• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.133-136
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• 2004

We are going to procedure various view from two frontal image using trifocal tensor. We found that warping is effective to produce synthesized poses of a face with the small number of mesh point of a given image in previous research[1]. For this research, fundamental matrix is important to calculate trifocal tensor. So, in this paper, we investigate two existing algorithms: Hartley's[2] and Kanatani's[3]. As an experimental result, Kenichi Kantani's algorithm has better performance of fundamental matrix than Harley's algorithm. Then we use the fundamental matrix of Kenichi Kantani's algorithm to calculate trifocal tensor. From trifocal tensor we calculate new trifocal tensor with rotation input and translation input and we use warping to produce new virtual views.

• 디지털융복합연구
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• 제18권4호
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• pp.315-323
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• 2020

본 연구는 상호동료 교수학습법을 기본간호학실습 교육에 적용하여 간호대학생의 핵심간호술 수행자신감, 숙련도 및 실습만족도에 미치는 효과를 규명하기 위한 비동등성 대조군 전·후 유사실험연구이다. 자료수집은 P 대학교 간호학과 2학년 학생 83명으로, 연구기간은 2019년 3월 11일부터 5월 17일까지이다. 연구결과 상호동료 교수학습법을 적용한 실험군은 수술전간호, 개인위생, 수술후간호의 핵심간호술 수행자신감과 수술전간호, 수술후간호의 숙련도가 대조군에 비해 유의하게 향상되었고, 실험군의 실습만족도가 대조군보다 높게 나타났다. 결과적으로 상호동료 교수학습법을 적용한 기본간호학실습 교육은 난이도 '중' 정도의 핵심간호술의 수행자신감과 숙련도에 효과가 있으며 실습만족도에 긍정적인 효과가 있었다. 이에 상호동료 교수학습법을 다양한 실습교과에 확대 적용하고 핵심기본간호술의 난이도에 따른 효과를 검정하는 연구를 제언한다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1989년도 가을 학술발표회 논문집
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• pp.15-20
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• 1989

The effect of superplasticizing agents on the sorkability performance in fresh concrete have been analyzed and investigated under various mix proportions of water cement ratio of 0.40, 0.50, 0.60 and 0.70, superplasticizing agents of NL-4000 and Rheobuild-716, and addition rate of sp. agents of 0.0, 0.5, 1.0, 1.5 and 2.0 in the practical range. It is the aim of this study to provide the fundamental data on the fluidity performance and workability improvement of superplasticized concrete such as time-dependent change of slump, flow value and compacting factor, air content, bleeding, mixing temperature and setting rate of fresh concrete comparing with base concrete and conventional concrete for the practical use and research data accumulation of superplasticized concrete in the side of development of concrete construction technology and management.

• 융복합기술연구소 논문집
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• 제9권1호
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• pp.1-6
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• 2019

Matrix multiplication is a fundamental mathematical operation that has numerous applications across most scientific fields. In this paper, we presents a parallel GPU computation algorithm for dense matrix-matrix multiplication using OpenGL compute shader, which can play a very important role as a fundamental building block for many high-performance computing applications. Experimental results on NVIDIA Quad 4000 show that the proposed algorithm runs about 208 times faster than previous CPU algorithm and achieves performance of 75 GFLOPS in single precision for dense matrices with matrix size 4,096. Such performance proves that our algorithm is practical for real applications.

• 대한조선학회논문집
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• 제51권3호
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• pp.231-238
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• 2014

In this study, the self-propulsion tests are performed in INHA towing tank. And the effective wake characteristics of the KVLCC2 and the KCS models are compared by the experimental results. The form factor is independent of Reynolds number. To estimate the hydrodynamic performance of a full scale ship, the form factor is determined to consider attendant on Reynolds number. In this research, the power predictions are carried out considering the form factor difference of model and full scale ship. The results of this research could be used as one of the fundamental data to the powering performance prediction.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2016년도 춘계 학술논문 발표대회
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• pp.28-29
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• 2016

Recently, the attention on high tensile, and high performance cementitious composite (HPFRCC) which can minimize the damage from explosion of inflammable gas and chemicals has been increased. In spite of outstanding tensile performance, HPFRCC has the drawbacks of fiber ball, undesirable cost, and high autogenous shrinkage. therefore, in this research, to develop the optimum HPFRCC, the fundamental properties and autogenous shrinkage of HPFRCC was analyzed depending on various combination and content of organic and inorganic fibers.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 추계 학술발표회 논문집
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• pp.1-12
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• 2006

For advanced civil engineering structures a service life of hundred up to hundred fifty and even two hundred years is sometimes required. The prediction of the performance of concrete structures over such a long period requires accurate and reliable predictive models. Most of the presently used, mostly experience based models don't have the quality and reliability that is required for reliable long-term predictions. The models designers are searching for should be based on an accurate description of the relevant degradation mechanisms. The starting point of such models is a realistic description of the microstructure of the concrete. In this presentation the need and the role of fundamental microstructural models for predicting the performance of concrete structures will be presented. An example will be given of a microstructural model with a proven potential for long-term predictions. Besides this also the role of models in general, i.e. in the whole design and execution process of concrete structures, will be dealt with. Finally recent trends in concrete research will be presented, like the research on self-healing cement-bases systems.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.