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IEEE International Conference on Artificial Intelligence and computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence distributed processing Software engineering

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On the effect of out-of-phase cyclic normal load and axial stress ratios on fretting fatigue crack initiation and propagation

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International Journal of Solids and Structures 2025年 320卷

作者： Can Wang Qiqi Xiao Dagang Wang Zhenbing Cai Lihua Wang Gregor Kosec Magd Abdel Wahab Soete Laboratory Department of Electrical energy metals mechanical constructions and systems Faculty of Engineering and Architecture Ghent University Belgium School of Mechanical and Electrical Engineering China University of Mining and Technology Xuzhou 221116 China Tribology Research Institute Southwest Jiaotong University Chengdu 610031 China School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai 200092 China Parallel and Distributed Systems Laboratory Jožef Stefan Institute Jamova cesta 39 SI-1000 Ljubljana Slovenija College of Engineering Yuan Ze University Taiwan China

In this paper, we present a novel investigation into the influence of out-of-phase cyclic normal load and axial stress ratios on fretting fatigue crack initiation and propagation behaviours. Unlike previous studies that primarily focus on constant normal load, our research employs advanced numerical models to provide for the first time a detailed analysis of the effect of out-of-phase cyclic normal load on fretting fatigue crack initiation and propagation behaviours. Due to the non-proportional loading conditions, the frequency of loading, normal load ratio, and cyclic stress ratio play important roles in both crack initiation and propagation phases, which are not yet fully understood. Some experimental results reflect the effect of normal load ratio and cyclic stress ratio on the total fretting fatigue lifetime and wear scar, but there are no direct conclusions. This paper uses the Critical Plane Method and the Theory of Critical Distance to estimate crack initiation behaviour and lifetime. At the same time, the Extended Maximum Tangential Stress criterion from Linear Elastic Fracture Mechanics theory is applied to analyse the impact of load stress ratio on crack propagation path and lifetime, saving experimental time through numerical modelling. The enhancement ratios of crack propagation lifetime are less significant than those of crack initiation lifetime with varying axial stress ratios. Moreover, the impact of the stress ratio of both loads shows discrepancies under high and low cycle fatigue regimes.

关键词： Cyclic loads

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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Variational Distillation for Multi-View Learning

arXiv

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arXiv 2022年

作者： Tian, Xudong Zhang, Zhizhong Wang, Cong Zhang, Wensheng Qu, Yanyun Ma, Lizhuang Wu, Zongze Xie, Yuan Tao, Dacheng School of Computer Science and Technology East China Normal University Shanghai200062 China The Distributed and Parallel Software Laboratory 2012 Labs Huawei Technologies Hangzhou China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Information Science and Technology Xiamen University Fujian361005 China School of Computer Science and Techology East China Normal University Shanghai China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China JD Exploer Academy China The University of Sydney Australia

Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions. However, its great success is generally attributed to estimate the multivariate mutual information which is intractable when the network becomes complicated. Moreover, the representation learning tradeoff, i.e., prediction-compression and sufficiency-consistency tradeoff, makes the IB hard to satisfy both requirements simultaneously. In this paper, we design several variational information bottlenecks to exploit two key characteristics (i.e., sufficiency and consistency) for multi-view representation learning. Specifically, we propose a Multi-View Variational Distillation (MV2D) strategy to provide a scalable, flexible and analytical solution to fitting MI by giving arbitrary input of viewpoints but without explicitly estimating it. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels, producing predictive and compact representations naturally. Also, our information-theoretic constraint can effectively neutralize the sensitivity to heterogeneous data by eliminating both task-irrelevant and view-specific information, preventing both tradeoffs in multiple view cases. To verify our theoretically grounded strategies, we apply our approaches to various benchmarks under three different applications. Extensive experiments to quantitatively and qualitatively demonstrate the effectiveness of our approach against state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Deep reinforcement learning for combinatorial optimization: Covering salesman problems

arXiv

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arXiv 2021年

作者： Li, Kaiwen Zhang, Tao Wang, Rui Wang, Yuheng Han, Yi College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKL-MSI2T Changsha410073 China Graduate College National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha410073 China

This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the Multi-head Attention to capture the structural patterns, and design a dynamic embedding to handle the dynamic patterns of the problem. Once the model is trained, it can generalize to various types of CSP tasks (different sizes and topologies) with no need of re-training. Through controlled experiments, the proposed approach shows desirable time complexity: it runs more than 20 times faster than the traditional heuristic solvers with a tiny gap of optimality. Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large-scale and require quick decisions. Copyright © 2021, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Cooperative Emergent Swarming Through Deep Reinforcement Learning

Cooperative Emergent Swarming Through Deep Reinforcement Lea...

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International Conference on Control and Automation (ICCA)

作者： Tony X. Lin Daniel M. Lofaro Donald A. Sofge School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Distributed Autonomous Systems Group U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper studies the problem of designing a decentralized controller that is able to induce a desired emergent behavior in robot swarms. We consider a robot swarm composing of agents that have identical dynamics and sensors and are unable to communicate among each other. Our proposed approach leverages deep reinforcement learning methods to search for an appropriate control policy that achieves our desired group behavior. We show that our method is able to capture targets in a predator-prey case-study. Simulation and experimental results using an autonomous blimp research platform are also provided.

