• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Korean Journal of Construction Engineering and Management
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• v.22 no.4
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• pp.3-10
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• 2021

According to the IPCC, humans are influencing the climate system. Such changes in the climate system can cause problems in the supply of food ingredients in the agricultural field by changing the existing growing environment. To solve this problem, vertical farms can be a good alternative for a stable supply of food ingredients. Although the vertical smart farm pays close attention to maintaining and managing the growing environment of crops, it is difficult to uniformly implement temperature, humidity, illumination, oxygen, and carbon dioxide concentrations in the building space. As a result of conducting computational fluid dynamics analysis to ensure air uniformity, a remarkable result is that it is advantageous to continuously spray suitable carbon dioxide CO2 concentrations for a long period of time for air uniformity in a vertical smart farm. Through this result, it is possible to efficiently plan a growing environment system optimized for a vertical smart farm. Based on this study, if efficient crops are produced by creating an optimized growing environment for vertical smart farms, it will be able to contribute to the development of the agricultural field.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.95-104
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• 2020

In this paper, we propose the real time indoor air quality monitoring and controlling platform on cloud using IoT sensor data such as PM10, PM2.5, CO2, VOCs, temperature, and humidity which has direct or indirect impact to indoor air quality. The system is connected to air ventilator to manage and optimize the indoor air quality. The proposed system has three main parts; First, IoT data collection service to measure, and collect indoor air quality in real time from IoT sensor network, Second, Big data processing pipeline to process and store the collected data on cloud platform and Finally, Big data analysis and visualization service to give real time insight of indoor air quality on mobile and web application. For the implication of the proposed system, IoT sensor kits are installed on three different public day care center where the indoor pollution can cause serious impact to the health and education of growing kids. Analyzed results are visualized on mobile and web application. The impact of ventilation system to indoor air quality is tested statistically and the result shows the proper optimization of indoor air quality.

• Journal of Mushroom
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• v.18 no.4
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• pp.311-316
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• 2020

The effect of the nitrogen content of sawdust medium (1.2~1.8%) and the restriction stage temperature (2, 4, and 6℃) on the growth and storage characteristics of Flammulina velutipes (winter mushroom) were investigated. With increased nitrogen content, the growth days shortened and the yield of the fruiting body increased. The effect of restriction temperature on the growth of the fruiting body differed depending on the nitrogen content. No difference in restriction temperature was evident for T1 with a low nitrogen content of 1.28%. In medium with a nitrogen content ≥1.55%, the yield and length of the pileus and stipe increased as the restriction temperature decreased. The weight loss ratio according to the storage period did not change according to the nitrogen content in the medium. A low weight loss ratio of 1.50 to 1.93% was observed at a restriction temperature <4℃. When T3 with high nitrogen content in the medium was treated at a restriction temperature of 4℃, the fruiting body color values after 31 days of storage were 84.81 (L) and 6.3 (ΔE). This color change was minute compared to that after other treatments. The sensory characteristics score was 5.2 after 31 days of storage, and the quality remained acceptable for consumption.

• Journal of Conservation Science
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• v.38 no.3
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• pp.180-191
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• 2022

Wooden cultural heritage are exposed to the external environment as they and there are many difficulties in conservation due to their location and size. Among them, biological damage caused by termites or mold consumes a lot of money and time. Select and use wood preservatives to prevent biological damage: Wood preservatives were selected and the worst environmental conditions, temperature 60±3℃, humidity 55±5%, and light intensity of 0.35 W/m², were subjected to aging treatment to analyze chemical changes. Through the deterioration process, it was confirmed that the change in color difference decreased in the wood preservative treatment compared to the Control group. As a result of measuring the content of the active ingredient contained in the deterioration process of the wood preservative, it was confirmed that the active ingredient content of Gori22 and Bondex Preserve III was higher than that of the comparative Wood Keeper A. Through experiments, the shelf life and treatment period can be predicted by measuring the extent to which wood preservatives affect the change of wood specimens during the deterioration process and the content of active ingredients. In conclusion, various wood preservatives were prepared, and the possibility of selectively selecting wood preservatives according to the environment, topography and period was presented as a major evaluation factor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.2
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• pp.52-59
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• 2023

The effects of dielectric curing temperature and temperature/humidity treatment conditions on the interfacial adhesion energies between Ti diffusion barrier/polybenzoxazole (PBO) dielectric layers were systematically investigated for Cu redistribution layer applications of fan-out wafer level package. The initial interfacial adhesion energies were 16.63, 25.95, 16.58 J/m² for PBO curing temperatures at 175, 200, and 225 ℃, respectively. X-ray photoelectron spectroscopy analysis showed that there exists a good correlation between the interfacial adhesion energy and the C-O peak area fractions at PBO delaminated surfaces. And the interfacial adhesion energies of samples cured at 200 ℃ decreased to 3.99 J/m² after 500 h at 85 ℃/85 % relative humidity, possibly due to the weak boundary layer formation inside PBO near Ti/PBO interface.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.593-600
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• 2006

Joint opening of jointed concrete pavement is caused by change in temperature and humidity of adjoined slab. The magnitude of joint opening influences on the load-transfer-efficiency and the behavior of sealant. If temperature or humidity decreases, joint opening increases. Generally maximum joint opening of a given joint is predicted by using AASHTO equation. While different magnitudes of joint opening at the individual joints have been observed in a given pavement section, AASHTO equation is limited to predict average joint opening in a given pavement section. Therefore the AASHTO equation may underestimate maximum joint for the half of joint in a given pavement section. Joints showing larger opening than the designed may experience early joint sealant failure, early faulting. Also unexpected spalling may be followed due to invasion of fine aggregate into the joints after sealant pop-off. In this study, the variation of the joint opening in a given pavement section was investigated based on the LTPP SMP data. Factors affecting on the variation are explored. Finally a probabilistic joint opening model is developed. This model can account for the reliability of the magnitude of joint opening so that the designer can select the ratio of underestimated joint opening.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1A
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• pp.53-59
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• 2010

Recently, NDTs (Non-Destructive Techniques) using infrared camera are widely studied for detection of damage and void in RC (reinforced concrete) structures and they are also considered as an effective techniques for maintenance of infrastructures. The temperature on concrete surface depends on material and thermal properties such as specific heat, thermal conductivity, and thermal diffusion coefficient. Different porosity on cement mortar due to different mixture proportions can show different heat behavior in cooling stage. The porosity can affect physical and durability properties like strength and chloride diffusion coefficient as well. In this paper, active thermography which uses flash for heat induction is utilized and thermal characteristics on surface are evaluated. Samples of cement mortar with W/C (water to cement ratio) of 0.55 and 0.65 are prepared and physical properties like porosity, compressive strength, and chloride diffusion coefficient are evaluated. Then infrared thermography technique is carried out in a constant room condition (temperature $20{\sim}22^{\circ}C$ and relative humidity 55-60%). The mortar samples with higher porosity shows higher residual temperature at the cooling stage and also shows reduced critical time which shows constant temperature due to back wall effect. Furthermore, simple equation for critical time of back wall effect is suggested with porosity and experimental constants. These characteristics indicate the applicability of infrared thermography as an NDT for quality assessment of cement based composite like concrete. Physical properties and thermal behavior in cement mortar with different porosity are analyzed in discussed in this paper.