• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.1-15
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• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.237-246
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• 2020

Recently, as high resolution satellites data have been serviced, frequent DSM (Digital Surface Model) generation over urban areas has been possible. In addition, it is possible to detect changes using a high-resolution DSM at building level such that various methods of building change detection using DSM have been studied. In order to detect building changes using DSM, we need to generate a DSM using a stereo satellite image. The change detection method using D-DSM (Differential DSM) uses the elevation difference between two DSMs of different dates. The D-DSM method has difficulty in applying a precise vertical threshold, because between the two DSMs may have elevation errors. In this study, we focus on the urban structure change detection using D-nDSM (Differential nDSM) based on nDSM (Normalized DSM) that expresses only the height of the structures or buildings without terrain elevation. In addition, we attempted to reduce noise using a morphological filtering. Also, in order to improve the roadside buildings extraction precision, we exploited the urban road network extraction from nDSM. Experiments were conducted for high-resolution stereo satellite images of two periods. The experimental results were compared for D-DSM, D-nDSM, and D-nDSM with road extraction methods. The D-DSM method showed the accuracy of about 30% to 55% depending on the vertical threshold and the D-nDSM approaches achieved 59% and 77.9% without and with the morphological filtering, respectively. Finally, the D-nDSM with the road extraction method showed 87.2% of change detection accuracy.

• Journal of Radiation Protection and Research
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• v.33 no.1
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• pp.13-20
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• 2008

The flow rate measurements in a multi-phase flow pipeline were evaluated quantitatively by means of a clamp-on sealed radioisotope based on a cross correlation signal processing technique. The flow rates were calculated by a determination of the transit time between two sealed gamma sources by using a cross correlation function following FFT filtering, then corrected with vapor fraction in the pipeline which was measured by the ${\gamma}$-ray attenuation method. The pipeline model was manufactured by acrylic resin(ID. 8 cm, L=3.5 m, t=10 mm), and the multi-phase flow patterns were realized by an injection of compressed $N_2$ gas. Two sealed gamma sources of $^{137}Cs$ (E=0.662 MeV, ${\Gamma}$ $factor=0.326\;R{\cdot}h^{-1}{\cdot}m^2{\cdot}Ci^{-1}$) of 20 mCi and 17 mCi, and radiation detectors of $2"{\times}2"$ NaI(Tl) scintillation counter (Eberline, SP-3) were used for this study. Under the given conditions(the distance between two sources: 4D(D; inner diameter), N/S ratio: $0.12{\sim}0.15$, sampling time ${\Delta}t$: 4msec), the measured flow rates showed the maximum. relative error of 1.7 % when compared to the real ones through the vapor content corrections($6.1\;%{\sim}9.2\;%$). From a subsequent experiment, it was proven that the closer the distance between the two sealed sources is, the more precise the measured flow rates are. Provided additional studies related to the selection of radioisotopes their activity, and an optimization of the experimental geometry are carried out, it is anticipated that a radioisotope application for flow rate measurements can be used as an important tool for monitoring multi-phase facilities belonging to petrochemical and refinery industries and contributes economically in the light of maintenance and control of them.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.305-314
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• 2023

With the development of medical technology and radiation treatment equipment, the frequency of high-precision radiation therapy such as intensity modulation radiation therapy has increased. Image-guided radiation therapy has become essential for radiation therapy in precise and complex treatment plans. In particular, with the introduction of imaging equipment for diagnosis in a linear accelerator, CBCT scanning became possible, which made it possible to calibrate and correct the patient's posture through 3D images. Although more precise reproduction of the patient's posture has become possible, the exposure dose delivered to the patient during the image acquisition process cannot be ignored. Radiation optimization is necessary in the field of radiation therapy, and efforts to reduce exposure are necessary. However, when acquiring 3D CBCT images by changing the imaging conditions to reduce exposure, there should be no image quality or artefacts that would make it impossible to align the patient's position. In this study, Rando phantom was used to scan and evaluate images for each shooting condition. The highest SNR was obtained at 100 kV 80 mA 25 ms F1 filter 180°. As the tube voltage and tube current increased, the noise decreased, and the bowtie filter showed the optimal effect at high tube current. Based on the actual scanned images, it was confirmed that patient alignment was possible under all imaging conditions, and that image-guided radiation therapy for patient alignment was possible under the condition of 70 kV 10 mA 20 ms F0 filter 180°, which showed the lowest SNR. In this study, image evaluation was conducted according to the imaging conditions, and low tube voltage, tube current, and small rotation angle scan are expected to be effective in reducing radiation exposure. Based on this, the patient's exposure dose should be kept as low as possible during CBCT imaging.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.