• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• International Area Studies Review
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• v.18 no.3
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• pp.175-201
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• 2014

This paper analyses the effect of R&D investment on local economies. R&D investment contributes to the regional local economy by increasing employment and production activity of the investees. The investees may end up with increased productivity, sales and employment. At the regional R&D level, the central government R&D fund and firm self R&D budget will be the source of R&D investment. Further positive effects are inter-related with local industries. This study carried out an empirical analysis on the effect of R&D investment on local economies using Korean panel data after comparing international literatures. The dynamic panel estimator is used to estimate an autoregressive model with lagged dependent variable. Using the Da Silva method, mixed variance-component moving-average error process is estimated and selected. R&D investment is very important factor to improve the productivity of a region and the size of the effect is dependent on the time periods within the Korean economic history.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• 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 Crystal Growth and Crystal Technology
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• v.26 no.6
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• pp.220-224
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• 2016

Anti-reflection (AR) thin films were fabricated on a glass substrate by using an ultrasonic spray. Glycidoxypropyl trimethoxysilane (GPTMS) and tetraethyl orthosilicate (TEOS) were used to synthesize a sol-gel hybrid coating solution. The moving speed of spray nozzle was changed from 15~25 mm/s to control the coating thickness of AR thin film. As the moving speed of spray nozzle increased, the thickness of AR thin film decreased from 138 nm to 86 nm. When the AR thin film was fabricated by nozzle moving speed of 20 mm/s, the refractive index and thickness of AR thin film was measured to be 1.31 and 104 nm, respectively. The average reflectance and transmittance of AR thin film coating glass was measured to be 0.75 % and 94 %, respectively into the visible light range of 380~780 nm.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.105-112
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• 2015

Sawmilling industry remained a high risk with the average 4.73% of industrial accidents in 2010-2012 that was eight times that of general manufacturing. Sawmilling industry had 200 industrial accidents victim in average. Manufacturing process in sawmill industry contained dangerous machinery such as conveyors, roller, saw ( band saw, circular saw) etc. It may be effective to figure out the type of industrial accidents occurred in the past and extend risk assessment which can predict hazard such as near miss when implementing exposure or potential dangers in sawmill industry. This study conducted research on the actual condition on the place of industrial accident occurrence, detailed work and contact object when injured, and injured part targeting 643 businesses which had industrial accidents in 2010-2012. As the results, RPN of general sawmill industry was the highest 'ganglip saw' with 36,157. RPN of the following order were 'moving truck' with 25,454, 'special machining operations' with 22,283. Also, probability of general sawmill industry was a lots within 1 year, while risk appeared a lots within 5 years. So, risk assessment shall be needed to emphasis on accident prevention of sawmill industry. And additional work will be needed on the risk assessment in hazard prevention work of supervisors.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.405-426
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• 2014

In this paper, a new meta-heuristic algorithm named Ranked Particles Optimization (RPO), is presented. This algorithm is not inspired from natural or physical phenomena. However, it is based on numerous researches in the field of meta-heuristic optimization algorithms. In this algorithm, like other meta-heuristic algorithms, optimization process starts with by producing a population of random solutions, Particles, located in the feasible search space. In the next step, cost functions corresponding to all random particles are evaluated and some of those having minimum cost functions are stored. These particles are ranked and their weighted average is calculated and named Ranked Center. New solutions are produced by moving each particle along its previous motion, the ranked center, and the best particle found thus far. The robustness of this algorithm is verified by solving some mathematical and structural optimization problems. Simplicity of implementation and reaching to desired solution are two main characteristics of this algorithm.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1874-1875
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• 2004

A high rate deposition sputtering process of magnesium oxide thin film in oxide mode has been developed using a 20 kW unipolar pulsed power supply. The powersupply was operated at a maximum constant voltage of 500 V and a constant current of 40 A. The pulse repetition rate and the duty were changed in the ranges of 10 ${\sim}$ 50 kHz and 10 ${\sim}$ 60 %, respectively. The deposition rate increased with increasing incident power to the target. Maximum incident power to the magnesium target was obtained by the control of frequency, duty and current. The deposition rate of a moving state was 9 nm m/min at the average power of 1.5 kW. This technique is proposed to apply high through-put sputtering system for plasma display panel.

• ETRI Journal
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• v.35 no.5
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• pp.748-756
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• 2013

This paper addresses the problem of online hop timing detection and frequency estimation of multiple frequency-hopping (FH) signals with antenna arrays. The problem is deemed as a dynamic one, as no information about the hop timing, pattern, or rate is known in advance, and the hop rate may change during the observation time. The technique of particle filtering is introduced to solve this dynamic problem, and real-time frequency and direction of arrival estimates of the FH signals can be obtained directly, while the hop timing is detected online according to the temporal autoregressive moving average process. The problem of network sorting is also addressed in this paper. Numerical examples are carried out to show the performance of the proposed method.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.201-208
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• 1995

The information obtained from the analysis of respiratory muscle elecromyographic (EMG) activities provides a mean for studying muscular activity in relation to the ventilatory process. Thus, in order to comprehend the airflow pattern and its brain control, signal processing is required to characterize respiratory muscle activity. This paper presents a computerized method for the analysis of the electrical activity of the respiratory muscles of premature lambs, and focuses upon the automatic determination of respiratory timing points such as onset and cessation points of the burst activity. Based on experimental results, reliable timing points can be obtained using the proposed methodology.