• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.124-127
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• 1997

In this paper, we propose the new face detection and tracking angorithm in sequential images which have complex background. In order to apply face deteciton algorithm efficently, we convert the conventional RGB coordiantes into CIE coordonates and make the input images insensitive to luminace. And human face shapes and colors are learned using ueural network's backpropagation. For variable face size, we make mosaic size of input images vary and get the face location with various size through neural network. Besides, in sequential images, we suggest face motion tracking algorithm through image substraction processing and thresholding. At this time, for accurate face tracking, we use the face location of previous. image. Finally, we verify the real-time applicability of the proposed algorithm by the simple simulation.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.73-82
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• 2022

This paper shows the hiding process of unlimited secret text size in an image using three methods: the first method is the traditional method in steganography that based on the concealing the binary value of the text using the least significant bits method, the second method is a new method to hide the data in an image based on Exclusive OR process and the third one is a new method for hiding the binary data of the text into an image (that may be grayscale or RGB images) using Exclusive and Huffman Coding. The new methods shows the hiding process of unlimited text size (data) in an image. Peak Signal to Noise Ratio (PSNR) is applied in the research to simulate the results.

• 대한산업공학회지
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• 제24권1호
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• pp.23-35
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• 1998

This article presents a network flow analysis to form flexible machine cells with minimum intercellular part moves and a neural network model to form part families. The operational sequences and production quantity of the part, and the number of cells and the cell size are taken into considerations for a 0-1 quadratic programming formulation and a network flow based solution procedure is developed. After designing the machine cells, a neural network approach for the integration of part families and the automatic assignment of new parts to the existing cells is proposed. A multi-layer backpropagation network with one hidden layer is used. Experimental results with varying number of neurons in hidden layer to evaluate the role of hidden neurons in the network learning performance are also presented. The comprehensive methodology developed in this article is appropriate for solving large-scale industrial applications without building the knowledge-based expert rule for the cellular manufacturing environment.

• 인터넷정보학회논문지
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• 제17권1호
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• pp.47-53
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• 2016

워크플로우 소속성 네트워크는 워크플로우 기반 조직의 수행자와 업무 사이의 연관관계를 나타내는 소셜 네트워크의 한 형태이며, 이를 기반으로 연결 중심도, 근접 중심도, 사이 중심도, 위세 중심도 등과 같은 다양한 분석 기법들이 제안되었다. 특히, 전사적 워크플로우 모델을 기반으로 하는 소속성 네트워크의 근접 중심도 분석은 워크플로우 소속성 네트워크의 규모가 증가함에 따라, 중심도 및 랭킹 계산의 시간 복잡도 문제점을 가진다는 것을 발견하였다. 본 논문에서는 근접 중심도 분석의 시간 복잡도 문제를 개선하기 위해, 근사치 추정 방법을 이용한 워크플로우 기반 소속성 네트워크의 추정 근접 중심도 기반 랭킹 알고리즘을 제안한다. 노드의 타입이 수행자인, 워크플로우 예제 모델을 추정 근접 중심도 기반 랭킹 알고리즘에 적용한 성능 분석을 실시하였다. 수행 결과, 네트워크 규모 관점에서의 정확도는 평균적으로 47.5% 향상되었고, 샘플 모집단 비율 관점에서는 평균적으로 9.44%정도의 향상된 수치를 보였다. 또한, 추정 근접 중심도 랭킹 알고리즘의 평균 계산 시간은 네트워크의 노드 수가 2400개, 샘플 모집단의 비율이 30%일 때, 기존 근접 중심도 랭킹 알고리즘의 평균 계산 시간보다 82.40%의 높은 성능을 보였다.

