• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Journal of the Korean earth science society
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• v.40 no.4
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• pp.353-381
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• 2019

The general world model for homogeneous and isotropic universe has been proposed. For this purpose, we introduce a global and fiducial system of reference (world reference frame) constructed on a (4+1)-dimensional space-time, and assume that the universe is spatially a 3-dimensional hypersurface embedded in the 4-dimensional space. The simultaneity for the entire universe has been specified by the global time coordinate. We define the line element as the separation between two neighboring events on the expanding universe that are distinct in space and time, as viewed in the world reference frame. The information that determines the kinematics of the geometry of the universe such as size and expansion rate has been included in the new metric. The Einstein's field equations with the new metric imply that closed, flat, and open universes are filled with positive, zero, and negative energy, respectively. The curvature of the universe is determined by the sign of mean energy density. We have demonstrated that the flat universe is empty and stationary, equivalent to the Minkowski space-time, and that the universe with positive energy density is always spatially closed and finite. In the closed universe, the proper time of a comoving observer does not elapse uniformly as judged in the world reference frame, in which both cosmic expansion and time-varying light speeds cannot exceed the limiting speed of the special relativity. We have also reconstructed cosmic evolution histories of the closed world models that are consistent with recent astronomical observations, and derived useful formulas such as energy-momentum relation of particles, redshift, total energy in the universe, cosmic distance and time scales, and so forth. The notable feature of the spatially closed universe is that the universe started from a non-singular point in the sense that physical quantities have finite values at the initial time as judged in the world reference frame. It has also been shown that the inflation with positive acceleration at the earliest epoch is improbable.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.75-98
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• 2021

This paper compares three statistical models that examine the relationship between national and provincespecific fertility rates. The three models are two of the regression models and a cointegration model. The regression model is by substituting Gompit transformation for the cumulative fertility rate by the average for ten years, and this model applies the raw data without transformation of the fertility data. A cointegration model can be considered when fitting the unstable time series of fertility rate in probability process. This paper proposes the following when it is intended to derive the relation of non-stationary fertility rate between the national and provinces. The cointegrated relationship between national and regional fertility rates is first derived. Furthermore, if this relationship is not significant, it is proposed to look at the national and regional fertility rate relationships with a regression model approach using raw data without transformation. Also, the regression model method of substituting Gompit transformation data resulted in an overestimation of fertility rates compared to other methods. Finally, Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon and Gyeonggi province are expected to show a total fertility rate of 1.0 or less from 2025 to 2030, so an urgent and efficient policy to raise this level is needed.

• BMB Reports
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• v.54 no.6
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• pp.311-316
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• 2021

Ethanol often causes critical health problems by altering the neuronal activities of the central and peripheral nerve systems. One of the cellular targets of ethanol is the plasma membrane proteins including ion channels and receptors. Recently, we reported that ethanol elevates membrane excitability in sympathetic neurons by inhibiting Kv7.2/7.3 channels in a cell type-specific manner. Even though our studies revealed that the inhibitory effects of ethanol on the Kv7.2/7.3 channel was diminished by the increase of plasma membrane phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂), the molecular mechanism of ethanol on Kv7.2/7.3 channel inhibition remains unclear. By investigating the kinetics of Kv7.2/7.3 current in high K⁺ solution, we found that ethanol inhibited Kv7.2/7.3 channels through a mechanism distinct from that of tetraethylammonium (TEA) which enters into the pore and blocks the gate of the channels. Using a non-stationary noise analysis (NSNA), we demonstrated that the inhibitory effect of ethanol is the result of reduction of open probability (P_O) of the Kv7.2/7.3 channel, but not of a single channel current (i) or channel number (N). Finally, ethanol selectively facilitated the kinetics of Kv7.2 current suppression by voltage-sensing phosphatase (VSP)-induced PI(4,5)P₂ depletion, while it slowed down Kv7.2 current recovery from the VSP-induced inhibition. Together our results suggest that ethanol regulates neuronal activity through the reduction of open probability and PI(4,5)P₂ sensitivity of Kv7.2/7.3 channels.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• Journal of Korea Trade
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• v.23 no.5
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• pp.87-101
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• 2019

