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您的检索式：作者名="Fu Tingyan"
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| 1 | 3-41 Bio-audiovisual Feedback Breathing Guidance Technique for Synchrotron-based Ion Beam Delivery显示文摘Based on the periodic synchrotron-based beam delivery,a bio-audiovisual feedback breathing guidance technique has been developed to mitigate the interplay between target motion and dynamic beam delivery for ion beam therapy,in which variable beam extraction and generation of individualized breathing guidance curve are two key factors.Shown in Fig.1 is the schematic diagram of the breathing guidance technique.Through the simulations with 15 healthy volunteers for the breathing guidance technique,we found not only the treatment efficiency of synchrotron-based pulsed ion beam could be increased effectively,but also the residual movement of respiration during gating window could be reduced. | Li Qiang He Pengbo Liu Xinguo Dai Zhongying Ma Yuanyuan Shen Guosheng Yan Yuanlin Huang Qiyan Fu Tingyan | 2014 | IMP & HIRFL Annual Report2014，，1： | 0 |
| 2 | 3-44 Monte Carlo Simulation Study on Optimizing the Active Beam Delivery System at HIRFL显示文摘To obtain carbon ion beam suitable for the active spot scanning beam delivery system at the Heavy Ion ResearchFacility in Lanzhou (HIRFL), the Monte Carlo (MC) program SHIELD-HIT12A was used to study the influencesof beam delivery distance and structure period of mini ridge filter on full width at the half maximum (FWHM) ofbeam spot and dose flatness at the iso-center of the treatment room. | Yan Yuanlin Liu Xingguo Dai Zhongying Ma Yuanyuan Huang Qiyan He Pengbo Shen Guosheng Fu Tingyan Li Qiang | 2014 | IMP & HIRFL Annual Report2014，，1： | 0 |
| 3 | A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment显示文摘As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization. | Tingyan Long Yong Ma Lei Wu Yunni Xia Ning Jiang Jianqi Li Xiaodong Fu Xiangmi You Bo Zhang | 2022 | Digital Communications and Networks2022，8，6： | 0 |
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