• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.49.1-49.1
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• 2010

The optical lightcurves of flare events can be regarded as a direct indicator about the existence of magnetic activity in low-mass stars. Stellar flares are generated by magnetodynamic processes in the stellar interiors as on the Sun and indicate that the locally intensified active regions still exist on the photosphere. However previous photometric observations are limited to a few selected active objects because of their faintness and randomness of the flare occurrence. Based on dedicated deep (r~23), long-term (24 night) time-series monitoring of the open cluster M37 from MMT 6.5m transit survey program, we searched for flare-like transient phenomena in the 3,052 M-dwarf lightcurves with relatively high-temporal resolution (30s-90s). In order to collect all statistical significant events, we applied the change-point analysis with filtering algorithm using local statistics. We found a number of flares from 412 M-dwarf stars that are probable cluster members. Nearly half of them have periodic brightness variations with a near or distorted sinusoidal shape. With a small exception of binary cases, most of these variations appear to reflect the presence of large starspots resulting in rotational brightness modulations. We will discuss the relationship among magnetic activity indicators and dependence on spectral type.

• Publications of The Korean Astronomical Society
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• v.32 no.3
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• pp.421-438
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• 2017

Through the short-period variability survey program, we obtained time-series BV CCD images for $1.5^{\circ}{\times}1.0^{\circ}$ region around the young open cluster NGC 457. As a result, we have detected 61 variable stars including 31 new ones after checking light curves of all stars by eyes. The 61 variable stars were included 14 ${\delta}$ Scuti variable stars, a ${\beta}$ Cephei variable star, 10 variable Be and slowly pulsating B stars, 13 eclipsing binary stars, 21 semi-long periodic or slow irregular variables and an RR Lyrae variable star, respectively. Many variable B-type stars were known through a well-defined zero-age main sequence to the ${\beta}$ Cepheid region of NGC 457. Most of the variable B-type stars found this paper were known variable stars. But, 11 out of 14 ${\delta}$ Scuti variable stars were newly discovered. The new variable stars except for ${\delta}$ Scuti stars were 4 variable B-type stars, 5 eclipsing binaries and 11 semi-long periodic or slow irregular variables. We have performed frequency analysis for all ${\delta}$ Scuti stars, a ${\beta}$ Cepheid star and an RR Lyrae star.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.335-346
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• 2016

This study produced the parameter maps of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall generation model across South Korea and developed and validated the web application that automates the process of rainfall generation based on the produced parameter maps. To achieve this purpose, three deferent sets of parameters of the MBLRP model were estimated at 62 ground gage locations in South Korea depending on the distinct purpose of the synthetic rainfall time series to be used in hydrologic modeling (i.e. flood modeling, runoff modeling, and general purpose). The estimated parameters were spatially interpolated using the Ordinary Kriging method to produce the parameter maps across South Korea. Then, a web application has been developed to automate the process of synthetic rainfall generation based on the parameter maps. For validation, the synthetic rainfall time series has been created using the web application and then various rainfall statistics including mean, variance, autocorrelation, probability of zero rainfall, extreme rainfall, extreme flood, and runoff depth were calculated, then these values were compared to the ones based on the observed rainfall time series. The mean, variance, autocorrelation, and probability of zero rainfall of the synthetic rainfall were similar to the ones of the observed rainfall while the extreme rainfall and extreme flood value were smaller than the ones derived from the observed rainfall by the degree of 16%-40%. Lastly, the web application developed in this study automates the entire process of synthetic rainfall generation, so we expect the application to be used in a variety of hydrologic analysis needing rainfall data.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.47-56
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• 2015

In an energy saving server cluster environment, the power modes of servers are controlled according to load situation, that is, by making ON only minimum number of servers needed to handle current load while making the other servers OFF. This algorithm works well under normal circumstances, but does not guarantee QoS under abnormal circumstances such as sharply rising or falling loads. This is because the number of ON servers cannot be increased immediately due to the time delay for servers to turn ON from OFF. In this paper, we propose a new prediction algorithm of the power consumption for improving QoS under not only normal but also abnormal circumstances. The proposed prediction algorithm consists of two parts: prediction based on the conventional time series analysis and prediction adjustment based on trend analysis. We performed experiments using 15 PCs and compared performance for 4 types of conventional time series based prediction methods and their modified methods with our prediction algorithm. Experimental results show that Exponential Smoothing with Trend Adjusted (ESTA) and its modified ESTA (MESTA) proposed in this paper are outperforming among 4 types of prediction methods in terms of normalized QoS and number of good reponses per power consumed, and QoS of MESTA proposed in this paper is 7.5% and 3.3% better than that of conventional ESTA for artificial load pattern and real load pattern, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.43-48
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• 1999

