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Survey on the distribution of ancient tombs using LiDAR measurement method (라이다(LiDAR) 측량기법을 활용한 고분분포현황 조사)

  • SIM Hyeoncheol
    • Korean Journal of Heritage: History & Science
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    • v.56 no.4
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    • pp.54-70
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    • 2023
  • Surveys and studies on cultural assets using LiDAR measurement are already active overseas. Recently, awareness of the advantages and availability of LiDAR measurement has increased in Korea, and cases of using it for surveys of cultural assets are gradually increasing. However, it is usually restricted to surveys of mountain fortresses and is not actively used for surveys of ancient tombs yet. Therefore, this study intends to emphasize the need to secure fundamental data from LiDAR measurement for the era from the Three Kingdoms to Unified Silla in which recovery, maintenance, etc., in addition to the actual surveys, are unfulfilled due to the sites being mainly distributed in mountainous areas. For this, LiDAR measurement was executed for the area of Jangsan Ancient Tombs and Chunghyo-dong Ancient Tombs in Seoak-dong, Gyeongju, to review the distribution and geographical conditions of ancient tombs. As a result, in the Jangsan Ancient Tombs, in which a precision archaeological (measurement) survey was already executed, detailed geographic information and distribution conditions could be additionally identified, which could not be known only with the layout indicated by the topographic map of the existing report. Also, in the Chunghyo-dong Ancient Tombs, in which an additional survey was not conducted after 10 tombs were found during the Japanese colonial period, the location of the ancient tombs initially excavated was accurately identified, and the status and additional information was acquired, such as on the conditions of ancient tombs not surveyed. Such information may also be used as fundamental data for the preservation and maintenance of future ancient tombs in addition to the survey and study of the ancient tombs themselves. LiDAR measurement is most effective for identifying the condition of ancient tombs in mountainous areas where observation is difficult or access is limited due to the forest zone. It may be executed before on-site surveys, such as archaeological surveys, to secure data with high availability as prior surveys or pre-surveys. Therefore, it is necessary to secure fundamental data from LiDAR measurement in future surveys of ancient tombs and to establish a survey and maintenance/utilization plan based on this. To establish survey/study and preservation/maintenance measures for ancient tombs located in mountainous areas, a precision archaeological survey is currently executed to draw up a distribution chart of ancient tombs. If LiDAR measurement data is secured before this and used, a more effective and accurate distribution chart can be drawn up, and the actual conditions can be identified. Also, most omissions or errors in information can be prevented in on-site surveys of large regions. Therefore, it is necessary to accumulate fundamental data by actively using LiDAR measurement in future surveys of ancient tombs.

A Study on the Digital Drawing of Archaeological Relics Using Open-Source Software (오픈소스 소프트웨어를 활용한 고고 유물의 디지털 실측 연구)

  • LEE Hosun;AHN Hyoungki
    • Korean Journal of Heritage: History & Science
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    • v.57 no.1
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    • pp.82-108
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    • 2024
  • With the transition of archaeological recording method's transition from analog to digital, the 3D scanning technology has been actively adopted within the field. Research on the digital archaeological digital data gathered from 3D scanning and photogrammetry is continuously being conducted. However, due to cost and manpower issues, most buried cultural heritage organizations are hesitating to adopt such digital technology. This paper aims to present a digital recording method of relics utilizing open-source software and photogrammetry technology, which is believed to be the most efficient method among 3D scanning methods. The digital recording process of relics consists of three stages: acquiring a 3D model, creating a joining map with the edited 3D model, and creating an digital drawing. In order to enhance the accessibility, this method only utilizes open-source software throughout the entire process. The results of this study confirms that in terms of quantitative evaluation, the deviation of numerical measurement between the actual artifact and the 3D model was minimal. In addition, the results of quantitative quality analysis from the open-source software and the commercial software showed high similarity. However, the data processing time was overwhelmingly fast for commercial software, which is believed to be a result of high computational speed from the improved algorithm. In qualitative evaluation, some differences in mesh and texture quality occurred. In the 3D model generated by opensource software, following problems occurred: noise on the mesh surface, harsh surface of the mesh, and difficulty in confirming the production marks of relics and the expression of patterns. However, some of the open source software did generate the quality comparable to that of commercial software in quantitative and qualitative evaluations. Open-source software for editing 3D models was able to not only post-process, match, and merge the 3D model, but also scale adjustment, join surface production, and render image necessary for the actual measurement of relics. The final completed drawing was tracked by the CAD program, which is also an open-source software. In archaeological research, photogrammetry is very applicable to various processes, including excavation, writing reports, and research on numerical data from 3D models. With the breakthrough development of computer vision, the types of open-source software have been diversified and the performance has significantly improved. With the high accessibility to such digital technology, the acquisition of 3D model data in archaeology will be used as basic data for preservation and active research of cultural heritage.

