• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.93-100
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• 2022

The collision behaviors of the tripod and jacket structures, which are considered as support structures for offshore wind towers at the Southwest sea of Korea, were compared by nonlinear dynamic analysis. These structures, designed for the 3 MW capacity of the wind towers, were modeled using shell elements with nonlinear behaviors, and the tower structure including the nacelle, was modeled by beam and mass elements with elastic materials. The mass of the tripod structure was approximately 1.66 times that of the jacket structure. A barge and commercial ship were modeled as the collision vessel. To consider the tidal conditions in the region, the collision levels were varied from -3.5 m to 3.5 m of the mean sea level. In addition, the collision behaviors were evaluated as increasing the minimum collision energy at the collision speed (=2.6 m/s) of each vessel by four times, respectively. Accordingly, the plastic energy dissipation ratios of the vessel were increased as the stiffness of collision region. The deformations in the wind tower occurred from vibration to collapse of conditions. The tripod structure demonstrated more collision resistance than the jacket structure. This is considered to be due to the concentrated centralized rigidity and amount of steel utilized.

• Journal of the Korean Geosynthetics Society
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• v.20 no.4
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• pp.19-32
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• 2021

In this study, evaluation and consideration of domestic/overseas design, construction, and quality control performed by the authors on the deep cement mixing method were performed, and improvements for the development of the DCM method were suggested in the future. As a result of this study, it was found that the cross-sectional area correction for strength is required during the laboratory test of mix proportion, and caution is required because the extrapolation method may lead to different results from the actual one. Applicable design methods should be selected in consideration of both the improvement ratio and the type of improvement during design, and it was confirmed that the allowable compressive strength to which the safety factor was applied refers to the standard value for stability review and not the design parameters. In the case of the stress concentration ratio, rather than applying a conventional value, it was possible to perform economical design by calculating the experimental and theoretical stress concentration ratio reflecting the design conditions. In the case where pre-boring is expected during construction, if the increased water content is not large compared to the original, there were cases where a major problem did not occur even if the result that did not consider the increase in water content was used. In addition, it was confirmed that when the ratio of the top treatment length to the improved length is high, a small amount of design cement contents per unit length can be injected during construction. In the case of quality control, it was evaluated that D/4~2D/4 for single-axis and D/4 point for multi-axis were optimal for coring of grouting mixtures. As an item for quality control, it is judged that the standard that considers the TCR along with the unconfined compressive strength of grouting mixtures is more suitable for the domestic situation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1489-1503
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• 2022

Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.394-401
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• 2021

The construction of new renewable energy facilities is steadily increasing every year. In particular, the offshore wind farm market, which has abundant development scalability and a high production coefficient, is growing rapidly. The southwest sea has the highest possible offshore wind power potential, and related projects are to be promoted. This study presents a basic design procedure by the EUROCODE and considers structural safety in the development of an effective of shore wind foundation in the clay layer. In a previous study, the wind power generator of 5MW class was the main target, but the 8MW of wind turbine generator, which meets the technical trend of the wind turbine market in the Southwest sea, was selected as the standard model. Furthermore, a foundation that fulfills the geological conditions of the Southwest sea was developed. The structural safety of this foundation was verified using finite element method. Moreover, structural safety was secured by proper reinforcement from the initial design. Based on the results of this study, structural safety check for various types of foundations is possible in the future. Additionally, specialized structural design and evaluation guidance were also established.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.25-38
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• 2021

The aim of this study is to protect workers and pedestrians from accidents at night or bad weather by attaching optical fiber to existing safety clothing that is made only with fluorescent fabrics and retroreflective materials. A safety vest was designed and manufactured by applying optical fiber, and energy-harvesting technology was developed. The safety vest was designed to emit light using the automatic flashing of optical fibers attached to the film, and an energy harvester was manufactured and attached to drive the light emission of the optical fiber more continuously. As a result, first, the vest wearer' body was recognized from a distance through the optical fiber and retroreflection, which helped prevent accidents. Thus, this concept helps in saving lives by preventing accidents during night-time work on the roadside or activities of rescue crew and sports activities, or by quickly finding the point of an accident with a signal that changes the optical fiber light emission. Second, to use the wasted energy, a piezoelectric-element power generation system was developed and the piezoelectric-harvesting device was mounted. Potentially, energy was efficiently produced by activating the effective charging amount of the battery part and charging it auxiliary. In the existing safety vest, detecting the person wearing the vest is almost impossible in the absence of ambient light. However, in this study, the wearer could be found within 100 m by the light emission from the safety vest even with no ambient light. Therefore, in this study, we will help in preventing and reducing accidents by developing smart safety clothing using optical fiber and energy harvester attached to save lives.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.1-6
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• 2021

Mercury, once released, is not destroyed but accumulates and circulates in the natural environment, causing serious harm to ecosystems and human health. In the United States, sulfur-impregnated activated carbon is being considered for the removal of vapor mercury from the flue gas of coal-fired power plants, which accounts for about 32 % of the anthropogenic emissions of mercury. In this study, a high-efficiency porous mercury adsorption material was developed to reduce the mercury vapor in the exhaust gas of coal combustion facilities, and the mercury adsorption characteristics of the material were investigated. As a result of the investigation of the vapor mercury adsorption capacity at 30℃, the silica nanotube MCM-41 was only about 35 % compared to the activated carbon Darco FGD commercially used for mercury adsorption, but it increased to 133 % when impregnated with 1.5 % sulfur. In addition, the furnace fly ash recovered from the waste copper regeneration process showed an efficiency of 523 %. Furthermore, the adsorption capacity was investigated at temperatures of 30 ℃, 80 ℃, and 120 ℃, and the best adsorption performance was found to be 80 ℃. MCM-41 is a silica nanotube that can be reused many times due to its rigid structure and has additional advantages, including no possibility of fire due to the formation of hot spots, which is a concern when using activated carbon.

• Applied Chemistry for Engineering
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• v.34 no.1
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• pp.23-29
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• 2023

Microfluidic reactors have been made to achieve significant development for the generation of new functional materials to apply in a variety of fields. Over the last decade, microfluidic reactors have attracted attention as a user-friendly approach that is enabled to control physicochemical parameters such as size, shape, composition, and surface property. Here, we develop a centrifugal microfluidic reactor that can control the flow of fluid based on centrifugal force and generate multifunctional particles of various sizes and compositions. A centrifugal microfluidic reactor is fabricated by combining microneedles, micro- centrifuge tubes, and conical tubes, which are easily obtained in the laboratory. Depending on the experimental control param- eters, including centrifuge rotation speed, alginate concentration, calcium ion concentration, and distance from the needle to the calcium aqueous solution, this strategy not only enables the generation of size-controlled microparticles in a simple and reproducible manner but also achieves scalable production without the use of complicated skills or advanced equipment. Therefore, we believe that this simple strategy could serve as an on-demand platform for a wide range of industrial and academic applications, particularly for the development of advanced smart materials with new functionalities in biomedical engineering.