• Journal of the Korean Chemical Society
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• v.59 no.5
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• pp.423-428
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• 2015

(1S)-(+)-10-camphorsulfonic acid (CSA) and HCl were used together as a binary dopant in the electrodeposition of polyaniline (PAni). (+)-CSA and HCl were added in different mole ratios (9:1 and 6:4). (+)-CSA-doped and binary-doped PAni exhibited markedly different ultraviolet-visible and circular dichroism spectral characteristics due to differences in their conformations. Distinct helical structures are observed in the scanning electron microscopy images of (+)-CSA-doped PAni. The X-ray diffraction pattern of (+)-CSA-doped PAni exhibited remarkably higher crystallinity than that of HCl-doped PAni which is associated with the helical ordering along the polymer chains. The conformational changes due to the binary doping in chiral PAni had a significant effect on its chiroptical and electrochemical properties, morphology, and crystallinity, thus determined its conductivity.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.147.2-147.2
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• 2011

We have initiated a long-term identification campaign of supernova remnant candidates in X-ray regime. In the short-listed unidentified sources from the ROSAT All Sky Survey, we have chosen the brightest candidate, G308.3-1.4, as our pilot target for a dedicated investigation with Chandra X-ray Observatory. Our observation has revealed an incomplete shell-like X-ray structure which well-correlated with the radio feature. Together with the spectral properties of a shocked heated plasma, we confirm that G308.3-1.4 is indeed a supernova remnant. A bright X-ray point source which locates close to the remnant center is also uncovered in this observation. Its spectral behavior conform with those observed in a rare class of neutron stars. The properties of its optical/infrared counterpart suggests the evidence for a late-type companion star. Interestingly, possible excesses in B-band and H-alpha have been found which indicate this can be an accretion-powered system. With the further support from the putative periodicity of ~1.4 hrs, this source can possibly provide the direct evidence of a binary system survived in a supernova explosion for the first time.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1062-1075
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• 2010

In the medical image application the difference of intensity is widely used for the image segmentation and feature extraction, and a well known method is the threshold technique that determines a threshold value and generates a binary image based on the threshold. A frequently-used threshold technique is the Otsu algorithm that provides efficient processing and effective selection criterion for choosing the threshold value. However, we cannot get good segmentation results by applying the Otsu algorithm to chest X-ray images. It is because there are various organic structures around lung regions such as ribs and blood vessels, causing unclear distribution of intensity levels. To overcome the ambiguity, we propose in this paper an effective algorithm to extract pulmonary regions that utilizes the Otsu algorithm after removing the background of an X-ray image, constructs intensity-level maps, and uses them for segmenting the X-ray image. To verify the effectiveness of our method, we compared it with the existing 1-dimensional and 2-dimensional Otsu algorithms, and also the results by expert's naked eyes. The experimental result showed that our method achieved the more accurate extraction of pulmonary regions compared to the Otsu methods and showed the similar result as the naked eye's one.

• 한국전기화학회:학술대회논문집
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• 2002.07a
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• pp.211-218
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• 2002

In this study, Pt/Pd (1.1), PtPd (2:1) and PtPd (3:1) binary catalysts and Pt/Ru/Pd (5:4:1) ternary catalyst were designed. The catalysts were synthesized by impregnation method using $NaBH_4$ as a reducing agent. A good catalyst for methanol oxidation requires low on-set potential, stable durability and low activation energy. In order to investigate the catalytic activity for the methanol oxidation, electrochemical measurements such as cyclic voltammetry and chronoamperometry were peformed in sulfuric acid with/without methanol solution. In order to calculate the activation energy of the reaction, electrochemical measurements were also tested at different temperatures. For investigation of the structural analysis such as particle size and alloying, X-ray diffraction and transmission electron microscopy analysis were used. In order to identify the role of the Pd and to determine the composition of the surface of the Pt/Pd nanoparticles, X-ray photoelectron spectroscopy (XPS) analysis was investigated. The XPS spectra of Pd showed that Pd appears only as a metallic state in the binary catalysts. The chemical states of Pt in PtPd catalysts are both metallic and oxidative. Polarization curves and power density data were obtained by testing the DMFC unit cell performance of PtPd and PtRuPd catalysts. These data showed that Pt/Pd (2:1) and Pt/Ru/Pd (5:4:1) have better performance than Pt and Pt/Ru, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Analytical Science and Technology
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• v.11 no.3
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• pp.189-193
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• 1998

