• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.107-116
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• 2020

In this data paper, we share the dataset obtained during 2019 from the test-bed to develop soil moisture estimation technology for upland fields, which was built in Seosan and Taean, South Korea on May 3. T his dataset includes various eco-hydro-meteorological variables such as soil moisture, evapotranspiration, precipitation, radiation, temperature, humidity, and vegetation indices from the test-bed nearby the Automated Agricultural Observing System (AAOS) in Seosan operated by the Korea Meteorological Administration. T here are three remarkable points of the dataset: (1) It can be utilized to develop and evaluate spatial scaling technology of soil moisture because the areal measurement with wide spatial representativeness using a COSMIC-ray neutron sensor as well as the point measurement using frequency/time domain reflectometry (FDR/TDR) sensors were conducted simultaneously, (2) it can be used to enhance understanding of how soil moisture and crop growth interact with each other because crop growth was also monitored using the Smart Surface Sensing System (4S), and (3) it is possible to evaluate the surface water balance by measuring evapotranspiration using an eddy covariance system.

• Journal of Wetlands Research
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• v.22 no.4
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• pp.312-320
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• 2020

Various LIDs with natural water circulation function are applied to reduce urban environmental problems and environmental impact of development projects. However, excessive Infiltration and evaporation of LID facilities dry the LID internal soil, thus reducing plant and microbial activity and reducing environmental re duction ability. The purpose of this study was to develop a real-time measurement system with complex sensors to derive the management plan of LID facilities. The test of measurable sensors and Internet of Things (IoT) application was conducted in artificial wetlands shaped in acrylic boxes. The applied sensors were intended to be built at a low cost considering the distributed LID and were based on Arduino and Raspberry Pi, which are relatively inexpensive and commercialized. In addition, the goal was to develop complex sensor measurements to analyze the current state o f LID facilities and the effects of maintenance and abnormal weather conditions. Sensors are required to measure wind direction, wind speed, rainfall, carbon dioxide, Micro-dust, temperature and humidity, acidity, and location information in real time. Data collection devices, storage server programs, and operation programs for PC and mobile devices were developed to collect, transmit and check the results of measured data from applied sensors. The measurements obtained through each sensor are passed through the Wifi module to the management server and stored on the database server in real time. Analysis of the four-month measurement result values conducted in this study confirmed the stability and applicability of ICT technology application to LID facilities. Real-time measured values are found to be able to utilize big data to evaluate the functions of LID facilities and derive maintenance measures.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.285-290
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• 2016

In rural area, our farmers confront decreasing benefits owing to imported crops and increased cost. Recently, the government encourage the 6th Industry that merges farming, rural resources, and information and communication technology. Therefor the government makes an investment in supplying 'smart greenhouse' in which a farmer monitor growing crops and environment information to control growing condition. The objective of this study is developing an Moving Monitor and Control System for crops in green House. This system includes a movable sensing unit, a controlling unit, and a server PC unit. The movable sensing unit contains high resolution IP camera, temperature and humidity sensor and WiFi repeater. It rolls on a rail hanging beneath the ceiling of a green house. The controlling unit contains embedded PC, PLC module, WiFi router, and BLDC motor to drive the movable sensing unit. And the server PC unit contains a integrated farm management software and home pages and databases in which the images of crops and environment informations. The movable sensing unit moves widely in a green house and gathers lots of information. The server saves these informations and provides them to customers with the direct commercing web page. This system will help farmers to control house environment and sales their crops in online market. Eventually It will be helpful for farmers to increase their benefits.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.719-729
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• 2017

