• Economic and Environmental Geology
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• v.51 no.4
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• pp.371-379
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• 2018

In many countries, recycling coal ashes as backfill materials for subsided lands, abandoned mine tunnels, and road pipeline constructions by making low-strength concretes with minimal amounts of cement is frequently considered for massive treatment of coal ashes. This study investigates the variation of heavy metals in the concrete test pieces prepared for the cases of using only Portland cement as binding material, fly ash as a replacement of the cement, sand as aggregates, and disposed ashes in the ash ponds as a replacement of aggregates. Heavy metal contents were measured based on the aqua regia extraction technique following the Korean Standard for Fair Testing of Soil Contamination and the influences of each materials on the total heavy metal contents were also assessed. Results show that the cement has the highest Cu, Pb, and Zn concentrations than any other materials. Therefore, the test pieces show significant concentration decreases for those metals when the cement was replaced by fly ash. Ponded ash shows low concentrations relative to fly ash in most of the parameters but shows higher Cu and Ni, and lower Pb levels than the sand aggregate. In overall, heavy metal levels of the test pieces are regulated by mixing among the used materials. Test pieces prepared during this study always show concentrations much lower than the Worrisome Level of Soil Contamination (Area 1), which was designated by the Soil Environment Conservation Act of Korea.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.75-85
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• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2960-2965
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• 2013

Customized services for individuals in analysis of recently collected GPS information have been investigated in various aspects. As the size of collected GPS data gets larger, a variety of services is being released accordingly. Existing studies, however, are limited to presenting service models for users while there is little study on developing intelligent computing technologies in the introduction of GPS information into the system. This study suggests an algorithm to analyze traffic information by introducing GPS information into the system in order to take the lead among intelligent computing technologies. The suggested algorithm analyzes a map by means of the collected vehicle GPS information and sectional traffic information interpretation method; thus, the computer judges the traffic information collected by humans. The experiment result shows that the traffic information was properly analyzed upon the utilization of the given data. Although a small quantity of analyzed data was less reliable, the system maintained high reliability as the data was sufficient.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.593-600
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• 2022

Various sensors such as lidar, radar, and camera are fused and used in autonomous vehicles. Rider and radar sensors are difficult to popularize because they are expensive equipment. In order to popularize autonomous vehicles, research that can replace expensive equipment is continuously being conducted. In this paper, we use a single camera that is inexpensive and can be easily mounted. We propose a method for detecting the wheels and adjacent lanes of a front-side vehicle of a driving vehicle and estimating distances. Our proposed method detects lanes and wheels from frame images after frame extraction via input images. In addition, the distance is measured and compared with the actual distance measured in the actual road environment. The distance could be calculated relatively accurately within the error range of ± 3 cm. Through this, it is expected that the camera can be used as an alternative means when the cost of autonomous vehicles is reduced or when the lidar or radar sensor fails.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1245-1254
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• 2023

Significant research has been conducted on the W-band synthetic aperture radar (SAR) system that utilizes the 77 GHz frequency modulation continuous wave (FMCW) radar. To reconstruct the high-resolution W-band SAR image, it is necessary to transform the point cloud acquired from the stereo cameras or the LiDAR in the direction of 6 degrees of freedom (DOF) and apply them to the SAR signal processing. However, there are difficulties in matching images due to the different geometric structures of images acquired from different sensors. In this study, we present the method to extract an optimized depth map by obtaining 6 DOF of the point cloud using a gradient descent method based on the entropy of the SAR image. An experiment was conducted to reconstruct a tree, which is a major road environment object, using the constructed W-band SAR system. The SAR image, reconstructed using the entropy-based gradient descent method, showed a decrease of 53.2828 in mean square error and an increase of 0.5529 in the structural similarity index, compared to SAR images reconstructed from radar coordinates.

• Journal of Soil and Groundwater Environment
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• v.11 no.5
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• pp.20-34
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• 2006

This study was undertaken to assess the anthropogenic impact on trace metal concentrations (Zn, Cu, Pb, Cr, Ni, and Cd) of roadside sediments (N = 70) from No.7 national road within the watershed of Hoidong Reservoir in Pusan City and to estimate the potential mobility of selected metals using sequential extraction. We generally found high concentrations of metals, especially Zn, Cu and Pb, affected by anthropogenic inputs. Compared to the trace metal concentrations of uncontaminated stream sediments, arithmetic mean concentrations of roadside sediments were about 7 times higher for Cu, 4 times higher for Zn, 3 times higher for Pb and Cr and, 2 times higher for Ni and As. Speciation data on the basis of sequential extraction indicate that most of the trace metals considered do not occur in significant quantities in the exchangeable fraction, except for Cd and Ni whose exchangeable fractions are appreciable (average 29.3 and 25.8%, respectively). Other metals such as Zn (51.4%) and Pb (45.2%) are preferentially bound to the reducible fraction, and therefore they can be potentially released by a pH decrease and/or redox change. Copper is mainly found in the organic fraction, while Cd is highest in the exchangeable fraction, and Cr and Ni in the residual fraction. Considering the proportion of metals bound to the exchangeable and carbonate fractions, the comparative mobility of metals probably decreases in the order of Cd>Ni>Pb>Zn>Cr>Cu. Although the total concentration data showed that Zn was typically present in potentially harmful concentration levels, the data on metal partitioning indicated that Cd, Ni and Pb pose the highest potential hazard for runoff water. As potential changes of redox state and pH may remobilize the metals bound to carbonates, amorphous oxides, and/or organic matter, and may release and flush them through drain networks into the watershed of Hoidong Reservoir, careful monitoring of environmental conditions appears to be very important.