• 한국국방경영분석학회지
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• 제33권2호
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• pp.87-100
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• 2007

IT 발전에 따른 장차전 양상은 네트워크 중심전(NCW : Network Centric Warfare)으로서 국방부는 이러한 NCW 구현을 위한 기반체계로 차세대 국방정보통신망(M-BcN : Military Broadband convergence Network)을 2008년 말까지 구축할 계획을 갖고 있다. IPv6 주소체계는 NCW의 필수 요소로 체계적으로 관리되어야 하는데 기존 연구에서는 국방 조직 구조 기반으로 계층적 설계를 했거나, 현재의 국방정보통신망 토폴로지를 기준으로 설계하여 토폴로지 구조가 상이한 M-BcN에 적용하는 것이 제한되고, 서비스망을 구분하는 필드부터 할당하여 경로 요약(route aggregation)이 비효율적이고 라우팅 정보 크기가 커지는 단점이 있었다. 본 논문에서는 M-BcN의 네트워크 토폴로지 기반으로 계층적 주소를 할당하고 서비스망 구분 필드의 위치를 조정하여 경로 요약 및 라우팅 테이블 크기를 기존 연구보다 효율적으로 개선했으며, 네트워크 시뮬레이션 프로그램(OPNET 12.0)으로 검증하였다.

• 상하수도학회지
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• 제13권3호
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• pp.73-82
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• 1999

In this study, the optimal analysis for pipe network is performed for the combined ideal pipe network system(CASE 1, CASE 2 and CASE 3) which is composed of 25 nodes, 41 elements, and 1 fixed nodal head with evaluating pressure variation distribution of main and branch in grid composed drainage pipe network. The linear analysis technique used as the analysis method in this study, the KYPIPE being used extensively as the linear technique to design and analysis of pipe network is applied. Firstly, in the analysis of pipe network, the CASE 2 and CASE 3 supply same thing(value) in the result of considering the total flow provided each pipeline, but in the general intension in the case of CASE 2, relative width of supply is more large than CASE 1 and CASE 3. Secondly, in the analysis technique of pipe network, CASE 3 is analysed largest as a result of comparing with same heads, and in the order of their size CASE 2 and CASE 1 were determined but the difference doesn't appear to be obvious. Thirdly, as the result of determining main factor, pressure in the design and analysis of net work. CASE 3 is from Node 3 to 25 than CASE 1 and CASE 2 and it is determined in the order of their size, CASE 2 and CASE 1. Finally, in this study, discharge flow distribution is evaluated in the same condition with 3-type CASE in the case of branch position for designing optimal composed drainage pipe network. As the result of that, branch pipe perform. Therefore, it is thought that the efficient and reasonable management of water supply and sewerage design will be possible if it give all our energies to study at the pipe system design in and out of country in the future.

• ETRI Journal
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• 제39권6호
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• pp.841-850
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• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• ETRI Journal
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• 제34권1호
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• pp.142-145
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• 2012

The main challenge at present in constructing hierarchical identity-based encryption (HIBE) is to solve the trade-off between private-key size and ciphertext size. At least one private-key size or ciphertext size in the existing schemes must rely on the hierarchy depth. In this letter, a new hierarchical computing technique is introduced to HIBE. Unlike others, the proposed scheme, which consists of only two group elements, achieves constant-size private keys. In addition, the ciphertext consists of just three group elements, regardless of the hierarchy depth. To the best of our knowledge, it is the first efficient scheme where both ciphertexts and private keys achieve O(1)-size, which is the best trade-off between private-key size and ciphertext size at present. We also give the security proof in the selective-identity model.

• 대한음성학회지:말소리
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• 제50호
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• pp.51-69
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• 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.

• 한국통신학회논문지
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• 제29권11B
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• pp.967-973
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• 2004

DOCSIS와 IEEE 802.16 MAC 프로토콜의 MAP 메시지는 CM의 상향 채널 데이터 전송 영역을 할당하므로 프로토콜 성능에 영향을 미치게 된다. 하지만 MAP의 구성요소와 용도에 대한 정의만 표준안에 언급이 되어있고, MAP 메시지의 크기나 MAP 메시지 내의 경쟁슬롯의 수에 대한 정의는 언급되어 있지 않다. 본 논문에서는 MAP의 크기와 경쟁슬롯의 수에 따른 프로토콜의 성능을 분석하곤 분석 결과를 바탕으로 최적의 MAP 크기와 경쟁슬롯의 수를 구하였다. 실험 결과로 MAP의 크기는 2msec이고 경쟁슬롯의 수는 8개일 때 최적의 프로토콜 성능을 보임을 알 수 있었다. 본 연구의 결과는 네트워크 시스템 파라미터로 사용할 수 있으며 실험에 사용하였던 시뮬레이터는 케이블 네트워크, BWA 및 WiBro 시스템 파라미터의 최적화에 사용할 수 있다.