• 제목/요약/키워드: Multiple silos

검색결과 2건 처리시간 0.019초

월성 중저준위 처분시설 다중사일로 안정성 평가 모델 - 1단계: 모델개발 (Multiple-Silo Performance Assessment Model for the Wolsong LILW Disposal Facility in Korea - PHASE I: Model Development)

  • 임두현;김지연;박주완
    • 방사성폐기물학회지
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    • 제9권2호
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    • pp.99-105
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    • 2011
  • 중저준위 방사성폐기물 처분장의 안전성 평가를 위하여 지하 사일로와 그 주변의 굴착손상영 역 (EDZ) 및 단열암반을 고려한 지하수유동해석과 핵종이동해석의 통합모델을 개발하였다. 사일로를 다중방벽개념으로 고려하여 사일로를 구성하는 3개의 특성지역 (waste, buffer, concrete)으로 구분하여 해석하였고, EDZ는 사일로 주변과 건설운영 터널 주변의 손상영역을 고려하였다. 단열암반의 불균일성은 분리단열 (discrete fractures)로 부터 해석된 불균일한 지하수 유속계로 도출하였고, 그 결과를 핵종의 이동경로를 모사하는데 사용하였다. 현 모델은 핵종누출에 따른 사일로 배치의 최적화와 안전성의 정량화를 도출하는데 사용가능하다.

Collaborative Modeling of Medical Image Segmentation Based on Blockchain Network

  • Yang Luo;Jing Peng;Hong Su;Tao Wu;Xi Wu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권3호
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    • pp.958-979
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    • 2023
  • Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.