• 대한기계학회논문집B
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• 제25권11호
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• pp.1527-1534
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• 2001

Phase averaged velocity fields in the near wake region behind a square cylinder have been (successfully) obtained using randomly sampled PIV data sets. The Reynolds number based on the flow velocity and the vertex height was 3,900. To identify the phase information, we examined the magnitude of circulation and the center of peak vorticity. The center of vorticity was estimated from lowpass filtered vorticity contours (LES decomposition) adopting a sub-pixel searching algirithm. Due to the sinusoidal nature of firculation which is closely related to the instantaneous vorticity, the location of peak voticity fits well with a sine curve of the circulation magnitude. Conditionally-averaged velocity fields represent the barman vortex shedding phenomenon very well within 5 degrees phase uncertainty. The oscillating nature of the separated shear layer and the separation bubble at the top surface are clearly observed. With the hot-wire measurements of Strouhal frequency, we found thats the convection velocity changes its magnitude very rapidly from 25 to 75 percent of the free stream velocity along the streamwise direction when the flow passes by the recirculation region.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.83-93
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• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.203-209
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• 2006

The aim of this study is to investigate the feasibility of ex vivo MR diffusion tensor imaging technique in order to observe the diffusion-contrast characteristics of human gastric tissues. On normal and pathologic gastric tissues, which have been fixed in a polycarbonate plastic tube filled with 10% formalin solution, laboratory made 3D diffusion tensor Turbo FLASH pulse sequence was used to obtain high resolution MR images with voxel size of $0.5{\times}0.5{\times}0.5mm^3\;using\;64{\times}32{\times}32mm^3$ field of view in conjunction with an acquisition matrix of $128{\times}64{\times}64$. Diffusion weighted- gradient pulses were employed with b values of 0 and $600s/mm^2$ in 6 orientations. The sequence was implemented on a clinical 3.0-T MRI scanner(Siemens, Erlangen, Germany) with a home-made quadrature-typed birdcage Tx/Rx rf coil for small specimen. Diffusion tensor values in each pixel were calculated using linear algebra and singular value decomposition(SVD) algorithm. Apparent diffusion coefficient(ADC) and fractional anisotropy(FA) map were also obtained from diffusion tensor data to compare pixel intensities between normal and abnormal gastric tissues. The processing software was developed by authors using Visual C++(Microsoft, WA, U.S.A.) and mathematical/statistical library of GNUwin32(Free Software Foundation). This study shows that 3D diffusion tensor Turbo FLASH sequence is useful to resolve fine micro-structures of gastric tissue and both ADC and FA values in normal gastric tissue are higher than those in abnormal tissue. Authors expect that this study also represents another possibility of gastric carcinoma detection by visualizing diffusion characteristics of proton spins in the gastric tissues.

• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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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.

• 한국정보전자통신기술학회논문지
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• 제15권3호
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• pp.211-216
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• 2022

스테가노그래피 기술은 커버 매체의 특정 위치에 비밀 메시지를 대체시켜 숨겨진 정보의 존재를 추적할 수 없도록 보호 조치를 한다. 암호화와 스테가노그래피를 기반으로 다양한 복합적인 방법을 적용하여 보안성과 저항성을 강화한다. 특히 보안성을 향상시키기 위해 혼돈과 무작위성을 높이는 기법이 필요하다. 실제로 이산코사인변환(DCT)과 최하위 비트(LSB) 기반에서 셔플링 방식이 적용된 경우는 연구가 진행되어야 할 영역이다. 메시지 숨김의 복잡성을 추가할 수 있는 비트 정보 셔플링 방식을 통합하고, 공간 영역 기법을 스테가노그래피에 적용하여 한글 메시지의 비트 정보를 은닉하는 새로운 접근 방법을 제시한다. 메시지를 추출할 때 역셔플링을 적용한다. 이 논문에서, 삽입하려는 한글 메시지를 초성, 중성, 종성으로 분리한다. 대응된 정보에 기반한 선택적 셔플링 과정을 적용하여 보안성과 혼돈성을 향상시킨다. 제안된 방법의 성능을 확인하기 위해 상관계수와 PSNR을 이용하였다. 기준값과 비교했을 때 제안한 방법의 PSNR 값이 타당하다는 것을 확인하였다.

• 한국광학회지
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• 제29권3호
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• pp.110-118
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• 2018

항공용 전자 광학 타겟팅 시스템을 위한 중적외선 광학계를 설계하였다. 본 광학계는 이중 시야를 갖도록 설계되었으며, 빔 축소 전단 광학계, 줌 렌즈 그룹, 릴레이 렌즈 그룹, 콜드스탑 공액 광학계 및 냉각 적외선 검출기로 구성된다. 적외선 검출기는 단일 화소의 크기가 $15{\times}15{\mu}m$ 인 $1280{\times}1024$ 화소 배열을 가지며 잡음을 최소화하기 위하여, f/5.3의 냉각 콜드스탑이 적용된 제품으로 선정하였다. 이중 시야 ($1.50^{\circ}{\times}1.20^{\circ}$, $5.40^{\circ}{\times}4.23^{\circ}$)는 두 개의 렌즈를 삽입하는 방식으로 구현했으며, 줌 배율 변경 시 모든 시야에 걸쳐 f/5.3의 콜드스탑의 효율을 유지하도록 설계하였다. 열 효과가 이미지에 미치는 영향을 조사하기 위해 비열화 및 나르시서스 분석을 수행하였으며, 비열화 분석은 $-55{\sim}50^{\circ}C$의 작동 온도를 기준으로 초점 이동과 잔여 고차 파면 수차에 조사하였고 제르니케 다항식을 이용한 민감도 분석을 수행하여 최적의 보상자를 선정하였다. 선정된 보상자의 최적 이동을 고려한 MTF 해상력을 확인한 결과, 작동 온도 전 구간에 걸쳐 요구조건인 33 lp/mm에서 축상 10% 이상의 성능을 유지하는 것을 확인하였으며, 나르시서스 분석 결과, NITD (Narcissus Induced Temperature Difference) 값이 $1.5^{\circ}C$ 이하가 되도록 설계 된 것을 확인하였다.