• 한국컴퓨터정보학회논문지
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• 제10권6호
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• pp.209-218
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• 2005

링형 광 액세스망에서 능동형 광네트워크(AON: Active Optical Network)는 DWDM(Dense Wavelength Division Multiplexing)을 이용하여 요구대역폭(BoD: Bandwidth on Demand)에 따라서 가입자에게 원활한 서비스를 제공할 수 있다. 이를 위하여 기존의 광 기가비트 이더넷 스위치에서 다수개의 파장 및 서브캐리어(Sub-Carrier) 접속을 지원하며, 특정 파장을 분기하는 WADM (Wavelength Add Drop Multiplexer)과 링의 형태로 연결된다. WADM에서 분기된 특정 파장은 가입자단에 이르러 서브캐리어별로 역다중화 되어 가입자에게 분배되므로 가입자망의 분배가 시작되는 광 기가비트 이더넷 스위치와 가입자 단말 접속 장치간의 능동적인 연결이 가능한 구조를 가진다 본 논문에서는 이러한 AON 구조에서 BoD에 따라서 달라지는 버퍼의 크기를 비교 분석하고 또한 비트의 지체시간을 서버의 처리율과 비교 분석한다. 이러한 실험을 통하여 소요 시간의 한계를 결정함으로써 가입자에게 요구 대역폭에 따른 원활한 서비스를 제공할 수 있는 네트워크의 동적 운용 프로토콜 및 효율적인 알고리즘 구현을 위한 기준을 제시한다.

• ETRI Journal
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• 제40권2호
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• pp.227-236
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• 2018

Ultra-dense small cell networks (UD-SCNs) have been identified as a promising scheme for next-generation wireless networks capable of meeting the ever-increasing demand for higher transmission rates and better quality of service. However, UD-SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software-defined networking (SDN)-based hierarchical agglomerative clustering (SDN-HAC) framework, which leverages SDN to centrally control all sub-channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non-cooperative scenarios, respectively.

• Journal of the Optical Society of Korea
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• 제12권4호
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• pp.351-354
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• 2008

We demonstrate an ultra-dense wavelength-division- multiplexed (UD-WDM) passive optical network (PON) where 12.5-GHz spaced 1 GbE ${\times}$ 256 optical channels are distributed using 12.5- and 200-GHz arrayed waveguide gratings in series. For the generation of upstream signals, we use reflective semiconductor optical amplifiers. We use two optical fiber amplifiers at the optical line terminal to amplify downstream and upstream channels.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.646-647
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• 2022

Multiview stereo (MVS) 3D reconstruction of a scene from images is a fundamental computer vision problem that has been thoroughly researched in recent times. Traditionally, MVS approaches create dense correspondences by constructing regularizations and hand-crafted similarity metrics. Although these techniques have achieved excellent results in the best Lambertian conditions, traditional MVS algorithms still contain a lot of artifacts. Therefore, in this study, we suggest using a transformer network to accelerate the MVS reconstruction. The network is based on a transformer model and can extract dense features with 3D consistency and global context, which are necessary to provide accurate matching for MVS.

• 로봇학회논문지
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• 제19권2호
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• pp.188-195
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• 2024

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.

• 한국지역지리학회지
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• 제12권6호
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• pp.693-703
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• 2006

본 연구는 혁신유형과 네트워크 형태간의 관계를 탐색적으로 고찰하였다. 즉, 기업의 혁신유형에 따라 사회적 자본의 특성이 어떻게 다르게 나타나는지를 실증 분석하고 있다. 이 관계를 검증하기 위해 2005년 6월에서 7월 사이에 실시된 "경북 IT기업 기술혁신활동 조사"에서 나타난 168개 기업 자료를 실증적으로 분석하였다. 분석은 IT기업의 기술혁신 유형변수로 '탐색형 기업', '활용형 기업'으로 구분하였고, 사회적 자본은 네트워크의 형태를 나타내는 '구조적 변수'와 강도를 나타내는 '관계적 변수'로 구분하여 구성형태를 분석하였다. 분석 결과, 탐색형 기업에서는 네트워크의 범위가 넓고(sparse network) 약한 연계(weak tie) 관계를 가지는 반면, 활용형 기업에서는 네트워크가 범위를 좁고(dense network) 강한 연계(strong tie) 관계를 가지는 것으로 나타났다.

• 대한전자공학회논문지SD
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• 제38권6호
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• pp.412-419
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• 2001

본 논문에서는 유리집적광학을 이용하여 채널 도파로, Y-분리기, CWDM 등의 개별소자와 이들을 하나의 유리기판위에 평면형으로 집적하겨 제작함으로써 1.31/1.55㎛ CWDM(Coarse Wavelength Division Multiplexing) 및 1.55㎛ 대역 DWDM (Dense WDM) 수동 광 망에 적용할 수 있도록 하였다. CWDM에 적용한 결과, 1.55㎛ 파장에서는 30㏈, 1.31㎛ 파장에서는 15㏈ 이상의 교차 비를 얻을 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.21-28
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• 2019

Recent years with the development of artificial intelligence and the success of the deep model, they have been deployed in all fields of computer vision. Action recognition, as an important branch of human perception and computer vision system research, has attracted more and more attention. Action recognition is a challenging task due to the special complexity of human movement, the same movement may exist between multiple individuals. The human action exists as a continuous image frame in the video, so action recognition requires more computational power than processing static images. And the simple use of the CNN network cannot achieve the desired results. Recently, the attention model has achieved good results in computer vision and natural language processing. In particular, for video action classification, after adding the attention model, it is more effective to focus on motion features and improve performance. It intuitively explains which part the model attends to when making a particular decision, which is very helpful in real applications. In this paper, we proposed a 3D dense convolutional network based on attention mechanism(ADD-Net), recognition of human motion behavior in the video.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2240-2254
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• 2021

We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.656-658
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• 2022

The purpose of infrared and visible image fusion is to integrate images of different modes with different details into a result image with rich information, which is convenient for high-level computer vision task. Considering many deep networks only work in a single scale, this paper proposes a novel image fusion based on three-scale dense network to preserve the content and key target features from the input images in the fused image. It comprises an encoder, a three-scale block, a fused strategy and a decoder, which can capture incredibly rich background details and prominent target details. The encoder is used to extract three-scale dense features from the source images for the initial image fusion. Then, a fusion strategy called l₁-norm to fuse features of different scales. Finally, the fused image is reconstructed by decoding network. Compared with the existing methods, the proposed method can achieve state-of-the-art fusion performance in subjective observation.