关键词： Reinforcement learning Decentralized control Neural networks Predator prey systems Vehicle dynamics Training Testing

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ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers

arXiv

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arXiv 2024年

作者： Chen, Zihao Xiao, Zhili Akl, Mahmoud Leugring, Johannes Olajide, Omowuyi Malik, Adil Dennler, Nik Harper, Chad Bose, Subhankar Gonzalez, Hector A. Eshraghian, Jason Pignari, Riccardo Urgese, Gianvito Andreou, Andreas G. Shankar, Sadasivan Mayr, Christian Cauwenberghs, Gert Chakrabartty, Shantanu Department of Electrical and Systems Engineering Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States Department of Bioengineering University of California 9500 Gilman Dr La Jolla San DiegoCA92093 United States SpiNNcloud Systems GmbH Freibergerstr. 37 Dresden01067 Germany Department of Electrical and computer engineering Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Electrical and Electronic Engineering Imperial College London Exhibition Rd LondonSW7 2AZ United Kingdom International Centre for Neuromorphic Engineering Western Sydney University Penrith Second Ave KingswoodNSW2747 Australia Department of Physics University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Department of Electrical and Computer Engineering University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Politecnico di Torino Address: Corso Duca degli Abruzzi 24 Torino10129 Italy Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Biocomputation Group University of Hertfordshire Exhibition Rd LondonSW7 2AZ United Kingdom Materials Science and Engineering Stanford University 450 Jane Stanford Way StanfordCA94305 United States Highly-Parallel VLSI-Systems and Neuro-Microelectronics Technische Universität Dresden Mommsenstraße 12 Dresden01069 Germany Scads.AI: Center for scalable data analytics and artificial intelligence Strehlener Street 12 14 Dresden01069 Germany

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-Nordheim (FN). The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing (SA) dynamics onto a network of integrate-and-fire (IF) neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer which replicates the optimal escape mechanism and convergence of SA, particularly at low temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved various benchmark MAX-CUT combinatorial optimization problems. Across multiple runs, NeuroSA consistently generates solutions that approach the state-of-the-art level with high accuracy (greater than 99%), and without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform. © 2024, CC BY-NC-SA.

关键词： Simulated annealing

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parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

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A modified low cycle fatigue Chaboche model for irradiation hardening behaviour of EUROFER97 at elevated temperatures

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European Journal of Mechanics - A/Solids 2025年 114卷

作者： Jianxin Liu Xiaowei Wang Aleksandr Zinovev Dmitry Terentyev Lihua Wang Magd Abdel Wahab Soete Laboratory Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University 9000 Ghent Belgium School of Mechanical and Power Engineering Nanjing Tech University Nanjing 211816 China Reliability Centered Manufacturing Research Institute Nanjing Tech University Nanjing 211816 China Structural Materials Group Institute for Nuclear Energy Technology Belgian Nuclear Research Centre SCK CEN 2400 Mol Belgium School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai 200092 China Parallel and Distributed Systems Laboratory Jožef Stefan Institute Jamova cesta 39 SI-1000 Ljubljana Slovenia College of Engineering Yuan Ze University Taiwan Republic of China

This study investigates the irradiation hardening mechanical behaviour of EUROFER97, a Reduced Activation Ferritic/Martensitic (RAFM) steel, using a modified Chaboche model within the temperature range of 250–350 °C. Structural materials for nuclear fusion reactors must maintain their performance under neutron irradiation. However, EUROFER97 faces challenges such as irradiation-induced hardening and embrittlement at irradiation temperatures below 350 °C, which lead to reduced ductility and toughness. This research incorporates an irradiation hardening factor into a modified Chaboche constitutive model to accurately simulate EUROFER97's behaviour under irradiation. A unified viscoplastic model is employed for finite element analysis, with variable parameters used to better represent the material's stress-strain evolution during cyclic loading and irradiation-induced hardening. Simulations are conducted using ABAQUS, with a UMAT subroutine developed to simulate fatigue, and a hybrid acceleration strategy is adopted to accelerate the calculation of different stages of Low Cycle Fatigue (LCF). Key findings demonstrate that the modified Chaboche model effectively predicts the irradiation hardening and cyclic softening behaviour of EUROFER97 under various irradiation doses and strain amplitudes. The model's predictions are in good agreement with experimental data, capturing the stress-strain hysteresis loops and irradiation-induced changes in mechanical properties, thereby validating its applicability not only to EUROFER97 but also to other nuclear structural materials, such as F82H ferritic/martensitic steel. The contribution of this study lies in refining the Chaboche model by incorporating irradiation effects, providing a robust tool for predicting the mechanical behaviour of structural materials under irradiation, which is crucial for the safety and design of fusion reactor components.

关键词： Neutron irradiation

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What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?

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Journal of Integrative Agriculture 2020年第7期19卷 1789-1801页

作者： WANG Xiu-juan KANG Meng-zhen FAN Xing-rong YANG Li-li ZHANG Bao-gui HUANG San-wen Philippe DE REFFYE WANG Fei-yue The State Key Laboratory of Management and Control for Cormplex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China Bejjing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBejing 100190P.R.China Innovation Center for Parallel Agriculture Qingdao Academy of Intelligent IndustriesQingdao 266109P.R.China School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing 400067P.R.China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083P.R.China College of Land Science and Technology China Agricultural UniversityBeijing 100193P.R.China Agricultural Genomes Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhen 518124P.R.China AMAP University MontpellierCIRADCNRSINRAIRDMontpellier 34000France The School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049P.R.China

To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.

关键词： cucumber biomass production functional-structural plant model source-sink ratio fruit-setting PSO heterosis

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