• 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.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.569-582
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• 2020

Recently, Korea has faced a change in the pattern of water use due to urbanization, which has caused difficulties in understanding the rainfall-runoff process and optimizing the allocation of available water resources. In this perspective, spatially downscaled analysis of the water balance is required for the efficient operation of water resources in the National Water Management Plan and the River Basin Water Resource Management Plan. However, the existing water balance analysis does not fully consider water circulation and availability in the basin, thus, the obtained results provide limited information in terms of decision making. This study aims at developing a novel water circulation analysis model that is designed to support a quasi-real-time assessment of water availability along the river. The water circulation model proposed in this study improved the problems that appear in the existing water balance analysis. More importantly, the results showed a significant improvement over the existing model, especially in the low flow simulation. The proposed modeling framework is expected to provide primary information for more realistic hydrological drought monitoring and drought countermeasures by providing streamflow information in quasi-real-time through a more accurate natural flow estimation approach with highly complex network.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.368-376
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• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.495-507
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• 2021

With the recent industrial development, accidental pollution in riverine environments has frequently occurred. It is thus necessary to simulate pollutant transport and dispersion using water quality models for predicting pollutant residence times. In this study, we conducted a field experiment in a meandering reach of the Sum River, South Korea, to validate the field applicability and prediction accuracy of RAMS+ (River Analysis and Modeling System+), which is a two-dimensional (2D) stream flow/water quality analysis program. As a result of the simulation, the flow analysis model HDM-2Di and the water quality analysis model CTM-2D-TX accurately simulated the 2D flow characteristics, and transport and mixing behaviors of the pollutant tracer, respectively. In particular, CTM-2D-TX adequately reproduced the elongation of the pollutant cloud, caused by the storage effect associated with local low-velocity zones. Furthermore, the transport model effectively simulated the secondary flow-driven lateral mixing at the meander bend via 2D dispersion coefficients. We calculated the residence time for the critical concentration, and it was elucidated that the calculated residence times are spatially heterogeneous, even in the channel-width direction. The findings of this study suggest that the 2D water quality model could be the accidental pollution analysis tool more efficient and accurate than one-dimensional models, which cannot produce the 2D information such as the 2D residence time distribution.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.148-163
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• 2021

The diversification of the New Deal urban regeneration projects, that started in 2017 in accordance with the "Special Act on Urban Regeneration Activation and Support", generated the increased demand for the accuracy of data-driven diagnosis and project type forecast. Thus, this research was conducted to develop an application model able to identify the most appropriate New Deal project type for "eup", "myeon" and "dong" across the country. Data for application model development were collected through Statistical geographic information service(SGIS) and the 'Urban Regeneration Comprehensive Information Open System' of the Urban Regeneration Information System, and data for the analysis model was constructed through data pre-processing. Four models were derived and simulations were performed through polynomial regression analysis and multinomial logistic regression analysis for the application of the appropriate New Deal project type. I verified the applicability and validity of the four models by the comparative analysis of spatial distribution of the previously selected New Deal projects by targeting the sites located in Seoul by each model and the result showed that the DI-54 model had the highest concordance rate.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• Journal of The Korean Association For Science Education
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• v.40 no.5
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• pp.543-563
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• 2020

This study explores how the teaching practices of two teachers changed during scientific modeling classes. It also aims to understand these changes in terms of the teachers' modeling pedagogical content knowledge (PCK) development. The study participants were two elementary school teachers and their fifth-grade students. The teachers taught eight lessons of scientific modeling classes about the human body. The data analysis was conducted for lessons 1-2 and 7-8, which best showed the change in teaching practice. The two teachers' teaching practices were analyzed in terms of feedback frequency, feedback content, and the time allocated for each stage of model generation, evaluation, and modification. Teacher A led the evaluation and modification stages in a teacher-driven way throughout the classes. In terms of feedback, teacher A mainly used answer evaluation feedback in lesson 1-2; however, in lesson 7-8, the feedback content changed to thought-provoking feedback. Meanwhile, teacher B mostly led a teacher-driven model evaluation and modification in lesson 1-2; however, in lesson 7-8, she let her students lead the model evaluation and modification stages and helped them develop models through various feedbacks. The analysis shows that these teaching changes were related to the development of modeling PCK components. Furthermore, the two teachers' modeling PCK differed in teaching orientation, in understanding the modeling stages, and in recognizing the value of modeling, suggesting the importance of these in modeling teaching practice. This study can help improve the understanding of modeling classes by revealing the relationship between teaching practices and modeling PCK.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.31-40
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• 2014

