• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.4
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• pp.435-446
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• 2014

Tidal turbulence was examined using three-dimensional tidal velocity data observed at a trench offshore of Tongyoung, Korea. The kinetic energy and intensity, including the variation period of the flow velocity and direction, were used to investigate the relationships between tidal turbulence and fishing gear dynamics, including the effects of swimming fish during fishing operations. As the resultant velocity increased from 0.2 to 0.9 m/s, the kinetic energy also significantly increased, while the turbulence intensity decreased from 50 to 10%. Tidal flow in strong flow fields displayed shorter periods of between 4 and 10 s, as determined by fast Fourier transform, the global wavelet method, and peak event analysis, and the periods were compared with the period of response to swimming fish and to oscillation of fishing gear. As mean velocity increased, velocity amplitude also increased from 0.1 to 0.6 m/s, and its directional amplitude changed markedly from 20 and $90^{\circ}$. Our study suggests that tidal turbulence can influence fish behavior or fishing gear geometry during fishing operations, although our analysis considered only a limited area. In future work, observations should be carried out over a more extensive depth and area.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.7
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• pp.555-561
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• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.

• Geophysics and Geophysical Exploration
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• v.5 no.1
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• pp.18-22
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• 2002

As a single arrival traveltime, maximum energy arrival traveltime has been known as the most proper operator for Kirchhoff migration. In case of the model having the simple structure, both the first arrival traveltime and the maximum energy arrival traveltime can be used as the correct operators for Kirchhoff migration. However for some model having the complex and high velocity contrast structure, the migration using the first arrival traveltime can't give the correct depth section. That is, traveltime to be required in Kirchhoff migration is the maximum energy traveltime, but, needs considerably more calculation time than that of first arrival. In this paper, we propose the method for calculating the traveltime approximated to the maximum energy arrival using one-way wave equation. After defining the WAS(Wrap Around Suppression) factor to be used for calculating the first arrival traveltime using one-way wave equation as the function of lateral grid interval and depth and considering the delay time of source wavelet. we calculate the traveltime approximated to the maximum energy arrival. to verify the validity of this traveltime, we applied this to the migraion for simple structure and complex structure and compared the depth section with that obtained by using the first arrival traveltime.

• Corrosion Science and Technology
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• v.16 no.4
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• pp.194-200
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• 2017

Stress-corrosion cracking (SCC) of alloy 600 and alloy 800 in 0.5 mol/L thiosulfate solution during constant strain was investigated using electrochemical noise (EN) combined with 3-D microscope techniques. The in-situ morphology observation and EN results indicate that the SCC process could be divided into three stages: (1) passive film stabilization and growth, (2) crack initiation, (3) and crack growth. Power Spectral Density (PSD) and the probability distribution obtained from EN were used as the "fingerprint" to distinguish the different processes. During passive film stabilization and growth, the current noise signals resembled "white noise": when the crack initiated, many transient peaks could be seen in the current noise and the wavelet energy at low frequency as well as the noise resistance decreased. After crack propagation, the noise amplitudes increased, particularly the white noises at low and high frequencies ($W_L$ and $W_H$) in the PSDs. Finally, the detection of metal structure corrosion in a simulated sea splash zone and pipeline corrosion in the atmosphere are established.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.520-529
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• 2007

In this paper, we study the characteristics of parameters which are related to performance of MCTF which is a key technique for wavelet-based scalable video coding, and propose an improved MCTF method. The proposed MCTF method adopts the motion estimation of which motion vector field is distributed more uniformly using variable block sizes. By using the proposed method, the decomposition performance of temporal filter is improved, and the energy in high-frequency frames is reduced. It can help the entropy coder to generate lower bitrate. From simulation results, we verify the decomposed energy on high-frequency frame using the proposed method is reduced by 25.86% at the most in terms of variance of the high-frequency frame.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.821-827
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• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.712-721
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• 2010

This paper proposes a new fire-flame detection method using probabilistic membership function of visual features and Fuzzy Finite Automata (FFA). First, moving regions are detected by analyzing the background subtraction and candidate flame regions then identified by applying flame color models. Since flame regions generally have continuous and an irregular pattern continuously, membership functions of variance of intensity, wavelet energy and motion orientation are generated and applied to FFA. Since FFA combines the capabilities of automata with fuzzy logic, it not only provides a systemic approach to handle uncertainty in computational systems, but also can handle continuous spaces. The proposed algorithm is successfully applied to various fire videos and shows a better detection performance when compared with other methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.1072-1086
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• 1998

Transform, subband, and wavelet transform decompositions are powerful linear transformation tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirat/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visulal system. Thus, a growingbody of research has bee performed to extend these tools in various kinds of modified formations. Hence, in this paper, an overall survey to achieve a general view on these transformation tools have been attempted. Starting from basic tools such as orthogonal transforms, lapped transforms, QMF(quadrature mirror filter) subband filter banks, and wavelet transforms, their hierarchical extensions, vector extensions, and linear time-varying extensions are investugated in detail.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.

• Composites Research
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• v.24 no.6
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• pp.49-55
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• 2011

The use of advanced composite materials in main structures of military and civil aircraft has been increased rapidly because of their considerable metals in high specific strength and stiffness. However, the mechanical properties of composite materials may severely degrade in the presence of damage. Especially, the high-velocity impact such as a hailstorm, and a small piece of tire or stone during high taxing, can cause considerable damage to the structures and sub-system in spite of a very small mass. However, it is not easy to detect the damage in composite plates using a single sensor or any conventional methods. In this paper, the PVDF sensors and AE sensors were used for monitoring high-velocity impact damage initiation and propagation in composite laminates. The WT(wavelet transform) is used to decompose the sensor signals. In the PVDF sensor and AE sensor signal analysis, amounts of high-frequency signals are increased when the impact energy is increased. PVDF sensor and AE sensor signal appeared similar results. This study shows how various sensing techniques can be used to characterize high-velocity impact damage of advanced composite laminates.