• Journal of the Korean Society of Earth Science Education
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• v.14 no.2
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• pp.202-211
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• 2021

The purpose of this study is to find out the Learning elements in 'Curriculum Reconstruction' of Elementary Pre-service Teachers in Connection with 'The weather and our daily life'. The pre-service teachers who participated in the study formed a research group of 29 students in 2nd grade who are attending the first semester of A university of education and taking courses in 'teaching research 1'. Participants described the learning topics and contents they would like to add to curriculum 'The weather and our daily life'. Each response was analyzed and classified based on scientific terms related to weather or climate. The results of the study were as follows. First, there were three learning topics related to weather, such as water phenomena in the atmosphere, fine dust and yellow dust phenomena, and light or electricity phenomena, and two topics related to climate such as abnormal climate and global warming. Second, interest in the problem of fine dust and yellow dust in the atmosphere was relatively high. Third, the interest in learning in the knowledge area was relatively higher than in the learning in the function or attitude area. Through these research results, it can be confirmed that it is necessary to develop a climate change or climate crisis education program.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.528-531
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• 2021

Nowadays recommendation systems are so ubiquitous, where our many decisions are being done by the means of them. We can see recommendation systems in all areas of our daily life. Therefore the research of this sphere is still so active. So far many research papers were published for clothing recommendations as well. In this paper, we propose the clothing-recommendation system according to user emotion and weather information. We used social media to analyze users' 6 basic emotions according to Paul Eckman theory and match the colour of clothing. Moreover, getting weather information using visualcrossing.com API to predict the kind of clothing. For sentiment analysis, we used Emotion Lexicon that was created by using Mechanical Turk. And matching the emotion and colour was done by applying Hayashi's Quantification Method III.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.203-211
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• 2006

Today, Internet covers a world wide range and most appliances of our life are linked to network from enterprise server to household electric appliance. Therefore, the importances of administrable framework that can grasp network state by real-time is increasing day by day. Our objective in this paper is to describe a network weather report framework that monitors network traffic and performance state to report a network situation including traffic status in real-time. We also describe a mobile agent architecture that collects state information in each network segment. The framework could inform a network manager of the network situation. Through the framework. network manager accumulates network data and increases network operating efficiency.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.211-225
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• 2020

The purpose of this study was to investigate and analyze pre-service teachers' eye-movement in science textbooks in the learning process, whether they perceived "arrow" presented in textbooks, and changes in eye-movement by arrows. For the study, an eye-tracker, a eye-movement tracking device, was used, and 10 pre-service teachers attending a teacher training college were selected and conducted. The science textbook unit used in the research was the "Weather and Our Life" unit, one of the areas of earth science. As a result of the study, first, it was investigated that pre-service teachers devote more time to texts rather than illustrations in the learning process of science textbooks. Second, it was analyzed that pre-service teachers did not pay attention to the "arrow" presented in science textbooks. Third, it was confirmed that in order for "affordance of the eye-movement by arrow" to occur, sufficient concentration on "arrow" should be made. These findings suggest the importance of the learner's visual attention to learning elements in science textbooks such as "arrow". In addition, it suggests the importance of developing a textbook editing design that can induce visual attention to learning elements in textbooks through eye-movement research data for effective learning.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.772-779
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• 2015

Advancing information technology have rapidly changed our service environment of life, culture, and industry. Computer information communication system is applied in medical, health, distribution, and business transaction. Smart is using new information by combining ability of computer and information. Although agriculture is labor intensive industry that requires a lot of hands, agriculture is becoming knowledge-based industry today. In agriculture field, computer communication system is applied on facilities farming and machinery Agricultural. In this paper, we designed and implemented application that provides personalized agriculture related information at the actual farming field. Also, this provides farmer a system that they can directly auction or sell their produced crops. We designed and implemented a system that parsing information of each seasonal, weather condition, market price, region based, crop, and disease and insects through individual setup on ubiquitous environment using location-based sensor network and processing data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.133-144
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• 2017

Recently, weather information is getting closer to our real life, and it is a very important factor especially in the transportation field. Although the damage caused by the abnormal climate changes around the world has been gradually increased and the correlation between the road risk and the possibility of traffic accidents is very high, the domestic research has been performed at the level of basic research. The Purpose of this study is to develop a risk map for the road hazard forecasting service of weather situation by linking real - time weather information and traffic information based on accident analysis data by weather factors. So, we have developed a collection and analysis about related data, processing, applying prediction models in various weather conditions and a method to provide the road hazard map for national highways and provincial roads on a web map. As a result, the road hazard map proposed in this study can be expected to be useful for road managers and users through online and mobile services in the future. In addition, information that can support safe autonomous driving by continuously archiving and providing a risk map database so as to anticipate and preemptively prepare for the risk due to meteorological factors in the autonomous driving vehicle, which is a key factor of the 4th Industrial Revolution, and this map can be expected to be fully utilized.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.62.1-62.1
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• 2021

