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

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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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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Accelerating FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture

Accelerating FDTD simulation of microwave pulse coupling int...

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IEEE Pacific Rim Conference on Communications, Computers and Signal processing, PACRIM 2015

作者： Wang, Qinglin Liu, Jie Cui, Xiantao Fu, Guitao Gong, Chunye Xing, Zuocheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Beijing Satellite Navigation Center Beijing100094 China

ISBN: (纸本)9781467377881

The coupling of microwaves into apertures plays an important part in many electromagnetic physics and engineering fields. When the width of apertures is very small, Finite Difference Time Domain (FDTD) simulation of the coupling is very time-consuming. As a many-core architecture, the Intel's Many Integrated Core (MIC) architecture owns 512-bit vector units and more than 200 threads. In this paper, we parallelize FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture. In the implementation, the parallel programming model OpenMP is used to exploit thread parallelism while loop unrolling and SIMD intrinsic functions are utilized to accomplish vectorization. Compared with the serial version on Intel Xeon E5-2670 CPU, the implementation on the MIC coprocessor including 57 cores obtains a speedup of 11.57 times. The experiment results also demonstrate that the parallelization has good scalability in performance. Additionally, how binding relationship between OpenMP threads and hardware threads in MIC influences performance is also reported. © 2015 IEEE.

关键词： Scalability

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Grid computing in China

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Journal of Grid Computing 2004年第2期2卷 193-206页

作者： Yang, Guangwen Jin, Hai Li, Minglu Xiao, Nong Li, Wei Wu, Zhaohui Wu, Yongwei Tang, Feilong Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China College of Computer Science Zhejiang University Hangzhou 310027 China

Grid computing presents a new trend to distributed computation and Internet applications, which can construct a virtual single image of heterogeneous resources, provide uniform application interface and integrate widespread computational resources into super, ubiquitous and transparent aggregation. In the adoption of Grid computing, China, who is facing more resource heterogeneity and other specific demands, has put much effort on both research and practical utilization. In this paper, we introduce the major China Grid research projects and their perspective applications. First we give the overview of the four government-sponsored programs in Grid, namely the China National Grid, ChinaGrid, NSFC Grid, and ShanghaiGrid. Then we present six representative ongoing Grid systems in details, which are categorized into Grid middleware and Grid application. This paper provides the general picture of Grid computing in China, and shows the great efforts, devotion and confidence in China to use Grid technology to boost the society, economics and scientific research. © Springer 2004.

关键词： Grid computing

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A secure light-weight public auditing scheme in cloud computing with potentially malicious third party auditor

A secure light-weight public auditing scheme in cloud comput...

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作者： Wu, Yilun Lin, Xinye Lu, Xicheng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology China School of Computer Science McGill University Montreal Canada National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China

Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient. © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Cloud security

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ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation

ECDT: Exploiting Correlation Diversity for Knowledge Transfe...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shichang He Xuan Liu Xinning Chen Ying Huang College of Computer Science and Electronic Engineering Hunan University P.R. China The Science and Technology on Paraller and Distributed Processing Laboratory Tsinghua University P.R. China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.

关键词： Correlation Handheld computers Tagging Sensors Task analysis Knowledge transfer

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Highly stretchable strain sensors using conductive MoS 2 composited PVA-PAA-Li + hydrogels for information interaction

Transactions of Materials Research

引用

Transactions of Materials Research 2025年第3期1卷

作者： Zhixiang Zhang Jianhua Zhou Xiaoling Ke Lili Zhao Xiaojiang Mu Yulian Chen Lida Jia Fengmei He Jie Gao Jing Liu Jun-Liang Chen Xiaoyang Wang Lei Miao Libo Yuan Guangxi Key Laboratory of Information Materials Guangxi Collaborative Innovation Center of Structure and Property for New Energy Materials School of Material Science and Engineering Guilin University of Electronic Technology Guilin 541004 China College of Optoelectronic Engineering Guilin University of Electronic Technology Guilin 541004 China Guangdong Provincial Key Laboratory of Distributed Energy Systems Dongguan University of Technology Dongguan 523808 China Guangxi Novel Battery Materials Research Center of Engineering Technology State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures School of Physical Science and Technology Guangxi University Nanning 530004 China Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin 541004 China