Binary alloys, $MoRu_3$ and $MoRh_3$, have been prepared using arc melting furnace. Mo and the noble metals Ru and Rh are the constituents of metallic insoluble residues, which were found in the early days of the post-irradiation studies on uranium oxide fuels. Detailed structural informations about these alloys have not been reported on JCPDS files of ICDD (International Centre for Diffraction Data). The results of X-ray diffraction study showed that the alloy was crystallized in hexagonal close-packing, well known as ${\varepsilon}$-phase. The X-ray diffraction patterns of these alloys matched well to that of $WRh_3$ with $P6_3/mmc$ of space group. The lattice parameters, a and c, were calculated using the least squares extrapolation. It was found from X-ray photoelectron spectroscopic measurements that Mo on the surface of the alloy was oxidized to Mo(6+), which could be removed by sputtering with Ar ions for approximately 15 minutes. The changes in binding energy of Mo, Ru, and Rh on the surface of the alloy were not observed. Magnetic susceptibility measurements resulted in the typical Pauli-paramagnetic behavior in the temperature range of 2 to 300 K.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.277-285
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• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• Bulletin of the Korean Space Science Society
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• 1992.10a
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• pp.21-21
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• 1992

The X-ray binary pulsar Her X-1 was observed with Ginga on 1988 August 28 during the orbital phase of 0.76 to 0.85 at the main-on phase of the 35 day cycle. During the observations the X-ray intensity varied by a factor of five or more on a time scale as short as 30 sec, due mostly to the soft X-ray absorption in the pre-eclipse phase. From the studies of pulse profiles and energy spectra, we revealed that there exists in the dipphase an unpulsed component which is "3% of the intensity at the non-absorbed high-level. We suggest that scattering of the source continuum by the optically thin hot corona is responsible for the unpoised component. In the spectral analysis, we find that the high-state non-absorbed spectra can be fitted by a power-law without absorption, and the spectra observed in the different abgorption states by two components of a power-law with the same photon index. An iron-K emission line is required in to the cases of fitting. The estimated equivalent width of the iron line varies from 0.18 to 0.51 key according to the change in the absorption column density along the line of sight. We suggest that the fluorescent iron line arises in a cool and relatively small region, like the Alfvensur face, and may be partially intercepted by the optically thick gas cloud passing across the line of sight.1 Korea Astronomy Observatorya The Institute of Space and Astronautical Science in Japan3 Korea Advanced Institute of Science and Technology

• Journal of Powder Materials
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• v.10 no.1
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• pp.46-50
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• 2003

Lee et al. reported that a mixture of Cu and Ta, the combination of which is characterized by a positive heat of mixing, $\{Delta}H_{mix}$ of +2 kJ/㏖, can be amorphized by mechanical alloying(MA). It is our aim to investigate to what extent the MA is capable of producing a non-equilibrium phase with increasing the heat of mixing. The system chosen is the binary $Cu_{30}Mo_{70}$ with $\{Delta}H_{mix}$=+19 kJ/㏖. The mechanical alloying was carried out using a Fritsch P-5 planetary mill under Ar gas atmosphere. The vial and balls are made of Cu containing 1.8-2.0 wt.%Be to avoid contaminations arising mainly from Fe when steel balls and vial are used. The MA powders were characterized by the X-ray diffraction, EXAFS and thermal analysis. We conclude that two phase mixture of nanocrystalline fcc-Cu and bcc-Mo with grain size of 10 nm is formed by the ball-milling for a 3:7 mixture of pure Cu and Mo, the evidence for which has been deduced from the thermodynamic and structural analysis based on the DSC, X-ray diffraction and EXAFS spectra.