• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• 국제지역연구
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• 제14권3호
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• pp.57-80
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• 2010

이 연구에서는 개방형 정책조정방법이 사회적 포용의 영역에 도입된 이후, 과정의 변화와 정책효과에 대한 평가를 시도한다. 2000년 리스본이사회에서 결정된 개방형 정책조정방법의 정책도구들은 결과지향적인 신공공관리론과 과정지향적인 숙의적 다중질서라는 거버넌스이론을 배경으로 한다고 볼 수 있다. 역사적 변화과정을 살펴볼 때 결과지향적인 신공공관리론의 정책도구인 목표설정, 수범사례의 벤치마킹, 분권적 의사결정의 경우 애매하거나 아예 거부되었고, 제도적 틀을 넘지 못함으로써 효율성을 추구할 수 없었다. 아울러 규범적인 숙의적 다중질서이론이 제시하는 것처럼 학습을 위해 숙의와 상호검토를 추구하고 있으나, 실제 운영은 성찰적인 숙의과정에 미치지 못하고, 상호검토도 제도적 한계를 보임으로써 원활한 학습이 이루어지지 않는다. 10여 년간 개방형 정책조정방법이 집행되었지만 정책효과의 측면에서도 유의미한 결과를 찾을 수 없다. 그렇지만 빈곤문제에 대한 인식이 변하고 시민사회가 활발하게 조직되어 참여가 확대되고 있는 점은 긍정적인 효과이다.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1061-1080
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• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.