• Journal of Information Processing Systems
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• v.8 no.4
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.431-434
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• 2016

Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.135-135
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• 2017

최근 강우-유출 모형은 물리적 현상에 근거한 확정론적 모의 모형과 물리적 성분으로 설명할 수 없는 내용에 대해 통계적으로 접근하는 추계학적 모의 모형 등이 계속 연구되고 있어 자연현상에 가까운 결과를 기대할 수 있게 되었다. 하지만 우리나라의 경우 많은 연구에도 불구하고 돌발성 집중호우, 여름철 집중되는 강우 등으로 인해 재난이 반복적으로 발생하고 있어 모형의 정확성에 대한 논의가 지속되고 있다. 동일한 유역에 동일한 입력자료를 사용하더라도 사용하는 모형에 따라 유출 분석결과는 상이하며 이는 유출 해석에 대한 불확실성으로 작용한다. 본 연구에서는 앙상블 및 블랜딩 기법을 사용하여 각 강우-유출 모형의 불확실성을 고려하여 최적 유출량을 산정하고자 한다. 대상 유역으로는 한강 수계에 있는 중랑천 유역을 선정하였으며, Distributed 모형인 Vflo 모형과 Lumped 모형인 저류함수 모형, SSARR모형, TANK 모형을 이용하여 유출 분석을 실시하였다. 그 후, Multi-Model Super Ensemble(MMSE), Simple Model Average(SMA), Mean Square Error(MSE) 방법 등의 blending 기법을 이용하여 하나의 통합된 형태의 유출 분석 결과를 제시하였으며, 최적 유출량 산정을 위한 blending 기법을 선정하였다. 본 연구를 통해 동일한 강우 시나리오에 대한 여러 강우-유출 모형에 대한 정확도를 확인하였으며, 앙상블 및 블랜딩 기법을 사용하여 유출 분석에 대한 정확도를 향상시킬 수 있을 것으로 판단된다.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.79-92
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• 2022

In this paper, the impact of a vernacular pozzolanic kaolinite mine on concrete alkali-silica reaction and strength has been evaluated. For making the samples, kaolinite powder with various levels has been used in the quality specification test of aggregates based on the ASTM C1260 standard in order to investigate the effect of kaolinite particles on reducing the reaction of the mortar bars. The compressive strength, X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM) experiments have been performed on concrete specimens. The obtained results show that addition of kaolinite powder to concrete will cause a pozzolanic reaction and decrease the permeability of concrete samples comparing to the reference concrete specimen. Further, various machine learning methods have been used to predict ASR-induced expansion per different amounts of kaolinite. In the process of modeling methods, optimal method is considered to have the lowest mean square error (MSE) simultaneous to having the highest correlation coefficient (R). Therefore, to evaluate the efficiency of the proposed model, the results of the support vector machine (SVM) method were compared with the decision tree method, regression analysis and neural network algorithm. The results of comparison of forecasting tools showed that support vector machines have outperformed the results of other methods. Therefore, the support vector machine method can be mentioned as an effective approach to predict ASR-induced expansion.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.255-255
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• 2020

아프리카 대륙에서 존재하는 가장 큰 사하라 사막(Sahara desert)의 면적은 지난 1세기 동안 기후변화로 인하여 10% 정도 증가하였고, 미래에도 기온상승으로 인하여 증가할 것으로 판단된다. 사하라 사막 면적의 증가로 인하여 아프리카의 자연식생과 수자원뿐만 아니라 아프리카에 거주하는 사람들의 삶에 많은 영향을 미치기에 사막의 면적 또는 경계선의 위치를 예측함은 매우 중요하다. 본 연구에서는 Coupled Model Intercomparison Project Phase 5 (CMIP5)의 36개 Global Climate Models (GCMs)과 ERA-interim 재분석 자료의 1979~2000년 강수 자료들을 이용하여 사헬(Sahel) 지대 서쪽(15W~15E, 10N~20N)과 동쪽(15E~35E, 10N~20N)의 강수량과 사막경계선을 비교하였다. 또한, 각 모델의 과거 모의 성능을 평가하여 미래 기후 예측성을 판단하고자 한다. 본 연구에서는 22년 평균 강수량이 200mm 이하인 지역을 사막이라 정의하고, 모델별로 연평균 강수량과 사막경계선에 대한 root mean square error(RMSE)를 산정하여 평가하였다. 또한, 습윤 정적 에너지(Moist. Static Energy; MSE), 바람(풍속 및 풍향) 자료를 이용하여 각 모델의 사막경계선의 오차에 대한 이유를 분석하였다. 이 연구를 바탕으로 하여 사헬 지대의 강수량 및 사막면적 모의의 불확실성 요소를 이해하고, 미래 상세 지역 수문기후 변화 예측에 활용 가능한 GCMs을 선별할 수 있을 것으로 판단한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4A
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• pp.322-331
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• 2010

