• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.1030-1050
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• 2021

The edges with "bridge characteristic" play the role of connecting the communication between regions in power optical cable network. To solve the problem of mining edges with "bridge characteristic" in provincial power optical cable network, the complex power optical cable network model is constructed. Firstly, to measure the generated potential energy of all nodes in n-level neighborhood local structure for one edge, the n-level neighborhood local structure topological potential is designed. And the minimum connected chain attenuation is designed to measure the attenuation degree caused by substituted edges. On the basis of that, the minimum connected chain attenuation topological potential based measurement is designed. By using the designed measurement, a bridge-edges mining algorithm is proposed to mine edges with "bridge characteristic". The experiments are conducted on the physical topology of the power optical cable network in Jilin Province. Compared with that of other three typical methods, the network efficiency and connectivity of the proposed method are decreased by 3.58% and 28.79% on average respectively. And the proposed method can not only mine optical cable connection with typical "bridge characteristic" but also can mine optical cables without obvious characteristics of city or voltage, but it have "bridge characteristic" in the topology structure.

• 한국통신학회논문지
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• 제40권6호
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• pp.1142-1147
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• 2015

무선 센서 네트워크에서 네트워크의 확장성과 효율성을 높이기 위한 방법으로 네트워크 구조를 계층적으로 구성하는 방법에 관심이 높다. 무선 네트워크를 계층 구조로 구성하는 방법은 특정 노드들을 선별하여 이들을 백본 네트워크로 구성하는 방법을 중심으로 연구가 진행되었다. 백본을 구성하는 노드들은 연결되어 있어서 자신들 간에 통신이 직접적으로 가능해야하며, 백본에 속하지 않은 모든 노드들이 백본을 통해 통신이 가능해야한다. 이러한 조건을 만족하는 최소 크기의 노드 집합을 선출하는 문제를 최소연결지배집합선출 문제라 한다. 최소연결지배집합선출 문제는 복잡도가 NP-hard로 알려져 있으며, 현재 효율적인 알고리즘이 존재하지 않는다. 본 논문은 최소연결지배집합선출 문제를 해결하기 위한 다중시작 지역탐색 알고리즘을 제안하다. 제안 방법의 성능 측정을 위해 다양한 조건에서 실험하고 결과를 제시한다.

• 제어로봇시스템학회논문지
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• 제9권3호
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• pp.196-203
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• 2003

Recent increase in accessibility to the internet makes it easy to use the internet-connected devices. The internet could allow any user can reach and command any device that is connected to the network. But these teleoperation systems using the internet connected device have several problems such as the network time delay, data loss and development cost of an application for the communication with each other. One feasible solution is to use local and external network line for the network time delay, transmission control protocol for data loss and Java language to reduce the development period and cost. In this study, web-based remote control system using Java language is newly proposed and implemented to a pneumatic servo control system to solve the time delay, data loss and development cost. We have conducted several experiments using pneumatic rodless cylinder through the internet and verified that the proposed remote control system was very effective.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 춘계종합학술대회
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• pp.729-732
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• 2003

무선 통신망의 데이터를 보호하기 위한 암호화의 방법 및 비밀 통신을 위한 인중 메카니즘에 대한 방법을 제안하였다. 무선 통신망의 경우 기존의 유선망에 비해, 설치, 이동성 등이 우수하여 많은 기술적인 발전을 보이고 있다. 따라서 이에 대한 데이터의 보호에 대한 관심이 고조되고 있다. 본 논문에서는 가정, 사무실, 건물과 같은 전형적인 외부 환경에 대해 정보를 보호할 수 있는 시스템의 구조를 설계하여 제안하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권5호
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• pp.289-294
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• 2002

다층신경회로망 구조의 재구성은 회로망의 일반화 능력이나 효율성의 관점에서 중요한 문제로 연구되어왔다. 본 논문에서는 신경회로망에 학습된 은닉 지식들을 추출하여 조합함으로써 신경회로망의 구조를 재구성하는 새로운 방법을 제안한다. 먼저, 각 노드별로 학습된 대표적인 지역 규칙을 추출하여 각 노드의 불필요한 연결구조들을 제거한 후, 이들의 논리적인 조합을 통하여 중복 또는 상충되는 노드와 연결구조를 제거한다. 이렇게 학습된 지식을 분석하여 노드와 연결구조를 재구성한 신경회로망은 처음의 신경회로망에 비하여 월등히 감소된 구조 복잡도를 가지며 일반적으로 더 우수한 일반화 능력을 가지게 됨을 실험결과로서 제시하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.77-79
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• 2004

Because robot hardware architecture generally is consisted of a few sensors and motors connected to the central processing unit, this type of structure is led to time consuming and unreliable system. For analysis, one of the fundamental difficulties in real-time system is how to be bounded the time behavior of the system. When a distributed control network controls the robot, with a central computing hub that sets the goals for the robot, processes the sensor information and provides coordination targets for the joints. If the distributed system supposed to be connected to a control network, the joints have their own control processors that act in groups to maintain global stability, while also operating individually to provide local motor control. We try to analyze the architecture of network-based humanoid robot's leg part and deal with its application using the CAN(Controller Area Network) protocol.

• ETRI Journal
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• 제42권5호
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• pp.658-668
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• 2020

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

• ETRI Journal
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• 제41권4호
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• pp.473-482
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• 2019

High availability and reliability have been considered promising requirements for the support of seamless network services such as real-time video streaming, gaming, and virtual and augmented reality. Increased availability can be achieved within a local area network with the use of the virtual router redundancy protocol that utilizes backup routers to provide a backup path in the case of a master router failure. However, the network may still lose a large number of packets during a failover owing to a late failure detections and lazy responses. To achieve an efficient failover, we propose the implementation of fast detection with virtual router redundancy protocol (FDVRRP) in which the backup router quickly detects a link failure and immediately serves as the master router. We implemented the FDVRRP using open neutralized network operating system (OpenN2OS), which is an open-source-based network operating system. Based on the failover performance test of OpenN2OS, we verified that the FDVRRP exhibits a very fast failure detection and a failover with low-overhead packets.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.