• 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.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.9
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• pp.148-157
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• 2007

A new method which uses distance fields scheme and marching cube algorithm is proposed in order to get an accurate offset model of arbitrary shapes composed of triangular net. In the method, the space bounding the triangular net is divided into smaller cells. For the efficient calculation of distance fields, valid cells which will generate a portion of offset model are selected previously by the suggested detection algorithm. These valid cells are divided again into much smaller voxels which assure required accuracy. At each voxel distance fields are created by calculating the minimum distances between corner points of voxels and triangular net. After generating the whole distance fields, the offset surface were constructed by using the conventional marching cube algorithm together with mesh smoothing scheme. The effectiveness and validity of this new offset method was demonstrated by performing numerical experiments for the various types of triangular net.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.233-239
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• 2016

Color enhancement basically deals with color manipulation in digital images. Recently, the technique has become widely used as a result of the increasing use of digital cameras. Retinex-based colorenhancement algorithms are a popular technique. In this paper, retinex with bilateral filtering is proposed to improve the quality of poorly illuminated images. Generally, it consists of three main steps: first, a retinex-based algorithm with color restoration; second, transformation mapping using histogram matching; and finally, smoothing the image using a bilateral filter. The experimental results demonstrate that the proposed method can successfully enhance image contrast while avoiding the halo effect and maintaining the color distribution in the image.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.121-126
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• 1996

The hydrodynamic coefficients estimation (HCE) is important to design the autopilot and to predict the maneuverability of an underwater vehicle. In this paper, a system identification is proposed for an HCE of an underwater vehicle. First, we attempt to design the HCE algorithm which is insensitive to initial conditions and has good convergence, and which enables the estimation of the coefficents by using measured displacements only. Second, the sensor and measurement system which gauges the data from the full scale trials is constructed and the data smoothing algorithm is also designed to filter the noise due to irregular fluid flow without changing the data characteristics itself. Lastly the hydrodynamic coefficients are estimated by applying the measured data of full scale trials to the developed algorithm, and the estimated coefficients are verified by full scale trials.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.301-303
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• 2004

In this work, we develop on-line traffic prediction algorithm for real-time VBR traffic. There are a number of important issues: (i) The traffic prediction algorithm should exploit the stochastic characteristics of the underlying traffic and (ii) it should quickly adapt to structural changes in underlying traffic. GOP ARIMA model effectively addresses this issues and it is used as basis in our bandwidth prediction. Our prediction model deploy Kalman filter to incorporate the prediction error for the next prediction round. We examine the performance of GOP ARIMA based prediction with linear prediction with LMS and double exponential smoothing. The proposed prediction algorithm exhibits superior performam againt the rest.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.1-11
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• 1980

For an ordinary engineer or researcher there are too diversified branches or even disciplines which have their own jargon to complicate an easy access, Nevertheless in many cases an isomorphism or even identity of notions exist to escape our grasp when expressed in different discipline or context, In this paper the masterwork of Box and Jenkins is introduced to accustom a few terms of statisticiens, to be followed by the technique of smoothing filtering of Wiener and Kalman - Bucy. The advantages of a transform (for example Hadamard) technique are explaned as well as authors personal philosophical views.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.361-364
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• 1997

Bayesian MAP-EM approaches have been quite useful or tomographic reconstruction in that they can stabilize the instability of well-known ML-EM approaches, and can incorporate a priori information on the underlying emission object. However, MAP reconstruction algorithms with expressive priors often suffer from the optimization problem when their objective unctions are nonquadratic. In our previous work [1], we showed that the use of deterministic annealing method greatly reduces computational burden or optimization and provides a good solution or nonquadratic objective unctions. Here, we further investigate the convergence of the deterministic annealing algorithm; our experimental results show that, while the solutions obtained by a simple quenching algorithm depend on the initial conditions, the estimates converged via deterministic annealing algorithm are consistent under various initial conditions.

• Journal of Satellite, Information and Communications
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• v.3 no.2
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• pp.32-37
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• 2008

Global Navigation Satellite System (GNSS) is been developing in many countries. The satellite navigation system has the importance in economic and military fields. For utilizing satellite navigation system properly, the technology of GNSS Ground Station is needed. GNSS Ground Station monitors the signal of navigation satellite and analyzes navigation solution. This study deals with the navigation software for GNSS Ground Station. This paper will introduce the navigation solution algorithm for GNSS Ground Station. The navigation solution can be calculated by the code-carrier smoothing method, the Kalman-filter method, the least-square method, and the weight least square method. The performance of each navigation algorithm in this paper is presented.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.393-396
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• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.171-176
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• 2005

This paper proposes an automatic liver segmentation method using improved partial histogram threshold (PHT) algorithms. This method removes neighboring abdominal organs regardless of random pixel variation of contrast enhanced CT images. Adaptive multi-modal threshold is first performed to extract a region of interest (ROI). A left PHT (LPHT) algorithm is processed to remove the pancreas, spleen, and left kidney. Then a right PHT (RPHT) algorithm is performed for eliminating the right kidney from the ROI. Finally, binary morphological filtering is processed for removing of unnecessary objects and smoothing of the ROI boundary. Ten CT slices of six patients (60 slices) were selected to evaluate the proposed method. As evaluation measures, an average normalized area and area error rate were used. From the experimental results, the proposed automatic liver segmentation method has strong similarity performance as the MSM by medical Doctor.