• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.558-569
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• 2020

This study examined the frequency of a broiler's stay in each area as stock density using an ICT-based image analysis technique from the perspective of precision livestock farming (PLF) according to the increase in the domestic broiler farms to understand the normal behavior patterns of broilers by age. The broiler was used in the experimental box (3.3×2.7 m) in a poultry house in Gyeonggi province. The stock densities were 9.5 birds/㎡ (n=85) and 19 birds/㎡ (n=170), respectively, and the frequency of stay by feeding, water, and rest area was monitored using a top-view camera. The image data of three-colored-specific broilers identified as the stock density were acquired by age (12, 16, 22, 27, and 29 days) for six hours. In the collected image data, the object tracking technique was used to record the cumulative movement path by connecting approximately 640,000 frames at 30 fps to quantify the frequency of stay in each area. In each stock density, it was significant in the order of the rest area, feeding, and water area (p<0.001). In 9.5 birds/㎡, it was at 57.9, 24.2, and 17.9 %, and 73.2, 16.8, and 10 % in 19 birds/㎡. The frequency of a broiler's stay could be evaluated in each area as the stock density using an ICT-based image analysis technique that minimizes stress. This method is expected to be used to provide basic material for developing an ICT-based management system through real-time monitoring.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.722-732
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• 2021

Purpose: Due to the coronavirus infection-19 (COVID) pendemics, all educational institutions were required to provide full non-face-to-face real-time education, and fire officials were required to provide fire-fighting education by applying non-face-to-face education. In this difficult situation, the National Fire Service Academy tries to find the direction of the non-face-to-face real-time education and suggest ways to improve it through a survey of the status of non-face-to-face real-time education conducted by the NFSA to fire officials. Method: A survey was conducted on fire officials under the theme of "Consciousness Survey for Improving the Quality and Specialization of Non-face-to-face Real-Time Remote Education" and an in-depth analysis was conducted based on the results. Result & Conclusion: First, professors or educational operators shall actively utilize remote education programs suitable for educational characteristics by utilizing various programs. Second, a dedicated notebook for non-face-to-face training should be provided to provide an educational environment where all learners can participate in the training without difficulty. Third, in the case of education and training that requires the use of equipment due to the nature of fire officials' education and training, it is necessary to consider it as a non-face-to-face training place by arranging educational equipment at each fire station. Fourth, it is hard to expect a satisfactory educational effect to cope with practical education with theoretical education. Therefore, facilities and programs that enable non-face-to-face real-time hands-on training should be developed. It is worth considering the proper combination of face-to-face education while maintaining the social distance as much as possible until such non-face-to-face training is possible. Fifth, non-face-to-face education is considered to have high eye fatigue due to the light and electromagnetic waves of the computer screen, and as time goes by, the concentration level decreases. Therefore, it is necessary to form an education time to reduce the eye fatigue of learners and increase concentration through proper class and rest time. Finally, professors should operate a learner participation-oriented education that allows professors and learners to interact rather than one-sided knowledge transfer education. In addition, technical problems of non-face-to-face remote education should be thoroughly prepared through preliminary system checks to ensure that education is not disrupted.

• Journal of Naturopathy
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• v.11 no.1
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• pp.18-30
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• 2022

Background: It was to develop an air purification system (APS) using an underground air purification layer to verify the effect of basalt forest's underground air (sumgol) on a volcanic Jeju. Finally, it is necessary to analyze these purified air components and their usefulness to the human body in an air experience center. Purpose: It was to collect basalt forest air, analyze its composition, and explore its effect on the human body. Methods: We APS devices installed at four points in the Papaville area of Jeju. The air discharged from the APS was collected and analyzed the recycling components. An installed experience room filled with negative ions is about 5,000 ions/m³. After allowing the participants to stay for 60 to 120 minutes, we investigated the state of blood vessels. Results: In the analysis of the underground air, the O₂ concentration was 21.18%, which was higher than the average oxygen concentration of 20.94% in the atmosphere. However, Formaldehyde was not detected, and the CO₂ was 419 ppm, which was lower than that of indoor air. The PM2.5 concentration was less than 24 ㎍/m³ and detected anions over 5.000 /m³. The experiencer's vascular states improved, and the increase in pulse rate and stress relief were high. Conclusions: The valuable ingredients identified by analyzing the air were precious for natural healing. The experience results showed that it effectively improved the pulse rate, blood vessels, and stress. These conditions may be highly beneficial as a new area for expanding the basalt lava forest in the Jeju area into the natural healing and wellness industry.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.447-460
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• 2022

It is well known that satellite synthetic aperture radar interferometry (InSAR) has been widely used for the observation of surface displacement owing to earthquakes, volcanoes, and subsidence very precisely. In wetlands where vegetation exists on the surface of the water, it is possible to create a water level change map with high spatial resolution over a wide area using the InSAR technique. Currently, a number of imaging radar satellites are in operation, and most of them support a ScanSAR mode observation to gather information over a large area at once. The Cienaga Grande de Santa Marta (CGSM) wetland, located in northern Colombia, is a vast wetland developed along the Caribbean coast. The CGSM wetlands face serious environmental threats from human activities such as reclamation for agricultural uses and residential purposes as well as natural causes such as sea level rise owing to climate change. Various restoration and protection plans have been conducted to conserve these invaluable environments in recognition of the ecological importance of the CGSM wetlands. Monitoring of water level changes in wetland is very important resources to understand the hydrologic characteristics and the in-situ water level gauge stations are usually utilized to measure the water level. Although it can provide very good temporal resolution of water level information, it is limited to fully understand flow pattern owing to its very coarse spatial resolution. In this study, we evaluate the L-band ALOS-2 PALSAR-2 ScanSAR mode to observe the water level change over the wide wetland area using the radar interferometric technique. In order to assess the quality of the interferometric product in the aspect of spatial resolution and coherence, we also utilized ALOS-2 PALSAR-2 stripmap high-resolution mode observations.

