• International Journal of Aeronautical and Space Sciences
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• v.8 no.1
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• pp.54-65
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• 2007

Parameter optimization technique is applied to planning UAVs(Unmanned Aerial Vehicles) path under artificial enemy radar threats. The ground enemy radar threats are characterized in terms of RCS(Radar Cross Section) parameter which is a measure of exposure to the radar threats. Mathematical model of the RCS parameter is constructed by a simple mathematical function in the three-dimensional space. The RCS model is directly linked to the UAVs attitude angles in generating a desired trajectory by reducing the RCS parameter. The RCS parameter is explicitly included in a performance index for optimization. The resultant UAVs trajectory satisfies geometrical boundary conditions while minimizing a weighted combination of the flight time and the measure of ground radar threat expressed in RCS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2115-2127
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• 2021

Although significant progress has been made in synthesizing visually realistic face images by Generative Adversarial Networks (GANs), there still lacks effective approaches to provide fine-grained control over the generation process for semantic facial attribute editing. In this work, we propose a novel cross channel self-attention based generative adversarial network (CCA-GAN), which weights the importance of multiple channels of features and archives pixel-level feature alignment and conversion, to reduce the impact on irrelevant attributes while editing the target attributes. Evaluation results show that CCA-GAN outperforms state-of-the-art models on the CelebA dataset, reducing Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) by 15~28% and 25~100%, respectively. Furthermore, visualization of generated samples confirms the effect of disentanglement of the proposed model.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.1
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• pp.101-110
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• 2022

For this purpose, the authors proposed and proved usefulness of the modified mean skin temperature which is integrated mean radiation temperature and the effect of floor contacted heat conduction. The mean radiation temperature is applied form factor between half cross-legged human body and surrounding wall of indoor. In addition the floor contacted heat conduction is applied heat transfer coefficient of half cross-legged human body. Eight Korean young men were targeted for the experiment. From the experiment the authors excerpted physiological reaction and psychological reaction in Ondol environment which is combined physiccal environmental factor of artificial climate chamber, air and floor temperature. As a result of the experiment it is confirmed that heat conduction has more impact than heat exchange from existing research for the heat exchange between half cross-legged human body and surrounding wall in Ondol thermal environment. Thereby, it is proved the effectiveness of the modified mean skin temperature which is added floor contacted temperature to the Ondol thermal environmental evaluation index.

• ETRI Journal
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• v.46 no.2
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.1-7
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• 2012

Hemorrhagic shock is a common cause of death in emergency rooms. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. Therefore, the purpose of this study was to select an optimal survival prediction model using physiological parameters for the two analyzed periods: two and five minutes before and after the bleeding end. We obtained heart rates, mean arterial pressures, respiration rates and temperatures from 45 rats. These physiological parameters were used for the training and testing data sets of survival prediction models using an artificial neural network (ANN) and support vector machine (SVM). We applied a 5-fold cross validation method to avoid over-fitting and to select the optimal survival prediction model. In conclusion, SVM model showed slightly better accuracy than ANN model for survival prediction during the entire analysis period.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.4
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• pp.266-273
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• 2016

Two-dimensional hydraulic experiments were performed to assess the impact of artificial seaweed on wave energy attenuation, and coastal erosion prevention. In this experimental study, erosion geometry and wave reflection coefficients were determined for normal and stormy incident waves, with and without artificial seaweed. The coastline of beaches without artificial vegetation was observed to retreat, and the longshore bar height increased in normal and stormy conditions. Through the introduction of artificial seaweed (of widths 0.8 m, and 1.6 m), the coastline was found to advance in the offshore direction due to material deposition. From these results, it is shown that artificial seaweed alters the cross-section of beaches, such that it is possible to prevent coastline erosion.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.532-541
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• 2021

Recently, research on the application of artificial intelligence in the chemical process has been increasing rapidly. However, overfitting is a significant problem that prevents the model from being generalized well to predict unseen data on test data, as well as observed training data. Cross validation is one of the ways to solve the overfitting problem. In this study, the time-series cross validation method was applied to optimize the number of batch and epoch in the hyperparameters of the prediction model for the 2,3-BDO distillation process, and it compared with K-fold cross validation generally used. As a result, the RMSE of the model with time-series cross validation was lower by 9.06%, and the MAPE was higher by 0.61% than the model with K-fold cross validation. Also, the calculation time was 198.29 sec less than the K-fold cross validation method.

• Journal of the Korean Geotechnical Society
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• v.33 no.6
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• pp.27-36
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• 2017

Gravel compaction pile method has been widely used to improve the soft ground on the land or sea as one of the soft ground improvement technique. The ultimate bearing capacity of the ground reinforced by gravel compaction piles is affected by the soil strength, the replacement ratio of pile, construction conditions, and so on, and various prediction equations have been proposed to predict this. However, the prediction of the ultimate bearing capacity using the existing models has a very large error and variation, and it is not suitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by gravel compaction pile, and the most efficient input variables are selected through evaluation of error by leave one out cross validation, and a multiple regression equation for the prediction of ultimate bearing capacity was proposed. In addition, the prediction error was evaluated by applying artificial neural network using the selected input variables, and the results were compared with those of the existing model.

• Korean Journal of Medicinal Crop Science
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• v.28 no.4
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• pp.298-309
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• 2020

Background: Safflower (Carthamus tinctorius L.) is a useful medicinal and oil crop in Korea. However, when safflower is cultivated, the flowering period overlaps with the rainy season, and seed maturation is poor. Therefore, this study aimed to use basic research data to develop superior varieties using agronomic characteristics and crossing method. Methods and Results: A total of 34 safflower germplasms were sown and their agronomic characteristics were investigated. Based on these investigations, the cultivar 'ui-san-hong-hwa' was selected as the mother plant, and 'Myanmar safflower' (Hsu Pan) was selected as the father plant. In addition, we developed a floret-protecting cap to cover florets after emasculation during the artificial crossing. When florets were protected by the cap, the seed setting rate increased in comparison to that in the non-covered florets. Conclusions: Agronomic characteristics can contribute to developing suitable varieties. The results suggest that the protection cap will be helpful in breeding without the floral organ drying. This study contributes an efficient breeding method to develop new safflower varieties.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.