• International Journal of Aeronautical and Space Sciences
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• 제8권1호
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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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• 제15권6호
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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.

• 한국산업융합학회 논문집
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• 제25권1호
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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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• 제46권2호
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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.

• 대한의용생체공학회:의공학회지
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• 제33권1호
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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.

• 한국해양환경ㆍ에너지학회지
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• 제19권4호
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• pp.266-273
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• 2016

본 연구는 인공식생에 의한 파 에너지 저감과 해빈침식방지를 평가하기 위해서 2차원 수리모형실험을 수행하였다. 실험에서는 인공식생 유무 및 평상파/폭풍파 입사 조건의 규칙파 영향하에서 해빈단면 변화와 파고 반사율을 조사하였다. 주요 연구 결과로는 1) 인공식생이 없는 조건에서는 파 조건에 의해서 연안사주 높이가 증가하고 해안선의 후퇴가 나타났으나 2) 1B(폭=0.8 m) 또는 2B(폭=1.6 m)의 인공식생을 설치한 조건에서는 해안선의 전진 및 퇴적현상이 발생하였다. 이를 통해 인공식생이 해빈 단면에 영향을 줄 수 있으며 해빈침식방지공법으로써 적용가능함을 알 수 있었다.

• Korean Chemical Engineering Research
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• 제59권4호
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• pp.532-541
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• 2021

최근 인공지능에 대한 관심이 높아짐에 따라 화학공정분야에서도 인공지능을 활용한 연구가 많아지고 있다. 그러나 인공지능 기반 모델이 충분히 일반화되지 않아 학습에 이용되지 않은 새로운 데이터에 대한 예측률이 떨어지는 과적합 현상이 빈번하게 일어나고 있으며, 교차검증은 과적합을 해결하는 방법 중 하나이다. 본 연구에서는 2,3-BDO 분리 공정 온도 예측 모델의 초매개변수 중에서 배치 개수와 반복횟수를 조정하기 위해 시계열 교차검증을 적용하고 일반적으로 사용되는 K 겹 교차검증과 비교하였다. 결과적으로 K 겹 교차검증을 사용했을 때 보다 시계열 교차검증 방식을 사용했을 때 MAPE는 0.61% 증가한 반면 RMSE는 9.06% 감소하였고 학습 시간은 198.29초 적게 소요되었다.

• 한국지반공학회논문집
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• 제33권6호
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• pp.27-36
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• 2017

자갈다짐말뚝(Gravel Compaction Pile) 공법은 연약지반 개량공법 중의 하나로 육상 및 해상에서 연약 지반을 개량하기 위해 많이 사용되어 왔다. 자갈다짐말뚝으로 보강된 지반의 극한 지지력은 자갈다짐말뚝 및 지반의 강도, 치환율, 시공조건 등에 영향을 받으며 이를 예측하기 위한 다양한 예측식이 제안되었다. 하지만 기존 예측식을 활용한 극한지지력 예측은 오차율 및 변동성이 매우 크며, 실제 설계에 활용하기에는 부적합한 것으로 나타났다. 본 연구에서는 자갈다짐말뚝으로 보강된 지반의 극한 지지력을 예측하기 위하여 현장 재하시험결과를 활용한 다중회귀분석을 수행하였으며, 단일잔류 교차검증에 따른 예측오차평가를 통하여 가장 효율적인 입력변수를 선정하고 이에 대한 극한 지지력 예측식을 제안하였다. 또한 선정된 입력변수를 활용하여 인공신경망 적용에 따른 극한 지지력 예측오차를 평가하고 이를 기존 예측식에 따른 결과와 비교 분석하였다.

• 한국약용작물학회지
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• 제28권4호
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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.

• 한국물환경학회지
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• 제27권4호
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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.