the LP-EM, our method further reduces the possible redundancy by identifying such simple paths that could give no increase of maximum capacity flow on the current subnetwork and excluding those simple paths from consideration in the process of constructing composite paths.

• Journal of Power Electronics
• /
• v.18 no.2
• /
• pp.588-603
• /
• 2018

This paper presents the idea of a smart load that can adjust the input power flow based on the intermittent power available from RESs (Renewable Energy Resources) to regulate the line voltage, and draw a constant power from the grid. To this effect, an innovative power flow controller is presented based on a Resistive ES (Electric Spring) in combination with a PEAT (Power Electronics based Adjustable Transformer), which can effectively shape the load power flow at the subnetwork level. With a PEAT incorporated in the step down transformer at the grid side, the proposed controller can supply non-critical loads through local RESs, and the critical loads can draw a relatively constant power from the grid. If there is an abundance of power produced by the RESs, the controller can supply both non-critical loads and critical loads through the RES, which significantly reduces the power demand from the grid. The principle, practicality, stability analysis, and controller design are presented. In addition, simulation results show that the power flow controller performs well in shaping the load power flow at the subnetwork level, which decreases the power demand on the grid. Experimental results are also provided to show that the controller can be realized.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.11C
• /
• pp.1046-1054
• /
• 2002

In this paper, we propose a subnetwork-based segment restoration scheme to reduce the restoration time and restoration resources. And we compare and analyze the restoration performance according to the size of divided subnetworks. Segment restoration is based on network partitioning where a large network is divided into several small subnetworks and the end-to-end data path is divided into multiple segments according to the subnetworks. In segment restoration, the link/node failure is restored by segment instead of end-to-end path. Because most faults are restored within the subnetwork, the restoration performance can be improved. From the simulation analysis, we verified that the proposed segment restoration has advantage of restoration time and backup resource utilization.

• Korean Journal of Computational Design and Engineering
• /
• v.2 no.2
• /
• pp.77-84
• /
• 1997

This paper is concerning the development of multiple neural networks system of problem domains where the complete input space can be decomposed into several different regions, and these are known prior to training neural networks. We will adopt oblique decision tree to represent the divided input space and sel ect an appropriate subnetworks, each of which is trained over a different region of input space. The overall architecture of multiple neural networks system, called the federated architecture, consists of a facilitator, normal subnetworks, and tile networks. The role of a facilitator is to choose the subnetwork that is suitable for the given input data using information obtained from decision tree. However, if input data is close enough to the boundaries of regions, there is a large possibility of selecting the invalid subnetwork due to the incorrect prediction of decision tree. When such a situation is encountered, the facilitator selects a tile network that is trained closely to the boundaries of partitioned input space, instead of a normal subnetwork. In this way, it is possible to reduce the large error of neural networks at zones close to borders of regions. The validation of our approach is examined and verified by applying the federated neural networks system to the configuration design of a midship structure.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.4
• /
• pp.111-122
• /
• 1999

In this paper, we consider a queueing network model. where the population constraint within each subnetwork is controlled by a semaphore queue. The total number of customers that may be present in the subnetwork can not exceed a given value. Each node has a constant service time and the arrival process to the queueing network is an arbitrary distribution. A major characteristics of this model is that the lower layer flow is halted by the state of higher layer. We present some properties that the inter-change of nodes does not make any difference to customer's waiting time in the queueing network under a certain condition. The queueing network can be transformed into a simplified queueing network. A dramatic simplification of the queueing network is shown. It is interesting to see how the simplification developed for sliding window flow control, can be applied to multi-layered queueing network.

• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.879-882
• /
• 1988

This paper describes a fast security analysis techique for voltage security assessment. The new method identifies the location of buses with potential voltage problems and thereby defines a voltage-sensitive subnetwork for contingency screening. The efficieny of this method is derived from the use of a voltage subnetwork to drastically reduce the number of bus voltages to be solved; and subsequently from the use of compensation techniques and sparse-vectors methods for screening. Results demonstrating the effectiveness of the method on the IEEE-14 bus model system are presented.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2021.06a
• /
• pp.176-179
• /
• 2021

최근 딥 러닝 기법의 하나인 합성곱 신경망(Convolutional Neural Network, CNN)은 영상 잡음(Noise) 제거 분야에서 전통적인 기법보다 좋은 성능을 나타내고 있지만 학습하는 과정에서 영상 내 디테일한 부분이 손실될 수 있다. 본 논문에서는 웨이블릿 변환(Wavelet Transform)을 기반으로 영상 내 디테일 정보도 같이 학습하여 영상 디테일을 향상하는 잡음 제거 합성곱 신경망 네트워크를 제안한다. 제안하는 네트워크는 디테일 향상 서브 네트워크(Detail Enhancement Subnetwork)와 영상 잡음 추출 서브 네트워크(Noise Extraction Subnetwork)를 이용하게 된다. 실험을 통해 제안하는 방법은 기존 알고리듬보다 디테일 손실 문제를 효과적으로 해결할 수 있었고 객관적 품질 평가인 PSNR(Peak Signal-to-Noise Ratio)와 주관적 품질 비교에서 모두 우수한 결과가 나온 것을 확인하였다.

• Information and Communications Magazine
• /
• v.11 no.4
• /
• pp.71-79
• /
• 1994

• MALSORI
• /
• no.50
• /
• pp.51-69
• /
• 2004

In this paper, we present a one-pass semi-dynamic network decoding framework that inherits both advantages of fast decoding speed from static network decoders and memory efficiency from dynamic network decoders. Our method is based on the novel language model network representation that is essentially of finite state machine (FSM). The static network derived from the language model network [1][2] is partitioned into smaller subnetworks which are static by nature or self-structured. The whole network is dynamically managed so that those subnetworks required for decoding are cached in memory. The network is near-minimized by applying the tail-sharing algorithm. Our decoder is evaluated on the 25k-word Korean broadcast news transcription task. In case of the search network itself, the network is reduced by 73.4% from the tail-sharing algorithm. Compared with the equivalent static network decoder, the semi-dynamic network decoder has increased at most 6% in decoding time while it can be flexibly adapted to the various memory configurations, giving the minimal usage of 37.6% of the complete network size.

• Journal of information and communication convergence engineering
• /
• v.9 no.5
• /
• pp.530-538
• /
• 2011

Performance of TCP can be severely degraded in Mobile IP-based wireless networks where packet losses not related to network congestion occur frequently during inter-subnetwork handoffs by user mobility. To solve such a problem in the networks using Mobile IP, the packet buffering method at a base station(BS) recovers those packets dropped during handoff by forwarding the buffered packets at the old BS to the mobile users. But, when the mobile user moves to a congested BS in a new foreign subnetwork, those buffered packets forwarded by the old BS are dropped and TCP transmission performance of a mobile user degrades severely. In this paper, we propose a Modified BLUE(MBLUE) buffer required at a BS to increase TCP throughput in Mobile IP-based networks. When a queue length exceed a threshold and congestion grows, MBLUE increases its packet drop probability. But, when a TCP connection is added at new BS by a handoff, the old BS marks the buffered packets. And new BS receives the marked packets without dropping. Simulation results show that MBLUE buffer reduces congestion during handoffs and increases TCP throughputs.