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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear Science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety Technology of the Chinese Academy of Sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear Engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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Learning generic diffusion processes for image restoration 29

Learning generic diffusion processes for image restoration

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29th British Machine Vision Conference, BMVC 2018

作者： Qiao, Peng Dou, Yong Chen, Yunjin Feng, Wensen Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China ULSee Inc. Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Image restoration problems are typical ill-posed problems where the regularization term plays an important role. The regularization term learned via generative approaches is easy to transfer to various image restoration, but offers inferior restoration quality compared with that learned via discriminative approaches. On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained. To address this issue, we propose a generic diffusion process (genericDP) to handle multiple Gaussian denoising problems based on the Trainable Non-linear Reaction Diffusion (TNRD) models. Instead of one model, which consists of a diffusion and a reaction term, for one Gaussian denoising problem in TNRD, we enforce multiple TNRD models to share one diffusion term. The trained genericDP model can provide both promising denoising performance and high training efficiency compared with the original TNRD models. We also transfer the trained diffusion term to non-blind deconvolution which is unseen in the training phase. Experiment results show that the trained diffusion term for multiple Gaussian denoising can be transferred to image non-blind deconvolution as an image prior and provide competitive performance. © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Diffusion

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Adaptive Self-Supervised Continual Learning 26

Adaptive Self-Supervised Continual Learning

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Lilei Wang, Zhen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9781643684369

Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and data perspectives, we introduce a framework, Adaptive Self-supervised Continual Learning (ASCL). Specifically, we devise an Adaptive Sharpness-Aware Minimization (ASAM) module responsible for identifying flatter local minima in the loss landscape with a smaller memory over-fitting risk. Additionally, we design an Adaptive Memory Enhancement (AME) module responsible for rebalancing self-supervised loss with new and old tasks from a data perspective. Finally, the adaptive mechanisms in AME and ASAM modules dynamically adjust the loss landscape sharpness and memory enhancement strength with the feedback of intermediate training results. The results of our extensive experiments demonstrate the state-of-the-art performance of our methods in continual self-supervised learning scenarios across multiple datasets. © 2023 The Authors.

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Crowd intelligence in AI 2.0 era

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 15-43页

作者： Wei LI Wen-jun WU Huai-min WANG Xue-qi CHENG Hua-jun CHEN Zhi-hua ZHOU Rong DING State Key Laboratory of Software Development Beihang University National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University National Key Laboratory for Novel Software Technology Nanjing University

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

关键词： Crowd intelligence Artificial intelligence 2.0 Crowdsourcing Human computation

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Fast flow field prediction of pollutant leakage diffusion based on deep learning

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Environmental Science and Pollution Research 2024年第36期31卷 49393-49412页

作者： YunBo, Wan Zhong, Zhao Jie, Liu KuiJun, Zuo Yong, Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China School of Aeronautics Northwestern Polytechnical University Xi’an710072 China

Abstract: Predicting pollutant leakage and diffusion processes is crucial for ensuring people’s safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges. Graphical abstract: A deep learning method for simulating pollutant leakage diffusion. (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diffusion

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Modelling with relational calculus of object and component systems - RCOS

Modelling with relational calculus of object and component s...

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作者： Chen, Zhenbang Hannousse, Abdel Hakim Van Hung, Dang Knoll, Istvan Li, Xiaoshan Liu, Zhiming Liu, Yang Nan, Qu Okika, Joseph C. Ravn, Anders P. Stolz, Volker Yang, Lu Zhan, Naijun International Institute for Software Technology United Nations University China Faculty of Science and Technology University of Macau Department of Computer Science Aalborg University Denmark Lab. of Computer Science Institute of Software CAS China National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)3540852883

This chapter presents a formalization of functional and behavioural requirements, and a refinement of requirements to a design for CoCoME using the Relational Calculus of Object and Component Systems (rCOS). We give a model of requirements based on an abstraction of the use cases described in Chapter 3.2. Then the refinement calculus of rCOS is used to derive design models corresponding to the top level designs of Chapter 3.4. We demonstrate how rCOS supports modelling different views and their relationships of the system and the separation of concerns in the development. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Calculations

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 IEEE.

关键词： Data privacy Smart contracts Data communication Cryptography

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A 55 TFLOPS simulation of amyloid-forming peptides from yeast prion Sup35 with the special-purpose computer system MDGRAPE-3

A 55 TFLOPS simulation of amyloid-forming peptides from yeas...

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2006 ACM/IEEE Conference on Supercomputing, SC'06

作者： Narumi, Tetsu Ohno, Yousuke Okimoto, Noriaki Koishi, Takahiro Suenaga, Atsushi Futatsugi, Noriyuki Yanai, Ryoko Himeno, Ryutaro Fujikawa, Shigenori Taiji, Makoto Ikei, Mitsuru Intel K.K. High Performance Molecular Simulation Team Genomic Sciences Center Innovative Nanopatterning Laboratory Topochemical Design Laboratory Frontier Research System Advanced Center for Computing and Communication Software and Solutions Group Parallel and Distributed Solutions Group

ISBN: (纸本)0769527000

We have achieved a sustained performance of 55 TFLOPS for molecular dynamics simulations of the amyloid fibril formation of peptides from the yeast Sup35 in an aqueous solution. For performing the calculations, we used the MDGRAPE-3 system - -a special-purpose computer system for molecular dynamics simulations. Its nominal peak performance was 415 TFLOPS for Coulomb force calculations;this is the highest-ever performance reported for classical molecular dynamics simulations. Amyloid fibril formation is known to be related to the occurrence of severe diseases such as Alzheimer's, Parkinson's, and Creutzfeldt-Jakob diseases. The Sup35 protein is a "yeast prion protein," which forms mini-crystals due to aggregation;it forms an effective platform for studying the formation process of amyloid fibrils. In these simulations, we first elucidate that the amyloid-forming peptides GNNQQNY aggregate at a higher frequency than non-amyloid-forming peptides SQNGNQQRG;further, the GNNQQNY peptides tend to form parallel two-stranded -sheets that would grow into a cross- amyloid nucleus. The results are consistent with those obtained experimentally. Furthermore, we could observe an early elongation of the amyloid nucleus. This result is expected to contribute toward a deeper understanding of the amyloid growth mechanism. © 2006 IEEE.

关键词： Peptides

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Asynchronous parallelization of Guo's algorithm for function optimization

Asynchronous parallelization of Guo's algorithm for function...

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Congress on Evolutionary Computation

作者： Lishan Kang Zhuo Kang Yan Li Pu Liu Yuping Chen State Key Laboratory of Parallel and Distributed Processing State Key Laboratory of Software Engineering Wuhan University of China Wuhan China

Recently Tao Guo (1999) proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for the overall situation, and the latter maintains the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the high accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments are performed using Guo's algorithm to demonstrate the theoretical results. Three asynchronous parallel algorithms with different granularities for MIMD machines are designed by parallelizing Guo's algorithm.

关键词： Algorithm design and analysis parallel algorithms Constraint optimization software algorithms Optimization methods Laboratories Design optimization distributed processing software engineering Electronic mail

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Modeling full information with graph network for joint entity-relation extraction 4

Modeling full information with graph network for joint entit...

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Wan, Qian Wei, Luona Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Hunan Changsha China College of Systems Engineering National University of Defense Technology Hunan Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9781450385053

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the CoNLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model. © 2021 ACM.

关键词： Natural language processing systems

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