• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.150-157
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• 2019

This study was carried out to investigate at the micrometeorology change of the orchard according to the net installation. Two weather stations were installed at the inside of the anti-insect nets(2 mm, 4 mm), one was installed at the outside(control). They were observed the temperature, humidity, wind speed and solar radiation from April to September 2018. Daily mean temperature at the experimental group was higher than control group by $0.2^{\circ}C$. Daily mean humidity at the experimental group was higher than control group by 3.5 to 4.7%. Daily mean the solar radiation at the experimental group(2 mm) was lower than control group by 50%. The wind speed was decreased from 12% to 50% of the external wind speed at 4 mm, and from 25% to 59% at 2 mm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.687-693
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• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea. showed: (1) average RMSE error of the approach was 0.435 pixel; (2) the average number of matching points was over 25,573; (3) the average processing time was 4.2 min per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.591-596
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• 2022

Various COVID-19 quarantine guidelines and measures are being taken by country at the WHO, but the number of confirmed cases has not decreased significantly. In order to prevent the inflow and outflow of COVID-19 through individual droplets, it is mandatory to wear a mask anytime, anywhere. However, as virus bacteria entering the mask amplify, it pollutes the mask and causes a disgusting smell. In this paper, a new method of preventing the spread of COVID-19 was proposed by sterilizing the mask with a dental gait spray introduced into the mask that has been used for a long time. Dental gargle water is usually produced by electrolysis of tap water, and the unstable ion water (HOCl) dissolved in water penetrates the cell barrier of various viruses and fails to act in its nucleus, causing water to self-purify. As a result of the experiment, when the mask used for a long time was washed with gargle water spray, the washed mask was dried after 10 minutes, and the smell of virus droplets or saliva almost disappeared. In particular, as a result of MOS testing the fit of the subjects who participated in the mask cleaning, it was excellent at 4.4 on average. Therefore, the mask was disposable, but if the spray was washed in the proposed method more than twice a day, the mask could be used in a comfortable state for more than a week.

• Progress in Medical Physics
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• v.26 no.4
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• pp.273-279
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• 2015

Recently, medical devices for a smart health development and dissemination are becoming increasingly frequent use of devices and their's thermal stability, durability, the external splendors are required. Industrial demand for smart health medical devices uses high-functioning electrodeposition technology that expressed pearl-like feeling is rapidly increasing. Generally, pearl powder is added to electrodeposition pigment in order to form a coating which shows pearl-like feeling. On the other hand, the electrodeposition technology for the smart health medical devices uses a new method that can express pearl-like feeling without using pearl powder. In this study, we was tried to find out the most appropriate texture formation, the right dilution recipe. We've tried various ptoportions of pigments (ED-600, ED-600S, ED-MX, ED-M). As a result, we found out that ED-600 and ED-MX (15% solid) in appropriate concentration showed the best adherence rate. By several samples tests and experiments which include washing the fixed pigment in various temperature levels ($20{\sim}40^{\circ}C$) and drying, we were able to get the best results in drying condition of $180{\pm}10^{\circ}C$ and $30{\pm}5min$. The research showed that it is mush more competitive and cost effective to use the new method that produces natural pearl-like feeling on the surface than to add pearl powder to high-functioning electrodeposition pigment, which is a method that has been used for the smart health medical devices so far.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.487-496
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• 2020

For the purpose of next generation technology for robot perfomances, a RAoRA (Robot Actor on Robot Arm) structure was proposed using a robot arm joined with a humanoid robot actor. Mechanical analysis, machine design and fabrication were performed for motions combined with the robot arm and the humanoid robot actor. Kinematical analysis for 3D model, spline interpolation of positions, motion control algorithm and control devices were developed for movements of the robot actor. Preliminary visualization, simulation tools and integrated operation of consoles were constructed for the non-professionals to produce intuitive and safe contents. Air walk was applied to test the developed platform. The air walk is a natural walk close to a floor or slow ascension to the air. The RAoRA also executed a performance with 5 minute-running time. Finally, the proposed platform of robot performance presented intensive and live motions which was impossible in conventional robot performances.

• Applied Chemistry for Engineering
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• v.23 no.3
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• pp.259-265
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• 2012

Differently from fly ash, the bottom ash produced from thermal power generation has been treated as an industrial waste matter, and almost reclaimed or was applied with the additive of the part concrete. Bottom ash has various problems to use with the aggregate. Bottom ash is lighter than typically the sand or the gravel and it's physical properties (compressive strength etc.) is somewhat low because of high absorptance. In order to manufacture the ash concrete, we used a bottom ash as a main material and a pure sulfur as a binder. In this study, fundamental research methods that vary the grain-size of bottom ash and the ratio of sulfur vs ash were investigated to improve the quality of ash concrete such as compressive strength. Bottom ash in this research which occurs from domestic 4 place power plants, was checked physical and chemical properties. The compressive strength seems the result which simultaneously undergoes an influence in content of the sulfur and Bottom ash grain-size. We got the result of the maximum 92 MPa. The compressive strength was high result for grain size below 1.2 mm and high sulfur content.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.12-19
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• 2012

Recently, the market of skin-care device has been steadily growing up. In this paper, we tried to develop a personal compound stimulus device more competitive than existing products. As the compound stimulus, biochemical stimulus of herbal extraction fluid, thermal stimulus of plate-shaped carbon fiber heater, and optical stimulus of near infrared LED were selected. By some evaluation tests, the thermal stimulation part and the optical stimulation part were found to be developed properly. Additionally, the efficacy of the mixed stimulus of thermal and optical stimulation was tested in C2C12 mouse myoblast. Through RT-PCR analysis, it was found that, by the developed compound stimulus, the expression of collagen I mRNA and collagen III mRNA increased by 4.9 and 1.3 times respectively.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.4
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• pp.253-263
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• 2021

Concrete structures are damaged by aging and external environmental factors. This type of damage is to appear in the form of cracks, to proceed in the form of spalling. Such concrete damage can act as the main cause of reducing the original design bearing capacity of the structure, and negatively affect the stability of the structure. If such damage continues, it may lead to a safety accident in the future, thus proper repair and reinforcement are required. To this end, an accurate and objective condition inspection of the structure must be performed, and for this inspection, a sensor technology capable of detecting damage area is required. For this reason, we propose a deep learning-based image processing algorithm that can detect spalling. To develop this, 298 spalling images were obtained, of which 253 images were used for training, and the remaining 45 images were used for testing. In addition, an improved loss function and data augmentation technique were applied to improve the detection performance. As a result, the detection performance of concrete spalling showed a mean intersection over union of 80.19%. In conclusion, we developed an algorithm to detect concrete spalling through a deep learning-based image processing technique, with an improved loss function and data augmentation technique. This technology is expected to be utilized for accurate inspection and diagnosis of structures in the future.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.