• Journal of Aerospace System Engineering
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• v.16 no.3
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• pp.105-114
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• 2022

The purpose of this study was to present a conceptual design of the 6U micro-satellite system for optical image of 3 m GSD. An optical camera system with a payload of 3 m GSD image was designed and optimized. The optical system has a diameter of Ø78 mm, length 250 mm, and 1400 mm focal length. The requirement and constraints were configured for the 6U micro-satellite bus system with the payload. Satisfying the requirement and constraints, the subsystems of the 6U bus were designed such as attitude and orbit control, propulsion, command and data handling, electrical power, communication, structures and mechanisms, and thermal control subsystem. The mass budget, power budget, and communication link budget were also confirmed for the 6U micro-satellite comprising the optical payload and the subsystems of bus. To take optical images, a mission operation concept is proposed for the 6U micro-satellite in a low-Earth orbit. A constellation comprising many 6U micro-satellites studied in this paper, can provide with various data for reconnaissance and disaster tracking.

• Geophysics and Geophysical Exploration
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• v.26 no.1
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• pp.8-17
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• 2023

Various sources, such as wind, waves, ships, and gas leaks from the seafloor, forms bubbles in the ocean. Underwater bubbles cause signal scattering, considerably affecting acoustic measurements. This characteristic of bubbles is used to block underwater noise by attenuating the intensity of the propagated signal. Recently, researchers have been studying the large-scale release of methane gas as bubble plumes from the seabed. Understanding the physical properties and distribution of bubble plumes is crucial for studying the relation between leaked methane gas and climate change. Therefore, a water tank experiment was conducted to estimate the distribution of bubble plumes using seismic imaging techniques and acoustic signals obtained from artificially generated bubbles using a bubble generator. Reverse time migration was applied to image the bubble plumes while the acquired acoustic envelope signal was used to effectively estimate bubble distribution. Imaging results were compared with optical camera images to verify the estimated bubble distribution. The water tank experiment confirmed that the proposed system could successfully image the distribution of bubble plumes using reverse time migration and the envelope signal. The experiment showed that the scattering signal of artificial bubble plumes can be used for seismic imaging.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.9-20
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• 2009

Navigation system providing route-guidance and traffic information is one of the most widely used driver-support system these days. Most of the navigation system is based on the 2D map paradigm so the information is ed and encoded from the real world. As a result it imposes a cognitive burden to the driver to interpret and translate the ed information to real world information. As a new concept of navigation system, augmented-reality navigation system (AR navigation) is suggested recently. It provides navigational guidance by imposing graphical information on real image captured by camera mounted on a vehicle in real-time. The ultimate goal of navigation system is to assist the driving task with least driving workload whether it is based on the abstracted graphic paradigm or realistic image paradigm. In this paper, we describe the comparative studies on how map navigation and AR navigation affect for driving tasks by experimental research. From the result of this research we obtained a basic knowledge about the two paradigms of navigation systems. On the basis of this knowledge, we are going to find the optimal design of navigation system supporting driving task most effectively, by analyzing characteristics of driving tasks and navigational information from the human-vehicle interface point of view.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.114-123
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• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.445-455
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• 2005

Purpose: We developed an animal SPECT system using clinical Philips ARGUS scintillation camera and pinhole collimator with specially manufactured small apertures. In this study, we evaluated the physical characteristics of this system and biological feasibility for animal experiments. Materials and Methods: Rotating station for small animals using a step motor and operating software were developed. Pinhole inserts with small apertures (diameter of 0.5, 1.0, and 2.0 mm) were manufactured and physical parameters including planar spatial resolution and sensitivity and reconstructed resolution were measured for some apertures. In order to measure the size of the usable field of view according to the distance from the focal point, manufactured multiple line sources separated with the same distance were scanned and numbers of lines within the field of view were counted. Using a Tc-99m line source with 0.5 mm diameter and 12 mm length placed in the exact center of field of view, planar spatial resolution according to the distance was measured. Calibration factor to obtain FWHM values in 'mm' unit was calculated from the planar image of two separated line sources. Te-99m point source with i mm diameter was used for the measurement of system sensitivity. In addition, SPECT data of micro phantom with cold and hot line inserts and rat brain after intravenous injection of [I-123]FP-CIT were acquired and reconstructed using filtered back protection reconstruction algorithm for pinhole collimator. Results: Size of usable field of view was proportional to the distance from the focal point and their relationship could be fitted into a linear equation (y=1.4x+0.5, x: distance). System sensitivity and planar spatial resolution at 3 cm measured using 1.0 mm aperture was 71 cps/MBq and 1.24 mm, respectively. In the SPECT image of rat brain with [I-123]FP-CIT acquired using 1.0 mm aperture, the distribution of dopamine transporter in the striatum was well identified in each hemisphere. Conclusion: We verified that this new animal SPECT system with the Phlilps ARGUS scanner and small apertures had sufficient performance for small animal imaging.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.57-67
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• 2013

Purpose: Replacing the film which used to be used for checking the set-up of the patient and dosimetry during radiation therapy, more and more EPID equipped devices are in use at present. Accordingly, this article tried to evaluated the accuracy of the position check-up and the usefulness of dosimetry during the use of an electronic portal imaging device. Materials and Methods: On 50 materials acquired with the search of Korea Society Radiotherapeutic Technology, The Korean Society for Radiation Oncology, and Pubmed using "EPID", "Portal dosimetry", "Portal image", "Dose verification", "Quality control", "Cine mode", "Quality - assurance", and "In vivo dosimetry" as indexes, the usefulness of EPID was analyzed by classifying them as history of EPID and dosimetry, set-up verification and characteristics of EPID. Results: EPID is developed from the first generation of Liquid-filled ionization chamber, through the second generation of Camera-based fluoroscopy, and to the third generation of Amorphous-silicon EPID imaging modes can be divided into EPID mode, Cine mode and Integrated mode. When evaluating absolute dose accuracy of films and EPID, it was found that EPID showed within 1% and EDR2 film showed within 3% errors. It was confirmed that EPID is better in error measurement accuracy than film. When gamma analyzing the dose distribution of the base exposure plane which was calculated from therapy planning system, and planes calculated by EDR2 film and EPID, both film and EPID showed less than 2% of pixels which exceeded 1 at gamma values (r%>1) with in the thresholds such as 3%/3 mm and 2%/2 mm respectively. For the time needed for full course QA in IMRT to compare loads, EDR2 film recorded approximately 110 minutes, and EPID recorded approximately 55 minutes. Conclusion: EPID could easily replace conventional complicated and troublesome film and ionization chamber which used to be used for dosimetry and set-up verification, and it was proved to be very efficient and accurate dosimetry device in quality assurance of IMRT (intensity modulated radiation therapy). As cine mode imaging using EPID allows locating tumors in real-time without additional dose in lung and liver which are mobile according to movements of diaphragm and in rectal cancer patients who have unstable position, it may help to implement the most optimal radiotherapy for patients.