• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.12
• /
• pp.1004-1012
• /
• 2014

In this study, blowoff was investigated in a ducted combustor with the bluff body when acoustic excitation was forced. To observe the flame structure, OH radical chemiluminescence was used and the image was analyzed by using POD (Proper Orthogonal Decomposition) algorithm. Natural gas mainly composed of methane was used as fuel. Blowoff occurred when the equivalence ratio was reduced. Equivalence ratio causing blowoff was measured by changing air flow rate, excitation frequency and sound pressure. Blowoff equivalence ratio was varied depending on the experimental conditions. Vortex frequency behind the bluff body and resonance effect in combustor are the main factors that affect the blowoff equivalence ratios with the excitation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.11C
• /
• pp.1118-1122
• /
• 2003

In this paper, we propose a new invisible digital watermarking scheme based on wavelet transform using singular value decomposition. Embedding process is started by decomposing the lowest frequency band image with 3${\times}$3 block among which we define the watermark block chosen by a key set; entropy and condition number of the block. A watermark is embedded in the singular values of each watermark blocks. This provides a robust watermarking in lowest possible time-frequency domain. To detect the watermark, we are locally modeling an attack as 3${\times}$3 matrices on the watermark blocks. Combining with the SVD and the attack matrices, we estimate watermark set corresponding to the watermark blocks. In each watermark block, we determine an optimal watermark which is justified by the T-testing. A numerical experiment shows that the proposed watermarking scheme efficiently detects the watermarks from several JPEG attacks.

• Journal of IKEEE
• /
• v.18 no.2
• /
• pp.240-247
• /
• 2014

Intraocular pressure is measured after a cornea air puff by observing biomechanical properties such as thickness or displacement of the cornea. In this paper, we deal with a frequency domain analysis of corneal deformation in the air puff tonometry that is used to diagnose glaucoma or lasik. We distinguish the patient from the normal by measuring the oscillation frequency in the neighborhood of the central cornea section. A binary image was obtained from the video images, and cornea vertical oscillation profile was extracted from the difference between the vertical displacement data and the curve fitting. In terms of Fourier transform, a vibration frequency of 479.2Hz for the patient was obtained as well as more higher 702.8Hz for the normal due to stiffness. Hilbert-Huang transform's empirical mode decomposition generally describes local, nonlinear, and nonstationary data. After the data were decomposed into intrinsic mode functions, a spectrum and power were analysed. Finally, we confirm that the patient has 6 times more higher power ratio for the specific intrinsic mode function between the patient and the normal.

• Polymer(Korea)
• /
• v.33 no.1
• /
• pp.1-6
• /
• 2009

We report the template-based synthesis of well-dispersed CdS nanoparticles (NPs) in the interior of poly (2-acetamidoacrylic acid) (PAAA) hydrogel as a novel type of nanocomposite without particle aggregation via ion exchange in a aqueous system. As revealed by the TEM image analysis, the mean crystallite diameter of CdS NPs embedded in hydrogel composite was 4.5 nm, and the composite did not suffer any observable change after 6 months. Desorption/decomposition of CdS/PAAA hydrogel composite was studied by evolved gas analysis-gas chromatography-mass spectrometry (EGA-GC-MS), and thermogravimetric analysis (TGA) methods. From the TGA data, the thermal stability of the composite system increased by ca. 100 $^\circ$C and the content of CdS NPs in a dry composite gel was over 70 wt%. In addition, the chemical pathway was proposed for the entire decomposition process.