Interstellar objects originate from other stellar systems. Thus, they contain information about the stellar systems that cannot be directly explored; the information includes the formation and evolution of the stellar systems and the possibility of life. The examples observed so far are 1l/Oumuamua in 2017 and 2l/Borisov in 2019. In this talk, we present the possibility of detecting interstellar objects using the Heliospheric Imagers designed for space weather research and forecasting by observing solar wind in interplanetary space between the Sun and Earth. Because interstellar objects are unpredictable events, the detection requires observations with wide coverage in spatial and long duration in temporal. The near-real time data availability is essential for follow-up observations to study their detailed properties and future rendezvous missions. Heliospheric Imagers provide day-side observations, inaccessible by traditional astronomical observations. This will dramatically increase the temporal and spatial coverage of observations and also the probability of detecting interstellar objects visiting our solar system, together with traditional astronomical observations. We demonstrate that this is the case. We have used data taken from Solar TErrestrial RElation Observatory (STEREO)/Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) HI-1. HI-1 is off-pointed from the Sun direction by 14 degrees with 20 degrees of the field of view. Using images observed from 2007 to 2019, we have found a total of 223 small objects other than stars, galaxies, or planets, indicative of the potential capability to detect interstellar objects. The same method can be applied to the currently operating missions such as the Parker Solar Probe and Solar Orbiter and also future L5 and L4 missions. Since the data can be analyzed in near-real time due to the space weather purposes, more detailed properties can be analyzed by follow-up observations in ground and space, and also future rendezvous missions. We discuss future possible rendezvous missions at the end of this talk.

• Journal of The Korean Astronomical Society
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• v.36 no.spc1
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• pp.125-133
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• 2003

New Jersey Institute of Technology (NJIT), in collaboration with the University of Hawaii (UH), is upgrading Big Bear Solar Observatory (BBSO) by replacing its principal, 65 cm aperture telescope with a modern, off-axis 1.6 m clear aperture instrument from a 1.7 m blank. The new telescope offers a significant incremental improvement in ground-based infrared and high angular resolution capabilities, and enhances our continuing program to understand photospheric magneto-convection and chromospheric dynamics. These are the drivers for what is broadly called space weather - an important problem, which impacts human technologies and life on earth. This New Solar Telescope (NST) will use the existing BBSO pedestal, pier and observatory building, which will be modified to accept the larger open telescope structure. It will be operated together with our 10 inch (for larger field-of-view vector magnetograms, Ca II K and Ha observations) and Singer-Link (full disk H$\alpha$, Ca II K and white light) synoptic telescopes. The NST optical and software control design will be similar to the existing SOLARC (UH) and the planned Advanced Technology Solar Telescope (ATST) facility led by the National Solar Observatory (NSO) - all three are off-axis designs. The NST will be available to guest observers and will continue BBSO's open data policy. The polishing of the primary will be done in partnership with the University of Arizona Mirror Lab, where their proof-of-concept for figuring 8 m pieces of 20 m nighttime telescopes will be the NST's primary mirror. We plan for the NST's first light in late 2005. This new telescope will be the largest aperture solar telescope, and the largest aperture off-axis telescope, located in one of the best observing sites. It will enable new, cutting edge science. The scientific results will be extremely important to space weather and global climate change research.

• Korean Journal of Ecology and Environment
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• v.38 no.4 s.114
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• pp.494-502
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• 2005

Antialgal bacteria and ciliates were tested alone and in combination for their abilities to decrease the densities of the warm-weather cyanobacterium, Microcystis aeruginosa, and the cold-weather centric diatom, Stephanodiscus hantzschii. Our results indicate that the density of M. aeruginosa was effectively suppressed by the bacterium, Streptomyces neyagawensis, and the heterotrich ciliate, Stentor roeselii. However, co-treatment with both bio-agents stimulated the algal density rather than decreasing it, suggesting that S. neyagawensis and S. roeselii may have an antagonistic relationship. Additional experiments revealed that the density of S. hantzschii was effectively suppressed by the bacterium, Pseudomonas putida, and by the above mentioned strain of S. roeselii. Co-treatment with both bio-agents had a higher antialgal activity than treatment with each alone, indicating that the bio-agents may act synergistically. These results suggest that the anti-alge efficacy of co-treatment with multiple biological control agents is likely to differ depending on the bio-agents and target organisms.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.