• Korean Journal of Heritage: History & Science
• /
• v.55 no.2
• /
• pp.82-100
• /
• 2022

With the crisis of the COVID-19 pandemic and the era of the 4th industrial revolution, archaeological heritage education has entered a new phase. This article responds to the trends in the post-COVID-19 era, seeking ways to develop archaeological heritage education and sustainable strategies necessary in the era of the 4th industrial revolution. The program of archaeological heritage education required in the era of the 4th industrial revolution must cultivate creative talent, solve problems, and improve self-efficacy. It should also draw attention to archaeological heritage maker education. Such maker education should be delivered based on constructivism and be designed by setting specific learning goals in consideration of various age-specific characteristics. Moreover, various ICT-based contents applying VR, AR, cloud, and drone imaging technologies should be developed and expanded, and, above all, ontact digital education(real-time virtual learning) should seek ways to revitalize communities capable of interactive communication in non-face-to-face situations. The development of such ancient heritage content needs to add AI functions that consider learners' interests, learning abilities, and learning purposes while producing various convergent contents from the standpoint of "cultural collage." Online archaeological heritage content education should be delivered following prior learning or with supplementary learning in consideration of motivation or field learning to access the real thing in the future. Ultimately, archaeological ontact education will be delivered using cutting-edge technologies that reflect the current trends. In conjunction with this, continuous efforts are needed for constructive learning that enables discovery and question-exploration.

• Journal of the Microelectronics and Packaging Society
• /
• v.27 no.4
• /
• pp.83-89
• /
• 2020

For the past few decades, as part of efforts to protect the environment where fossil fuels, which have been a key energy resource for mankind, are becoming increasingly depleted and pollution due to industrial development, ecofriendly secondary batteries, hydrogen generating energy devices, energy storage systems, and many other new energy technologies are being developed. Among them, the lithium-ion battery (LIB) is considered to be a next-generation energy device suitable for application as a large-capacity battery and capable of industrial application due to its high energy density and long lifespan. However, considering the growing battery market such as eco-friendly electric vehicles and drones, it is expected that a large amount of battery waste will spill out from some point due to the end of life. In order to prepare for this situation, development of a process for recovering lithium and various valuable metals from waste batteries is required, and at the same time, a plan to recycle them is socially required. In this study, we introduce a nanoscale pattern transfer printing (NTP) process of Li2CO3, a representative anode material for lithium ion batteries, one of the strategic materials for recycling waste batteries. First, Li2CO3 powder was formed by pressing in a vacuum, and a 3-inch sputter target for very pure Li2CO3 thin film deposition was successfully produced through high-temperature sintering. The target was mounted on a sputtering device, and a well-ordered Li2CO3 line pattern with a width of 250 nm was successfully obtained on the Si substrate using the NTP process. In addition, based on the nTP method, the periodic Li2CO3 line patterns were formed on the surfaces of metal, glass, flexible polymer substrates, and even curved goggles. These results are expected to be applied to the thin films of various functional materials used in battery devices in the future, and is also expected to be particularly helpful in improving the performance of lithium-ion battery devices on various substrates.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1135-1144
• /
• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Korean Journal of Ecology and Environment
• /
• v.56 no.3
• /
• pp.207-217
• /
• 2023

This study was conducted to investigate the current status of seagrass species in the Nakdong River estuary from May to June 2023. To survey the seagrass habitat area, the Nakdong River estuary was divided into seven zones. Aerial photography using drones was conducted to find seagrass areas, GPS tracking was carried out on foot in the intertidal zone and by boat and SCUBA diving in the subtidal zone. To analyze the seagrass status, we measured the morphological characteristics, shoot density, and biomass of representative seagrass species in each zone. Four seagrass species were found in this area: Zostera japonica, Z. marina, Ruppia maritima, and Phyllospadix japonicus. The distribution areas of each species was 338.2 ha, 92.9 ha, 0.9 ha, and 1.4 ha, respectively, with a total area of 432.5 ha. Z. japonica was widely distributed in most of the tidal flats and mudflats of the Nakdong River estuary, while Z. marina was restricted to Nulcha-do, Jinu-do, and Dadae-dong. R. maritima occurred within the habitat of Z. japonica in Eulsukdo and Myeongji mudflats, and P. japonicus inhabited rocky areas in Dadae-dong. The shoot density of each species was 4,575.8±338.3 shoots m^-2, 244.8±12.0 shoots m^-2, 11,302.1±290.0 shoots m^-2, and 2862.5±153.5 shoots m^-2, respectively. The biomass of each species was 239.7±18.5 gDW m^-2, 362.3±20.5 gDW m^-2, 33.3±1.2 gDW m^-2, and 1,290.0±37.0 gDW m^-2, respectively. The results of this study revealed that Z. japonica was dominant in the Nakdong River estuary. In particular, Z. japonica habitats of Eulsukdo, Daema-deung, and Myeongji mudflats were identified as the largest in Korea. The Nakdong River estuary is an important site of ecological, environmental, and economic value, and will require continuous investigation and management of the native seagrasses.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.32 no.3
• /
• pp.213-220
• /
• 2014

This is the study to conduct the topographical analysis using the orthophotographic data from the waypoint flight using the UAV and constructed the system required for the automatic waypoint flight using the multicopter.. The results of the waypoint photographing are as follows. First, result of the waypoint flight over the area of 9.3ha, take time photogrammetry took 40 minutes in total. The multicopter have maintained the certain flight altitude and a constant speed that the accurate photographing was conducted over the waypoint determined by the ground station. Then, the effect of the photogrammetry was checked. Second, attached a digital camera to the multicopter which is lightweight and low in cost compared to the general photogrammetric unmanned airplane and then used it to check its mobility and economy. In addition, the matching of the photo data, and production of DEM and DXF files made it possible to analyze the topography. Third, produced the high resolution orthophoto(2cm) for the inside of the river and found out that the analysis is possible for the changes in vegetation and topography around the river. Fourth, It would be used for the more in-depth research on landscape analysis such as terrain analysis and visibility analysis. This method may be widely used to analyze the various terrains in cities and rivers. It can also be used for the landscape control such as cultural remains and tourist sites as well as the control of the cultural and historical resources such as the visibility analysis for the construction of DSM.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.925-938
• /
• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.