Recently, the field of agriculture has been gaining a new leap with the integration of ICT technology in agriculture. In particular, smart farms, which incorporate the Internet of Things (IoT) technology in agriculture, are in the spotlight. Smart farm technology collects and analyzes information such as temperature and humidity of the environment where crops are cultivated in real time using sensors to automatically control the devices necessary for harvesting crops in the control device, Environment. Although smart farm technology is paying attention as if it can solve everything, most of the research focuses only on increasing crop yields. This paper focuses on the development of a system architecture that can harvest high quality crops at the optimum stage rather than increase crop yields. In this paper, we have developed an architecture using apple trees as a sample and used the color information and weight information to predict the harvest time of apple trees. The simple board that collects color information and weight information and transmits it to the server side uses Arduino and adopts model-driven development (MDD) as development methodology. We have developed an architecture to provide services to PC users in the form of Web and to provide Smart Phone users with services in the form of hybrid apps. We also developed an architecture that uses beacon technology to provide orchestration information to users in real time.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.437-444
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• 2017

Electric Pole is a supporting beam used for power transmission/distribution which is sensitive to external force change of environmental factors. Therefore, power facilities have many difficulties in terms of maintenance/conservation from external environmental changes and natural disasters that cause a great economic impact. The aerial wire cause elasticity due to the influence of temperature, or factors such as wind speed and wind direction, that weakens the electric pole. The situation may lead to many safety risk in day-to-day life. But, the safety assessment of the pole is carried out at the design stage, and aftermath is not considered. For the safety and maintenance purposes, it is very important to analyze the influence of weather factors on external forces periodically. In this paper, we analyze the acceleration data of the sensor nodes installed in electric pole for maintenance/safety purpose and use Kalman filter as noise compensation method. Fast Fourier Transform (FFT) is performed to analyze the influence of each meteorological factor, along with the meteorological factors on frequency components. The result of the analysis shows that the temperature, humidity, solar radiation, hour of daylight, air pressure, wind direction and wind speed were influential factors. In this paper, the influences of meteorological factors on frequency components are different, and it is thought that it can be an important factor in achieving the purpose of safety and maintenance.

• Journal of Environmental Health Sciences
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• v.46 no.4
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• pp.444-456
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• 2020

Objectives: This study developed an index for the indoor air quality management of city buses to allow the provision of indoor air quality information to city bus users. Methods: Nine city buses in Seoul were measured for PM₁₀, PM_2.5, CO₂, temperature, and relative humidity through IoT sensors. Big data collected through the sensors was analyzed to identify indoor air quality on city buses and graded through the index. Results: As a result of dividing the measured city bus data into five grades through the IAQ index, PM₁₀ was rated "good" for 30.4% of the total measured values, and 9.2% were rated "risky". For PM_2.5, 67.7 percent were rated "good" and 0.4 percent were rated "risky". For CO₂, 0.9% were 'good' and 1.1% were 'risky'. The results of the classification through the IAQ index for city buses showed that the impact of good, normal, sensitive, bad, and dangerous were 2.7, 38.8, 46.0, 12.4, and 0.1%, respectively. According to the analysis by measurement area, Seocho-gu, Gangnam-gu, Seongdong-gu, Gwangjin-gu, and Dobong-gu are "normal" and other areas (Seodaemoon-gu, Jongno-gu, Yongsan-gu, Jung-gu, Seongbuk-gu, Dongdaemun-gu, Junggye-gu, Gangbuk-gu, and Nowon-gu) are all rated "sensitive". Conclusions: When analyzing cases where PM₁₀ and CO₂ indices are in the "bad" zone, the concentration is generally found to increase during rush hour, during which there are a large number of passengers. It is expected that indoor air quality management in vehicles will be necessary during rush hour.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.536-539
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• 2018

In this paper, we propose a system that analyzes environment information by using deep learning and then provides a suitable environment for users by predicting environmental information change. As the level of living improves, interest in improving the quality of life is increasing. In particular, as the air quality deteriorated due to the recent occurrence of dust, smog, fine dust, and ultrafine dust, the indoor air quality as well as the outdoor air became a serious problem. The increase of indoor pollution due to the lack of ventilation and the use of chemicals is a serious problem for modern people who have a lot of indoor living. In order to solve this indoor air pollution, a system has been proposed that measures the state of air quality through sensors and maintains proper temperature and humidity. However, existing system has a difficulty to apply most of the atmospheric environment information to various users depending on sensors only. The system proposed in this paper predicts the indoor environment by analyzing the indoor pollution information collected through the sensor using the deep learning. Then, the predicted indoor environment is modeled and learned in this system, and the environment suitable for the user is suggested. Afterwards, the system receives feedback from the user and repeats the process of re-learning the proposed environment so that it can create the optimal environment for the user.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.173-180
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• 2015

