• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.3
• /
• pp.237-246
• /
• 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 the Korean Association of Geographic Information Studies
• /
• v.20 no.4
• /
• pp.115-125
• /
• 2017

In current, as the release of national base map with an equivalent scale and accuracy for the whole territory areas in South Korea, rapid spatial information industry such as national land development, GIS, and car navigation are used in a variety of spatial information industry as decision making method, and a lot of research and policies are proposed for the wide expansion of spatial information industry. For this, as of 2013, it contributes to the latest trend of spatial information field in order to solve the problems for the latest trend of spatial information, replacing modification of base maps as dividing the whole territory to zone with policy transformation by ordinary modifications. Therefore, this paper evaluates the possibility of modification and renewal of national base maps(scale: 1:5,000) using drones which currently get the limelight from a variety of research fields and industries. In particular, as a result of overlapping orthophoto, 3D point clouds extracted from images acquired by low-cost drones, and digital maps which are applied for the tasks of modification and renewal, it presents 0.2m precision and 0.1m accuracy. This means that drone-based photorgammetry technique can be fully utilized in the tasks of digital map modification and renewal because it conforms the error range of work regulation in making the national base maps(scale 1: 5000).

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.7
• /
• pp.271-278
• /
• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.10 no.1
• /
• pp.35-46
• /
• 2007

In the study digital imagery was used to examine asphalt concrete pavements. With digitally mastered-image information that was filmed with a video camera fixed on a car travelling on road at a consistent speed, a road surface management system that can gain road surface information (Crack, Rutting, IRI) was developed using an object-oriented language "Delphi". This system was designed to improve visualized effects by animations and graphs. After analyzing the accuracy of 3-D coordinates of road surfaces that were decided using multiple image orientation and bundle adjustment method, the average of standard errors turned out to be 0.0427m in the X direction, 0.0527m in the Y direction and 0.1539m in the Z direction. As a result, it was found to be good enough to be put to practical use for maps drawn on scales below 1/1000, which are currently producted and used in our country, and GIS data. According to the analysis of the accuracy in crack width on 12 spots using a digital video camera, the standard error was found to be ${\pm}0.256mm$, which is considered as high precision. In order to get information on rutting, the physically measured cross sections of 4 spots were compared with cross sections generated from digital images. Even though a maximum error turned out to be 10.88mm, its practicality is found in work efficiency.

• Journal of the Korean Geotechnical Society
• /
• v.38 no.7
• /
• pp.5-24
• /
• 2022

The current performance evaluation of slope anchors qualitatively determines the physical bonding between the anchor head and ground as well as cracks or breakage of the anchor head. However, such performance evaluation does not measure these primary factors quantitatively. Therefore, the time-dependent management of the anchors is almost impossible. This study is an evaluation of the 3D numerical model by SfM which combines UAS images with terrestrial LiDAR to collect numerical data on the damage factors. It also utilizes the data for the quantitative maintenance of the anchor system once it is installed on slopes. The UAS 3D model, which often shows relatively low precision in the z-coordinate for vertical objects such as slopes, is combined with terrestrial LiDAR scan data to improve the accuracy of the z-coordinate measurement. After validating the system, a field test is conducted with ten anchors installed on a slope with arbitrarily damaged heads. The damages (such as cracks, breakages, and rotational displacements) are detected and numerically evaluated through the orthogonal projection of the measurement system. The results show that the introduced system at the resolution of 8K can detect cracks less than 0.3 mm in any aperture with an error range of 0.05 mm. Also, the system can successfully detect the volume of the damaged part, showing that the maximum damage area of the anchor head was within 3% of the original design guideline. Originally, the ground adhesion to the anchor head, where the z-coordinate is highly relevant, was almost impossible to measure with the UAS 3D numerical model alone because of its blind spots. However, by applying the combined system, elevation differences between the anchor bottom and the irregular ground surface was identified so that the average value at 20 various locations was calculated for the ground adhesion. Additionally, rotation angle and displacement of the anchor head less than 1" were detected. From the observations, the validity of the 3D numerical model can obtain quantitative data on anchor damage. Such data collection can potentially create a database that could be used as a fundamental resource for quantitative anchor damage evaluation in the future.

• The Journal of Korean Society for Radiation Therapy
• /
• v.31 no.1
• /
• pp.25-32
• /
• 2019

Purpose: The size, shape, and volume of prosthetic appliance depend on the metal artifacts resulting from dental implant during head and neck treatment with radiation. This reduced the accuracy of contouring targets and surrounding normal tissues in radiation treatment plan. Therefore, the purpose of this study is to obtain the images of metal representing the size of tooth through MVCT, SMART-MAR CT and KVCT, evaluate the volumes, apply them into the proton therapy plan, and analyze the difference of dose distribution. Materials and Methods : Metal A ($0.5{\times}0.5{\times}0.5cm$), Metal B ($1{\times}1{\times}1cm$), and Metal C ($1{\times}2{\times}1cm$) similar in size to inlay, crown, and bridge taking the treatments used at the dentist's into account were made with Cerrobend ($9.64g/cm^3$). Metal was placed into the In House Head & Neck Phantom and by using CT Simulator (Discovery CT 590RT, GE, USA) the images of KVCT and SMART-MAR were obtained with slice thickness 1.25 mm. The images of MVCT were obtained in the same way with $RADIXACT^{(R)}$ Series (Accuracy $Precision^{(R)}$, USA). The images of metal obtained through MVCT, SMART-MAR CT, and KVCT were compared in both size of axis X, Y, and Z and volume based on the Autocontour Thresholds Raw Values from the computerized treatment planning equipment Pinnacle (Ver 9.10, Philips, Palo Alto, USA). The proton treatment plan (Ray station 5.1, RaySearch, USA) was set by fusing the contour of metal B ($1{\times}1{\times}1cm$) obtained from the above experiment by each CT into KVCT in order to compare the difference of dose distribution. Result: Referencing the actual sizes, it was appeared: Metal A (MVCT: 1.0 times, SMART-MAR CT: 1.84 times, and KVCT: 1.92 times), Metal B (MVCT: 1.02 times, SMART-MAR CT: 1.47 times, and KVCT: 1.82 times), and Metal C (MVCT: 1.0 times, SMART-MAR CT: 1.46 times, and KVCT: 1.66 times). MVCT was measured most similarly to the actual metal volume. As a result of measurement by applying the volume of metal B into proton treatment plan, the dose of $D_{99%}$ volume was measured as: MVCT: 3094 CcGE, SMART-MAR CT: 2902 CcGE, and KVCT: 2880 CcGE, against the reference 3082 CcGE Conclusion: Overall volume and axes X and Z were most identical to the actual sizes in MVCT and axis Y, which is in the superior-Inferior direction, was regular in length without differences in CT. The best dose distribution was shown in MVCT having similar size, shape, and volume of metal when treating head and neck protons. Thus it is thought that it would be very useful if the contour of prosthetic appliance using MVCT is applied into KVCT for proton treatment plan.