• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Korea Journal of Hospital Management
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• v.28 no.2
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• pp.44-65
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• 2023

Purposes: As super-aging population and low fertility rates are threatening the sustainability of the National Health Insurance funds, enhancing the efficiency of hospital management is paramount. In the past, studies analyzing the efficiencies of hospitals primarily made inter-hospital comparisons, but it is important to assess hospitals' internal efficiency and develop improvement measures in order to attain practical improvements in hospital efficiencies. The purpose of this study is to analyze the efficiencies of specialists by medical specialty in a hospital in order to provide foundational data for efficient hospital management. Methodology/Approach: We used the activity-based costing (ABC) data and hospital statistical data from one tertiary hospital in Seoul to analyze the efficiency of specialists by medical specialty. Efficiency was analyzed and compared among specialists using the data envelopment analysis developed by Charnes, Cooper, and Rhodes (DEA-CCR) model and the slacks-based measure (SBM) models. The input variables were labor cost, material cost, and operational expenses, and the output variables were the number of outpatients, number of inpatients, outpatient revenue, and inpatient revenue. Findings: First, there was a marked deviation in efficiency across specialists. Second, there was a marked deviation in efficiency across medical specialties. Third, there was little difference in efficiency according to the specialist's sex, age, and job position. Fourth, the SBM model produced more conservative results and better explained efficiency parameters than the CCR model. Practical Implications: The efficiency of a specialist was more influenced by their medical specialty than their personal characteristics, namely sex, age, and job position. Therefore, Further research is needed to analyze the efficiencies of each subspecialty and identify factors that contribute to the variations in efficiencies across medical specialties, such as clinical practices and fee structures.

• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.1
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• pp.19-27
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• 2023

The interest in greenhouse gases (GHG) emitted from all industries is emerging as a very important issue worldwide. This is affecting not only the global warming, but also the environmentally friendly competitiveness of the industry. The fisheries sector is increasingly interested in greenhouse gas emissions also due to the Paris Climate Agreement in 2015. Korean industry and government are also making a number of effort to reduce greenhouse gas emissions so far, but the effort to reduce GHG in the fishery sector is insufficient compared to other fields. Especially, the investigation on the GHG emissions from Korean fisheries did not carry out extensively. The studies on GHG emissions from Korean fishery are most likely dealt with the GHG emissions by fishery classification so far. However, the forthcoming research related to GHG emissions from fisheries is needed to evaluate the GHG emission level by species to prepare the adoption of Environmental labels and declarations (ISO 14020). The purpose of this research is to investigate which degree of GHG emitted to produce the species (swimming crab and snow crab) from various fisheries. Here, we calculated the GHG emission to produce the species from the fisheries using the life cycle assessment (LCA) method. The system boundary and input parameters for each process level are defined for LCA analysis. The fuel use coefficients of the fisheries for the species are also calculated according to the fuel type. The GHG emissions from sea activities by the fisheries will be dealt with. Furthermore, the GHG emissions for producing the unit weight species and annual production are calculated by fishery classification. The results will be helpful to establish the carbon footprint of seafood in Korea.

• Membrane Journal
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• v.33 no.1
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• pp.34-45
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• 2023

Two computational intelligence techniques namely artificial neural networks (ANN) and support vector machine (SVM) are employed to model the permeate flux based on seven input variables including time, transmembrane pressure, rotating velocity, the pore diameter of the membrane, dynamic viscosity, concentration and density of the feed fluid. The best-fit model was selected through the trial-error method and the two statistical parameters including the coefficient of determination (R²) and the average absolute relative deviation (AARD) between the experimental and predicted data. The obtained results reveal that the optimized ANN model can predict the permeate flux with R² = 0.999 and AARD% = 2.245 versus the SVM model with R² = 0.996 and AARD% = 4.09. Thus, the ANN model is found to predict the permeate flux with high accuracy in comparison to the SVM approach.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.205-208
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• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.1-11
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• 2023

Due to climate change and aging of reservoirs, damage to embankment slopes is increasing. However, the safety diagnosis of the reservoir slope is mainly conducted by visual observation, and the time and economic cost are formidable to apply soil mechanical tests and slope stability analysis. Accordingly, this study presented a predicting method for the compaction and strength characteristics of the reservoir embankment soil using a portable static cone penetration test. The predicted items consisted of dry density, cohesion, and internal friction angle, which are the main factors of slope stability analysis. Portable static cone penetration tests were performed at 19 reservoir sites, and prediction equations were constructed from the correlation between penetration resistance data and test results of soil samples. The predicted dry density and strength parameters showed a correlation with test results between R² 0.40 and 0.93, and it was found to replace the test results well when used as input data for slope stability analysis (R² 0.8134 or more, RMSE 0.0320 or less). In addition, the prediction equations for the minimum safety factor of the slope were presented using the penetration resistance and gradient. As a result of comparing the predicted safety factor with the analysis results, R² 0.5125, RMSE 0.0382 in coarse-grained soil, R² 0.4182 and RMSE 0.0628 in fine-grained soil. The results of this study can be used as a way to improve the existing slope safety diagnosis method, and are expected to be used to predict the characteristics of various soils and inspect slopes.

• Earthquakes and Structures
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• v.26 no.5
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• pp.401-416
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• 2024

Long-span structures, such as bridges, can experience different seismic excitations at the supports due to spatially variability of ground motion. Regarding current bridge designing codes, it is just EC 2008 that suggested some regulations to consider it and in the other codes almost ignored while based on some previous studies it is found that the effect of mentioned issue could not be neglected. The current study aimed to perform a comprehensive study about the effect of spatially varying ground motions on the dynamic response of a reinforced concrete bridge under asynchronous input motions considering soil-structure interactions. The correlated ground motions were generated by an introduced method that contains all spatially varying components, and imposed on the supports of the finite element model under different load scenarios. Then the obtained results from uniform and non-uniform excitations were compared to each other. In addition, the effect of soil-structure interactions involved and the corresponding results compared to the previous results. Also, to better understand the seismic response of the bridge, the responses caused by pseudo-static components decompose from the total response. Finally, an incremental dynamic analysis was performed to survey the non-linear behavior of the bridge under assumed load scenarios. The outcomes revealed that the local site condition plays an important role and strongly amplifies the responses. Furthermore, it was found that a combination of wave-passage and strong incoherency severely affected the responses of the structure. Moreover, it has been found that the pseudo-static component's contribution increase with increasing incoherent parameters. In addition, regarding the soil condition was considered for the studied bridge, it was found that a combination of spatially varying ground motions and soil-structure interactions effects could make a very destructive scenarios like, pounding and unseating.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.166-173
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• 2024

This study explores lip-sync detection, focusing on the synchronization between lip movements and voices in videos. Typically, lip-sync detection techniques involve cropping the facial area of a given video, utilizing the lower half of the cropped box as input for the visual encoder to extract visual features. To enhance the emphasis on the articulatory region of lips for more accurate lip-sync detection, we propose utilizing a pre-trained visual attention-based encoder. The Visual Transformer Pooling (VTP) module is employed as the visual encoder, originally designed for the lip-reading task, predicting the script based solely on visual information without audio. Our experimental results demonstrate that, despite having fewer learning parameters, our proposed method outperforms the latest model, VocaList, on the LRS2 dataset, achieving a lip-sync detection accuracy of 94.5% based on five context frames. Moreover, our approach exhibits an approximately 8% superiority over VocaList in lip-sync detection accuracy, even on an untrained dataset, Acappella.