• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.681-692
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• 2021

Vegetation processes have a significant impact on rainfall runoff processes through evapotranspiration control, but are rarely considered in the conceptual lumped hydrological model. This study evaluated the model performance of the Hapcheon Dam watershed by integrating the ecological module expressing the leaf area index data sensed remotely from the satellite into the hydrological partition module. The proposed eco-hydrological model has three main features to better represent the eco-hydrological process in humid regions. 1) The growth rate of vegetation is constrained by water shortage stress in the watershed. 2) The maximum growth of vegetation is limited by the energy of the watershed climate. 3) The interaction of vegetation and aquifers is reflected. The proposed model simultaneously simulates hydrologic components and vegetation dynamics of watershed scale. The following findings were found from the validation results using the model parameters estimated by the SCEM algorithm. 1) Estimating the parameters of the eco-hydrological model using the leaf area index and streamflow data can predict the streamflow with similar accuracy and robustness to the hydrological model without the ecological module. 2) Using the remotely sensed leaf area index without filtering as input data is not helpful in estimating streamflow. 3) The integrated eco-hydrological model can provide an excellent estimate of the seasonal variability of the leaf area index.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.871-879
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• 2020

The purpose of this study was to analysis the correlation between kidneys function indicators and these size in ultrasonography. A total of 170 (male:86, female:84) patients of sex and age groups were examined by abdominal ultrasonography. The patients classified as those in their 20's, 30's, 40's, and over 50's. We measured the length, width, and cross-sectional height of the kidneys twice. At this time, the length of these were measured from the maximum upper to the maximum lower pole and the widest width in the same ultrasonography was measured to obtain the cross-sectional area. Other relevant indicators included body surface area, serum creatinine, glomerular filtration rate (GFR), MDRD (Modification of diet in renal disease) and C-G (Cockcroft-Gault). Significant comparisons of differences between relevant factors by age groups and sex were conducted with a one-way distribution analysis. Correlation analysis was also performed between relevant factors by using Pearson and Spearman correlation coefficient. It was defined as meaningful when the p-value was less than 0.05. As a result, the length, the width, and the cross-sectional area of kidneys were correlated with GFR, C-G, MDRD. Therefore, it is expected that the accuracy of diagnosis of kidneys disease will be increased if the relevant indicators are evaluated together rather than measuring only length of these in ultrasonography.

• Journal of Korea Society of Waste Management
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• v.35 no.7
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• pp.616-626
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• 2018

The quality standards of solid refuse fuel (SRF) define the values for 12 physico-chemical properties, including moisture, lower heating value, and metal compounds, according to Article 20 of the Enforcement Rules of the Act on Resource Saving and Recycling Promotion. These parameters are evaluated via various SRF Quality Test Methods, but problems related to the heavy metal content have been observed in the microwave acid digestion method. Therefore, these methods and their applicability need improvement. In this study, the appropriate testing conditions were derived by varying the parameters of microwave acid digestion, such as microwave power and pre-treatment time. The pre-treatment of SRF as a function of the microwave power revealed an incomplete decomposition of the sample at 600 W, and the heavy metal content analysis was difficult to perform under 9 mL of nitric acid and 3 mL of hydrochloric acid. The experiments with the reference materials under nitric acid at 600 W lasted 30 minutes, and 1,000 W for 20 or 30 minutes were considered optimal conditions. The results confirmed that a mixture of SRF and an acid would take about 20 minutes to reach $180^{\circ}C$, requiring at least 30 minutes of pre-treatment. The accuracy was within 30% of the standard deviation, with a precision of 70 ~ 130% of the heavy metal recovery rate. By applying these conditions to SRF, the results for each condition were not significantly different and the heavy metal standards for As, Pb, Cd, and Cr were satisfied.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Journal of Korean Society on Water Environment
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• v.37 no.3
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• pp.217-228
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• 2021

This paper discusses a methodology where an integrated modelling framework is used to quantify the risk derived from anthropic activities on habitats and species. To achieve this purpose, a tool comprising the Delft3D and HABITAT model, was applied in the Yeongsan river. Delft3D effectively simulated the operational condition and flow of weirs in river. In accuracy evaluation of the Delft3D-FLOW, the Bias, Pbias, Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Index of Agreement (IOA) were used, and the result was evaluated as grade above 'Satisfactory'. The HABITAT calculated Habitat Suitability Value (HSV) for the following eight species: mammal, fish, aquatic plant, and benthic macroinvertebrate. An Area was defined as a suitable habitat if the HSV was larger than 0.5. HABITAT was judged accurately by measuring the Correct Classification rate (CCR) and the area under the ROC curve (AUC). For benthic macroinvertebrate, the CCR and AUC were 77% and 0.834, respectively, at thresholds of 0.017 and 4 inds/m² for HSV and individuals per unit area. This meant that the HABITAT model accurately predicted the appearance of the benthic macroinvertebrates by approximately 77% and that the probability of false alarms was also very low. As a result of evaluating the suitability of habitats, in the Yeongsan river, if the annual "lowest level" (Seungchon weir: 2.5 EL.m/ Juksan weir: -1.35 EL.m) was maintained, the average habitat improvement effect of 6.5%P compared to the 'reference' scenario was predicted. Consequently, it was demonstrated that the integrated modelling framework for habitat suitability assessment is able to support the remedy aquatic ecological management.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.203-209
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• 2021

In this study, a prototype design model was proposed for developing an online recruitment system through multi-dimensional data crawling and social media analysis, and validates text information and video interview in job application process. This study includes a comparative analysis process through text mining to verify the authenticity of job application paperwork and to effectively hire and allocate workers based on the potential job capability. Based on the prototype system, we conducted performance tests and analyzed the result for key performance indicators such as text mining accuracy and interview STT(speech to text) function recognition rate. If commercialized based on design specifications and prototype development results derived from this study, it may be expected to be utilized as the intelligent online recruitment system technology required in the public and private recruitment markets in the future.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.