• Journal of Information Processing Systems
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• v.16 no.3
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• pp.557-571
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• 2020

The existing methods for detection of fire smoke in a video easily lead to misjudgment of cloud, fog and moving distractors, such as a moving person, a moving vehicle and other non-smoke moving objects. Therefore, an algorithm for detection of fire smoke in a video based on wavelet energy slope fitting is proposed in this paper. The change in wavelet energy of the moving target foreground is used as the basis, and a time window of 40 continuous frames is set to fit the wavelet energy slope of the suspected area in every 20 frames, thus establishing a wavelet-energy-based smoke judgment criterion. The experimental data show that the algorithm described in this paper not only can detect smoke more quickly and more accurately, but also can effectively avoid the distraction of cloud, fog and moving object and prevent false alarm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.9
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• pp.1032-1041
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• 2004

In this study we decompose the 3-dimensional velocity field of isotropic turbulent flow into the coherent and the incoherent structure using the discrete wavelet. It is shown that the coherent structure, 3% wavelet modes, has 98% energy and 88% enstrophy and its statistical characteristics are almost same as the original turbulence structure. And it is confirmed that the role of the coherent structure is that it produces the turbulent kinetic energy at the inertia range then transfers energy to the dissipation range. The incoherent structure, with residual wavelet modes, is uncorrelated and has the Gaussian probability density function but it dissipates the kinetic energy in dissipation range. On the procedure, we propose a new but easy way to get the threshold by applying the energy partition percentage concept about coherent structure. The vorticity field extracted from the wavelet-decomposed velocity field has the same structure as the result of the precedent studies which decomposed vorticity field directly using wavelet. Therefore it has been shown that velocity and vorticity field are on the interactive condition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.50-59
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• 1996

In this paper, a new hierarchical motion estiamtion scheme using the wavelet transformed multi-resolution image layers is proposed. Compared with the full search motion estimation method, the existing hierarchical methods remarkably reduce the amount of the computation but their efficiencies are depreciated by the local minima problem. In order to solve the local minima problem, the multi-resolution image layers are composed using the wavelet transform and the number of layers participated in the motion estimation for a block is determined by considering of its low band energy and higher band energy on the first wavelet transformed layer. The ratio between higher band energy and low band energy of each block is evaluated and in the case of the blocks which include relatively large higher band energy, the motion estimation is carried out in the high resolution layer. Otherwise, all layers are used. The final motion vectors are obtained in the first wavelet transformed layer. So less bits for motion vectors are transmitted, and the decomposition of received image using inverse wavelet transform decreases the blocking effect.

• Journal of Industrial Technology
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• v.19
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• pp.403-408
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• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.437-444
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have tried to enhance classification accuracy. Previous studies have shown that the classification technique based on wavelet transform is more effective than traditional techniques based on original pixel values, especially in complicated imagery. Various basis functions such as Haar, daubechies, coiflets and symlets are mainly used in 20 image processing based on wavelet transform. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we first computed the wavelet coefficients of satellite image using ten different basis functions, and then classified images. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis functions. The energy parameters of wavelet detail bands and overall accuracy are clearly correlated. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.236-237
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• 2019

In this paper, we propose a wavelet - based image noise reduction algorithm. We develop wavelet transform of existing Mallat Tree method. First, we propose a multiple filtering method. Maximizes the energy concentration characteristic of the wavelet transform considering the energy of each subband in the wavelet domain. We apply the proposed multiple filtering to the noise image. Finds energy subbands that can not be seen in normal images and removes them to remove noise.

• Journal of Welding and Joining
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• v.21 no.6
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• pp.33-39
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• 2003

Wavelet transform analysis results show a spectrum energy distribution of CWT along scale factors distinguish the partial, full and over penetration in a electron beam welding by analyzing the curve of spectrum energy at small scale, middle and large scale range, respectively. Two types of signals collected by Ion collector and x-ray sensors and analyzed. The acquired signals from sensors are very complicated since these signals are very closely related the dynamics of keyhole which interact the very high density energy with materials during welding. The results show the wavelet transform is more effective to diagnosis than Fourier Transform, further for the general welding defects which are not a periodic based, but a transient, non-stationary and time-varying phenomena.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.5
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• pp.471-478
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• 2015

The new islanding detection method using wavelet energy is proposed in this paper. The proposed method is based on the autocorrelation of the wavelet energies, which is obtained from the high-frequency components of the grid voltage. It has the enhanced detection capabilities in the UV/OV/UF/OF region, which the conventional passive methods cannot obtain. The mathematical theories on the wavelet are presented, and the performance effectiveness is proved by the experimental results.

• Earthquakes and Structures
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• v.17 no.4
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• pp.387-398
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• 2019

On the basis that ground motions may arrive at a structure from any horizontal direction and that different directions of seismic incidence would result in different structural dynamic responses, this paper focuses on orienting the crucial seismic incidence of transmission tower-line systems based on the wavelet energy method. A typical transmission tower-line system is chosen as the case study, and two finite element (FE) models are established in ABAQUS, with and without consideration of the interaction between the transmission towers and the transmission lines. The mode combination frequency is defined by considering the influence of the higher-order modes of the structure. Subsequently, wavelet transformation is performed to obtain the total effective energy input and the effective energy input rate corresponding to the mode combination frequency to further judge the critical angle of seismic incidence by comparing these two performance indexes under different seismic incidence angles. To validate this approach, finite element history analysis (FEHA) is imposed on both FE models to generate comparative data, and good agreement is found. The results demonstrate that the wavelet energy method can forecast the critical angle of seismic incidence of a transmission tower-line system with adequate accuracy, avoiding time-consuming and cumbersome computer analysis. The proposed approach can be used in future seismic design of transmission tower-line systems.