The warming of the climate is increasing the damage caused by termites to wooden buildings, cultural properties and houses. A group removal system can be installed around the building to detect and remove termite damage; however, if the site is not visited regularly, every one to two months, you cannot observe whether termites have spread within, and it is difficult to take prompt effective action. In addition, since the system is installed and operated in an exposed state for a long period of time, it may be ineffective or damaged, resulting in a loss of function. Furthermore if the system is installed near a cultural site, it may affect the aesthetic environment of the site. In this study, we created a detection system that uses wood, cellulose, magnets, and magnetic sensors to determine whether termites have entered the area. The data was then transferred to a low power LoRa Network which displayed the results without the necessity of visiting the site. The wood was made in the shape of a pile, and holes were made from the top to the bottom to make it easier for termites to enter and produce a cellulose sample. The cellulose sample was made in a cylindrical shape with a magnet wrapped in cellulose and inserted into the top of a hole in the wood. Then, the upper part of the wood pile was covered with a stopper to prevent foreign matter from entering. It also served to block external factors such as light and rainfall, and to create an environment where termites could add cellulose samples. When the cellulose was added by the termites, a space was created around the magnet, causing the magnet to either fall or tilt. The magnetic sensor inside the stopper was fixed on the top of the cellulose sample and measured the change in the distance between the magnet and the sensor according to the movement of the magnet. In outdoor experiments, 11 cellulose samples were inserted into the wood detection system and the termite inflow was confirmed through the movement of the magnet without visiting the site within 5 to 17 days. When making further improvements to the function and operation of the system it in the future, it is possible to confirm that termites have invaded without visiting the site. Then it is also possible to reduce damage and fruiting due to product exposure, and which would improve the condition and appearance of cultural properties.
The deep geological repository for high-level radioactive waste disposal is a multi barrier system comprised of engineered barriers and a natural barrier. The long-term integrity of the deep geological repository is affected by the coupled interactions between the individual barrier components. Erosion and piping phenomena in the compacted bentonite buffer due to buffer-rock interactions results in the removal of bentonite particles via groundwater flow and can negatively impact the integrity and performance of the buffer. Rapid groundwater inflow at the early stages of disposal can lead to piping in the bentonite buffer due to the buildup of pore water pressure. The physiochemical processes between the bentonite buffer and groundwater lead to bentonite swelling and gelation, resulting in bentonite erosion from the buffer surface. Hence, the evaluation of erosion and piping occurrence and its effects on the integrity of the bentonite buffer is crucial in determining the long-term integrity of the deep geological repository. Previous studies on bentonite erosion and piping failed to consider the complex coupled thermo-hydro-mechanical-chemical behavior of bentonite-groundwater interactions and lacked a comprehensive model that can consider the complex phenomena observed from the experimental tests. In this technical note, previous studies on the mechanisms, lab-scale experiments and numerical modeling of bentonite buffer erosion and piping are introduced, and the future expected challenges in the investigation of bentonite buffer erosion and piping are summarized.
With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.
A sediment control dam is an artificial structure built to prolong sedimentation in the main dam by reducing the inflow of suspended solids. These dams can affect changes in dissolved organic matter (DOM) in the water body by changing the river flow regime. The main DOM component for Yeongju Dam sediment control of the Naeseongcheon River was analyzed through 3D excitation-emission matrix (EEM) and parallel factor (PARAFAC) analyses. As a result, four humic-like components (C1~C3, C5), and three proteins, tryptophan-like components (C2, C6~C7) were detected. Among DOM components, humic-like components (autochthonous: C1, allochthonous: C2~C3) were found to be dominant during the sampling period. The total amount of DOM components and the composition ratio of each component did not show a difference for each depth according to the amount of available light (100%, 12%, and 1%). Throughout the study period, the allochthonous organic matter was continuously decomposing and converting into autochthonous organic matter; the DOM indices (fluorescence index, humification index, and freshness index) indicated the dominance of autochthonous organic matter in the river. Considering the relative abundance of cyanobacteria and that the number of bacteria cells and rotifers increased as autochthonous organic matter increased, it was suggested that the algal bloom and consequent activation of the microbial food web was affected by the composition of DOM in the water body. Research on DOM characteristics is important not only for water quality management but also for understanding the cycling of matter through microbial food web activity.
