• Journal of Information Processing Systems
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• v.14 no.3
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• pp.727-739
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• 2018

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.322-326
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• 2023

We propose a method of movie recommendation that involves an algorithm known as spectral bipartition. The Social Network is constructed manually by considering the similar movies viewed by users in MovieLens dataset. This kind of similarity establishes implicit ties between viewers. Because we assume that there is a possibility that there might be a connection between users who share the same set of viewed movies. We cluster users by applying a community detection algorithm based on the spectral bipartition. This study helps to uncover the hidden relationships between users and recommend movies by considering that feature.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.358-365
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• 2010

We develop a low complexity cooperative diversity protocol for low energy adaptive clustering hierarchy (LEACH) based wireless sensor networks. A cross layer approach is used to obtain spatial diversity in the physical layer. In this paper, a simple modification in clustering algorithm of the LEACH protocol is proposed to exploit virtual multiple-input multiple-output (MIMO) based user cooperation. In lieu of selecting a single cluster-head at network layer, we proposed M cluster-heads in each cluster to obtain a diversity order of M in long distance communication. Due to the broadcast nature of wireless transmission, cluster-heads are able to receive data from sensor nodes at the same time. This fact ensures the synchronization required to implement a virtual MIMO based space time block code (STBC) in cluster-head to sink node transmission. An analytical method to evaluate the energy consumption based on BER curve is presented. Analysis and simulation results show that proposed cooperative LEACH protocol can save a huge amount of energy over LEACH protocol with same data rate, bit error rate, delay and bandwidth requirements. Moreover, this proposal can achieve higher order diversity with improved spectral efficiency compared to other virtual MIMO based protocols.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.58.1-58.1
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• 2012

We present a comprehensive re-analysis of stellar photometric variability in the field of open cluster M37, using our new high-precision light curves. This dataset provides a rare opportunity to explore different types of variability between short (-minutes) and long (-one month) time-scales. To investigate the variability properties of -30,000 objects, we developed new algorithms for detecting periodic, aperiodic, and sporadic variability in their light curves. About 7.5% (2,284) of the total sample exhibits convincing variations that are induced by flares, pulsations, eclipses, starspots and, in some cases, unknown causes. The benefits of our new photometry and analysis package are evident. The discovery rate of new variables is increased by 63% in comparison with the existing catalog of variables, and 51 previously identified variables were found to be false positives resulting from time-dependent systematic effects. Based on extended and improved catalog of variables, we will review the basic properties (e.g., periodicity, amplitude, type) of the variability and how different they are for different spectral types and for cluster memberships.

• Journal of The Korean Astronomical Society
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• v.37 no.3
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• pp.119-129
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• 2004

A new method has been developed to solve the star cluster membership problem. It is based on synthetic photometry employing the Black Body concept as stellar radiation simulator. Synthetic color-magnitude diagram is constructed showing the main sequence band and the positions of binary star systems of combinations of various components through different photometric tracks. The method has been applied to the Hyades. The cluster membership problem has been re-appraised for the cluster (both single and binary) stars. For the binary members, the components' spectral types have been derived by the method. The results obtained agree very well with those found in literature, The method is simpler than the others and can be developed to undertake other cases as multiple star systems.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.32.2-32.2
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• 2021

Radio relics, Mpc-size elongated diffuse radio emissions found at galaxy cluster outskirts, are known as the result of shock acceleration during the cluster merger. Theories have claimed that low Mach number shocks are too inefficient to create the observed properties of radio relics. Alternative scenarios such as fossil cosmic ray electrons (CRes) from AGNs are required to explain the observations. However, how exactly the fossil CRes from AGNs can supply the Mpc-size radio relic is still an open question. In this study, we present our recent uGMRT radio observation results of the merging galaxy cluster Abell 514. We found three remarkable AGN jet tails that may have undergone multiple reorientations and extend nearly 800 kpc. Using multi-frequency data, we have performed spectral analysis along the AGN tails and track how the tails lose or gain energy as they propagate in the intracluster medium. We will discuss whether these AGN jets can provide sufficient seed CRes to radio relics.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.30.1-30.1
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• 2015

Radio relics are diffuse radio sources found in the outskirts of galaxy clusters and they are thought to trace synchrotron-emitting relativistic electrons accelerated at shocks. We explore a diffusive shock acceleration (DSA) model for radio relics in which a spherical shock with the parameters relevant for the Sausage radio relic in cluster CIZA J2242.8+5301 impinges on a magnetized cloud containing fossil relativistic electrons. This model is expected to explain some observed characteristics of giant radio relics such as the relative rareness, uniform surface brightness along the length of thin arc-like radio structure, and spectral curvature in the integrated radio spectrum. We find that the observed surface brightness profile of the Sausage relic can be explained reasonably well by shocks with speed $u_s{\sim}3{\times}10^3km/s$ and sonic Mach number $M_s{\sim}3$. These shocks also produce curved radio spectra that steepen gradually over $(0.1-10){\nu}_{br}$ with a break frequency ${\nu}_{br}{\sim}1GHz$, if the duration of electron acceleration is ~60-80 Myr. However, the abrupt increase in the spectral index above ~1.5 GHz observed in the Sausage relic seems to indicate that additional physical processes, other than radiative losses, operate for electrons with the Lorentz factor, ${\gamma}_e$ > $10^4$.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.450-453
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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 steps. 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 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 it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.437-438
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• 2011

Spectral segmentation is a major trend in image segmentation. Specially, constrained spectral segmentation, inspired by the user-given inputs, remains its challenging task. Since it makes use of the spectrum of the affinity matrix of a given image, its overall quality depends mainly on how to design the graphical model. In this work, we propose a sparse, multi-layer graphical model, where the pixels and the over-segmented regions are the graph nodes. Here, the graph affinities are computed by using the must-link and cannot-link constraints as well as the likelihoods that each node has a specific label. They are then used to simultaneously cluster all pixels and regions into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Although we incorporate only the adjacent connections in the multi-layer graph, the foreground object can be efficiently extracted in the spectral framework. The experimental results demonstrate the relevance of our algorithm as compared to existing popular algorithms.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.