• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Korean Journal of Optics and Photonics
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• v.29 no.3
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• pp.110-118
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• 2018

We have designed a mid-infrared optical system for an airborne electro-optical targeting system. The mid-IR optical system is a dual-field-of-view (FOV) optics for an airborne electro-optical targeting system. The optics consists of a beam-reducer, a zoom lens group, a relay lens group, a cold stop conjugation optics, and an IR detector. The IR detector is an f/5.3 cooled detector with a resolution of $1280{\times}1024$ square pixels, with a pixel size of $15{\times}15{\mu}m$. The optics provides two stepwise FOVs ($1.50^{\circ}{\times}1.20^{\circ}$ and $5.40^{\circ}{\times}4.23^{\circ}$) by the insertion of two lenses into the zoom lens group. The IR optical system was designed in such a way that the working f-number (f/5.3) of the cold stop internally provided by the IR detector is maintained over the entire FOV when changing the zoom. We performed two analyses to investigate thermal effects on the image quality: athermalization analysis and Narcissus analysis. Athermalization analysis investigated the image focus shift and residual high-order wavefront aberrations as the working temperature changes from $-55^{\circ}C$ to $50^{\circ}C$. We first identified the best compensator for the thermal focus drift, using the Zernike polynomial decomposition method. With the selected compensator, the optics was shown to maintain the on-axis MTF at the Nyquist frequency of the detector over 10%, throughout the temperature range. Narcissus analysis investigated the existence of the thermal ghost images of the cold detector formed by the optics itself, which is quantified by the Narcissus Induced Temperature Difference (NITD). The reported design was shown to have an NITD of less than $1.5^{\circ}C$.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.327-328
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• 2012

의료분야의 진단 방사선 장비는 초기의 필름방식 및 카세트에서 진보되어 현재는 디지털방식의 DR (Digital Radiography)이 널리 사용되며 이에 관한 연구개발이 활발히 진행 되고 있다. DR은 일반적으로 직접방식과 간접방식으로 나눌 수 있다. 직접방식의 원리는 X선을 흡수하면 전기적 신호를 발생 시키는 광도전체(Photoconductor)를 사용하여 광도전체 양단 전극에 전압을 인가하여 전기장을 유도한 가운데, X선을 조사하면 광도전체 내부에서 전자-전공쌍(Electron-hole pair)이 생성된다. 이것은 양단에 유도된 전기장의 영향으로 전자는 +극으로, 전공은 -극으로 이동하여 아래에 위치한 하부기판을 통하여 이미지로 변조된다. 간접방식은 X선을 흡수하면 가시광선으로 전환하는 형광체(Scintillator)를 사용하여 조사된 X선을 형광체에서 가시광선으로 전환하고, 이를 Photodiode와 같은 광변환소자로 전기적 신호로 변환하여 방사선을 검출하는 방식을 말한다. 본 연구에서는 직접방식에서 이용되는 광도전체 중 흡수효율이 높고 Mobility가 뛰어난 CdTe를 선정하여 PVD (Physical vapor deposition)방식으로 300 m의 두께를 목표로 하여 증착을 진행하였다. Chamber의 진공도가 $2.5{\times}10^{-2}$ Torr로 도달 시점부터, Substrate와 Boat에 열을 가하였다. Substrate온도는 $350^{\circ}C$, Boat온도는 $300^{\circ}C$도로 설정하여 11시간 동안 진행하였다. Substrate온도는 $303^{\circ}C$, Boat온도는 $297^{\circ}C$도부터 증착이 시작되어 선형적인 증가세 추이를 나타내어 Substrate 및 Boat온도가 설정 값에 도달 하였을 때, $25{\sim}34.4{\AA}/s$ 증착율을 나타내었다. 하부전극의 물질에 따른 CdTe증착 효율성 평가를 진행한 후, 그에 따른 전기적 특성을 알아보았다. 하부전극의 물질로는 ITO (Indium Tin Oxide), Parylene이 코팅 된 ITO, Au, Ag를 사용하였다. 하부전극의 물질 상단에 Thermal Evaporation System을 사용하여 CdTe를 증착한 후, Cdte 상단에 Au를 증착 시켜 민감도(Sensitivity)와 암전류(Dark current)를 측정하였다. 증착 결과 ITO와 Ag상단에 증착시킨 CdTe박막은 박리가 되었고, Au와 Parylene이 코팅 된 ITO에는 CdTe박막이 안정적이게 형성이 되었다. 이 두 샘플에 대하여 동일한 조건으로 민감도와 암전류를 측정 시, Parylene이 코팅된 ITO를 하부전극으로 사용한 CdTe박막은 0.1021 pA/$cm^2$의 암전류와 1.027 pC/$cm^2$의 민감도를 나타낸 반면, Au를 하부전극으로 사용한 CdTe박막은 0.0381 pA/$cm^2$의 암전류와 1.214 pC/$cm^2$의 민감도를 나타내어 Parylene이 코팅된 ITO보다 우수한 전기적 특성을 나타내었다. 따라서 Au는 CdTe박막 증착 시, 하부전극 기판으로서 뛰어난 특성을 나타내는 것을 알 수 있었다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Food Science and Preservation
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• v.20 no.4
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• pp.554-563
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• 2013