This paper introduces a new efficient design scheme for spatial multiplexing (SM) systems over closed loop multiple-input multiple-output (MIMO) wireless channels. Extending the orthogonalized spatial multiplexing (OSM) scheme which was developed recently for transmitting two data streams, we propose a new SM scheme where a larger number of data streams can be supported. To achieve this goal, we partition the data streams into several subblocks and execute the block-diagonalization process at the receiver. The proposed scheme still guarantees single-symbol maximum likelihood (ML) detection with small feedback information. Simulation results verify that the proposed scheme achieves a huge performance gain at a bit error rate (BER) of $10^{-4}$ over conventional closed-loop schemes based on minimum mean-square error (MSE) or bit error rate (BER) criterion. We also show that an additional 2.5dB gain can be obtained by optimizing the group selection with extra feedback information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.452-460
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• 2003

In this paper, an equalizer(RNE) using nonlinear multilayer combining techniques in Bayesian equalizer with a structure of radial basis function network is proposed in order to simplify the structure and enhance the performance of the equalizer(RE) using a radial basis function network. The conventional RE Produces its output using linear combining the outputs of the basis functions in the hidden layer while the proposed RNE produces its output using nonlinear combining the outputs of the basis function in the first hidden layer. The nonlinear combiner is implemented by multilayer perceptrons(MLPs). In addition, as an infinite impulse response structure, the RNE with decision feedback equalizer (RNDFE) is proposed. The proposed equalizer has simpler structure and shows better performance than the conventional RE in terms of bit error probability and mean square error.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.54-76
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• 1988

In Part Ⅰ of the paper, we have developed various block least mean-square (BLMS) adaptive digital filters (ADF's) based on a unified matrix treatment. In Part Ⅱ we analyze the convergence behaviors of the self-orthogonalizing frequency-domain BLMS (FBLMS) ADF and the unconstrained FBLMS (UFBLMS) ADF both for the overlap-save and overlap-add sectioning methods. We first show that, unlike the FBLMS ADF with a constant convergence factor, the convergence behavior of the self-orthogonalizing FBLMS ADF is governed by the same autocorrelation matrix as that of the UFBLMS ADF. We then show that the optimum solution of the UFBLMS ADF is the same as that of the constrained FBLMS ADF when the filter length is sufficiently long. The mean of the weight vector of the UFBLMS ADF is also shown to converge to the optimum Wiener weight vector under a proper condition. However, the steady-state mean-squared error(MSE) of the UFBLMS ADF turns out to be slightly worse than that of the constrained algorithm if the same convergence constant is used in both cases. On the other hand, when the filter length is not sufficiently long, while the constrained FBLMS ADF yields poor performance, the performance of the UFBLMS ADF can be improved to some extent by utilizing its extended filter-length capability. As for the self-orthogonalizing FBLMS ADF, we study how we can approximate the autocorrelation matrix by a diagonal matrix in the frequency domain. We also analyze the steady-state MSE's of the self-orthogonalizing FBLMS ADF's with and without the constant. Finally, we present various simulation results to verify our analytical results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.261-269
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• 2018

This paper presents a convenient method to estimate the internal parameters of plenoptic camera using CCD(charge-coupled device) camera model. The images used for plenoptic camera calibration generally use the checkerboard pattern used in CCD camera calibration. Based on the CCD camera model, the determinant of the plenoptic camera model can be derived through the relationship with the plenoptic camera model. We formulate four equations that express the focal length, the principal point, the baseline, and distance between the virtual camera and the object. By performing a nonlinear optimization technique, we solve the equations to estimate the parameters. We compare the estimation results with the actual parameters and evaluate the reprojection error. Experimental results show that the MSE(mean square error) is 0.309 and estimation values are very close to actual values.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1182-1188
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• 2006

Adaptive OFDM(Orthogonal Frequency Division Multiplexing) improves data capacity and system performance over multipath fading by adaptively changing modulation schemes according to channel state information(CSI). To achieve a good performance in adaptive OFDM systems, CSI should be transmitted from receiver to transmitter in real time through feedback channel. However, practically, the CSI feedback delay d which is the sum of the data processing delay and the propagation delay is not negligible and damages to the reliability of CSI such that the performance of adaptive OFDM is degraded. This paper presents an adaptive OFDM system with a multistep predictor on the frequency axis to effectively compensate the multiple feedback delays $d(\geq2)$. Via computer simulation we compare the proposed scheme and existing adaptive OFDM schemes with respect to data capacity and system performance.