• Journal of Platform Technology
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• v.8 no.4
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• pp.59-69
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• 2020

Recently, the seriousness of ultrafine dust and fine dust has emerged as a national disaster, but small and medium-sized cities in provincial areas lack fine dust monitoring stations compared to their area, making it difficult to manage fine dust. Although the computing resources for collecting and processing fine dust data are not large, it is necessary to utilize cloud and private and public data to share data. In this paper, we proposed a small edge computing system that can measure fine dust, ultrafine dust and temperature and humidity and process it to provide real-time control of fine dust and service to the public. Collecting fine dust data and using public and private data to service fine dust ratings is efficient to handle with edge computing using raspberry pie because the amount of data is not large and the processing load is not large. For the experiment, the experiment system was constructed using three sensors, raspberry pie and Thinkspeak, and the fine dust measurement was conducted in northern part of kyongbuk region. The results of the experiment confirmed the measured fine dust measurement results over time based on the GIS data of the private sector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.766-773
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• 2021

This paper presents an implementation result and performance evaluation of pavilion management services that does not only provide real-time status of the pavilion in the forest but also prediction services through machine learning. The developed hardware prototype detects whether the pavilion is occupied using a motion detection sensor and then sends it to a cloud database along with location information, date and time, temperature, and humidity data. The real-time usage status of the collected data is provided to the user's mobile application. The performance evaluation confirms that the average response time from the hardware module to the applications was 1.9 seconds. The accuracy was 99%. In addition, we implemented a pavilion availability prediction service that applied a machine learning-based SVM (Support Vector Model) model to collected data and provided it through mobile and web applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.374-380
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• 2022

This paper shows the results of a study that predicts changes in seawater conditions in sea farms using machine learning-based long short term memory (LSTM) algorithms. Hardware was implemented using dissolved oxygen, salinity, nitrogen ion concentration, and water temperature measurement sensors to collect seawater condition information from sea farms, and transferred to a cloud-based Firebase database using LoRa communication. Using the developed hardware, seawater condition information around fish farms in Tongyeong and Geoje was collected, and LSTM algorithms were applied to learning results using these actual datasets to obtain predictive results showing 87% accuracy. Flask and REST APIs were used to provide users with predictive results for each of the four parameters, including dissolved oxygen. These predictive results are expected to help fishermen reduce significant damage caused by fish group death by providing changes in sea conditions in advance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.547-554
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• 2022

Recently, the field of telemedicine is growing rapidly due to the COVID-19 pandemic. However, the cost of telemedicine services is relatively high, since cloud computing, video conferencing, and cyber security should be considered. Therefore, in this paper, we design and implement a cost-effective P2P-based telemedicine system. It is implemented using the widely used the open source computing platform, Raspberry Pi, and P2P network that frees users from security problems such as the privacy leakage by the central server and DDoS attacks resulting from the server/client architecture and enables trustworthy identifying connection system using SSL protocol. Also it enables users to check the other party's status including body temperature in real time by installing a thermal imaging camera using Raspberry Pi. This allows several medical diagnoses that requires visual aids. The proposed telemedicine system will popularize telemedicine service and meet the ever-increasing demand for telemedicine.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1653-1661
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• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.279-286
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• 2013

This paper introduces a new method for identification of building energy performance problems. The presented method is based on automated analysis and visualization of deviations between actual and expected energy performance of the building using EPAR (Energy Performance Augmented Reality) models. For generating EPAR models, during building inspections, energy auditors collect a large number of digital and thermal imagery using a consumer-level single thermal camera that has a built-in digital lens. Based on a pipeline of image-based 3D reconstruction algorithms built on GPU and multi-core CPU architecture, 3D geometrical and thermal point cloud models of the building under inspection are automatically generated and integrated. Then, the resulting actual 3D spatio-thermal model and the expected energy performance model simulated using computational fluid dynamics (CFD) analysis are superimposed within an augmented reality environment. Based on the resulting EPAR models which jointly visualize the actual and expected energy performance of the building under inspection, two new algorithms are introduced for quick and reliable identification of potential performance problems: 1) 3D thermal mesh modeling using k-d trees and nearest neighbor searching to automate calculation of temperature deviations; and 2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed EPAR v2.0 modeling method is validated on several interior locations of a residential building and an instructional facility. Our empirical observations show that the automated energy performance analysis using EPAR models enables performance deviations to be rapidly and accurately identified. The visualization of performance deviations in 3D enables auditors to easily identify potential building performance problems. Rather than manually analyzing thermal imagery, auditors can focus on other important tasks such as evaluating possible remedial alternatives.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.

