• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.507-514
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• 2011

A numerical model has been developed by employing a finite element method to simulate the depth-averaged 2-D dispersion of the heat pollutant, which is an important pollutant material in natural streams. Among the finite element methods, the Streamline Upwind/Petrov Galerkin (SUPG) method was applied. Also both linear and quadratic elements can be applied so that irregular river boundaries can be easily represented. To show the movement of heat pollutants, the reaction term describing heat transfer was represented as an equation in which sink/source term is proportional to the difference between the equilibrium temperature and water surface temperature. The equation was expressed so that the water surface temperature changes according to the temperature transfer coefficient and the equilibrium temperature. For the calibration of the model developed, analytic and numerical results from a case of rectangular channel with full width continuous injection have been compared in a steady state. The comparisons showed that the numerical results were in good agreement with analytical solutions. The application site was selected from the downstream of Paldang dam to Jamsil submerged weir, and overall length of this site is about 22.5 km. The change of water temperature caused by the discharge from the Guri sewage treatment plant has been simulated, and results were similar to the observed data. Overall it is concluded that the developed model can represent the water temperature changes due to heat transport accurately. But the verification using observed data will further enhance the validity of the model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.696-701
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• 2018

The nitrogen oxides generated during combustion reactions have a great influence on the generation of acid rain and fine dust. As an NOx reduction method, exhaust gas recirculation combustion using Coanda nozzles capable of recirculating a large amount of exhaust gas with a small amount of air has recently been utilized. In this study, for the burner outlet with dual end opening, the use of a recirculation burner was investigated for the distribution of the pressure, streamline, temperature, combustion reaction rate and nitrogen oxides using computational fluid analysis. The gas mixed with the combustion air and the recirculated exhaust gas flow in the tangential direction of the circular cylinder burner, so that there is a region with low pressure in the vicinity of the fuel nozzle exit. As a result, a reverse flow is formed in the central portion of the burner near the center of the circular cylinder burner and the exhaust gas is discharged to the outside region of the circular cylinder burner. The combustion reaction occurs on the right side of the burner and the temperature and NOx distribution are relatively higher than those on the left side of the burner. It was found that the average NOx production decreased from an air flow ratio of 1.0 to 1.5. When the air flow ratio is 1.8, the NOx production increases abruptly. It is considered that the NOx production reaction increases exponentially with temperature when the air ratio is more than 1.5 and the NOx production reaction rate increases rapidly on the right-hand side of the burner.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.697-707
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• 2007

In this study, we estimated the PMP(Probable Maximum Precipitation) and its transition in case of the typhoon Rusa which happened the biggest damage of all typhoons in the Korea. Specially, we analysed the moisture maximizing rate under the consideration of meteorological condition based on the orographic property when it hits in Gangneung region. The PMP is calculated by the rate of the maximum persisting 12 hours 1000 hPa dew points and representative persisting 12 hours 1000 hPa dew point. The former is influenced by the moisture inflow regions. These regions are determined by the surface wind direction, 850 hPa moisture flux and streamline, which are the critically different aspects compared to that of previous study. The latter is calculated using statistics program (FARD2002) provided by NIDP(National Institute for Disaster Prevention). In this program, the dew point is calculated by reappearance period 50-year frequency analysis from 5% of the level of significant when probability distribution type is applied extreme type I (Gumbel distribution) and parameter estimation method is used the Moment method. So this study indicated for small basin$(3.76km^2)$ the difference the PMP through new method and through existing result of established storm transposition and DAD(Depth-Area-Duration). Consequently, the moisture maximizing rate is calculated in the moisture inflow regions determined by meteorological fields is higher $0.20{\sim}0.40$ range than that of previous study. And the precipitation is increased $16{\sim}31%$ when this rate is applied for calculation.