• 한국통신학회논문지
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• 제34권9B호
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• pp.883-891
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• 2009

고속 네트워크 장비에 네트워크프로세서의 사용이 보편화되고 라우팅 기능과 패컷처리 기능을 분리하는 분산형 시스템 구조가 이용됨에 따라 대용량의 트래픽을 매우 빠르게 처리하는 향상된 성능을 보일 수 있으나, 제어평면과 데이터 평면 사이에 추가 메시지 교환이 필요하여 시스템 내부 통신 지연이 증가하는 문제가 발생한다. 본 논문에서 제안한 구조는 이더넷 프로텍션 스위칭 기술에 필요한 패킷 처리 기능 블록들을 효율적으로 배치하여 설계함으로써, 메시지 교환에 따른 지연의 증가에도 불구하고 단일 링크의 장애 발생 시 50 msec급 수준의 빠른 절체 성능을 보인다. 이에 따라 높은 신뢰성과 수십 기가비트의 광대역 전송이 요구되는 캐리어 이더넷 시스템에 적합한 선형 프로텍션 스위칭 기술의 설계 및 구현 방안을 제공할 수 있다.

• 전기학회논문지
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• 제64권8호
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• pp.1161-1167
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• 2015

Distribution Management System(DMS) ienhancing distribution automation system-based operation efficiency is an optimized system by various operational applications in a distribution network. DMS employs various applications like topology reconfiguration, volt/var control, and restoration at events such as overload, voltage violation, and a fault in a distribution system. An operation efficiency to employ multi-applications as restoration with short-term load forecasting is higher than a performance by a single application; and the applications are accomplished by an operator’s control. Applications’ combination is determined by various alarm information which means critical issues in order to operate a distribution system. Thus, this paper proposes an application management system which can configure application combination, control applications depending on alarm information and check their performance condition. The proposed application management system can be customized by operator easily and have high operation efficiency and reliability because it is worked by reviewed alarm information from operator.

• Journal of Communications and Networks
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• 제9권2호
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• pp.198-209
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• 2007

This paper provides a thorough study on shared segment protection (SSP) for mesh communication networks in the complete routing information scenario, where the integer linear program (ILP) in [1] is extended such that the following two constraints are well addressed: (a) The restoration time constraint for each connection request, and (b) the switching/merging capacity constraint at each node. A novel approach, called SSP algorithm, is developed to reduce the extremely high computation complexity in solving the ILP formulation. Basically, our approach is to derive a good approximation on the parameters in the ILP by referring to the result of solving the corresponding shared path protection (SPP) problem. Thus, the design space can be significantly reduced by eliminating some edges in the graphs. We will show in the simulation that with our approach, the optimality can be achieved in most of the cases. To verify the proposed formulation and investigate the performance impairment in terms of average cost and success rate by the additional two constraints, extensive simulation work has been conducted on three network topologies, in which SPP and shared link protection (SLP) are implemented for comparison. We will demonstrate that the proposed SSP algorithm can effectively and efficiently solve the survivable routing problem with constraints on restoration time and switching/merging capability of each node. The comparison among the three protection types further verifies that SSP can yield significant advantages over SPP and SLP without taking much computation time.

• 한국인터넷방송통신학회논문지
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• 제12권5호
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• pp.197-203
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• 2012

본 논문에서는 광파장다중화(WDM) 기반 광인터넷 환경에서 논리적인 다중 링 구조에 의한 통신망 복구 기법의 대역폭 효율성을 연구한다. 이 방법은 링 토폴로지의 특성을 이용해 빠르고 간단한 복구 동작을 제공하며, 지역적인 복구를 수행한다. 동시에 다중 링 구성을 통하여 대체 경로의 분산과 공유 정도를 높이고 단위 링크당 예약되어야 하는 예비 광파장의 수를 줄임으로써 전체 통신망 자원 이용의 효율성을 높일 수 있다. 본 논문에서는 위험공유링크그룹(SRLG) 개념을 이용하여 링크 손실을 복구하는데 필요한 예비대역폭 비율을 토폴로지상의 Cut-Set을 이용해 계산하고 이를 실험을 통해 비교 확인하였다.