Purpose - This paper examines the effectiveness of the foreign exchange risk insurance system in the promotion of SME exports in Korea. The purpose of this study is to analyze the short-term and long-term responses of SME exports to foreign exchange risk insurance support policies. Based on these empirical studies, we would like to present some operational improvements to the operation of the foreign exchange risk insurance system. Design/methodology - In order to analyze the effect of exchange risk insurance on the exports of SMEs, a VAR model consisting of foreign exchange risk insurance underwriting values, export relative price, and domestic demand pressure, including export volume, was established. The study began with tests of the stationarity of time series data. The unit root tests showed that all concerned variables were non-stationary. Accordingly, the results of the cointegration test showed that the tested variables are not cointegrated. Finally, an impulse response function and variance decomposition analysis were conducted to analyze the impulse of foreign exchange risk insurance on exports of SMEs. Findings - As a result of estimating the VAR (1) model, foreign exchange risk insurance was found to be significant at a 1% significance level for SME' export promotion. In the impulse response analysis, SMEs' export response to the impulse of foreign exchange risk insurance showed that exports gradually increased until the third quarter, and then slowed down. However, the impulse did not disappear, and appeared continuously. Originality/value - This study analyzed the effect of foreign exchange insurance on exports of SMEs by applying the VAR model. In particular, this study is the first to analyze the short-term and long-term effects of foreign exchange risk insurance on exports of SMEs. The empirical evidence in the current study have a policy implication for the policy authority to support and promote the foreign exchange risk insurance in the effect of exchange rate volatility on Korea' export SMEs.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.85-85
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• 2023

유량 측정을 위해 도섭법, 횡측선법 등의 인력에 의한 방법이 적용되고 있으나, 이는 야간 및 휴일 측정, 인력 부족 등 여러 제약으로 인해 고수위 홍수를 측정하는 데에 한계가 있다. 이를 해결하기 위해 시공간적 제약이 없는 도플러 방식 초음파유속계(Acousitc Doppler Velocity Meter, ADVM)와 자동유속관측시스템(Portable Automatic Velocity Observation System; PAVOs)이 제안되었다. 이 방법들은 교량에 설치된 장치를 통해 실시간으로 유속이 계측되어 시공간적 제약이 없으며 홍수 관리에 유용하게 사용될 수 있다. 실시간으로 계측된 유속 데이터는 오·결측 값이 발생하며 ADVM의 경우 수위-유량관계식을 활용하는 등 전처리 방법이 활용되고 있지만 전자파표면유속계를 활용한 PAVOs 데이터의 전처리 방법에 대한 연구는 부족하다. 따라서 본 연구에서는 PAVOs에서 실시간으로 계측된 유속 데이터의 전 처리 과정(Pre-processing)을 개발하였다. PAVOs를 통해 측정된 데이터는 5분 단위로 10개의 유속이 한번에 측정되며 비정상성(Non-stationary)인 특징을 가진다. 이 데이터의 전처리 과정으로 오·결측값에 대한 처리 및 보간법 적용 이후 10개 값 중 실제 유속을 판단하고 잡음제거(Denoising)를 수행하였다. 이를 강원도 홍천강에 위치한 홍천교에서 계측된 유속 데이터에 적용하였다. 그 결과 데이터의 상승부와 하강부에서 일정한 경향성을 파악할 수 있다. 이 데이터를 통해 산정한 유량과 실측 기반의 평균유속과 관계를 통해 계산한 유량을 비교해 보았을 때 낮은 편차율을 가지는 것을 확인하였다. 전 처리 된 실시간 유속 데이터를 활용한다면 최고수위가 발생하였을 경우 홍수량을 산정할 수 있을 것이다. 또한, 강우 또는 하천 공사에 의해 변동하는 수위-유량관계곡선식을 실시간으로 개발할 수 있을 것이며 이는 효과적인 홍수관리에 큰 역할을 할 수 있을 것이다.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.43.1-43.1
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• 2020

Cassiopeia A (Cas A) is a young supernova remnant (SNR) where we observe the interaction of SNR blast wave with circumstellar medium. From the early optical studies, dense, slowly-moving, N-rich "quasi-stationary flocculi" (QSF) have been known. These are probably dense CNO-processed circumstellar knots that have been engulfed by the SNR blast wave. We have carried out near-infrared, high-resolution (R=45,000) spectroscopic observations of ~40 QSF, and here we present the result on a QSF knot (hereafter 'Knot 24') near the SNR boundary of Cas A. The average [Fe II] 1.644 um spectrum of Knot 24 has a remarkable shape with a narrow (~8 km/s) line superposed on the broad (~200 km/s) line emitted from shocked gas. The spatial morphology and the line parameters indicate that Knot 24 has been partially destroyed by a shock wave and that the narrow line is emitted from the unshocked material heated/ionized by the shock radiation. This is the first detection of the emission from the pristine circumstellar material of the Cas A supernova progenitor. We also detected H Br gamma and other [Fe II] lines corresponding to the narrow [Fe II] 1.644 um line. For the main clump where we can clearly identify the shock emission associated with the unshocked material, we analyze the observed line ratios using a shock model that includes radiative precursor. The analysis indicates that the majority of Fe in the unshocked material is in the gas phase, not depleted onto dust grains as in the general interstellar medium. We discuss the non-depletion of Fe in QSF and its implications on the immediate progenitor of the Cas A supernova.