• Communications of the Korean Mathematical Society
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• v.37 no.2
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• pp.551-566
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• 2022

We apply Fourier-Legendre-based integration methods that had been given by Campbell in 2021, to evaluate new rational double hypergeometric sums involving ${\frac{{1}}{\pi}}$. Closed-form evaluations for dilogarithmic expressions are key to our proofs of these results. The single sums obtained from our double series are either inevaluable $_2F_1({\frac{4}{5}})$- or $_2F_1({\frac{1}{2}})$-series, or Ramanujan's ₃F₂(1)-series for the moments of the complete elliptic integral K. Furthermore, we make use of Ramanujan's finite sum identity for the aforementioned ₃F₂(1)-family to construct creative new proofs of Landau's asymptotic formula for the Landau constants.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• Progress in Superconductivity and Cryogenics
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• v.11 no.4
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• pp.25-28
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• 2009

In order to apply superconducting electric machineries such as a Superconducting Fault Current Limiter (SFCL) to the power grid, the single module should be connected in series to have reasonable size. Superconducting tapes in the module also should be stacked to satisfy requirements such as large operation current of the power grid. This is because a single superconducting tape has restricted applicable current capacity. Moreover especially in SFCL at the fault, there should be equal voltage distribution in series-connected SFCL modules. In this paper, we investigated the voltage distribution in fault current of series-connected YBCO coated conductors (CC). Depending on characteristics of the CC samples such as critical current, even voltage distribution could be achieved or not. In addition, the effect of stacked CC on the change of voltage distribution comparing to non-stack cases in series connection was confirmed by experiments. As the CC stacked, voltage difference could be reduced.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.381-382
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• 2020

In the battery energy storage system, battery cells are connected in series to increase the operating voltage. Due to the difference in characteristics, the performance degradation of cells is dissimilar. This paper proposes an online DC internal impedance estimation for battery cells in the series string using a matrix-switched capacitor converter, which is already verified as useful for the series balancing of the cells. The simulation in the hardware in the loop test rig shows good accuracy and the feasibility of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.377-379
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• 2023

Concept drift detection on data stream is the major issue to maintain the performance of the machine learning model. Since the online stream is to be a function of time, the classical statistic methods are hard to apply. In particular case of seasonal time series, a novel window strategy with Fourier analysis however, gives a chance to adapt the classical methods on the series. We explore the KS-test for an adaptation of the periodic time series and show that this strategy handles a complicate time series as an ordinary tabular dataset. We verify that the detection with the strategy takes the second place in time delay and shows the best performance in false alarm rate and detection accuracy comparing to that of arbitrary window sizes.

• The Journal of Korean Medical History
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• v.35 no.1
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• pp.119-134
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• 2022

This study is about Cho Heon-yeong's 『Eastern Medicine Series (東洋醫學叢書)』. It consists of 5 clinical books, 『Therapy for People (民衆醫術理療法)』, 『Tuberculosis Eastern Medical Treatment (肺病漢方治療法)』, 『Neurasthenia Treatment (神經衰弱症治療法)』, 『Gastrointestinal Disease Treatment (胃腸病治療法)』 and 『Gynecological Treatment (婦人病治療法)』, which were published between 1935-1941. This series mainly succeeded 『Donguibogam (東醫寶鑑)』, and was influenced by 『Kyeongakjeonseo (景岳全書)』, and 『Hwangjenegyeong (黃帝內經)』. Cho Heon-yeong's medical philosophy appears in two ways. First, he emphasized invigoration in treatment for the people who lacked nutrition and medical care at the time. Second, eclecticism of Korean Medicine and Western medicine is specifically revealed through this series. He aimed for a comprehensive medicine that consists mainly of Korean medicine and includes only a part of Western medicine.

• Honam Mathematical Journal
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• v.45 no.4
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• pp.619-628
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• 2023

In this paper, the fractional order time-varying linear dynamical systems are investigated by using a residual power series method. A residual power series method (RPSM) is constructed for this problem. The exact solution is obtained by the Laplace transform method and the analytical solution is calculated via the residual power series method (RPSM). As an application, some examples are tested to show the accuracy and efficacy of the proposed methods. The obtained result showed that the proposed methods are effective and accurate for this type of problem.

• International Journal of Concrete Structures and Materials
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• v.2 no.1
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• pp.41-48
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• 2008

This study investigates the spalling properties of high strength concrete designed with various types of mineral admixture and diverse content ratios of polypropylene (PP) fiber. Experimental factors considered in series I are four pozzolan types of mineral admixture and series II consists of three shrinkage reducing types of mineral admixture. PP fiber was added 0.05, 0.10 and 0.15vol. % in each mixture of series I and series II, so that totally 27 specimens including control concretes in each series were prepared. Test results showed that the increase of fiber content decreased the slump flow of fresh concrete and increased or decreased the air content depending on the declining ratio of slump flow. For the properties of compressive strength, all specimens were indicated at around 50 MPa, which is high strength range; especially all specimens in series II were 60 MPa. Fire test was conducted in standard heating curve of ISO 834 with ${\phi}100{\times}200\;mm$ size of cylinder moulds for 1 hour. The specimens incorporating silica fume exhibited severe spalling and most specimens without the silica fume could be protected from the spalling occurrence in only 0.05vol % of PP fiber content. This fire test results demonstrated that the spalling occurrence in high strength concrete was not only affected by concrete strength related to the porosity of microstructure but also, even more influenced by micro pore structure induced by the mineral admixtures.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1-10
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• 2020

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.