To clarify the motion characteristics of free-style Okinawan dance“Kachaasi”, first the subjective impression was quantitatively evaluated with semantic differential technique to cluster its types. Then, the contingency of joint rotation in shoulder, elbow and wrist joints was examined with multivariate autoregressive model. The time-series data of positions and angels of three joints were calculated according to the deforming conditions and shielding directions of the ring lights. As the results, in an excellent dancer, the motions of shoulder and elbow were highly synchronized and smoothly controlled. The low-frequency output of the shoulder and elbow were mutually interacted. Meanwhile, the wrist behaved independently of other joints' rotation.

• Journal of Auto-vehicle Safety Association
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• v.16 no.3
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• pp.18-24
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• 2024

Recent advancements in vehicle driving data analysis have attracted significant attention as a key research area aimed at optimizing driving behavior and enhancing vehicle performance. This study aims to collect operational data from drivers, including steering angle, longitudinal acceleration, brake usage, and wheel speed, and to cluster driving segments based on their characteristics using deep learning techniques. To achieve this, we employed a methodology that integrates the LSTM-Autoencoder model, which captures essential features of time-series data through compression and reconstruction, with an Attention mechanism. The driving segments were clustered and visually represented with distinct colors for clarity. Experimental results from the proposed approach demonstrated an over 96% accuracy in aligning input and output values, facilitating the clustering of driving segments based on their distinctive features. These findings provide valuable insights for improving driver behavior and optimizing vehicle assistance systems, potentially contributing to significant advancements in this field of research.

• Journal of The Korean Astronomical Society
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• v.49 no.6
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• pp.295-306
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• 2016

We present the results of BV time-series photometry of the globular cluster NGC 288. Observations were carried out to search for variable stars using the Korea Microlensing Telescope Network (KMTNet) 1.6-m telescopes and a 4k pre-science CCD camera during a test observation from August to December, 2014. We found a new SX Phe star and confirmed twelve previously known variable stars in NGC 288. For the semi-regular variable star V1, we newly determined a period of 37.3 days from light curves spanning 137 days. The light-curve solution of the eclipsing binary V10 indicates that the system is probably a detached system. The pulsation properties of nine SX Phe stars were examined by applying multiple frequency analysis to their light curves. We derived a new Period-Luminosity (P-L) relation, ${\langle}M_V{\rangle}=-2.476({\pm}0.300){\log}P-0.354({\pm}0.385)$, from six SX Phe stars showing the fundamental mode. Additionally, the period ratios of three SX Phe stars that probably have a double-radial mode were investigated; $P_{FO}/P_F=0.779$ for V5, $P_{TO}/P_{FO}=0.685$ for V9, $P_{SO}/P_{FO}=0.811$ for V11. This paper is the first contribution in a series assessing the detections and properties of variable stars in six southern globular clusters with the KMTNet system.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.487-500
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• 2020

Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.535-543
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• 2019

Recently, growing social interest in alley markets, which have shown rapid growth like Gyeonglidan-gil street in Seoul, has led to the need for an analysis of growth factors. This paper aims at exploring growing alley markets through time-series clustering using DTW (Dynamic Time Warping) and examining the growth factors through logistic regression. According to cluster analysis, the number of growing markets of the Northeast, the Southwest, and the Southeast were much more than the Northwest but the proportion in region of the Northwest, the Northeast, and the Southwest were much more than the Southeast. Logistic regression results show that people in 20s and 30s have a lower impact on sales than those in 50s, but have a greater impact on growth of alley market. Alley markets located in high-income areas often reached their growth limits, indicating a tendency to stagnate or decline. The proximity of a subway station effected positive on sales but negative on growth. This research is an advanced study in that the causes of sales growth of alley markets is examined, which has not been examined in the preceding study.