• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.106-115
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• 2000

This paper presents the measuring technique of time and velocity when rocket motor is burnt out for 2.751" rocket. This technique use doppler effect, frequency spectrum analysis and curve fitting. In this study, we use muzzle velocity radar for doppler signal acquisition, short-time fourier transform for spectrum analysis and curve fitting for smoothing.

• Journal of Integrative Natural Science
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• v.9 no.3
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• pp.215-222
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• 2016

When the linear correlation of the quality variables are considerably high, multivariate control charts may be a more effective way than univariate charts which operate quality variables and process parameters individually. Performances and efficiencies of the multivariate control charts under multivariate normal process has been considered. Some numerical results are presented under small scale of the shifts in the process to see the improvement of the efficiency of EWMA chart and CUSUM chart, which use past quality information, comparing to Shewart chart, which do not use quality information. We can know that the decision of the optimum value of the smoothing constant in EWMA structure or the reference value in CUSUM structure are very important whether we adopt combine-accumulate technique or accumulate-combine technique under the given condition of process.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.433-438
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• 1998

본 연구에서는 time averaging과 depth profiling기술을 음향현미경에 적용하여 5MHz 대역의 초음파 영상처리 시스템을 구성하였으며 기존의 피크값 검출기술과 상호 비교, 분석하였다. time averaging기술에서는 한 지점에서 반사된 tone burst파 전체를 디지털 오실로스코프를 통해 시간영역에서 A/D변환하고 변환된 512개 데이터들의 평균값을 취함으로써 영상을 얻을 수 있었으며, 이 기술은 시간영역에서 smoothing효과를 이용하여 산란이 심한 영역에 대한 영상을 개선시킬 수 있었다. depth profiling기술은 기준신호에 대한 반사신호의 시간 지연값을 최소 분해능 2ns로 검출함으로써 샘플의 3차원적인 실제 기하학적인 모양을 상대적인 크기로 얻을 수 있었다.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.289-295
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• 2000

The triangulation problem is to make triangles using the given points on the space. The Minimum Weight Triangulation(MWT) is the problem of finding a set of triangles with the minimum weight among possible set of the triangles. In this paper, a modified genetic algorithm(GA) based on an existing genetic algorithm and multispace smoothing technique is proposed. Through the computational results, we can find the tendency that the proposed GA finds good solutions though it needs longer time than the existing GA does as the problem size increases.

• 한국전산유체공학회:학술대회논문집
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• 1999.05a
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• pp.23-28
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• 1999

A mutigrid convergence acceleration technique is presented for computing hypersonic inviscid and viscous flows in equilibrium state. The governing equations are solved using an explicit Runge-Kutta method. Curve fitting data in NASA Reference Publication 1181, 1260 are used to calculate equilibrium properties. In order to ensure stability, damped prolongation and modified implicit residual smoothing are proposed. Blunt body test cases are presented to demonstrate the robustness and the efficiency in performance of the proposed methods

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.413-420
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• 2012

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.680-685
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• 2009

In this paper, we propose a efficient noise power estimation technique for an integrated acoustic echo and noise suppression system in a frequency domain. The proposed method uses speech absence probability (SAP) derived from the microphone input signal as the smoothing parameter updating noise power to reduce the noise power estimation error resulted from the distortions in the unified structure where the noise suppression (NS) operation is placed after the acoustic echo suppression (AES) algorithm. Therefore, in the proposed approach, the smoothing parameter based on SAP derived from the input signal instead of echo-suppressed signal should stop updating noise power estimates during the distorted noise spectrum periods. The performance of the proposed algorithm is evaluated by the objective test under various environments and yields better results compared with the conventional scheme.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1025-1039
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• 2010

Measuring displacement response for civil structures is very important for assessing their performance, safety and integrity. Recently, video-based techniques that utilize low-cost high-resolution digital cameras have been developed for such an application. These techniques however have relatively low sampling frequency and the results are usually contaminated with noises. In this study, an integrated visual-inertial measurement method that combines a monocular videogrammetric displacement measurement technique and a collocated accelerometer is proposed for displacement and velocity measurement of civil engineering structures. The monocular videogrammetric technique extracts three-dimensional translation and rotation of a planar target from an image sequence recorded by one camera. The obtained displacement is then fused with acceleration measured from a collocated accelerometer using a multi-rate Kalman filter with smoothing technique. This data fusion not only can improve the accuracy and the frequency bandwidth of displacement measurement but also provide estimate for velocity. The proposed measurement technique is illustrated by a shake table test and a pedestrian bridge test. Results show that the fusion of displacement and acceleration can mitigate their respective limitations and produce more accurate displacement and velocity responses with a broader frequency bandwidth.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.