• Journal of Service Research and Studies
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• v.12 no.1
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• pp.25-35
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• 2022

Covid-19, of which the first patient in Korea occurred in January 2020, has affected various fields. Of these, the tourism sector might havebeen hit the hardest. In particular, since tourism-based industrial structure forms the basis of the region, Gangwon-province, and the tourism industry is the main source of income for small businesses and small enterprises, the damage is great. To check the situation and extent of such damage, targeting the Chuncheon region, where public access is the most convenient among the Gangwon regions, one-day tours are possible using public transportation from Seoul and the metropolitan area, with a general image that low expense tourism is recognized as possible, this study conducted empirical analysis through data analysis. For this, the general status of the region was checked based on the visitor data of Chuncheon city provided by the tourist information system, and to check the levels ofinterest in 2019, before Covid-19, and in 2020, after Covid-19, by comparing keywords collected from the web service sometrend of Vibe Company Inc., a company specializing in keyword collection, with SK Telecom's T-map search site data, which in parallel provides in-vehicle navigation service and communication service, this study analyzed the general regional image of Chuncheon-city. In addition, by comparing data from two years by developing a tourism popularity index applying keywords and T-map search site data, this study examined how much the Covid-19 situation affected the level of interest of visitors to the Chuncheon area leading to actual visits using a data analysis approach. According to the results of big data analysis applying the tourism popularity index after designing the data mart, this study confirmed that the effect of the Covid-19 situation on tourism popularity in Chuncheon-city, Gangwon-provincewas not significant, and confirmed the image of tourist destinations based on the regional characteristics of the region. It is hoped that the results of this research and analysis can be used as useful reference data for tourism economic policy making.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.993-1003
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• 2023

Due to the increasing greenhouse gas emissions, the global mean temperature has risen by 1.1℃ compared to pre-industrial levels, and significant changes are expected in functioning of water supply systems. In this study, we assessed impacts of climate change and instreamflow management on water supply reliability in the Geum River basin, Korea. We proposed univariate climate response functions, where mean precipitation and potential evaporation were coupled as an explanatory variable, to assess impacts of climate stress on multiple water supply reliabilities. To this end, natural streamflows were generated in the 19 sub-basins with the conceptual GR6J model. Then, the simulated streamflows were input into the Water Evaluation And Planning (WEAP) model. The dynamic optimization by WEAP allowed us to assess water supply reliability against the 2020 water demand projections. Results showed that when minimizing the water shortage of the entire river basin under the 1991-2020 climate, water supply reliability was lowest in the Bocheongcheon among the sub-basins. In a scenario where the priority of instreamflow maintenance is adjusted to be the same as municipal and industrial water use, water supply reliability in the Bocheongcheon, Chogang, and Nonsancheon sub-basins significantly decreased. The stress tests with 325 sets of climate perturbations showed that water supply reliability in the three sub-basins considerably decreased under all the climate stresses, while the sub-basins connected to large infrastructures did not change significantly. When using the 2021-2050 climate projections with the stress test results, water supply reliability in the Geum River basin was expected to generally improve, but if the priority of instreamflow maintenance is increased, water shortage is expected to worsen in geographically isolated sub-basins. Here, we suggest that the climate response function can be established by a single explanatory variable to assess climate change impacts of many sub-basin's performance simultaneously.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.2
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• pp.121-128
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• 2008

As an aquatic ecotoxicity test method, a bioassay using the inhibition of sporualtion of the green macroalga, Ulva pertusa, has been developed. Optimal test conditions determined for photon irradiance, pH, salinity and temperature were $100\;{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, $7{\sim}9$, $25{\sim}35\;psu$ and $15{\sim}20^{\circ}C$, respectively. The validity of the test endpoint was evaluated by assessing the toxicity of four metals (Cd, Cu, Pb, Zn) and elutriates of sewage or waste sludge collected from 9 different locations. When the metals were assayed, the $EC_{50}$ values indicated the following toxicity rankings: Cu ($0.062\;mg{\cdot}L^{-1}$) > Cd ($0.208\;mg{\cdot}L^{-1}$) > Pb ($0.718\;mg{\cdot}L^{-1}$) > Zn ($0.776\;mg{\cdot}L^{-1}$). When compared with other commonly used bioassays of metal pollution listed on US ECOTOX database, the sporualtion test proved to be the most sensitive. Ulva sporulation was significantly inhibited in all elutriates with the greatest and least effects observed in elutriates of sludge from industrial waste ($EC_{50}=6.78%$) and filtration bed ($EC_{50}=15.0%$), respectively. The results of the Spearman rank correlation analysis for $EC_{50}$ data versus the concentrations of toxicants in the sludge presented a significant correlation between toxicity and four heavy metals(Cd, Cu, Pb, Zn). The method described here is sensitive to toxicants, simple to use, easy to interpret and economical. It is also easy to procure samples and maintain cultures. The present method would therefore probably make a useful assessment of aquatic toxicity of a wide range of toxicants. In addition, the genus Ulva has a wide geographical distribution and species have similar reproductive processes, so the test method would have a potential application worldwide.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.