• Journal of Internet of Things and Convergence
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• v.4 no.1
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• pp.1-5
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• 2018

This paper is concerned with the modeling and identification of time series data corrupted by noise. As modeling techniques, nonsingleton fuzzy logic system (NFLS) is employed for the modeling of corrupted time series. Main characteristic of the NFLS is a fuzzy system whose inputs are modeled as fuzzy number. So the NFLS is especially useful in cases where the available training data or the input data to the fuzzy logic system are corrupted by noise. Simulation results of the Mackey-Glass time series data will be demonstrated to show the performance of the modeling methods. As a result, NFLS does a much better job of modeling noisy time series data than does a traditional Mamdani FLS.

• Proceedings of the KIEE Conference
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• summer
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• pp.806-808
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• 2004

The series and parallel connection is essential for increasing power ratings of resistive type for fault current limiters. To increase voltage class, components are connected in series and to increase current level to the nominal value, they are connected in parallel. There are two ways to connect components in series and parallel. First, connected in series and then the module connects to the parallel. Second, connected in parallel and the module connects to the series. We have studied for the two ways. In this paper, we particularly investigated way to connect components in parallel first This way has the advantage of inducing effective simultaneous quench without any other devices, for example, the thing which is inducing magnetic field to the limiting and shunt resistors. And also we studied for the endurance of component which is patterned to the bi-spiral for prospective fault current. It is very important to understand this, because SFCL will use as the only device to decrease burden of circuit breaker. As experimental results, limiting component patterned to bi-spiral endures fault current up to 30kA and it works well, in parallel to series connection,

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.727-732
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• 2007

Most of previous work on indexing and searching time series focused on the similarity matching and retrieval of one-attribute time series. However, multimedia databases such as music, video need to handle the similarity search in multi-attribute time series. The limitation of the current similarity models for multi-attribute sequences is that there is no consideration for attributes' sequences. The multi-attribute sequences are composed of several attributes' sequences. Since the users may want to find the similar patterns considering attributes's sequences, it is more appropriate to consider the similarity between two multi-attribute sequences in the viewpoint of attributes' sequences. In this paper, we propose the similarity search method based on attributes's sequences in multi-attribute time series databases. The proposed method can efficiently reduce the search space and guarantees no false dismissals. In addition, we give preliminary experimental results to show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1096-1098
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• 2003

In this paper, the control algorithm and control methods for a combined system of shunt passive filter and series active filter in 3-phase 4-wire system are discussed. Moreover, the 3-phase 4-wire system is widely employed in distributing electric energy to several office building and manufacturing plants. In such systems, the third harmonic and odd multiples of $3^{rd}$($9^{th}$, $15^{th}$, etc.) are termed as triple and zero sequence components that do not cancel each other in the system neutral. As a result, the triple harmonics add together creating a primary source of excessive neutral current. Regarding this concern, this paper presents a new control scheme for a series hybrid active system. This series active power filter acts not only as a harmonic compensator but also as a harmonic isolator. Hence the required rating of the series active filter is much smaller than that of a conventional shunt active filter. However, the performance of the combined system is greatly influenced by the filtering algorithm employed in the active power filter. This paper proposes a series active power filter scheme based on performance function. Some experiments was executed and experimental results from a prototype active power filter confirm the suitability of the proposed approach.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.7
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• pp.554-559
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• 2010

In this paper, we analyzed the frequency spectrum of radiated electromagnetic pulses generated by series arc- and corona- discharges as a basic study to develop an on-line diagnostic technique for power facilities installed inside closed-switchboards. To simulate series arc and corona discharges, five types of electrode system which consists of needle and plane electrodes were arranged. The experiment was carried out in an electromagnetic shielding room, and the measurement system consists of an ultra log antenna and an EMI receiver. From the experimental results, the frequency spectrum exists in ranges from 30 MHz to 2 GHz for a series arc discharge and 30 MHz to 1.2 GHz depending on defects for a corona discharge. The peak frequency of the series arc- and corona- discharges were 100 MHz to 160 MHz and 40 MHz to 80 MHz, respectively.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.115-122
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• 2015

This article is concerned with count time series taking values in non-negative integers. Along with the first order mean of the count time series, conditional variance (volatility) has recently been paid attention to and therefore various integer-valued GARCH(generalized autoregressive conditional heteroscedasticity) models have been suggested in the last decade. We introduce diverse integer-valued GARCH(INGARCH, for short) processes to count time series and a real data application is illustrated as a case study. In addition, zero inflated INGARCH models are discussed to accommodate zero-inflated count time series.

• Communications of Mathematical Education
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• v.31 no.1
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• pp.71-84
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• 2017

Taylor series has a complicated structure comprising of various concepts in college major mathematics. This subject is a strong tool which has usefulness and applications not only in calculus, analysis, and complex analysis but also in physics, engineering etc., and other study. However, students have difficulties in understanding mathematical structure of Taylor series convergence correctly. In this study, after classifying students' mathematical characteristic into three categories, we use structural image of Taylor series convergence which associated with mathematical structure and operation acted on that structure. Thus, we try to analyze the understanding of Taylor series convergence and present the results of this study.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• Bulletin of the Korean Chemical Society
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• v.9 no.3
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• pp.167-171
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• 1988

Formation of intermolecular charge-transfer complexes between 7,7,8,8-tetracyanoquinodimethane (TCNQ) and two different series of stilbene derivatives has been studied spectroscopically at $25^{\circ}$C in 1,2-dichloroethane. The compounds of Series I include stilbene and derivatives which have fused phenyl rings on one end of the central ethylene structure and a phenyl ring on the other end. The other Series, II, is comprised of stilbenes which have various para substituents on one of the two phenyl rings. The equilibrium constant, $K_c^{AD}$ and the molar extinction coefficient, ${\varepsilon}_{\lambda}^{AD}$, were determined using the Scott equation. The values of the charge-transfer transition frequency, ${\vu}_AD$ and $K_c{AD}$ correlated well respectively with the ionization potentials of the fused rings of Series Ⅰ or of the compounds of Series II and with the values of ${\sigma}_p$, the Hammett constants of the Series II substituents. trans-4-N,N-Dimethylaminostilbene and trans-4-nitrostilbene were found to be able to participate in electron transfer reaction with TCNQ forming the corresponding anion radical, TCNQ$^-$:

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.1-11
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• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.