• Journal of the Korean earth science society
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• v.21 no.6
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• pp.703-718
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• 2000

In order to investigate interannual variation of total ozone and reflectivity over the globe, Nimbus-7/TOMS data were used on the monthly mean and its anomaly for the period of 1979-92. This study also examined MSU channel 4(Ch4; lower-stratosphere) brightness temperature data and two model reanalyses of NCEP and GEOS to compare the ozone variation with atmospheric thermal condition. In addition, the MSU channel 1(Ch1 ; lower-troposphere) brightness temperature was used to compare with the reflectivity. The ozone showed strong annual cycle with downward trend(-6.3${\pm}$0.6 DU/decade) over the globe, and more distinct response to volcanic eruption than El Ni${\tilde{n}$o. The relationship between total ozone and MSU Ch4 observation, and between the ozone and model reanalyses of lower stratosphere temperature showed positive correlation(0.2-0.7) during the period of 1980-92. Reflectivity increased interannually by 0.2${\pm}$0.06%/decade over the globe during the above period and reflected El Ni${\tilde{n}$o(1982-83, 1991-92) well. Its variability in annual cycle was remarkably smaller in tropics than in higher latitudes. This is inferred due to cloud suppression and tropical upwelling regions. Reflectivity correlated negatively(-0.9) to the Ch1 temperature over the globe, but positively(0.2) over tropical ocean. The positive value over the ocean results from the effect of microwave emissivity which increases the Ch1 temperature with enhanced hydrometeor activity. Significant correlations between total ozone and the Ch4 temperature, and between reflectivity and the Ch1 Suggest that the TOMS data may use valuably to better understand the feedback mechanism of climate change.

• Korean Chemical Engineering Research
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• v.40 no.6
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• pp.703-708
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• 2002

High pressure phase behavior data for poly(propyl acrylate) and poly(propyl methacrylate) with supercritical $CO_2$, ethylene, propane, butane, propylene, 1-butene, dimethyl ether, and $CHClF_2$ were measured in the temperature range from $23^{\circ}C$ to $186^{\circ}C$ and at pressures up to 2,400 bar. The cloud point were obtained at dissolved pressure below 2,070, 1,400, 1,880, 450, 2,200, 250, and 150 bar for poly(propyl acrylate) in supercritical $CO_2$, ethylene, propane, propylene, butane, 1-buthen, and dimethyl ether, respectively. The temperature range is $23-175^{\circ}C$. The poly(propyl methacrylate) does not dissolve in $CO_2$ at temperature of $240^{\circ}C$ and pressure 2,900 bar. The poly(propyl methacrylate)-propane, poly(propyl methacrylate)-butane, poly(propyl methacrylate)-propylene, poly(propyl methacrylate)-1-butene, and poly(propyl methacrylate)-$CHClF_2$ systems were dissolved at the pressures less than 2,390 bar, below 2,100 bar, below 570 bar, below 310 bar, below 300 bar, and below 170 bar, respectively. The temperature range shows from 40 to $186^{\circ}C$. The phase behavior of between binary poly(propyl acrylate)-$CO_2$ and poly(propyl acrylate)-dimethyl ether system were measured from upper critical solution temperature region to lower critical solution temperature region with added dimethyl ether concentrations of 5, 15 and 50 wt%.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.342-350
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• 2018

The purpose of this study was to identify the environmental factors that affect the beginning and end of calling by Hyalessa fuscata and Cryptotympana atrata, which are dominant cicada species in the central urban areas of Korea. The study area was Banpo Apartments in Seoul. The research period included two months, being from the end of July to the end of August 2015. We analyzed the start and end time of cicada calling, and on average H. fuscata started calling at 5:21 am and C. atrata started at 7:40 am. The average end time of calling was 6:31 pm for H. fuscata and 7:51 pm for C. atrata. From the scatter plot and box plot results, H. fuscata started calling at 05:00 am, whereas C. atrata consistently stopped calling at 20:00 pm compared to H. fuscata. Multiple regression analysis of the start and end time of cicada calling showed that sunrise time was a factor affecting the start of H. fuscata calling. The end time of H. fuscata calling was affected by sunset time and total cloud cover. The starting time of C. atrata calling was mostly affected by temperature and sunrise time. The effect of temperature was greater than that of sunrise time. The end time of C. atrata calling was strongly affected by sunset time, whereas peak temperature was also shown to affect the end time. From the above results, sunrise and sunset are thought to be the critical factor affecting the start and end time of H. fuscata calling. Therefore, H. fuscata started calling with sunrise, and the end time was also affected by sunset. Temperature was the factor most affecting the start of C. atrata calling and sunset was identified as the factor affecting the end time. Therefore, the start time of C. atrata calling shows variation with daily temperature changes, and C. atrata stop calling simultaneously with sunset.