• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.2
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• pp.198-203
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• 2010

In this paper, study on the mode characteristics analysis of the SH-EMAT (shear horizontal, electromagnetic acoustic transducer) waves for evaluating the thickness reduction in plates such as corrosion and friction is presented. Noncontact methods for ultrasonic wave generation and detection have been a great concern and highly demanded due to their capability of wave generation and reception on surface of high temperature or on rough surface. Mode identification of the SH-EMAT wave is carried out in an aluminum plate with thinning defects using time frequency analysis method such as wavelet transform, compared with theoretically calculated group velocity dispersion curve. The changes of various wave features such as the amplitude and the time-of-flight have been observed and the correlations with the thickness reduction have been investigated. Firstly, experiments have been conducted to confirm that it is possible to selectively generate and receive specific desired SH modes. These modes have then been analyzed to select the parameters that are sensitive to the thickness change. The results show that the mode cutoff and the time-of-flight changes are feasible as key parameters to evaluate the thickness reduction.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.341-348
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• 2011

ECG(Electrocardiogram), a field of Bio-signal, is generally experimented with classification algorithms most of which are SVM(Support Vector Machine), MLP(Multilayer Perceptron). But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm. The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed. As a result, modified RF Classifier showed better consequences than SVM classifier, MLP classifier and other researches' results in accuracy category. The Band-pass filter is used to extract R-R interval in pre-processing stage. However, the Wavelet transform, median filter, and finite impulse response filter in addition to Band-pass filter are often used in experiment of ECG. After this study, selection of the filters efficiently deleting the baseline wandering in pre-processing stage and study of the methods correctly extracting the R-R interval are needed.

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.509-523
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• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.5
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• pp.21-30
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• 2008

The fuel cell generation convert fuel source, and gas directly to electricity in an electro-chemical process. Unlike traditional and conventional turbine engines, the process of fuel cell generation do not burn the fuel and run pistons or shafts, and it has not revolutionary machine, so have fewer efficiency losses, low emissions and no noisy moving parts. A high power density allows fuel cells to be relatively compact source of electric power, beneficial in application with space constraints. In this system, the fuel cell itself is nearly small-sized by other components of the system such as the fuel reformer and power inverter. So, the fuel cell energy's stationary fuel cells produce reliable electrical power for commercial and industrial companies as well as utilities. In this paper, a fuel cell system has been modeled using PSCAD/EMTDC to analyze its electric signals and characteristics. Also the power quality of the fuel cell system has been evaluated and the problems which can be occurred during its operation have been studied by modeling it more detailed. Particularly, we have placed great importance on its power quality and signal characteristics when it is connected with a power grid.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.89-94
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• 2013

This paper presents color video flame detection algorithm based on wavelet transform to remove detection of flickering non-flame objects. Conventional flame detection algorithms consist of simple or mixed functions using colors, temporal and spatial characteristics. But those algorithms detect non-flame objects as flame regions sometimes. False alarm reasons are flame-like objects with regular flickering lights such as car signal lamps, alarm lights etc. The proposed algorithm is to reduce false detection which is occurred in periodic flickering lights. At first, It segments the candidate flame regions by using frame difference, flame colors. Then it distinguish flame regions and non flame regions including flickering car lights by analyzing wavelet coefficients. Computer simulation results showed that the proposed algorithm removes false detection due to the periodic flickering lamps by performing 97.9% of correct detection rate while false detection rate is 7.3%.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.936-941
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• 2006

To apply object recognition techniques to real environment, illumination compensation method should be developed. As effective illumination compensation model, we focused our attention on Retinex model and illumination-Reflectance model, implemented them, and experimented on their performance. We implemented Retinex model with Single Scale Retinex, Multi-Scale Retinex, and Retinex Neural Network and Multi-Scale Retinex Neural Network, neural network model of Retinex model. Also, we implemented illumination-Reflectance model with reflectance image calculation by calculating an illumination image by low frequency filtering in frequency domain of Discrete Cosine Transform and Wavelet Transform, and Gaussian blurring. We compare their illumination compensation performance to facial images under nine illumination directions. We also compare their performance after post processing using Principal Component Analysis(PCA). As a result, illumination Reflectance model showed better performance and their overall performance was improved when illumination compensated images were post processed by PCA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1342-1347
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• 2010

Various researches have been studied on WSN(wireless sensor network) for barbed wire entanglements surveillance applications such as industry facilities, security area, prison, military area, airport, etc. Currently, barbed wire entanglements surveillance is formed wire sensor network environment. Traditional wire sensor network guarantee high data transmission rate. Therefore, wire sensor network use fast fourier transform of data of high transmission rate for extraction of feature parameter. However, wireless sensor network in comparison with wire sensor network has very low data transmission rate. Therefore, wireless sensor network doesn't use fast fourier transform of wire sensor network for extraction of feature parameter. In this paper, proposed method use 1 level approximation coefficient of DTW(dynamic time-warped) algorithms based on DWT(discrete wavelet transform) for extraction of detection feature parameter and classification feature parameter for barbed wire entanglements surveillance. l level approximation coefficient have time information and frequency information of signal. Therefore, Dynamic time-warped algorithms based on discrete wavelet transform improve detection and classification of target rather than using energy of signal.

• Journal of the Korea Concrete Institute
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• v.23 no.2
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• pp.219-224
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• 2011

In this study, a piezoelectric smart sensor that can be embedded inside of concrete structures is developed to investigate the early stage of concrete curing. A waterproof coating is used to protect the piezoelectric sensor from moistures of concrete mixture. Also, a mortar case is utilized to encapsulate the sensor to protect it from impact loads. To estimate the strength of concrete, a self-sense guided-wave actuated sensing technique is applied. In the guided wave, its velocity is varied according to the mechanical properties of concrete such as modulus of elasticity. Because modulus of elasticity directly affects the strength of concrete, the guidedwave's velocity also affects the concrete strength development. To verify the feasibility of using the proposed approach, the smart sensor was embedded into a 100MPa concrete cylinder and the self-sense guided wave is continuously measured throughout the curing process. The measurements showed that the propagation time (TOF) of the measured guided waves gradually decreased as the curing age increased. Especially, at the early age of the curing process, the variation of the TOF was very significant. Furthermore, the results showed that there is a linear relationship between the TOF of the self-sense guided waves and the strength of concrete existed. It is safe to conclude that the proposed approach can be used very effectively in monitoring of the strength development of high strength concrete structures.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.105-114
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• 2007

Two-dimensional imaging of $V_s$ profile becomes more important in Korea because of the large horizontal variation of soil stiffness. To obtain a shear-wave velocity profile in geotechnical practice, various seismic nondestructive investigation methods are being frequently used. In this study, harmonic wavelet analysis of wave (HWAW) method is applied to the determination of $V_s$ profile to overcome some of weaknesses in the existing surface wave methods. HWAW method which is based on time-frequency analysis using harmonic wavelet transform has been developed to determine phase and group velocities of waves. Field testing of this method is relatively simple and fast because one experimental setup which consists of one pair of receivers is needed to determine $V_s$ profile of site. The proposed method uses the signal portion of the maximum local signal/noise ratio to evaluate the phase velocity to minimize the effects of noise, and uses single array inversion which considers receiver locations. Field tests were performed in 2 sites in order to evaluate accuracy of test method and estimate the applicability of 2-D imaging by HWAW method. Through field applications and comparison with other test results, the good accuracy and applicability of the proposed method were verified.