• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.281-288
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• 2016

The natural moving objects are the most non-rigid shapes with randomly time-varying deformation, and its types also very diverse. Methods of non-rigid shape reconstruction have widely applied in field of movie or game industry in recent years. However, a realistic approach requires moving object to stick many beacon sets. To resolve this drawback, non-rigid shape reconstruction researches from input video without beacon sets are investigated in multimedia application fields. In this regard, our paper propose novel CPSRF(Chained Partial Stereo Rigid Factorization) algorithm that can reconstruct a non-rigid 3D shape. Our method is focused on the real-time reconstruction of non-rigid 3D shape and motion from stereo 2D video sequences per frame. And we do not constrain that the deformation of the time-varying non-rigid shape is limited by a Gaussian distribution. The experimental results show that the 3D reconstruction performance of the proposed CPSRF method is superior to that of the previous method which does not consider the random deformation of shape.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1037-1048
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• 2022

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (¹³³Ba, ²²Na, ¹³⁷Cs, and ⁶⁰Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an insitu monitoring system.

• Journal of radiological science and technology
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• v.32 no.3
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• pp.325-334
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• 2009

Purpose : This study was conducted to assess how effective the permeability ratio and relative cerebral blood volume ratio are to tumor through perfusion MRI by measuring and reflecting the grade assessment and differential diagnosis and the permeability and relative cerebral blood volume of contrast media plunged from blood vessel into organ due to breakdown of blood-brain barrier in cerebral. Subject and Method : Subject of study was 29 patients whose diagnosis were confirmed by biopsy after surgery and 550 (11 slice$\times$50 image) perfusion MRI were used to make image of relative cerebral blood volume with the program furnished on instrument. The other method was to transmit to private computer and the image analysis was made additionally by making image of relative cerebral blood volume-reformulated singular value decomposition, rCBV-rSVD and permeability using IDL.6.2. In addition, Kruskal-wallis test tonggyein non numerical average by a comparative analysis of brain tumors Results : The rCBV ratio (Functool PF; GE Medical Systems and IDL 6.2 program by analysis) and permeability ratio of tumors were as follows; high grade glioma(n=4), (14.75, 19.25) 13.13. low grade astrocytoma(n=5) (14.80, 15.90) 11.60, glioblastoma(n=5) (10.90, 18.60), 22.00, metastasis(n=6) (11.00, 15.08). 22.33. meningioma(n=6) (18.58, 7.67), 5.58. oliogodendroglioma(n=3) (23.33, 16.33, 15.67. Conclusion : It was not easy to classify the grade with the relative cerebral blood volume ratio measured by using the relative cerebral blood image by type of tumors, however, permeability ratio measured by permeability image revealed that the higher the grade of tumor, the higher the measured permeability ratio, showing the assessment of tumor grade is more effective to differential diagnosis.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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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.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.4 no.3
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• pp.170-179
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• 1999

The SUS (Signal, Underwater Sound)-OAT experiment was carried out in the Ulleung Basin of the East Sea on 3 June 1997. The SUS-OAT system consisted of aircraft deployed shots as sources and a vertical line array (VLA) tethered by a receiver ship was used to survey a large area where a mesoscale warm eddy appears frequently. The experiment was carried out such that explosive charges set to detonate at 800 ft depth were dropped in a rectangular ($120{\times}120$ km). Sources were a rapidly deployable SUS charge (MK 61 MOD 0), and receiver is a fixed VLA, 90 m in length (150-240 m in receiver depth), composed of 10 elements equally spaced. The reference ray paths are computed by range-dependent acoustic model in canonical ocean based on the historical data. The singular value decomposition (SVD) method is used to obtain the horizontal perturbation of the temperature fields. Horizontal distributions of temperature fields at 150 m and 200 m depth show a weak warm eddy observed by AXBT and the inversely estimated temperature shows similar patterns in terms of the location of the warm eddy. In conclusion, the SUS-OAT experiment has been successful to estimate the position of warm eddy and its temperature field in the East Sea of Korea.

• Journal of radiological science and technology
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• v.39 no.2
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• pp.227-236
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• 2016

Dynamic positron emission tomography(dPET) is widely used medical imaging modality that can provide both physiological and functional neuro-image for diagnosing various brain disease. However, dPET images have low spatial-resolution and high noise level during spatio-temporal analysis (three-dimensional spatial information + one-dimensional time information), there by limiting clinical utilization. In order to overcome these issues for the spatio-temporal analysis, a novel computational technique was introduced in this paper. The computational technique based on singular value decomposition classifies multiple independent components. Signal components can be distinguished from the classified independent components. The results show that signal to noise ratio was improved up to 30% compared with the original images. We believe that the proposed computational technique in dPET can be useful tool for various clinical / research applications.

• Journal of radiological science and technology
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• v.39 no.3
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• pp.369-376
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• 2016

Positron Emission Tomography(PET) is nuclear medical tests which is a combination of several compounds with a radioactive isotope that can be injected into body to quantitatively measure the metabolic rate (in the body). Especially, Phenomena that increase (sing) glucose metabolism in cancer tissue using the $^{18}F$-FDG (Fluorodeoxyglucose) is utilized widely in cancer diagnosis. And then, Numerous studies have been reported that incidence seems high availability even in the modern diagnosis of dementia and Parkinson's (disease) in brain disease. When using a dynamic PET iamge including the time information in the static information that is provided for the diagnosis many can increase the accuracy of diagnosis. For this reason, clinical researchers getting great attention but, it is the lack of tools to conduct research. And, it interfered complex mathematical algorithm and programming skills for activation of research. In this study, in order to easy to use and enable research dPET, we developed the software based graphic user interface(GUI). In the future, by many clinical researcher using DIA-Tool is expected to be of great help to dPET research.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.367-373
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• 2016

We propose an improved direction of arrival (DoA) estimation algorithm based on co-prime array and propagator method. The propagator method with co-prime array does not require singular value decomposition (SVD) requiring much less computational complexity but exhibiting somewhat worse performance in comparison with MUSIC based on co-prime array. We notice that one cause of the performance degradation was in the avoidance of the usage of the diagonal elements of the signal autocorrelation matrix that contains the noise power spectral density. So we propose an algorithm with the diagonal elements of the signal autocorrelation matrix based on the fact that the noise power spectral density can be estimated using noise observation over a long period of time. We observe, through simulations, that the proposed scheme in this paper improves the performance, with 4 times more computational requirement, by signal-to-noise ratio of 1.5dB and by DoA resolution of $0.7^{\circ}$ at the detection probability of 95% compared with the previously introduced co-prime array propagator scheme, resulting in performance much closer to that of co-prime array-based MUSIC scheme.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.