The demand for high-strength steel is rising due to its economic efficiency. Low-cycle fatigue (LCF) tests have been conducted to investigate the nonlinear behaviors of high-strength steel. Accurate material models must be used to obtain reliable results on seismic performance evaluation using numerical analyses. This study uses the combined hardening model to simulate the LCF behavior of high-strength steel. However, it is challenging and complex to determine material model parameters for specific high-strength steel because a highly nonlinear equation is used in the model, and several parameters need to be resolved. This study used the particle swarm algorithm (PSO) to determine the model parameters based on the LCF test data of HSA 650 steel. It is shown that the model with parameter values selected from the PSO accurately simulates the measured LCF curves.

In general, the design response spectrum in seismic design codes is based on the mean-plus-one-standard deviation response spectrum to secure high safety. In this study, response spectrum analysis was performed using seismic wave records adopted in domestic horizontal design spectrum development studies, while three response spectra were calculated by combining the mean and standard deviation of the spectra. Seismic wave spectral matching generated seismic wave sets matching each response spectrum. Then, seismic fragility was performed by setting three damage levels using a single-degree-of-freedom system. A correlation analysis was performed using a comparative analysis of the change in the response spectrum and the seismic fragility concerning the three response spectra. Finally, in the case of the response spectrum considering the mean and standard deviation, like the design response spectrum, the earthquake load was relatively high, indicating that conservative design or high safety can be secured.

This study aimed to assess and determine the optimal model for predicting the full bloom date of ‘Fuji’ apples across South Korea. We evaluated the performance of four distinct models: the Development Rate Model (DVR)1, DVR2, the Chill Days (CD) model, and a sequentially integrated approach that combined the Dynamic model (DM) and the Growing Degree Hours (GDH) model. The full bloom dates and air temperatures were collected over a three-year period from six orchards located in the major apple production regions of South Korea: Pocheon, Hwaseong, Geochang, Cheongsong, Gunwi, and Chungju. Among these models, the one that combined DM for calculating chilling accumulation and the GDH model for estimating heat accumulation in sequence demonstrated the most accurate predictive performance, in contrast to the CD model that exhibited the lowest predictive precision. Furthermore, the DVR1 model exhibited an underestimation error at orchard located in Hwaseong. It projected a faster progression of the full bloom dates than the actual observations. This area is characterized by minimal diurnal temperature ranges, where the daily minimum temperature is high and the daily maximum temperature is relatively low. Therefore, to achieve a comprehensive prediction of the blooming date of ‘Fuji’ apples across South Korea, it is recommended to integrate a DM model for calculating the necessary chilling accumulation to break dormancy with a GDH model for estimating the requisite heat accumulation for flowering after dormancy release. This results in a combined DM+GDH model recognized as the most effective approach. However, further data collection and evaluation from different regions are needed to further refine its accuracy and applicability.

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

본 연구에서는 지역 영역 기상 수치 예보 모델의 여러 수평 영역 및 수평 해상도에 따른 이상적인 열대저기압 의 진로와 베타자이어의 민감도를 조사하였다. 모델의 이상적인 초기 조건은 경험적인 함수로 생성된 3차원 축대칭 모 조 소용돌이와 허리케인 활동 시기의 평균 대기 조건으로 구성된다. 이때 모델 설정에 따른 이상적인 열대저기압의 변 화를 분석하기 위하여 배경 흐름은 제거되었다. 수치 모델의 수평 영역 및 수평 해상도에 따른 이상적인 열대저기압의 민감도 실험을 수행하기 위해, 지역 영역 수치 모델로서 W RF (Weather Research a nd F orecasting) 모델을 사용하였다. 모의된 열대저기압의 바람장으로부터 베타자이어를 추출하기 위해, DFS (Double-Fourier Series) 국지 영역 고차 필터 를 사용하였다. 모델의 수평 영역의 크기가 감소할수록 베타자이어의 구조와 강도가 약해졌으며, 이는 열대저기압 진로 의 차이를 발생시켰다. 수평 영역의 크기를 본 연구의 실험에서 가장 작은 영역인 3,000 km3,000 km로 설정하였을 경 우에 베타자이어 통풍류의 서진 성분이 크게 감소하였으며, 수평 영역을 더 넓게 설정한 실험들에 비해 열대저기압의 진로가 동쪽으로 편향되었다. 본 결과는 열대저기압과 관련된 바람장 전체를 포함하지 못할 정도로 매우 작은 수평 영 역을 사용할 경우, 열대저기압의 진로가 적절히 모의 될 수 없음을 시사한다. 반면, 5,000 km5,000 km와 6,000 km 6,000 km의 수평 영역에서는 그 민감도가 매우 작게 나타났다. 수평 해상도가 감소할수록 이상적인 열대저기압의 진 로는 매우 서쪽으로 편향되었다. 베타자이어의 크기와 강도도 수평 해상도가 감소할수록 크고 더 강하게 나타났다.

This study aimed to determine the anti-obesity effect of adding Wolfiporia extensa Ginns (W) to fermented pollack skin products in an obesity-induced animal model. The experimental groups were the normal diet group (C), high-fat diet group (HF), dried pollack skin (H1), fermented pollack skin (H2), and W of 0.1 (F2-WL), 0.3 (F2-WM), and 0.5 (F2), respectively. It was confirmed that adding W to fermented pollack skin reduced blood triglycerides, total cholesterol, and LDL levels, while increasing HDL levels. Wolfiporia extensa Ginns was effective in controlling weight and improving blood lipids in a dose-dependent manner. In histological analysis, findings of fatty liver induced by a high-fat diet were improved by the addition of H2 and W. Size and density of fat globules in the epididymis were decreased. In addition, the concentration of TNF-α was increased in the high-fat diet group, but decreased by the addition of fermented pollack skin and W. In conclusion, adding fermented dried pollack skin and Wolfiporia extensa Ginns was effective for weight control and blood lipid improvement. Thus, the use of by-products in functional foods is expected to have a high value in the future.

본 연구에서는 한옥의 해석모델 구축을 지원하고, 구축된 해석모델의 활용도를 높이고자 이를 활용한 가상현실 소프트웨어를 개발 하였다. 한옥의 구조해석 모델은 범용 구조해석 소프트웨어인 midas Gen으로 생성하고, 이를 문자기반 입력파일로 변환한 후 본 연구 에서 개발된 소프트웨어에서 한옥 해석모델의 검토에 필요한 자료들을 저장한다. 개발된 가상현실 소프트웨어 내에서 3차원으로 표 현된 한옥의 해석모델은 시점을 변경하며 살펴볼 수 있고 특정 부재를 선택하여 관련된 자료를 확인할 수 있다. 이러한 과정을 통해 해 석모델의 오류를 확인 및 수정하여 완결된 해석모델을 구축할 수 있다. 개발된 소프트웨어는 3개의 한옥 사례에 적용하여 그 적용성 과 효용성을 검증하였으며, 구조분야 이외의 타 분야에서도 활용될 수 있다.