• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.612-614
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• 2000

하이퍼큐브의 성능을 좌우하는 중요한 요소 중 하나가 프로세서간의 통신이다. 그리고 병렬 컴퓨터에서 프로세서의 수가 증가함에 따라, 구성요소들이 오류가 날 확률도 높아졌다. 이러한 이유로, 오류 난 구성요소들이 있어도 다중 전송이 가능하게 효율적으로 설계하는 것이 중요하다. 본 논문에서는 최근에 제안된 완전 도달성 정보와 새로 추가한 국지적 정보를 이용해서 라우팅 알고리즘을 제안하고, 이것을 바탕으로 다중 전송 성공률이 높은 새로운 다중 전송 알고리즘을 제안하였다. 시뮬레이션을 통하여 제안한 기법은 기존의 기법 보다 통신량의 차이는 거의 없으면서, 다중 전송 성공률이 목적지 노드 수에 따라 5~15% 가량 향상시킬 수 있었다.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.243-246
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• 2002

In this paper, an advanced load balancing algorithm in nth order Hypercube distributed system has been proposed. The new algorithm uses centralized load-balancing to avoid blocking phenomenon and processor thrashing, and shows the results which makes loads to approach average value of loads. The new algorithm os compared with several other algorithm and it shows a merit in cost function value.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1078-1081
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• 1993

The Objective of this paper is to provide fuzzy control designers with a design tool for stable fuzzy logic controllers. Given multiple sets of data disturbed by vagueness uncertainty, we generate the implicative rules that guarantee stability and robustness of closed-loop fuzzy dynamic systems. We propose the cell-state transition method which utilizes Hsu's cell-to-cell mapping concept [1]. As a result, a generic and implementable design methodology for obtaining a fuzzy feedback gain K, a fuzzy hypercube [2], is provided and illustrated with simple examples.

• The KIPS Transactions:PartA
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• v.14A no.4
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• pp.249-254
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• 2007

In this paper, we consider the embedding problem of postorder Fibonacci circulant. We show that Fibonacci linear array, Fibonacci mesh, Fibonacci tree, Fibonacci cubes and Hypercube are a subgraph of postorder Fibonacci circulant.

• Honam Mathematical Journal
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• v.43 no.4
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• pp.679-689
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• 2021

The first author suggested an exact volume formula of the hypercubes [0, 1]ⁿ clipped by several hyperplanes expressed directly in terms of linear coefficients of the hyperplanes. However, it requires awkward assumptions to apply the formula to various situations. We suggest a concrete method to overcome those restrictions for two or three hyperplanes using 𝜖-perturbation, which gives an exact value applicable for any kind of arrangement of hyperplanes with no consideration.

• The Journal of Korean Association of Computer Education
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• v.17 no.2
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• pp.115-124
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• 2014

Hypercube and star graph are widely known as interconnection network. The embedding of an interconnection network is a mapping of a network G into other network H. The possibility of embedding interconnection network G into H with a low cost, has an advantage of efficient algorithms usage in network H, which was developed in network G. In this paper, we provide an embedding algorithm between HCN and HON. HCN(n,n) can be embedded into HON($C_{n+1},C_{n+1}$) with dilation 3 and HON($C_d,C_d$) can be embedded into HCN(2d-1,2d-1) with dilation O(d). Also, star graph can be embedded to half pancake's value of dilation 11, expansion 1, and average dilation 8. Thus, the result means that various algorithms designed for HCN and Star graph can be efficiently executed on HON and half pancake, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3106-3106
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• 2001

Imaging spectrometry or hyperspectral imaging is a recent development that makes possible quantitative and qualitative measurement for food quality and safety. This paper presents the research results that a hyperspectral imaging system can be used effectively for detecting fecal (from duodenum, cecum, and colon) and ingesta contamination on poultry carcasses from the different feed meals (wheat, mile, and corn with soybean) for poultry safety inspection. A hyperspectral imaging system has been developed and tested for the identification of fecal and ingesta surface contamination on poultry carcasses. Hypercube image data including both spectral and spatial domains between 430 and 900 nm were acquired from poultry carcasses with fecal and ingesta contamination. A transportable hyperspectral imaging system including fiber optically fabricated line lights, motorized lens control for line scans, and hypercube image data from contaminated carcasses with different feeds are presented. Calibration method of a hyperspectral imaging system is demonstrated using different lighting sources and reflectance panels. Principal Component and Minimum Noise Fraction transformations will be discussed to characterize hyperspectral images and further image processing algorithms such as image band ratio of dual-wavelength images and its histogram stretching with thresholding process will be demonstrated to identify fecal and ingesta materials on poultry carcasses. This algorithm could be further applied for real-time classification of fecal and ingesta contamination on poultry carcasses in the poultry processing line.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.3_4
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• pp.130-137
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• 2003

This paper presents the problem of partitioning the Single-Stage Shuffle-Exchange Network(SSEN). An algorithm, named SSEN_to_PSEN, is devised to transform an SSEN into a Partitionable Shuffle-Exchange Network (PSEN). The proposed algorithm presents that the SSEN can be partitioned into independent sub-networks without additional links for N $\leq$ 8. Additional links are needed in order to partition an SSEN, but only when N $\geq$ 16. The running time of the algorithm SSEN_to_PSEN is $\theta$(NlogN). By comparing with a hypercube network, the PSEN is less expensive than a hypercube network even when some additional links are added. By partitioning, a large PSEN in a massively parallel machine can compute various problems for multiple users simultaneously, thereby the processing efficiency of the machine is improved.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.556-564
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• 2003

A Star and Pancake graph also have such a good property of a hypercube and have a low network cost than the hypercube. A Macro-star graph which has the star graph as a basic module has the node symmetry, the maximum fault tolerance, and the hierarchical decomposition property. And, it is an interconnection network which improves the network cost against the Star graph. In this paper, we propose a method to embed between Star graph, Pancake graph, and Macro-star graph using the edge definition of graphs. We prove that the Star graph $S_n$ can be embedded into Pancake graph $P_n$ with dilation 4, and Macro-star graph MS(2,n) can be embedded into Pancake graph $P_{2n＋1}$ with dilation 4. Also, we have a result that the embedding cost, a Pancake graph can be embedded into Star and Macro-star graph, is O(n).