• 천문학회지
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• 제37권5호
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• pp.315-322
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• 2004

Several arguments have been presented in the literature to support the connection between radio halos and cluster mergers. The spectral index distributions of the halos in A665 and A2163 provide a new strong confirmation of this connection, i.e. of the fact that the cluster merger plays an important role in the energy supply to the radio halos. Features of the spectral index (flattening and patches) are indication of a complex shape of the radiating electron spectrum, and are therefore in support of electron reacceleration models. Regions of flatter spectrum are found to be related to the recent merger. In the undisturbed cluster regions, instead, the spectrum steepens with the distance from the cluster center. The plot of the integrated spectral index of a sample of halos versus the cluster temperature indicates that clusters at higher temperature tend to host halos with flatter spectra. This correlation provides further evidence of the connection between radio emission and cluster mergers.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.134-137
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• 2005

Unsupervised spectral angle classification (USAC) is the algorithm that can extract ground object information with the minimum 'Spectral Angle' operation on behalf of 'Spectral Euclidian Distance' in the clustering process. In this study, our algorithm uses the unit vector instead of the spectral distance to compute the mean of cluster in the unsupervised classification. The proposed algorithm (MUSAC) is applied to the Hyperion and ETM+ data and the results are compared with K-Meails and former USAC algorithm (FUSAC). USAC is capable of clearly classifying water and dark forest area and produces more accurate results than K-Means. Atmospheric correction for more accurate results was adapted on the Hyperion data (Hyperion-FLAASH) but the results did not have any effect on the accuracy. Thus we anticipate that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but also hyperspectral images. Furthermore the cluster unit vector can be an efficient technique for determination of each cluster mean in the USAC.

• 천문학회지
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• 제29권2호
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• pp.157-170
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• 1996

In order to determine the metallicity of a globuar cluster, M3,by using the spectral indices, a kind of index grid has been establshed by stars in globular clusters, M3, M15, M71 and old open cluster, NGC 188. The indices were measured from the medium resolution spectra of about $2{\AA}$. The summed indices were used to determine metallicity in order to increase signals. It is found that the core depth index is measured more accurately and leads result more accurate than the pseudo-equivalent width index. This method can be further improved by including many more calibration globular clusters of various metallicity to make finer grids. By this method, the metallicity of M3 is determined as $[Fe/H] = -1.46\pm0.15$.

• 말소리와 음성과학
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• 제6권4호
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• pp.127-132
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• 2014

This paper describes a robust speech recognition technique by reconstructing spectral components mismatched with a training environment. Although the cluster-based reconstruction method can compensate the unreliable components from reliable components in the same spectral vector by assuming an independent, identically distributed Gaussian-mixture process of training spectral vectors, the presented method exploits the temporal dependency of speech to reconstruct the components by introducing a hidden-Markov-model prior which incorporates an internal state transition plausible for an observed spectral vector sequence. The experimental results indicate that the described method can provide temporally consistent reconstruction and further improve recognition performance on average compared to the conventional method.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.185-199
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• 2024

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

• 천문학회보
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• 제39권2호
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• pp.51.1-51.1
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• 2014

The Virgo cluster, the nearest galaxy cluster, is dynamically young, hosting numerous globular clusters in galaxies as well as intracluster globular clusters (IGCs). We obtained spectra of globular cluster candidates in the core region of the Virgo cluster using Hectospec at MMT to study the kinematics of the globular clusters. The targets are located at a large range (50 kpc < d < 500 kpc) from M87, the most massive galaxy in Virgo. We distinguish the genuine globular cluster population in the targets by inspecting their spectral features and radial velocities. As a result, a significant number of IGCs are found. We present preliminary results of the kinematics of globular clusters in the Virgo core region.

• 천문학회보
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• 제35권2호
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• pp.41.2-41.2
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• 2010

We present ultraviolet (UV) properties of dwarf galaxies in the Ursa Major cluster comparing with those in the Virgo cluster. We have constructed SDSS DR7/GALEX GR5 matched optical/UV catalog for dwarf galaxies with various morphologies in these two clusters. Membership of galaxies belonging to the Ursa Major cluster was made by hierarchical grouping method using SDSS spectroscopic data. We classified morphologies of dwarf galaxies using the combination of visual inspection of the images and spectral features returned from SDSS data. In contrast to the case of the Virgo cluster, majority of dwarf galaxies in the Ursa Major cluster lies in the blue cloud of the UV color-magnitude relations (CMRs) implying strong recent or on-going star formation. We discuss the cluster environment on the star formation history and evolution of dwarf galaxies.

• 대한전자공학회논문지SD
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• 제37권2호
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• pp.55-64
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• 2000

본 논문에서는 트랙 밀도를 최소화할 수 있는 효과적인 데이터패스 배치 알고리즘을 제안한다. 주어진 n개의 데이터패스 element 각각을 한 개의 클러스터라 놓고 이들 클러스터 중 가장 강하게 연결된 두 개를 선택하고 병합하는 과정을 한 개의 클러스터만 남을 때까지 반복한다. 병합될 두 클러스터내의 element들은 이미 각각 선형배열되어 있으므로 병합 시 이 두 선형배열을 연결하면 되며, 최종적으로 남은 클러스터의 선형배열의 처음과 끝을 연결하면 회전선형배열을 만들 수 있다. 이 회전선형배열에서 인접한 두 element 사이를 절단하면 서로 다른 n개의 선형배열을 만들 수 있으며 제안된 알고리즘에서는 이들 중 트랙밀도가 가장 낮은 선형배열을 선택한다. 본 논문에서는 스펙트럴방법을 이용해 d차원에 사상시킨 벡터의 내적이 최대가 되면 대응되는 두 클러스터가 강하게 연결되었음을 보였으며, 이를 이용해 병합될 두 클러스터를 찾는다. 기존 GA/SA/sup ［2］/방법과 비교하여 제안된 방법은 트랙밀도 면에서 유사한 성능을 내지만 수행시간 면에서 상당히 향상되었다.

• Korean Journal of Geomatics
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• 제5권1호
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• 천문학회보
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• 제42권1호
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• pp.36.4-37
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• 2017

Although most of observed properties of giant radio relics detected in the outskirts of galaxy clusters could be explained by relativistic electrons accelerated at merger-driven shocks, a few significant puzzles remain. In some relics the shock Mach number inferred from X-ray observations is smaller than that estimated from radio spectral index. Such a discrepancy could be understood, if either the shock Mach number is nder-estimated in X-ray observation due to projection effects, or if pre-existing electrons with a flat spectrum are re-accelerated by a weak shock, retaining the flat spectral form. In this study, we explore these two scenarios by comparing the results of shock acceleration simulations with observed features of the so-called Toothbrush relic in the merging cluster 1RXS J060303.3. We find that both models could reproduce reasonably well the observed radio flux and spectral index profiles and the integrated radio spectrum. Either way, the broad transverse relic profile requires additional post shock electron acceleration by downstream turbulence.