• Archives of design research
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• v.18 no.4 s.62
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• pp.287-296
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• 2005

The street image in a business area is so much affected by Facade that the front side of a roadside building makes. Recently, for the indiscreet and intemperate advertising signboard of the front side of roadside buildings, a streetscape becomes more disordered than before, so now we need to do research about signboards of roadside buildings for a streetscape image. In this research, we focused on a streetscape with difference of occupation density of signboard in the business area via investigation and analysis about occupation density of signboards of the front side of roadside buildings, and we suggested optimum occupation density of signboards for supporting the road image positively. An object of research is the street in the business area that has many pedestrians and active passing zone of cars. We investigated and analyzed how to feel street images on the rate of occupation density of roadside building's signboards of in the chosen street. As a result of using an adjective that we use for estimating street view images for extraction of street images, we could know 2 factors. We named that one is the image of recognition, and the other is the image of feelings. We knew that signboard density of street of heavily recognized images is from 20% to 30% and, signboard density of street of heavily feeling images is from 50% to 60%. We also could know that people feel both images of recognition and images of feeling in specific density, 30 to 50%. Through this result of research, we can suggest Facade on signboard density with the recognition and the feeling and use images of the street view as materials to be more specific and more special.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.1
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• pp.69-78
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• 2010

In order to investigate the degree of household dust contamination, 48 samples of household dust (24 from urban area and 24 from rural area) in Daegu city were collected in vacuum cleaner during January to February 2009. Samples were sieved below 100 ${\mu}m$, and 14 elements (Al, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, Zn) were analyzed using ICP after acid extraction. Results obtained from the source assessment of trace elements using enrichment factor showed that Ca, Fe, K, Mg, Mn, Na, and V were influenced by natural sources such as weathered rock and resuspended soil, while Cd, Cr, Cu, Ni, Pb and Zn were influenced by anthropogenic sources such as fuel combustion and waste incineration. Concentrations were remarkably higher in components from natural sources than in components from urban anthropogenic sources. Household dust in urban area was more affected by anthropogenic sources compared with that of rural area. Pollution index of heavy metals revealed that urban area was 1.8 times more contaminated with heavy metal components than rural area. The correlation analysis among trace elements indicated that components were correlated with natural sources-natural sources (Al-Mg, Al-Mn, Fe-Mn) and natural sources-anthropogenic sources (Al-V, Fe-Cr, V-Mn) in both urban area and rural area. Trace element components of rural area were more correlated than those of urban area. Houses that use oil for heating fuel had relatively higher contents of heavy metals rather than those using gas or electricity for heating fuel. Houses with children also had higher contents of heavy metals. In addition, the age of houses was found to influence the heavy metal levels in household dusts, with older houses (>10years) having higher concentrations than newer houses (<10years) and houses located near the major road (<10 m) were found to have relatively higher heavy metal levels in household dust.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.2
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• pp.135-145
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• 2023

Aggregate is used to produce stable materials like concrete and asphalt and is fundamental to meet the social needs of housing, industry, road, energy and health. A total of 42.35 billion tons of aggregate were produced in 2021 worldwide, an increase of 0.91% compared to the previous year. Among them, 2 billion tons were produced in China, India, European Union and United States, making up to 71.75% of the share. South Korea has witnessed a constant increase in aggregate production, overtaking Mexico and Japan for seventh place with 390 million tons and 0.85% of the share. The industrial sand and gravel produced globally amounted to 352.66 million tons. The top seven countries with the highest production were China, United States, Netherlands, Italy, India, Turkey and France, and their production exceeded 10 million tons and held a share of 74.69%. Exports of natural rock recorded $21.68 billion in 2021, increased by $2.3 billion compared to the previous year, while exports of artificial rock increased by $2.66 billion to $13.59 billion. Exports of sand reached $1.71 billion with United States, Netherlands, Germany and Belgium being the four countries with the highest exports of sand. The four countries exported more than $100 million in sand and took up 57.70% of the total amount. Exports of gravel totaled $2.75 billion, with China, Norway, Germany, Belgium, France and Austria in the lead, making up to 48.30% of the total share. The aggregate quarry started to surge in the 1950s due to the change in people's lifestyle such as population growth, urbanization and infrastructure delvelopment. Demand for aggregate is also skyrocketing to prevent land reclamation and flood caused by sea-level rise. Demand for aggregate, which was around 24 gigatons in 2011, is expected to double to 55 gigatons in 2060. However, it is likely that aggregate extraction will heavily damage the ecosystem and the world will eventually face a shortage of aggregate followed by tense social conflict.