Wearable sensors using conductive hydrogels have been widely used in advanced technologies such as artificial intelligence, information interaction, and medical monitoring. However, it remains a major challenge to strike a balance between mechanical properties and electrical conductivity. In this work, we propose and demonstrate a conductivity-enhanced strain sensor with molybdenum disulfide (MoS 2 ) compounded as a conductive filler in the hydrogel matrix. MoS 2 not only facilitates the molding of the dual-networked poly(vinyl alcohol) and poly(acrylic acid) but also provides a physical cross-linking point for the hydrogel matrix, which improves the mechanical properties of the hydrogel with tensile strains up to 780%. Upon further immersion in lithium chloride (LiCl) solution, the conductivity of the hydrogel sensors was significantly improved up to 3.88 S m −1 because the intercalation of Li + into MoS 2 induces the synergistic effect of ionic and electronic conductivity. The inherent -OH and -COOH groups in the network made the hydrogels also self-adhesive. This study provides a new approach for the development of advanced information-interaction devices, with potential applications extending to next-generation wearable electronics and responsive sensor technologies.

关键词： Strain sensors Wearable sensors Information interaction MoS 2 Conductive hydrogel

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Design and Implementation of distributed Stage DB:A High Performance distributed Key-Value Database

Design and Implementation of Distributed Stage DB:A High Per...

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2014 International Conference on Industrial Engineering and Information technology

作者： Hui-jun Wu Kai Lu Gen Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

With the development of high performance computing and Web 2.0 applications,unstructured data storage becomes more and more *** RDBMS isn't efficient for big data ***,RDBMS's scalability is ***' expansion often leads to a large scale of data *** paper designs and implements a high performance distributed key-value database,which is distributed Stage *** servers are organized by a consistent hashing ring and distributed with the support of Zookeeper,a distributed service *** has a high single-node read/write *** route information is calculated by clients,which reduces the expense of expansion.

关键词： distributed system,database,key-value,Zookeeper

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DISVMs: Fast SVMs Training on Large-scale Data Sets

DISVMs: Fast SVMs Training on Large-scale Data Sets

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International Conference on Tools with Artificial Intelligence

作者： Lijuan Cui Changjian Wang Ziyang Li Yuxing Peng Science and Technology on Parallel and distributed Processing Laboratory National University of Defence Technology

ISBN: (纸本)9781509044603

Support Vector Machines (SVMs) are powerful classification tools. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to screening out non-support vectors (non-SVs) to accelerate the training. But their processes rely on prior knowledge of other classifiers with different parameters to screen out non-SVs. In this paper, we propose Directional Indicator Support Vector Machines (DISVMs) to efficiently identify non-SVs. DISVMs employs a directional indicator, which points to the approximately orthogonal direction of the separating hyperplane, to qualitatively define the location of different samples and thus identify non-SVs. Furthermore, DISVMs leverages a two-stage algorithm: the first stage is to compute the directional indicator. The second stage is to identify non-SVs using the indicator. To avoid misjudgement, we propose CnSV method for non-SVs based on the majority rule. DISVMs screens out non-SVs with light computation and little accuracy loss. Experiments show that our approach significantly reduces the total computation cost.

关键词： Support vector machines Non-support vector Directional indicator Computation efficiency Data preprocessing

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Accelerating Embarrassingly Parallel Algorithm on Intel MIC

Accelerating Embarrassingly Parallel Algorithm on Intel MIC

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2014 IEEE International Conference on Progress in Informatics and Computing

作者： Qinglin Wang Jie Liu XiantuoTang Feng Wang Guitao Fu Zuocheng Xing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

The Embarrassingly Parallel(EP) algorithm which is typical of many Monte Carloapplications provides an estimate of the upper achievable limits for double precision performance of parallel supercomputers. Recently, Intel released Many Integrated Core(MIC) architecture as a many-core co-processor. MIC often offers more than 50 cores each of which can run four hardware threads as well as 512-bit vector instructions. In this paper,we describe how the EP algorithm is accelerated effectively on the platforms containing MIC using the offload execution model. The result shows that the efficientimplementation of EP algorithm on MIC can take full advantage of MIC's computational resources and achieves a speedup of 3.06 compared with that on Intel Xeon E5-2670 CPU. Based on the EP algorithm on MIC and an effective task distribution model, the implementation of EP algorithm on a CPU-MIC heterogeneous platform achieves the performance of up to2134.86 Mop/s and 4.04 times speedup compared with that on Intel Xeon E5-2670 CPU.

关键词： NPB embarrassingly parallel algorithm heterogeneous platform many integrated core architecture

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