A passive type of fence security system was developed, which was based on electric charge detection technique. The implemented fence security system was installed at outskirts of greenhouse laboratory in the University of Seoul. The purpose of this research is to minimize false alarms by analyzing environmental noise. The existing system determines the intrusion alarm by analyzing the power of amplified signal, but the alarm was seriously affected by natural strong wind and heavy rainfall. The SAU(Signal Analysis Unit) sends input signals to remote server which displays intrusion alarm and stores all the information in database. The environmental noise such as temperature, humidity and wind speed was separately gathered to analyze a correlation with input signal. The input signal was analyzed for frequency characteristic using FFT(Fast Fourier Transform) and the algorithm that differentiate between intrusion alarm and environmental noise signal is improved. The proposed algorithm is applied for the site for one month as the same as the existing algorithm and the false alarm data was gathered and analyzed. The false alarm number was decreased by 98% after new algorithm was applied to the fence. The proposed algorithm improved the reliability at the field regarding environmental noise signal.

• Journal of the Korean Wood Science and Technology
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• v.35 no.6
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• pp.65-72
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• 2007

The VOC (volatile organic compound) analyzer is devised to measure the four main aromatic hydrocarbon gases: toluene, ethylbenzene, xylene and styfene. It is not affected by ambient temperature and humidity. In addition, standby and measuring time of VOC Analyzer is a short as below 30 min and 8 min, respectively. Since the semiconductor gas sensor is supersensitive to gas components, it is not necessary to use a conventional gas concentrator or other complicated equipment. In this study, VOC emission behavior from 4 types paints (lacquer, urethane vanish, water-base paint, enamel paint) for wood-based panel was investigated using VOC Analyzer. After a specimen was spreaded on aluminum foil ($6.32{\times}6.32cm$) in $3{\ell}$ polyester bag, after 24 hours we could measure maximum VOC emission level that is a stabilized VOC value. Xylene of VOCs was high emitted from lacquer, urethane vanish and water-based paint, and TVOC (Toluene + Ethylbenzene + Xylene + Styrene) of lacquer was the highest emission concentration than another.

• Journal of the Korean earth science society
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• v.38 no.2
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• pp.129-140
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• 2017

Global solar radiation was calculated in this research using ground-base measurement data, meteorological satellite data, and GWNU (Gangneung-Wonju National University) solar radiation model. We also analyzed the accuracy of the GWNU model by comparing the observed solar radiation according to the total cloud cover. Our research was based on the global solar radiation of the GWNU radiation site in 2012, observation data such as temperature and pressure, humidity, aerosol, total ozone amount data from the Ozone Monitoring Instrument (OMI) sensor, and Skyview data used for evaluation of cloud mask and total cloud cover. On a clear day when the total cloud cover was 0 tenth, the calculated global solar radiations using the GWNU model had a high correlation coefficient of 0.98 compared with the observed solar radiation, but root mean square error (RMSE) was relatively high, i.e., $36.62Wm^{-2}$. The Skyview equipment was unable to determine the meteorological condition such as thin clouds, mist, and haze. On a cloudy day, regression equations were used for the radiation model to correct the effect of clouds. The correlation coefficient was 0.92, but the RMSE was high, i.e., $99.50Wm^{-2}$. For more accurate analysis, additional analysis of various elements including shielding of the direct radiation component and cloud optical thickness is required. The results of this study can be useful in the area where the global solar radiation is not observed by calculating the global solar radiation per minute or time.