• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.2
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• pp.264-268
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• 1986

A thchnique has been studied for the measurement of the spectral loss and the cut-off wave length, at which the first high-order mode disappears. The near-field patterns of a fiber which is excited by a variable wavelength source are used for the measurement of cut-off wavelength. Because of the absorptive phenomenon of OH- molecular vibration, spectral loss of a single mode optical fiber sample is 5.6dB/km at 1380 nm wavelength and 1dB/km at 1240 nm wavelength. From the near field intensity patterns and the mode field diameter graph for the half power width, the cut-off wavelength of the fiber is measured to be 1120 nm.

• Bulletin of the Korean Mathematical Society
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• v.60 no.4
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• pp.873-893
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• 2023

In 2017, Nikiforov proposed the A_𝛼-matrix of a graph G. This novel matrix is defined as A_𝛼(G) = 𝛼D(G) + (1 - 𝛼)A(G), 𝛼 ∈ [0, 1], where D(G) and A(G) are the degree diagonal matrix and adjacency matrix of G, respectively. Recently, Zhai, Xue and Liu [39] considered the Brualdi-Hoffman-type problem for Q-spectra of graphs with given matching number. As a continuance of it, in this contribution we consider the Brualdi-Hoffman-type problem for A_𝛼-spectra of graphs with given matching number. We identify the graphs with given size and matching number having the largest A_𝛼-spectral radius for ${\alpha}{\in}[{\frac{1}{2}},1)$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.729-738
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• 2014

In this paper, we propose interference avoidance resource allocation scheme based on graph-coloring algorithm to introduce performance gain using spatial reuse in D2D (Device-to-Device) system. By assigning multiple D2D pairs to a single D2D resource, interference from neighboring D2D pairs is inevitable, which leads to performance degradation. Therefore, we first introduce the feedback information and the method considering the amount of information that can be practically provided by a D2D pair. Then, we propose how to construct a graph, which is corresponding to the D2D system, using the feedback information and adopt a graph-coloring algorithm to efficiently avoid interference. Simulation results show that the proposed resource allocation scheme outperforms traditional resource allocation schemes in both overall sum rate and spectral efficiency of D2D system while reducing the outage probability. Moreover, the outage probability, which indicates a failure rate of D2D communication, can be reduced by adopting the proposed scheme.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.70-85
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• 2021

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.130-133
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• 2007

본 연구는 원격 탐사의 영상 처리에서 영상 분할의 상위 수준으로 웅집 계층 clustering의 dendrogram을 통한 무감독 영상 분류를 제안한다. 제안된 알고리즘은 분광 영역에서 정의된 RAG(Regional Agency Graph)와 min-heap 자료 구조를 이용하여 MCSNP(Mutual Closest Spectral Neighbor Pair)의 집 합을 검색하면서 합병을 수행하는 계층 clustering 방법이다. 계산 시간과 저장 기억의 사용에 대한 효율을 증가시키기 위해 분광적 인접성올 정의 하는 분광 공간(spectral space)내의 다중창을 사용하였고 RNV(Region Neighbor Vector)을 이용하여 합병에 의하여 변하는 RAG 갱신하였고 적정한 단계 수가 주어 진다면 제안된 알고리즘은 집단 합병의 계층적 관계를 쉽게 해석 할 수 있는 dendrogram을 생성한다. 본 연구는 생성된 dendrogram을 이용한 nested-hierarchical 분석을 통하여 피복 형태의 계층적 관계를 해석한다. 이러한 해석은 피복 형태의 정확한 분류를 위한 의사 결정에 중요한 정보를 공급한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1243-1258
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• 2017

A new medical materials scheduling system and its modeling method for the complex rescue are presented. Different from other similar system, first both the BeiDou Satellite Communication System (BSCS) and the Special Fiber-optic Communication Network (SFCN) are used to collect the rescue requirements and the location information of disaster areas. Then all these messages will be displayed in a special medical software terminal. After that the bipartite graph models are utilized to compute the optimal scheduling of medical materials. Finally, all these results will be transmitted back by the BSCS and the SFCN again to implement a fast guidance of medical rescue. The sole drug scheduling issue, the multiple drugs scheduling issue, and the backup-scheme selection issue are all utilized: the Kuhn-Munkres algorithm is used to realize the optimal matching of sole drug scheduling issue, the spectral clustering-based method is employed to calculate the optimal distribution of multiple drugs scheduling issue, and the similarity metric of neighboring matrix is utilized to realize the estimation of backup-scheme selection issue of medical materials. Many simulation analysis experiments and applications have proved the correctness of proposed technique and system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1837-1860
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• 2020

We propose a general framework for studying resource allocation problem and the tradeoff between spectral efficiency (SE) and energy efficiency (EE) for downlink traffic in power domain-non-orthogonal multiple access (PD-NOMA) and device to device (D2D) based heterogeneous cloud radio access networks (H-CRANs) under imperfect channel state information (CSI). The aim is jointly optimize radio remote head (RRH) selection, spectrum allocation and power control, which is formulated as a multi-objective optimization (MOO) problem that can be solved with weighted Tchebycheff method. We propose a low-complexity algorithm to solve user association, spectrum allocation and power coordination separately. We first compute the CSI for RRHs. Then we study allocating the cell users (CUs) and D2D groups to different subchannels by constructing a bipartite graph and Hungrarian algorithm. To solve the power control and EE-SE tradeoff problems, we decompose the target function into two subproblems. Then, we utilize successive convex program approach to lower the computational complexity. Moreover, we use Lagrangian method and KKT conditions to find the global optimum with low complexity, and get a fast convergence by subgradient method. Numerical simulation results demonstrate that by using PD-NOMA technique and H-CRAN with D2D communications, the system gets good EE-SE tradeoff performance.

• The Korean Journal of Ecology
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• v.13 no.2
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• pp.83-92
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• 1990

Two-dimensional analysis provides a comprehensive description of the structure, scales of pattern and directional components in a spatial data set. In spectral analysisi, four functions are illustrated,; the autocorrelation, the periodogram, the R-spectrum and the $\theta$ -spectrum. The R-spectrum and $\theta$ -spectrum function respectively summarize the periodogram in term of scale of pattern and directional components. Sampling is measured in the Naejang National Park area where the Daphniphyllum trees grow. 320 contiguous (15$\times$15)m plots are located along the transect and density of all trees over DBH 3 cm recorded respectively. 12 species of vascular plant are recorded in this survey area. The trend surface of density of all plant are estimated using polynomial regression and are exhibited in 3-dimensional graph and density contour map. Transformation to the corresponding polar spectrum from the periodogram emphasized the directional components and the scales to pattern. R-spectrum corresponding to the scale of pattern of periodogram showed a large peak 15.47 in the interval 9$\theta$-spectrum corresponding to directional components have two peaks 8.28 and 11.05 in the interval $35^{\circ}\theta <45^{\circ}and 125^{\circ}\theta< <135^{\circ}, respectively. Programs to compute all the analyses described in this study was obtained from Dr. Ranshow and was translated to BASIC by the author.