• Journal of Drive and Control
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• v.21 no.2
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• pp.36-43
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• 2024

The yield is basic and necessary information in precision agriculture that reduces input resources and enhances productivity. Yield information is important because it can be used to set up farming plans and evaluate farming results. Yield monitoring systems are commercialized in the United States and Japan but not in Korea. Therefore, such a system must be developed. This study was conducted to develop a yield monitoring system that improved performance by correcting a previously developed flow sensor using a grain tank-weighing system. An impact-plated type flow sensor was installed in a grain tank where grains are placed, and grain tank-weighing sensors were installed under the grain tank to estimate the weight of the grain inside the tank. The grain flow rate and grain weight prediction models showed high correlations, with coefficient of determinations (R2) of 0.9979 and 0.9991, respectively. A main controller of the yield monitoring system that calculated the real-time yield using a sensor output value was also developed and installed in a combine harvester. Field tests of the combine harvester yield monitoring system were conducted in a rice paddy field. The developed yield monitoring system showed high accuracy with an error of 0.13%. Therefore, the newly developed yield monitoring system can be used to predict grain weight with high accuracy.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• The Korean Journal of Air & Space Law and Policy
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• v.27 no.2
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• pp.67-106
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• 2012

Today air traffic at the airport is complicated including a significant increase in the volume of air transport, so aviation accidents are constantly occurring. Therefore, we should newly recognize importance of the Air Traffic Safety, the core values of the Air Traffic. The location of airport that is the basic infrastructure of the air traffic and the security of safety for facilities and equipments are more important than what you can. From this dimension, I analyze the step-by-step safety factors that are taken into account in the airport development projects from the construction or improvement of the airport within the current laws and institutions and give my opinion on the enhancement of safety in the design and construction of airport. The safety of air traffic, as well as airport, depends on location, development, design, construction, inspection and management of the airport including airport facilities because we have to carry out the national responsibility that prevents the risk of large social overhead capital for many and unspecified persons in modern society through legislation regarding intervention of specialists and locational criteria for aviation safety from the planning stage of airport development. In addition, well-defined installation standards of airports and air navigation facilities, the key points of the airport development phase, can ensure the safety of the airport and airport facilities. Of course, the installation standards of airport and air navigation facilities are based on the global standard due to the nature of air traffic. However, to prevent the chaos for the safety standards in design, construction, inspection of them and to ensure the aviation safety, the safety standards must be further subdivided in the course of domestic legislation. The criteria for installation of the Air Navigation facilities is regulated most specifically. However, to ensure the safety of the operation for Air Navigation Facilities, performance system proved suitable for the Safety of Air Navigation Facilities must change over from arbitrary restrictions to mandatory restrictions and be applied for foreign producers as well as domestic producers. Of course, negligence of pilots and defective aircraft maintenance lead to a large portion of the aviation accidents. However, I think that air traffic accidents can be reduced if the airport or airport facility is perfect enough to ensure the safety. Therefore, legal and institutional supplement to prioritize the aviation safety from the stage of airport development may be necessary.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.4
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• pp.388-395
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• 2016

This paper explores the photocatalytic sensitivity of cement mortar incorporated with recycled $TiO_2$ from waste water sludge. Basically, $TiO_2$ cluster sank down slowly to the bottom of cement mortar specimen before setting and hardening process. This leads the mismatch of $TiO_2$ concentration on the top and the bottom faces of a specimen. This poorly dispersed $TiO_2$-cement mortar naturally exhibits poor NOx removal efficiency especially on the top of cementitious structure. In architectural engineering application such as building or housing structures, one can simply filp over from the bottom so that more $TiO_2$ concentrated surface can be placed outward into the air. However, in highway pavement case, this could not be applicable due to in-situ installation of concrete pavement. Hence, the dispersion of $TiO_2$ cluster inside the cementitous material is getting important issue onto road construction application. To elaborate this issue, according to our results, silica fume, high-ranged water reducer, viscosity agent, blast furnace slag were not enhanced much of dispersion characteristics of $TiO_2$ cluster. The combination of foaming agent and accelerator of hardening with viscosity agent and small grain size of fine aggregate may help the dispersion of $TiO_2$ inside cementitious materials. Even though the enhanced dispersion were applied to the specimen, NOx removal efficiency doest not change much for the top surface of the specimen. This concurrently affected by the presence of tiny air voids and the dispersion of $TiO_2$ in that these voids could easily adsorbed NOx gas with the aid of large surface area.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

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

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of the Korea Concrete Institute
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• v.24 no.3
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• pp.293-303
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• 2012