In general, the REE were produced by mining conventional deposits, such as the carbonatite or the clay-hosted REE deposits. However, because of the recent demand increase for REE in modern industries, unconventional REE deposits emerged as a necessary research topic. Among the unconventional REE recovery methods, the REE-bearing coal deposits are recently receiving attentions. R-types generally have detrital originations from the bauxite deposits, and show LREE enriched REE patterns. Tuffaceous-types are formed by syngenetic volcanic activities and following input of volcanic ash into the basin. This type shows specific occurrence of the detrital volcanic ash-driven minerals and the authigenic phosphorous minerals focused at narrow horizon between coal seams and tonstein layers. REE patterns of tuffaceous-types show flat shape in general. Hydrothermal-types can be formed by epigenetic inflow of REE originated from granitic intrusions. Occurrence of the authigenic halogen-bearing phosphorous minerals and the water-bearing minerals are the specific characteristics of this type. They generally show HREE enriched REE patterns. Each type of REE-bearing coal deposits may occur by independent genesis, but most of REE-bearing coal deposits with high REE concentrations have multiple genesis. For the case of the US, the rare earth oxides (REO) with high purity has been produced from REE-bearing coals and their byproducts in pilot plants from 2018. Their goal is to supply about 7% of national REE demand. For the coal deposits in Korea, lignite layers found in Gyungju-Yeongil coal fields shows coexistence of tuff layers and coal seams. They are also based in Tertiary basins, and low affection from compaction and coalification might resulted into high-REE tuffaceous-type coal deposits. Thus, detailed geologic researches and explorations for domestic coal deposits are required.
In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.
Kim, Jongmin;Lee, Sang Ung;Kwon, Siyoon;Chung, Se Woong;Kim, Young Do
Journal of Korea Water Resources Association
/
v.55
no.11
/
pp.931-939
/
2022
In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.
Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.
This study compares and analyzes the Soil and Water Assessment Tool (SWAT) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) as coniferous, deciduous and mixed forest with Yongdam Dam upstream (904.4 km2). The hydrologic evaluation period was set to 10 years from 2010 to 2019, and the applicability of the 8-day MOD15A2 Leaf Area Index (LAI) data, 3 TDR (Time Domain Reflectometry) (GB, JC, CC), and 1 Flux Tower (DU) evaporation volume (YDD) data was simulated. As a result, the R2 of coniferous forest, deciduous forest and mixed forest are 0.95, 0.89, 0.90, soil moisture and evaportranspiration stations R2 were analyzed at 0.50 to 0.55 and 0.51, respectively, with R2 at 0.74, RMSE 2.75 mm/day, NSE 0.70 and PBIAS 14.3% for Yongdam inflow. Based on the calibrated and validated watersheds, the annual average evaportranspiration was calculated as coniferous 469.7 mm, deciduous 501. mm and 511.5 mm mixed forest, total runoff were estimated at coniferous 909.8 mm, deciduous 860.6 mm and 864.2 mm mixed forest. In the case of annual average evaportranspiration, it was evaluated that deciduous were high, but in the case of streamflow, it was evaluated that coniferous were high. Unlike other hydrologic with similar patterns throughout the year, the average annual evapotranspiration was about 7% higher than coniferous due to the higher evapotranspiration of deciduous with high leaf area index in summer and fall. In addition, deciduous were 9% and 6% higher for surface runoff and lateral flow, but the groundwater of coniferous was 77% higher. Therefore, it was confirmed that the total runoff was in order of coniferous, mixed forest, and deciduous.
The amount of the plastic waste has been increasing according to global demand for plastic. Microplastics are the most hazardous among all plastic pollutants due to their toxicity and unknown physicochemical properties. This study investigates the optimal methodology that can be applied to sewage samples for detecting microplastics before discussing reducing microplastics in MWTPs. In this study, the effect of different pretreatment methods while detecting microplastic analysis of MWTP influent samples was investigated; the samples were collected from the J sewage treatment plant. There are many pretreatment methods but two of them are widely used: Fenton digestion and hydrogen peroxide oxidation. Although there are many pretreatment methods that can be applied to investigate microplastics, the most widely used methods for sewage treatment plant samples are Fenton digestion and H2O2 oxidation. For each pretreatment method, there were factors that could cause an error in the measurement. To overcome this, in the case of the Fenton digestion pretreatment, it is recommended to proceed with the analysis by filtration instead of the density separation method. In the case of the H2O2 oxidation method, the process of washing with distilled water after the reaction is recommended. As a result of the analysis, the concentration of microplastics was measured to be 2.75ea/L for the sample using the H2O2 oxidation method and 3.2ea/L for the sample using the Fenton oxidation method, and most of them were present in the form of fibers. In addition, it is difficult to guarantee the reliability of measurement results from quantitative analysis performed via microscope with eyes. A calibration curve was created for prove the reliability. A total of three calibration curves were drawn, and as a result of analysis of the calibration curves, all R2 values were more than 0.9. This ensures high reliability for quantitative analysis. The qualitative analysis could determine the series of microplastics flowing into the MWTP, but could not confirm the chemical composition of each microplastic. This study can be used to confirm the chemical composition of microplastics introduced into MWTP in the future research.
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