The changes in quality properties and nutritional components for two mugworts, namely, Artemisia capillaris Thumberg Artemisiae asiaticae Nakai fermented by Bacillus strains were characterized followed by rapid pattern analysis of volatile flavor compounds through the SAW-based electronic nose sensor in the GC system. After fermentation, the pH has remarkably decreased from 6.0~6.4 to 4.6~5.1 and there has been a slight change in the total soluble solids. The L (lightness) and b (yellowness) values in the Hunter's color system significantly decreased, whilst the a (redness) value increased via fermentation. The HPLC analysis demonstrated that the total amino acids increased in quantity and the essential amino acids were higher in the A. asiaticae Nakai than in the A. capillaris Thumberg, specially with high contents of glutamic and aspartic acid. After fermentation, the monounsaturated fatty acid increased in the A. asiaticae Nakai and the polyunsaturated fatty acids increased in the A. capillaris Thumberg. While the total polyphenol contents have not been affected by fermentation, the total sugar contents have dramatically decreased. Scopoletin, which is one of the most important index components in mugworts, was highly abundant in the A. capillaris Thumberg; however, it was not detected in the A. asiaticae Nakai. Small pieces of plant tissue in the surface microstructure were found in the fermented mugworts through the use of the scanning electron microscope (SEM). Volatile flavor compounds via electronic nose showed that the intensity of several peaks has increased and additional seven flavor peaks have been produced after fermentation. The VaporPrintTM images demonstrated a notable difference in flavors between the A. asiaticae Nakai and A. capillaris Thumberg, and the fermentation enabled the mugworts to produce subtle differences in flavor.

• The Korean Journal of Nuclear Medicine
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• v.35 no.6
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• pp.378-388
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• 2001

Purpose: In this study, we developed a new method for the determination of renal depth with anterior and posterior renal scintigrams in a dual-head gamma camera, considering the attenuation factor $e^{-{\mu}x}$ of the conjugate-view method. Material and Method: We developed abdomen and kidney phantoms to perform experiments using Technetium-99m dimercaptosuccinic acid ($^{99m}Tc$-DMSA). The phantom images were obtained by dual-head gamma camera equipped with low-energy, high-resolution, parallel-hole collimators (ICONf, Siemens). The equation was derived from the linear integration of omission ${\gamma}$-ray considering attenuation from the posterior abdomen to the anterior abdomen phantom surface. The program for measurement was developed by Microsoft Visual C++ 6.0. Results : Renal depths of the phantoms were derived from the derived equations and compared with the exact geometrical values. Differences between the measured and the calculated values were the range of 0.1 to 0.7 cm ($0.029{\pm}0.15cm,\;mean{\pm}S.D.$). Conclusion: The present study showed that the use of the derived equations for renal depth measurements, combined with quantitative planar imaging using dual-head gamma camera, could provide more accurate results for individual variation than the conventional method.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.