• 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.38 no.6_1
• /
• pp.1285-1300
• /
• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Journal of Life Science
• /
• v.32 no.1
• /
• pp.1-10
• /
• 2022

Natural products have gained increasing attention due to their advantage of long-term safety and low toxicity for a very long time. Torreya nucifera is widespread in southern Korea and Jeju Island and its seeds are commonly used as edible food. Oriental ingredients have often been reported for their insecticidal, antioxidant and antibacterial properties, but there have not yet been any studies on their antidiabetic effect. In this study, we investigated several biological activities of T. nucifera pericarp (TNP) and seeds (TNS) extracts and proceeded to characterize the antidiabetic compounds of TNS. The initial results suggested that TNS extract at 15 and 10 ㎍/ml concentration has inhibitory effects on α-glucosidase and protein tyrosine phosphatase 1B, that is 14.5 and 4.35 times higher than TNP, respectively. Thus, the stronger antidiabetic TNS was selected for the subsequent experiments to characterize its active compounds. Ultrafiltration was used to determine the apparent molecular weight of the active compounds, showing 300 kDa or more. Finally the mixture was then partially purified using Diaion HP-20 column chromatography by eluting with 50~100% methanol. Therefore we concluded that the active compounds of TNS have potential as therapeutic agents in functional food or supplemental treatment to improve diabetic diseases.

• Journal of agricultural medicine and community health
• /
• v.47 no.3
• /
• pp.166-180
• /
• 2022

Objective: This study evaluated the effects of a non-face-to-face obesity management program, implemented during the pandemic. Methods: The non-face-to-face obesity management program used the Intervention mapping protocol (IMP). The program was put into effect over the course of eight weeks, from September 14 to November 13, 2020 in 48 overweight and obese adults, who applied to participate through the Daegu Citizen Health Support Center. Results: IMP was first a needs assessment was conducted; second, goal setting for behavior change was established; third, evidence-based selection of arbitration method and performance strategy was performed; fourth, program design and validation; fifth, the program was run; and sixth, the results were evaluated. The average weight after participation in the program was reduced by 1.2kg, average WC decreased by 3cm, and average BMI decreased by 0.8kg/m² (p<0.05). The results of the health behavior survey showed a positive improvement in lifestyle factors, including average daily intake calories, fruit intake, and time spent in walking exercise before and after participation in the program. A statistically significant difference was seen (p<0.05). The satisfaction level for program process evaluation was high, at 4.57±0.63 point. Conclusion: The non-face-to-face obesity management program was useful for obesity management for adults in communities, as it enables individual counseling by experts and active participation through self-body measurement and recording without restriction by time and place. However, the program had some restrictions on participation that may relate to the age of the subject, such as skill and comfort in using a mobile app.

• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.485-496
• /
• 2022

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO₂ enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.

• Resources Recycling
• /
• v.31 no.2
• /
• pp.20-32
• /
• 2022

This study implemented a chelating agent (Ethylenediaminetetraacetic acid, EDTA) in purification to obtain high-purity vanadium pentoxide (V₂O₅) for use in VRFB (Vanadium Redox Flow Battery). V₂O₅ (powder) was produced through the precipitation recovery of ammonium metavanadate (NH₄VO₃) from a vanadium solution, which was prepared using a low-purity vanadium raw material. The initial purity of the powder was estimated to be 99.7%. However, the use of a chelating agent improved its purity up to 99.9% or higher. It was conjectured that the added chelating agent reacted with the impurity ions to form a complex, stabilizing them. This improved the selectivity for vanadium in the recovery process. However, the prepared V₂O₅ powder exhibited higher contents of K, Mn, Fe, Na, and Al than those in the standard counterparts, thus necessitating additional research on its impurity separation. Furthermore, the vanadium electrolyte was prepared using the high-purity V₂O₅ powder in a newly developed direct electrolytic process. Its analytical properties were compared with those of commercial electrolytes. Owing to the high concentration of the K, Ca, Na, Al, Mg, and Si impurities in the produced vanadium electrolyte, the purity was analyzed to be 99.97%, lower than those (99.98%) of its commercial counterparts. Thus, further research on optimizing the high-purity V₂O₅ powder and electrolyte manufacturing processes may yield a process capable of commercialization.

• Journal of Korea Water Resources Association
• /
• v.55 no.8
• /
• pp.623-634
• /
• 2022

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.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.249-263
• /
• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.436-445
• /
• 2023

Methane (CH₄) emissions from rice paddies are mainly observed using the closed chamber method or the eddy covariance method. In this study, a new observation technique combining a portable gas analyzer (Model LI-7810, LI-COR, Inc., USA) and an automatic opening/closing chamber (Model Smart Chamber, LI-COR, Inc., USA) was introduced based on the strengths and weaknesses of the existing measurement methods. A cylindrical collar was manufactured according to the maximum growth height of rice and used as an auxiliary measurement tool. All types of measured data can be monitored in real time, and CH₄ flux is also calculated simultaneously during the measurement. After the measurement is completed, all the related data can be checked using the software called 'SoilFluxPro'. The biggest advantage of the new observation technique is that time-series changes in greenhouse gas concentrations can be immediately confirmed in the field. It can also be applied to small areas with various treatment conditions, and it is simpler to use and requires less effort for installation and maintenance than the eddy covariance system. However, there are also disadvantages in that the observation system is still expensive, requires specialized knowledge to operate, and requires a lot of manpower to install multiple collars in various observation areas and travel around them to take measurements. It is expected that the new observation technique can make a significant contribution to understanding the CH₄ emission pathways from rice paddies and quantifying the emissions from those pathways.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.933-948
• /
• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.