• 항공우주시스템공학회지
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• 제14권6호
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• pp.26-35
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• 2020

The national satellite operation network, which supports multiple satellites, was designated as a Critical Information Infrastructure (CII) in 2017. The network was designed independently from the control network and the information network to enhance physical security. Planning is underway to establish a bidirectional data interface between networks. The data transmission system allows data flow only to the physical layer and the data link layer; hence, only one file can be transferred at any one time. This means that when large amounts of data are being transmitted, no other data can be sent simultaneously in urgent situations. Thus, this paper discusses the design of flexible packets for the transmission of data between networks in an environment where physical security has been enhanced through network separation and based on this, presents a method for transmitting data effectively.

• 한국통신학회논문지
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• 제22권12호
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• pp.2744-2754
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• 1997

회선분배시스템(DCS: Digital Cross-connet System)을 이용한 mesh 구조의 동기식 전송망에서 망의 장애를 효과적으로 복구하기 위한 망 복구 방식 중 최근에 제안된 다중 계층 복구(MLR: Multi-Layer Restoration) 방식은 선계획(Pre-planned) 복구 방식과 다이나믹(Dynamic) 복구 방식을 단계별로 구성하여 보다 효율적인 망복구를 수행하기 위한 망 복구 전략으로 B.T(British Telecom)에서 제안되었다[1,2]. 이 망 복구 전략은 단일 선로 장애에 대해 효율적인 성능을 발휘할 수 있으나, 다중 선로 장애와 노드 장애의 경우 선계획 복구 단계에서 복구되지 못하고 다이나믹 복구 단계에서 복구됨으로 인해서 선계획 복구의 장점을 최대한 활용하지 못하여 복구 성능 감쇠를 초래한다. 본 논문에서는 기존의 다중 계층 복구 방식에서 사용되는 선계획 복구 단계에 선계획 Multi-chooser 복구 단계를 추가한 새로운 다중 계충 복구 방식을 제안하였으며, 이 방식은 다중 선로와 노드 장애에 대하여 빠른 복구 시간과 높은 복구율을 갖고 있다. 또한, 제안한 복구 알고리즘을 모델링하여 OPNET(OPtimized Network Enginnering Tool)을 이용한 시뮬레이션을 통해 이의 성능을 분석, 검증하였다.

• 정보처리학회논문지C
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• 제16C권2호
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• pp.223-228
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• 2009

무선 애드-혹 네트워크와 유선 네트워크를 연동하는 하이브리드 애드-혹 네트워크에서 효율적인 서비스 검색 기법은 중요하다. 본 논문에서는 무선 애드-혹 네트워크를 인터넷으로 확장한 하이브리드 애드-혹 네트워크를 위해 중복을 허용하는 다중 경로 소스 라우팅 프로토콜을 사용하는 효율적인 크로스-레이어 서비스 검색 기법을 제안한다. 이 기법은 다중 경로 소스 라우팅 프로토콜의 장점뿐만 아니라 크로스-레이어 서비스 검색 기법의 장점을 동시에 갖는 기법이다. 서비스와 경로 검색이 동시에 이루어지는 크로스-레이어 서비스 검색 기법이 서비스 검색 후 해당 경로를 다시 검색하여 서비스를 제공받는 기존 기법보다 적은 오버헤드를 가진다는 것은 직관적으로 이해할 수 있으므로, 본 시뮬레이션을 통해서는 이 기법이 각 노드에서 다중 라우팅 경로를 유지함으로써 경로 단절시 보다 빠른 라우팅 복구가 가능하며, 다중 경로의 수를 제한하고 링크/노드 중복을 허용하는 다중 경로를 유지하도록 함으로써 라우팅 오버헤드를 줄일 수 있음으로 보인다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 1989년도 한글날기념 학술대회 발표논문집
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• pp.267-271
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• 1989

최근 Scofield는 coulomb energy network에 적용할 수 있는 learning algorithm(supervised learning algorithm)을 제안하였다. 이 learning algorithm은 multi-layer network에도 쉽게 적용이 가능하고 한 layer 에서 발생한 error가 다른 layer에 영향을 주지 않아서 system을 modular하게 구성할 수가 있으며 각 layer를 독립적으로 learning 시킬 수 있는 특징이 있다. 본 논문에서는 coulomb energy network를 이용하여 한글인식을 위한 neural network를 구현하여 인식실험을 한 결과와 구현한 network 에서 인식율을 높이기 위한 방안 (2 stage learning) 을 제시한다.

• 한국기계기술학회지
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• 제20권6호
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• pp.776-782
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• 2018

After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3018-3036
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• 2013

In this paper, we investigate power allocation scheme and outage performance for a physical-layer network coding (PNC) relay based secondary user (SU) communication in cognitive multi-antenna relay networks (CMRNs), in which two secondary transceivers exchange their information via a multi-antenna relay using PNC protocol. We propose an optimal energy-efficient power allocation (OE-PA) scheme to minimize total energy consumption per bit under the sum rate constraint and interference power threshold (IPT) constraints. A closed-form solution for optimal allocation of transmit power among the SU nodes, as well as the outage probability of the cognitive relay system, are then derived analytically and confirmed by numerical results. Numerical simulations demonstrate the PNC protocol has superiority in energy efficiency performance over conventional direct transmission protocol and Four-Time-Slot (4TS) Decode-and-Forward (DF) relay protocol, and the proposed system has the optimal outage performance when the relay is located at the center of two secondary transceivers.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• 조명전기설비학회논문지
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• 제21권6호
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• pp.94-101
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• 2007

하드 디스크(Hard Disk) 결함의 표준 패턴 클래스는 6가지로 분류되며, 이는 하드 디스크 생산 공정의 불량 처리 과정에서 중요한 역할을 수행한다. 본 논문에서는 다층 퍼셉트론(Multi-Layer Perceptron)을 이용한 하드 디스크 결함 분포의 패턴 인식 기법을 제시한다. 결함 분포로부터 5가지의 특징들을 추출하고, 이를 이용하여 퍼셉트론의 입력을 구성하였으며, 미리 분류된 표준 패턴 클래스를 이용하여 퍼셉트론의 출력을 구성하였다. 구성된 입출력 데이터들은 오차 역전파(Error Back-Propagation) 알고리듬을 통하여 다층 퍼셉트론의 학습에 사용되었다. 테스트 결과 제시된 신경망은 하드 디스크의 패턴 분류에 만족할 만한 성능을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• 전기학회논문지
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• 제58권4호
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.