• Journal of Korean Society of Environmental Engineers
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• v.29 no.5
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• pp.577-583
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• 2007

To overcome certain disadvantages of past typical control techniques for toxic contaminants emitted from various industrial processes, the current study was conducted to establish a thermal catalytic system using mesh-type transition-metal platinum(Pt)/stainless steel(SS) catalyst and to evaluate catalytic thermal destruction of five chlorinated hydrocarbons[chlorobenzene(CHB), chloroform(CHF), perchloroethylene (PCE), 1,1,1-trichloroethane(TCEthane), trichloroethylene(TCE)]. In addition, this study evaluated the catalyst poison effect on the catalytic thermal destruction. Three operating parameters tested for the thermal catalyst system included the inlet concentrations, the incineration temperature, and the residence time in the catalyst system. The thermal decomposition efficiency decreased from the highest value of 100% to the lowest value of almost 0%(CHB) as the input concentration increased, depending upon the type of chlorinated compounds. The destruction efficiencies of the four target compounds, except for TCEthane, increased upto almost 100% as the reaction temperature increased, whereas the destruction efficiency for TCEthane did not significantly vary. For the target compounds except for TCEthane, the catalytic destruction efficiencies increased up to 30% to 97% as the residence time increased from 10 sec to 60 sec, but the increase of destruction efficiency for TCEthane stopped at the residence time of 30 sec, suggesting that long residence times are not always proper for thermal destruction of VOCs, when considering the destruction efficiency and operation costs of thermal catalytic system together. Conclusively, the current findings suggest that when applying the transition-metal catalyst for the better destruction of chlorinated hydrocarbons, VOC type should be considered, along with their inlet concentrations, and reaction temperature and residence time in catalytic system. Meanwhile, the addition of high methyl sulfide(1.8 ppm) caused a drop of 0 to 50% in the removal efficiencies of the target compounds, whereas the addition of low methyl sulfide (0.1 ppm), which is lower than the concentrations of sulfur compounds measured in typical industrial emissions, did not cause.

• Journal of agriculture & life science
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• v.46 no.2
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• pp.179-190
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• 2012

This study was performed to obtain a heat saving effect and enhance the efficiency of a greenhouse by using a hot water piping in order to minimize the operating costs of a greenhouse as oil prices continue to rise. This method also reduces the likelihood of accidents caused by snowdrifts in regions with heavy snowfall. In general, the experimental plot was $2.0{\sim}6.0^{\circ}C$ higher than the control plot. When the skylight felt was opened, the minimum temperature was in the range of $3.0{\sim}12.0^{\circ}C$. Therefore, we judged that damage caused by snowdrifts may be prevented partly by active heating. The temperature difference inside of the greenhouse by height was insignificant. The maximum heating load of the greenhouse according to crop was respectively about $37,000kcal{\cdot}h^{-1}$ and $41,700kcal{\cdot}h^{-1}$. During the experiment, the heat value of each designed temperature in the range of the minimum ambient temperature $-11.9{\sim}4.0^{\circ}C$ was about 95,000~322,000 kcal and it was in the range of $6,050{\sim}20,900kcal{\cdot}h^{-1}$. If it is compared with the maximum heating load, it can be shown that about 15~56% of the heating energy can be supplied. The total heat value and the amount of power consumption were 2,629,025 kcal and 677.3 kWh respectively during the experiment. If it is heated with diesel, a fossil fuel, the consumption during the experiment was 291 L and the cost was 331,700won. Total cost of using electric power was about 24,400 won and it is shown that it is about 7.5% of the cost of diesel consumption. Also, if the total amount of power consumption is converted into energy, it is approximately 582,200 kcal and the energy was just about 22% of the total heat value.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.180-187
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• 2022

In order to select suitable light in a plant factory, electric energy use efficiency and light use efficiency should be considered simultaneously to consider operating costs as well as quantitative and functional aspects. The growth characteristics, electric energy use efficiency, light use efficiency by light intensity, LED ratio, and photoperiod conditions were compared together. Light intensity is 60, 130, 230, and 320 µmol·m^-2·s^-1 treatments, and light quality is the mixing ratio of red light and blue light 8:2, 6:4, 4:6, and 2:8 treatments. Photoperiod is 9, 12, 15, and 18 hours treatments based on the daytime. In the light intensity experiment, the growth rate increased as the light intensity increased, but there was no significant difference in the light use efficiency. When comparing the leaf fresh weight per power consumption, only the 320 µmol·m^-2·s^-1 treatment group showed significantly low efficiency, and there was no significant difference in the other treatments, so 230 µmol·m^-2·s^-1, which produced the most, was the most efficient. In the light quality experiment, the ratio of red light and blue light was measured to be high at the same time as the growth rate and light use efficiency in RB 8:2, and there was no significant difference in color difference and flavonoids content, so a Red:Blue ratio of 8:2 was the most suitable condition. In the photoperiod experiment, the longer the photoperiod, the higher the growth rate. However, there was no significant difference in the growth rate over 12 hours of daytime, so 12 hours considering the light consumption efficiency was a suitable condition. Based on the above results, LED light environmental conditions for perilla growth in plant factories were light intensity, light quality, and day length of 230 µmol·m^-2·s^-1 or more, 8:2, and 12 hours or more, respectively.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.263-271
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• 2019

The recent phenomenon of job "Miss Match", which is inconsistent with knowledge in the demand of educational training institutes and industries, has spread to an increase in private education costs for reeducation and employment of new hires, resulting in weak individual job competency and poor employment capability, as well as economic and material waste at the national level. To compensate for these problems, the National Competency Standards(NCS), which are available immediately in practice and look for a standard point of national job competency with the aim of fostering human resources sought by industries, were developed, and even the NCS-based qualification system was launched in line with the stream of times. This study is intended to look into the importance and priority of competency units and competency unit elements at the NCS-based qualification L3 level in the skin care field for an overall check of the NCS-based qualification level at a time when educational institutes are organizing and operating the school curriculums according to the NCS and NCS-based qualification level. And it is attempted to provide basic data for the development of curriculum in fostering professional human resources required by industries. To analyze the needs for competency units and competency unit elements at the L3 level, a survey using AHP method was carried out to a group of field experts and a group of education experts. In addition, the SPSS(Statistical Package for Social Science) ver. 21.0 and Expert Choice 2000, an AHP-only solution was used to do statistical processing through the processes of data coding and data cleaning. The findings showed that there was a partial difference of opinion between a group of field experts and a group of education experts. This indicates that the inconsistencies between educational training institutes and industrial sites should be resolved at this time of change with the aim of fostering field customized human resources with professional skills. Consequently, the solution is to combine jobs at industrial sites and standardized educations of educational institutes with human resources required at industrial sites.