• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.589-597
• /
• 2023

Floating debris in large quantities from land during heavy rainfall has adverse social, economic, and environmental impacts, but the monitoring system for the concentration area and amount is insufficient. In this study, we proposed an efficient monitoring method for floating debris entering the river during heavy rainfall in Daecheong Lake, the largest water supply source in the central region, and applied remote sensing techniques to estimate the standing-stock of floating debris. To investigate the status of floating debris in the upper of Daecheong Lake, we used a tracking buoy equipped with a low-orbit satellite communication terminal to identify the movement route and behavior characteristics, and used a drone to estimate the potential concentration area and standing-stock of floating debris. The location tracking buoys moved rapidly during the period when the cumulative rainfall for 3 days increased by more than 200 to 300 mm. In the case of Hotan Bridge, which showed the longest distance, it moved about 72.8 km for one day, and the maximum moving speed at this time was 5.71 km/h. As a result of calculating the standing-stock of floating debris using a drone after heavy rainfall, it was found to be 658.8 to 9,165.4 tons, with the largest amount occurring in the Seokhori area. In this study, we were able to identify the main concentrations of floating debris by using location-tracking buoys and drones. It is believed that remote sensing-based monitoring methods, which are more mobile and quicker than traditional monitoring methods, can contribute to reducing the cost of collecting and processing large amounts of floating debris that flows in during heavy rain periods in the future.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.521-535
• /
• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Heritage: History & Science
• /
• v.56 no.4
• /
• pp.288-318
• /
• 2023

This work began with the aim of examining the history of the concept "cultural property" that is expected to disappear, and the main subject of research was the history that preceded the spread of this notion throughout society. The phrase "cultural property" first appeared in the 1920s, and was used in various fields such as literature, history, music, and philosophy in the context of cultural resources. Until immediately following liberation from the Japanese colonial era, the meaning of cultural assets was widely applied in the range of "cultural resources," and during this period, it was often used to help supplant the reality and history of Japanese occupation. Immediately after the Korean War, it was also employed for the purpose of 'restoration of cultural resources through war'. Recognition of cultural property directly influenced by Japan's Cultural Heritage Protection Act has occurred since 1950s. In the early 1960s, the enactment of various laws related to cultural properties and the establishment of the Cultural Heritage Administration caused the meaning of cultural property to be limited to 'cultural heritage'. In this way, the definition of state-led cultural property has continued to apply to this day. It has not been clearly confirmed whether the concept of cultural properties was imported from Japan through means such as the Cultural Heritage Protection Act. Cases in which several Japanese students endorsed the concept of cultural property within Korea serve to increase the likelihood that the concept was indeed imported from Japan. However, "coined language using multiple Chinese characters," "the phenomenon of cultural complex words in the 1920s,", and "cases of non-Japanese international students using the concept of cultural property" also open up the possibility of their own occurrence. Apart from the general importance of the concept of cultural property, intellectuals at the time used this concept to promote internal development and the overcoming of colonial Joseon. In this research, it was confirmed that the conceptual word cultural property was older and had a wider history than the general perception had indicated previously. The history of the conceptual term "cultural property" may appear to be more than 60 years old based on the enactment of the Cultural Heritage Protection Act, but in fact it is nearly 100 years old when traced back to on 1925, as established here. In general, the creation and disappearance of terms may proceed naturally with social change, but such terms may alternatively be created or erased through national policy. Identifying the origins of a phrase that is about to disappear represents a significant task for purposes of establishing its historical meaning.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.27 no.6
• /
• pp.152-161
• /
• 2023

