• Computers and Concrete
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• 제21권1호
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• pp.21-30
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• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.

• 한국화재소방학회논문지
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• 제26권2호
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• pp.40-52
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• 2012

본 논문에서는 원자력발전소의 모터제어반 스위치기어실 화재 모델링에 대한 입력변수 불확실성 분석을 수행하였다. 화재모델링은 FDS 5.5를 사용하였고 FDS 입력변수 램던 샘플링은 라틴하이퍼쿠브 몬테칼로 방법을 이용하였다. 본 연구에서 수행한 입력변수 불확실성 분석 결과를 비교하기 위해 NUREG-1934의 화재모델링 결정론적 불확실성 분석과 민감도 분석 방법을 이용한 분석도 수행하였다. 분석결과, 본 연구의 모터제어반 스위치 기어룸 화재 모델링에 대한 입력변수 불확실성 분석방법이 NUREG-1934의 방법보다 보수적인 결과를 얻을 수 있음을 확인하였다.

• 소성∙가공
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• 제21권8호
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• pp.499-505
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• 2012

The fuel cell is a very promising power generation system combining the benefits of extremely low emissions, high efficiency, ease of maintenance and durability. In order to promote the commercialization of fuel cells, a flexible forming process, in which a hyper-elastic rubber is adopted as a medium to transmit forming pressure, is suggested as an efficient and cost effective manufacturing method for fuel cell bipolar plates. In this study, the ability of this flexible forming process to produce the micro channel arrays on metallic bipolar plates was first demonstrated experimentally. Then, a finite element (FE) model was built and validated through comparisons between simulated and experimental results. The effects of key process parameters on the forming performance such as applied load and punch velocity were investigated. As a result, appropriate process parameter values allowing high dimensional accuracy without failure were suggested.

• 공업화학
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• 제21권5호
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• pp.586-589
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• 2010

본 연구에서는 1,3-dicarbonyl 화합물과 올레핀 화합물의 산화첨가반응에서 $Ag_2CO_3$/celite (SC)를 촉매로 하여 1,3-cycleohexandion (1,3-CHD)과 ethyl vinyl ether (EVE)를 acetonitrile (AN), dimethyl sulfoxide (DMSO), benzene (BZ), heptane (HT)을 각각 용매로 하여 반응을 진행시켜 보았다. 그 결과 수득률이 78, 40, 15, 10%로 나타났다. 따라서 이러한 용매의 효과에 따른 수득률의 변화를 알아보기 위하여 하이퍼캠의 반경험적 방법으로 PM3와 ZINDO/1 파라미터를 이용하여 이론적 고찰을 해 보았다.

• Design & Manufacturing
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• 제14권3호
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• Computers and Concrete
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• 제24권6호
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 추계학술대회
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• pp.15-17
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• 2018

VVC(Versatile Video Coding)는 입력된 영상을 CTU(Coding Tree Unit) 단위로 분할하여 코딩하며, 이를 다시 QTBTT(Quadtree plus binary tree and triple tree)로 분할하고, TU(Transform Unit)도 이와 같은 단위로 분할된다. 따라서 TU의 크기는 $4{\times}4$, $4{\times}8$, $4{\times}16$, $4{\times}32$, $8{\times}4$, $16{\times}4$, $32{\times}4$, $8{\times}8$, $8{\times}16$, $8{\times}32$, $16{\times}8$, $32{\times}8$, $16{\times}16$, $16{\times}32$, $32{\times}16$, $32{\times}32$, $64{\times}64$의 17가지 종류가 있다. 기존의 VVC 참조 Software인 VTM에서는 디블록킹필터와 SAO(Sample Adaptive Offset)로 이루어진 인루프필터를 이용하여 에러를 복원하는데, 본 논문은 TU 크기에 따라서 원본블록과 복원블록의 차이(에러)가 통계적으로 다름을 이용하여 서로 다른 CNN(Convolution Neural Network)을 구축하고 에러를 복원하는 방법으로 VTM의 인루프 필터를 대체한다. 복원영상의 에러를 감소시키기 위하여 TU 블록크기에 따라 DenseNet의 Dense Block기반 CNN을 구성하고, Hyper Parameter와 복잡도의 감소를 위해 네트워크 간에 일부 가중치를 공유하는 모양의 Network를 구성하였다.

• 한국기계가공학회지
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• 제18권4호
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• pp.34-40
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• 2019

We performed shape optimization of an anti-vibration rubber assembly which is used in the field option cabin of agricultural tractors to improve the vibration isolation capability. To characterize the hyper-elastic material property of rubber, we performed uniaxial and biaxial tension tests and used the data to calibrate the material model applied in the finite element analyses. We conducted a field test to characterize the input excitation from the tractor and the output response at the cabin frame. To account for the nonlinear behavior of rubber, we performed static analyses to derive the load-displacement curve of the anti-vibration rubber assembly. The stiffness of the rubber assembly could be calculated from this curve and was input to the harmonic analyses of the cabin. We compared the results with the test data for verification. We utilized Taguchi's parameter design method to determine the optimal shape of the anti-vibration rubber assembly and found two distinct shapes with reduced stiffness. Results show that the vibration at the cabin frame was reduced by approximately 35% or 47.6% compared with the initial design using the two optimized models.

• 상하수도학회지
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• 제35권6호
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• pp.507-516
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• 2021

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, at first the correlation analysis between Chl-a and water quality and quantity data was studied. we extracted ten factors of high importance for water quality and quantity data. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.