• 한국통신학회논문지
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• 제37B권12호
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• pp.1168-1173
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• 2012

본 연구에서는 2개의 OLT를 이용한 PON 보호/절체에 관해 제안하였다. 본 제안된 방식을 이용하면 고장 상황에서도 가입자 영역에 서비스를 지속적으로 할 수 있고, 정상적인 동작을 수행할 수 있게 된다. 기존 보호/절체 방식과의 차이점은 제안된 방식의 경우 두 OLT에 다른 파장을 적용함으로써 정상 상태에서 두 OLT를 모두 사용하여 이용 효율을 높인다는 점이다. 보호 모드에서는 Shared-bandwidth 할당 방식을 적용함으로써 효율을 극대화하였다. 성능 분석을 통해 제안된 시스템이 효율적으로 이더넷 기반 PON 시스템의 신뢰성을 높일 수 있음을 확인하였다.

• 한국음향학회지
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• 제37권6호
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• pp.512-517
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• 2018

음성 품질의 향상은 통신 분야의 주요 관심사이다. 본 논문에서는 VoIP(Voice-over-IP) 수신부에서의 배경잡음 및 패킷손실에 강인한 음질향상 방식을 제안한다. 제안된 방식에서는 하이브리드 마르코프 체인 기반 네트워크 지터추정, 추정된 지터를 이용한 적응적 플레이아웃 스케줄링, 그리고 진폭 및 위상 복원 기반의 음성 향상 방식 등을 결합하여 IP 네트워크를 통해 VoIP 수신부에 도착하는 음성신호의 품질을 향상시킨다. 실험결과는 제안된 방식이 송신부의 인코딩 전에 음성신호에 추가된 잡음을 제거하고 불안정한 네트워크 환경에서 양질의 음성을 제공하는 것을 확인할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2547-2567
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• 2021

It has been widely acknowledged that occlusion impairments adversely distress many face recognition algorithms' performance. Therefore, it is crucial to solving the problem of face image occlusion in face recognition. To solve the image occlusion problem in face recognition, this paper aims to automatically de-occlude the human face majority or discriminative regions to improve face recognition performance. To achieve this, we decompose the generative process into two key stages and employ a separate generative adversarial network (GAN)-based network in both stages. The first stage generates an initial coarse face image without an occlusion mask. The second stage refines the result from the first stage by forcing it closer to real face images or ground truth. To increase the performance and minimize the artifacts in the generated result, a new refine loss (e.g., reconstruction loss, perceptual loss, and adversarial loss) is used to determine all differences between the generated de-occluded face image and ground truth. Furthermore, we build occluded face images and corresponding occlusion-free face images dataset. We trained our model on this new dataset and later tested it on real-world face images. The experiment results (qualitative and quantitative) and the comparative study confirm the robustness and effectiveness of the proposed work in removing challenging occlusion masks with various structures, sizes, shapes, types, and positions.

• Journal of Multimedia Information System
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• 제8권2호
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• pp.101-110
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• 2021

In computer vision, single-image super resolution has been an area of research for a significant period. Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration. Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges. Furthermore, previous works haven't placed enough emphasis on real-number scalability. Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size. In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations. It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space. Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model. Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1246-1262
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• 2021

Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR). The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions. Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures. Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques. In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively. In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations. The network configuration allows the pipeline to propagate information from lower layers to higher ones. Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block. Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters. Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.

• 방송공학회논문지
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• 제26권7호
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• pp.868-876
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• 2021

Deploying deep neural network model training performs remarkable performance in the fields of Object detection and Instance segmentation. To train these models, features are first extracted from the input image using a backbone network. The extracted features can be reused by various tasks. Research has been actively conducted to serve various tasks by using these learned features. In this process, standardization discussions about encoding, decoding, and transmission methods are proceeding actively. In this scenario, it is necessary to analyze the response characteristics of features against various distortions that may occur in the data transmission or data compression process. In this paper, experiment was conducted to inject various distortions into the feature in the object recognition task. And analyze the mAP (mean Average Precision) metric between the predicted value output from the neural network and the target value as the intensity of various distortions was increased. Experiments have shown that features are more robust to distortion than images. And this points out that using the feature as transmission means can prevent the loss of information against the various distortions during data transmission and compression process.