The Korean government made the 2nd Energy Basic Plan to achieve 11% of new and renewable energies distribution rate until 2035 as a response to cope with international discussion about greenhouse gas emission reduction. Renewable energies include solar thermal, photovoltaic, bioenergy, wind power, small hydropower, geothermal energy, ocean energy, and waste energy. New energies contain fuel cells, coal gasification and liquefaction, and hydrogen. As public and private investment to enhance the distribution of new and renewable energies, it is necessary to clarify the economic effects of the new and renewable energies sector. To the end, this study attempts to apply an input-output analysis and analyze the economic effects of new and renewable energies sector using 2012 input-output table. Three topics are dealt with. First, production-inducing effect, value-added creation effect, and employment-inducing effect are quantified based on demand-driven model. Second, supply shortage effects are analyzed employing supply-driven model. Lastly, price pervasive effects are investigated applying Leontief price model. The results of this analysis are as follows. First, one won of production or investment in new and renewable energies sector induces 2.1776 won of production and 0.7080 won of value-added. Moreover, the employment-inducing effect of one billion won of production or investment in new and renewable energies sector is estimated to be 9.0337 persons. Second, production shortage cost from one won of supply failure in new and renewable energies sector is calculated to be 1.6314 won, which is not small. Third, the impact of the 10% increase in new and renewable energies rate on the general price level is computed to be 0.0123%, which is small. This information can be utilized in forecasting the economic effects of new and renewable energies sector.

• The Southeast Asian review
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• v.28 no.2
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• pp.213-247
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• 2018

Thailand in 2017 the public sentiment has turned against the military government. The four pledges the military declared immediately after the 2014 coup, restoration of democracy, addressing of divisive politics, eradication of corruption, and stimulation of the economy have all failed. In the same year, however, Thai military junta began to recover it's diplomatic relationship with western countries including US and EU owing to promulgation of the new constitution endorsed by King Maha Vajiralongkorn and the lavish funeral of late King Bhumibol Adulyadej which was attended by huge number of condolence delegations from around the world including US Defense Secretary James Mattis. Since the 2014 coup, US has sanctioned the country under military junta led by General Prayuth Chan-o-cha for urging them back to the barracks. EU also joined this sanction measures. US signaled change in it's policy when General Prayuth got the chance to visit US and meet President Donal Trump in 2017. General Prayuth Chan-o-cha's military junta could start to restore it's reputation internationally. Domestically, he used absolute powers based on section 44 of the interim constitution, also guranteed in the new constitution. Oversea and national human rights groups have criticized that the interim constitution for permitting the NCPO, Thai military junta's official name, to carry out policies and actions without any effective oversight or accountability for human rights violations. On 1 December 2017, Thailand marked the one-year anniversary of King Maha Vajiralongkorn's accession to the throne as the country's new monarch, Rama X. In the first year of King Rama X's reign, arrests, prosecutions, and imprisonment under Article 112 of Thailand's Criminal Code (lese-majeste) have continued unabated in Thailand. NCPO has continued to abuse Article 112 to detain alleged violators and curb any form of discussion regarding the monarchy, particularly on social media. In this worsening human rights environment General Prayuth Chan-o-cha enforced continuously campaign like Thai-style democracy- an effort to promote largely autocratic 'Thainess' in such a way that freedom of expression is threatened. It is a resurgence of 'Sarit Model'. In the beginning of 2017 Thai military government raised the slogan of 'opportunity Thailand' in the context of 'Thailand 4.0' project which attempts to transform Thai economy based on industry-driven to innovation-driven for recovering robust growth. To consider freedom and liberty as a source of innovation, 'Thailand 4.0' led by 'Sarit Model' without democracy would be skeptical.

• Geotechnical Engineering
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• v.12 no.6
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• pp.51-64
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• 1996

After the model open-ended pile attached with strain gages was driven into a pressure chamber, in which the saturated microfine sand was contained, the static compression loading test was performed for that pile. Based on the test results, ultimate pile capacity was determined. Then, either simulated earthquake shaking or sinusoidal shaking was applied to the pile with the sustained certain level OP ultimate pile load. Then, pile capacity degradations characteristics during shaking were studied. Pile capacity degradation during two different shakings were greatly different. During the simulated earthquake shaking, capacity degradation depended upon the magnitude of applied load. When the load applied to the pile top was less than 70% of ultimate pile capacidy, pile capacity degradation rate was less than 8%, and pile with the sustained ultimate pile load had the degradation rate of 90%. Also, most of pile capacity degradation was reduced in outer skin friction and degradation rate was about 80% of ultimate pile capacity reduction. During sinusoidal shaking, pile capacity degradation did not depend on the magnitude of applied load. It depended on the amplitude and the frequency , the larger the amplitude and the fewer the frequency was, the higher the degradation rate was. Reduction pattern of unit soil plugging (once depended on the mode of shaking. Unit soil plugging force by the simulated earthquake shaking was reduced in the bottom 3.0 D, of the toe irrespective of the applied load, while reduction of unit soil plugging force by sinusoidal shaking was occurred in the bottom 1.0-3.0D, of the toe. Also, the soil plugging force was reduced more than that during simulated earthquake shaking and degradation rate of the pile capacity depended on the magnitude of the applied load.