Geological Structures of the Hadong Northern Anorthosite Complex and its surrounding Area in the Jirisan Province, Yeongnam Massif, Korea (영남육괴 지리산지구에서 하동 북부 회장암복합체와 그 주변지역의 지질구조)

  • Lee, Deok-Seon;Kang, Ji-Hoon
    • The Journal of the Petrological Society of Korea
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    • v.21 no.3
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    • pp.287-307
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    • 2012
  • The study area, which is located in the southeastern part of the Jirisan province of the Yeongnam massif, Korea, consists mainly of the Precambrian Hadong northern anorthosite complex (HNAC) and the Jirisan metamorphic rock complex (JMRC) and the Mesozoic granitoids which intrude them. Its tectonic frame is built into NS trend, unlike the general NE-trending tectonic frame of Korean Peninsula. This paper researched the structural characteristics at each deformation phase to clarify the geological structures associated with the NS-trending tectonic frame which was built in the HNAC and JMRC. The result indicates that the geological structures of this area were formed at least through three phases of deformation. (1) The $D_1$ deformation formed the $F_1$ sheath or "A"-type folds in the HNAC and JMRC, and the $S_{0-1}$ composite foliation and the $S_1$ foliation and the $D_1$ ductile shear zone which are (sub)parallel to the axial plane of $F_1$ fold, and the $L_1$ stretching lineation which is parallel to the $F_1$ fold axis owing to the large-scale top-to-the SE shearing on the $S_0$ foliation. (2) The $D_2$ deformation (re)folded the $D_1$ structural elements under the EW-trending tectonic compression environment, and formed the NS-trending $F_2$ open, tight, isoclinal, intrafolial folds with the $S_{0-1-2}$ composite foliation and the $S_2$ foliation and the $D_2$ ductile shear zone with S-C-C' structure and the $L_2$ stretching lineation which is (sub)parallel to the axial plane of $F_2$ fold. The extensive $D_2$ ductile shear zone (Hadong shear zone) of NS trend was persistently developed along the eastern boundary of HNAC and JMRC which would be to the limb of $F_2$ fold on a geological map scale. The Hadong shear zone is no less than 1.4 km width, and was formed in the mylonitization process which produced the mylonitic structure and the stretching lineation with the reduction of grain size during the $F_2$ passive folding. (3) The $D_3$ deformation formed the EW-trending $F_3$ kink or open fold under the NS-trending tectonic compression environment and partially rearranged the NS-trending pre-$D_3$ structural elements into (E)NE or (W)NW direction. The regional trend of $D_1$ tectonic frame before the $D_2$ deformation would be NE-SW unlike the present, and the NS-trending tectonic frame in the HNAC and JMRC like the present was formed by the rearrangement of the $D_1$ tectonic frame owing to the $F_2$ active and passive folding. Based on the main intrusion age of (N)NE-trending basic dyke in the study area, these three deformation events are interpreted to have occurred before the Late Paleozoic.

A Study of Anomaly Detection for ICT Infrastructure using Conditional Multimodal Autoencoder (ICT 인프라 이상탐지를 위한 조건부 멀티모달 오토인코더에 관한 연구)

  • Shin, Byungjin;Lee, Jonghoon;Han, Sangjin;Park, Choong-Shik
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.57-73
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    • 2021
  • Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.