In general, large-capacity hydrogen storage vessels, typically in the form of vertical cylindrical vessels, are constructed using steel materials. These vessels are anchored to foundation slabs that are specially designed to suit the environmental conditions. This anchoring method involves pre-installed anchors on top of the concrete foundation slab. However, it's important to note that such a design can result in concentrated stresses at the anchoring points when external forces, such as seismic events, are at play. This may lead to potential structural damage due to anchor and concrete damage. For this reason, in this study, it selected an vertical hydrogen storage vessel based on site observations and created a 3D finite element model. Artificial seismic motions made following the procedures specified in ICC-ES AC 156, as well as domestic recorded earthquakes with a magnitude greater than 5.0, were applied to analyze the structural behavior and performance of the target structures. Conducting experiments on a structure built to actual scale would be ideal, but due to practical constraints, it proved challenging to execute. Therefore, it opted for an analytical approach to assess the safety of the target structure. Regarding the structural response characteristics, the acceleration induced by seismic motion was observed to amplify by approximately ten times compared to the input seismic motions. Additionally, there was a tendency for a decrease in amplification as the response acceleration was transmitted to the point where the centre of gravity is located. For the vulnerable components, specifically the sub-system (support columns and anchorages), the stress levels were found to satisfy the allowable stress criteria. However, the concrete's tensile strength exhibited only about a 5% margin of safety compared to the allowable stress. This indicates the need for mitigation strategies in addressing these concerns. Based on the research findings presented in this paper, it is anticipated that predictable load information for the design of storage vessels required for future shaking table tests will be provided.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.979-995
• /
• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.1009-1029
• /
• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Korea Water Resources Association
• /
• v.56 no.12
• /
• pp.981-992
• /
• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.12 no.2
• /
• pp.112-120
• /
• 2007

This paper reviews the use of parasites as 'biological tags' for studying stock analysis of salmonid fishes. Numerous definitions of stock concepts exist, but most of them essentially define a group of fish as having similar biological characteristics and being self-reproducing as stocks. It is important to manage fish stocks for human consumption and sustainable production and especially for salmonid fishes. Because these fry are considered as each country's property, it is necessary to identify and discriminate each fish stock in the open sea. Methods of separating fish stocks are very diverse. Artificial tags, parasites, otoliths scales and genetic characters have been used for stock analysis and each method has advantages and disadvantages. Of these parasites can be good biological tags because they are applied by nature at no cost. Parasites can be infected with susceptible host fishes when they enter into certain areas. Then if they move to the outside and are caught researchers can infer that the fish had been in the endemic area for a period of time during their life. Hence the host fish can be considered as naturally 'tagged' by parasites. However, if they do not pass the parasites-endemic. area, they will harbour no parasites. Therefore, researchers can discriminate each fish stocks and trace their migration routes with these biological tags. In this paper, several examples on the use of parasites as biological tags for studying salmonids, as well as other species, are listed. The advantages and limitations of parasites as biological tags are also discussed. Chum salmon (Oncorhynchus keta), the main salmonid species migrating to Korea, is distributed all around the North Pacific. Korean chum salmon are generally thought to move to the Sea of Okhotsk, the western North Pacific and the Bering Sea. However, there is no clear information on the distribution and migration pathways of Korean chum salmon, and no markers exist for separating them from others yet. Recent Korean chum salmon stock analysis including parasites information are mentioned.

• The Korean Society of Law and Medicine
• /
• v.24 no.4
• /
• pp.67-101
• /
• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.50 no.1
• /
• pp.59-65
• /
• 2024

For plasma cosmetics, it is important to ensure the long-term stability of plasma in the formulation. This study examined the suitability of containers for efficient plasma cosmetics development. By varying the surface area covered by the plasma, 4 cm², 25 cm², 75 cm², and 175 cm² containers were injected with cosmetic plasma, and the amount of nitric oxide (NO), the main active species of nitrogen plasma, was analyzed. As a result, the surface area and stability exposed to plasma tended to be inversely proportional, and it was most effective in a 4 cm² container. Furthermore, 25 mm, 40 mm, and 50 mm vials were treated with plasma, which resulted in relative long-term stability of NO at 25 mm, a smaller surface area of the container exposed to air. Water mist and stratified mist were selected as cosmetic formulations, and NO plasma was injected into the water layer to observe the changes in formulation properties and the state of the injected NO plasma. In both formulations, the amount of NO plasma injected was about 1.5 times higher in the water phase mist than in the stratified mist, and the stratified mist gradually decreased with time and was found to disappear after 3 weeks. The stability of the nitrogen plasma was studied at low temperature (4 ℃), room temperature (25 ℃), and high temperature (37 ℃, 50 ℃). As a result, it was found that the water mist did not affect the stability, but the stratified mist observed a color change in the oil phase layer. Overall, this study demonstrates the container suitability of nitrogen plasma and suggests the importance of ensuring the stability of injected nitrogen plasma in cosmetic formulations.