• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.38.2-38.2
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• 2011

In general, solar filaments are divided into two parts; one spine and several barbs. Barbs are seen as if they protrudes from the spine. Until now there are many controversies about the structures of a barb and spine. Recently, New Solar Telescope was installed at Big Bear Solar Observatory. Its clear aperture is about 1.6m and it is the largest telescope among ground-based solar telescopes. Fast Imaging Solar Spectrograph (FISS) developed by SNU and KASI was also installed in a vertical optical table in Coude room of the 1.6m NST. It is simultaneously able to record two lines; $H{\alpha}$ and Ca II 8542A lines. On 2010 July 29, we observed a portion of a solar filament located in northern hemisphere with FISS and it had a well-developed barb. And we also observed a potion of a spine. In order to analyze the data, we used the cloud model and obtained physical quantities of the solar filament. Temperature of the solar lament ranged between 4500K and 12000K and non-thermal velocity ranged between 3km/s and 6.5km/s. By comparing physical quantities of a barb and spine, we try to understand these structures of the solar filament.

• Journal of Internet of Things and Convergence
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• v.1 no.1
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• pp.21-25
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• 2015

The Internet of Thing (IoT) will be a very important core technology to implement Intelligent Smart Home Network and it will take charge of an important role connected to Smart Phone, Cloud Computing in the Ubiquitous environment. In this paper, Internal Autonomous Regulation by human autonomic nervous system was studied and its core mechanism was applied to the design of IoT based Autonomous Regulation System for Intelligent Smart Home Network. We proposed an autonomous regulating mechanism in which the factors of Temperature, Humidity and Illumination are automatically adjusted as they communicate with the connected things.

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.445-452
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• 2007

We proposed a detection scheme of weather information in CCTV video images in this paper. The scheme obtains the RGB distribution of shiny day and divide a target image into cloud, rain, snow and for RGB distributions. shiny day RGB distribution. Our scheme designed systematically to detection and separation special characteristics of images from complex weather information. Our algorithm has less overhead than the previous methods to use weather database DB at the view of time and space. And our algorithm can be use in real world system with low cost of implementation. Also, our algorithm use informations of temperature, humidity, date, and time to detect the information of weather with high quality.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.371-375
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• 2018

Objectives: To successfully introduce an Internet of Things (IoT) system in the hospital environment, this study aimed to identify issues that should be considered while implementing an IoT based on a user demand survey and practical experiences in implementing IoT environment monitoring systems. Methods: In a field test, two types of IoT monitoring systems (on-premises and cloud) were used in Department of Laboratory Medicine and tested for approximately 10 months from June 16, 2016 to April 30, 2017. Information was collected regarding the issues that arose during the implementation process. Results: A total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility. Conclusions: It is expected that, with sufficient consideration of the various issues derived from this study, IoT monitoring systems can be applied to other areas, such as device interconnection, remote patient monitoring, and equipment/environmental monitoring.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.4
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• pp.35-41
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• 2019

Contrail is a kind of cloud that is formed during the flight by vapor condensation of engine exhaust in a cold atmospheric condition. Owing to the negative effects of contrails on the environment and in military applications, several studies for contrail mitigation had been performed in developed countries. The goal of this research is to design a lab-scale contrail generator, and to validate the contrail mitigation technology suggested by previous studies. The contrail generator was made using superheated vapor and a low temperature wind tunnel. Using this generator, the ineffectiveness of ethanol and surfactant suggested in the previous paper on contrail mitigation was found experimentally.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.33-42
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• 2022

One of the most pressing socioeconomic problems confronting humanity on a worldwide scale is food security, particularly in light of the expanding population and declining land productivity. These causes have increased the number of people in the world who are at risk of starving and have caused the natural ecosystems to degrade at previously unheard-of speeds. Happily, the Internet of Things (IoT) development provides a glimmer of light for those worried about food security through smart agriculture-a development that is particularly relevant to automating food production operations in order to reduce labor expenses. When compared to conventional farming techniques, smart agriculture has the benefit of maximizing resource use through precise chemical input application and regulation of environmental factors like temperature and humidity. Farmers may make data-driven choices about the possibility of insect invasion, natural disasters, anticipated yields, and even prospective market shifts with the use of smart farming tools. The technical foundation of smart agriculture serves as a potential response to worries about food security. It is made up of wireless sensor networks and integrated cloud computing modules inside IoT.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.178-180
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• 2021

In this paper, we design a system to monitor environmental data in fish farms in real-time and provide machine learning-based prediction services to prevent damage on fish farms caused by changes in the sea environment. The proposed system will install an IoT device module consisting of sensors that can measure hydrogen concentration, salinity, dissolved oxygen, and water temperature, which can be transferred to Cloud DB using LTE or LoRa communication technology and then monitor the real-time condition through a web or mobile application. In addition, it has a function to prepare for changes within the environment of fish farms by applying machine learning-based prediction technology using collected data.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

• Atmosphere
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• v.26 no.4
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• pp.495-508
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• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.