• Journal of Powder Materials
• /
• v.27 no.6
• /
• pp.490-497
• /
• 2020

The 3D printing process provides a higher degree of freedom when designing ceramic parts than the conventional press forming process. However, the generation and growth of the microcracks induced during heat treatment is thought to be due to the occurrence of local tensile stress caused by the thermal decomposition of the binder inside the green body. In this study, an alumina columnar specimen, which is a representative ceramic material, is fabricated using the digital light process (DLP) 3D printing method. DTG analysis is performed to investigate the cause of the occurrence of microcracks by analyzing the debinding process in which microcracks are mainly generated. HDDA of epoxy acrylates, which is the main binder, rapidly debinded in the range of 200 to 500℃, and microcracks are observed because of real-time microscopic image observation. For mitigating the rapid debinding process of HDDA, other types of acrylates PETA, PUA, and MMA are added, and the effect of these additives on the debinding rate is investigated. By analyzing the DTG in the 25 to 300℃ region, it is confirmed that the PETA monomer and the PUA monomer can suppress the rapid decomposition rate of HDDA in this temperature range.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.737-746
• /
• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• Journal of Aerospace System Engineering
• /
• v.16 no.4
• /
• pp.17-27
• /
• 2022

In this study, a deep learning-based network that can predict the aerodynamic characteristics of airfoils was designed, and the feasibility of the proposed network was confirmed by applying aerodynamic data generated by Xfoil. The prediction of aerodynamic characteristics according to the variation of airfoil thickness was performed. Considering the angle of attack, the coordinate data of an airfoil is converted into image data using signed distance function. Additionally, the distribution of the pressure coefficient on airfoil is expressed as reduced data via proper orthogonal decomposition, and it was used as the output of the proposed network. The test data were constructed to evaluate the interpolation and extrapolation performance of the proposed network. As a result, the coefficients of determination of the lift coefficient and moment coefficient were confirmed, and it was found that the proposed network shows benign performance for the interpolation test data, when compared to that of the extrapolation test data.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.2
• /
• pp.94-101
• /
• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.8
• /
• pp.579-588
• /
• 2013

This paper proposes an effective watermarking approach for copyright protection of fire video images. The proposed watermarking approach efficiently utilizes the inherent characteristics of fire data with respect to color and texture by using a gray level co-occurrence matrix (GLCM) and fuzzy c-means (FCM) clustering. GLCM is used to generate a texture feature dataset by computing energy and homogeneity properties for each candidate fire image block. FCM is used to segment color of the fire image and to select fire texture blocks for embedding watermarks. Each selected block is then decomposed into a one-level wavelet structure with four subbands [LL, LH, HL, HH] using a discrete wavelet transform (DWT), and LH subband coefficients with a gain factor are selected for embedding watermark, where the visibility of the image does not affect. Experimental results show that the proposed watermarking approach achieves about 48 dB of high peak-signal-to-noise ratio (PSNR) and 1.6 to 2.0 of low M-singular value decomposition (M-SVD) values. In addition, the proposed approach outperforms conventional image watermarking approach in terms of normalized correlation (NC) values against several image processing attacks including noise addition, filtering, cropping, and JPEG compression.

• Investigative Magnetic Resonance Imaging
• /
• v.11 no.2
• /
• pp.110-118
• /
• 2007

Purpose: The objective of this work to construct eigenvalue maps that have information of magnitude of three primary diffusion directions using diffusion tensor images. Materials and Methods: To construct eigenvalue maps, we used a 3.0T MRI scanner. We also compared the Moore-Penrose pseudo-inverse matrix method and the SVD (single value decomposition) method to calculate magnitude of three primary diffusion directions. Eigenvalue maps were constructed by calculating of magnitude of three primary diffusion directions. We did investigate the relationship between eigenvalue maps and fractional anisotropy map. Results: Using Diffusion Tensor Images by diffusion tensor imaging sequence, we did construct eigenvalue maps of three primary diffusion directions. Comparison between eigenvalue maps and Fractional Anisotropy map shows what is difference of Fractional Anisotropy value in brain anatomy. Furthermore, through the simulation of variable eigenvalues, we confirmed changes of Fractional Anisotropy values by variable eigenvalues. And Fractional anisotropy was not determined by magnitude of each primary diffusion direction, but it was determined by combination of each primary diffusion direction. Conclusion: By construction of eigenvalue maps, we can confirm what is the reason of fractional anisotropy variation by measurement the magnitude of three primary diffusion directions on lesion of brain white matter, using eigenvalue maps and fractional anisotropy map.