Recently, as construction technology improved, concrete structures not only became larger, taller and longer but were able to perform various functions. However, if extreme loads such as impact, blast, and fire are applied to those structures, it would cause severe property damages and human casualties. Especially, the structural responses from extreme loading are totally different than that from quasi-static loading, because large pressure is applied to structures from mass acceleration effect of impact and blast loads. Therefore, the strain rate effect and damage levels should be considered when concrete structure is designed. In this study, the low velocity impact loading test of steel fiber reinforced concrete (SFRC) slabs including 0%~1.5% (by volume) of steel fibers, and strengthened with two types of FRP sheets was performed to develop an impact resistant structural member. From the test results, the maximum impact load, dissipated energy and the number of drop to failure increased, whereas the maximum displacement and support rotation were reduced by strengthening SFRC slab with FRP sheets in tensile zone. The test results showed that the impact resistance of concrete slab can be substantially improved by externally strengthening using FRP sheets. This result can be used in designing of primary facilities exposed to such extreme loads. The dynamic responses of SFRC slab strengthened with FRP sheets under low velocity impact load were also analyzed using LS-DYNA, a finite element analysis program with an explicit time integration scheme. The comparison of test and analytical results showed that they were within 5% of error with respect to maximum displacements.

• Clean Technology
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• v.27 no.4
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• pp.332-340
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• 2021

With the recent reinforcement of emission standards, it is necessary to make efforts to reduce NOx from air pollutant-emitting workplaces. The NOx reduction method mainly used in industrial facilities is selective catalytic reduction (SCR), and the most commercial SCR catalyst is the ceramic honeycomb catalyst. This study was carried out to reduce the NOx emitted from steel plants by applying De-NOx catalyst coated on metallic monolith. The De-NOx catalyst was synthesized through the optimized coating technique, and the coated catalyst was uniformly and strongly adhered onto the surface of the metallic monolith according to the air jet erosion and bending test. Due to the good thermal conductivity of metallic monolith, the De-NOx catalyst coated on metallic monolith showed good De-NOx efficiency at low temperatures (200 ~ 250 ℃). In addition, the optimal amount of catalyst coating on the metallic monolith surface was confirmed for the design of an economical catalyst. Based on these results, the De-NOx catalyst of commercial grade size was tested in a semi-pilot De-NOx performance facility under a simulated gas similar to the exhaust gas emitted from a steel plant. Even at a low temperature (200 ℃), it showed excellent performance satisfying the emission standard (less than 60 ppm). Therefore, the De-NOx catalyst coated metallic monolith has good physical and chemical properties and showed a good De-NOx efficiency even with the minimum amount of catalyst. Additionally, it was possible to compact and downsize the SCR reactor through the application of a high-density cell. Therefore, we suggest that the proposed De-NOx catalyst coated metallic monolith may be a good alternative De-NOx catalyst for industrial uses such as steel plants, thermal power plants, incineration plants ships, and construction machinery.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.43-54
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• 2019

In this study, results of completion inspection are investigated according to the performance of the firefighting construction supervision personnel and according to the necessity of clean air supply in a performance test of the firefighting construction supervision personnel. The results of the completion inspection are measured and the causes of defects in the fire protection facilities after issuance of a completion inspection certificate are analyzed. The findings of this investigation can be used to minimize the risk of apartment fires and improve the performance of firefighting construction supervision personnel. The results of this study are summarized as follows. First, the results of the completion inspection conducted by the firefighting construction supervision personnel varies according to particular occupation. The performance of fire facility supervisors from design companies and fire construction companies were significantly higher than the fire officers. Therefore, it is necessary to employ supervisors who have experience in supervising the construction of apartments, in order to improve the performance of fire construction supervision officers. Second, the analysis of the completion inspection results according to the necessity of a clean air supply for performance tests of firefighting construction supervision personnel showed that the necessity of a clean air supply was significantly higher for fire facility supervisors from design companies and fire construction companies than for the fire officers. In order to improve, the completion inspection should be carried out up until the completion date of the building. In addition, a system needs to be established to ensure that clients are not able to demand that a completion inspection certificate be issued on the construction completion date. Finally, it is found that defects of completion inspection after issuance of a completion inspection certificate affects the result of the completion inspection. The results of the completion inspections conducted by the fire facility supervisors from design companies and fire construction companies were affected significantly less by defects than that of the general contractors. Research shows that faults have occured in firefighting facilities after the issuance of a completion inspection certificate due to the client and the construction company demanding the issuance of the completion inspection certificate.