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arXiv 2024年

作者： Zhao, Sunan Li, Zhijie Fan, Boyu Wang, Yunpeng Yang, Huiyu Wang, Jianchun Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Turbulence Research and Applications Southern University of Science and Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Department of Biomedical Engineering Applications National University of Singapore Singapore117583 Singapore

Acquisition of large datasets for three-dimensional (3D) partial differential equations are usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great potential in a variety of partial differential equations. In this work, we employ physics-informed neural operator, encoding the large-eddy simulation (LES) equations directly into the neural operator for simulating three-dimensional incompressible turbulent flows. We develop the LESnets (Large-Eddy Simulation nets) by adding large-eddy simulation equations to two different data-driven models, including Fourier neural operator (FNO) and implicit Fourier neural operator (IFNO) without using label data. Notably, by leveraging only PDE constraints to learn the spatio-temporal dynamics problem, LESnets retains the computational efficiency of data-driven approaches while obviating the necessity for data. Meanwhile, using large-eddy simulation equations as PDE constraints makes it possible to efficiently predict complex turbulence at coarse grids. We investigate the performance of the LESnets with two standard three-dimensional turbulent flows: decaying homogeneous isotropic turbulence and temporally evolving turbulent mixing layer. In the numerical experiments, the LESnets model shows a similar or even better accuracy as compared to traditional large-eddy simulation and data-driven models of FNO and IFNO. Moreover, the well-trained LESnets is significantly faster than traditional LES, and has a similar efficiency as the data-driven FNO and IFNO models. Thus, physics-informed neural operators have a strong potential for 3D nonlinear engineering applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Large eddy simulation

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Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

arXiv

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arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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2D-DOA for a Monostatic ULA EMVS-MIMO Radar Based on RC-PARAFAC

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IEEE Sensors Journal 2025年第12期25卷 21763-21774页

作者： Wang, Jianlong Shi, Junpeng Wen, Fangqing China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hvdroelectric Engineering College of Computer and Information Technology Yichang443002 China National University of Defense Technology College of Electronic Countermeasure Hefei230037 China City University of Macau College of Computer and Information Technology China Three Gorges University Faculty of Data Science 999078 China

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar is an emerging technology capable of two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, we propose a low-complexity algorithm for monostatic uniform linear array (ULA) EMVS-MIMO radar to rapidly estimate 2D-DOA. First, we decompose the received signal into a third-order tensor using parallel factor analysis (PARAFAC). Next, we eliminate data redundancy through a reduced complexity (RC) process. Then, we use the least squares method to extract ambiguous elevation angle information from the spatial response matrix and apply the vector cross-product (VCP) technique to the electromagnetic vector to achieve unambiguous 2D-DOA estimation. Finally, we combine these estimates to achieve high-resolution, unambiguous elevation angle estimation. The proposed algorithm is suitable for large-scale and sparse EMVS-MIMO radar systems. © 2001-2012 IEEE.

关键词： Direction of arrival

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Research on E-commerce Recommended Algorithm Based on Knowledge Graph

Research on E-commerce Recommended Algorithm Based on Knowle...

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作者： Zhang, Yu Ye, Jingming Yue, Xin Wei, Sifan Wang, Yu Ren, Wanli Harbin University of Commerce Harbin150028 China Heilongjiang University of Science and Technology Harbin150022 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China Heilongjiang Cultural Big Data Theory Application Research Center Harbin150028 China

The traditional e-commerce recommendation algorithms are difficult to extract effective information from massive data to meet users’ needs. This paper proposes a new algorithm, which integrates user’s review with commodities knowledge graph, and constructs corresponding entities. It also extracts feature words and semantic relations to form a set, obtains product feature weights, and calculates the recommended value of commodities by using random walk algorithms. The experimental results show that the new algorithm has a certain degree of improvement in precision, recall rate, and F-value compared with the traditional recommendation algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electronic commerce

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EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs

arXiv

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arXiv 2022年

作者： Tian, Xiaobin Sun, Zequn Li, Guangyao Hu, Wei State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China

Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment. © 2022, CC BY.

关键词： Knowledge graph

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Research on Feature Words for IoT Device Recognition Based on Word2vec 7th

Research on Feature Words for IoT Device Recognition Based o...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Xu, Zi-Xiao Chen, Xiu-Bo Xu, Gang Yuan, Kai-Guo Cui, Jun Yang, Yi-Xian Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China

ISBN: (纸本)9783030786175

With the advent of the IoT era, more and more IoT device has begun to integrate into our daily life. Effective recognition of all kinds of IoT device is the premise to ensure the safety of IoT devices. Furthermore, if a certain device can be identified more finely, such as brand and model information. Then it can be associated with the device vulnerability library to obtain more detailed device security information. According to the existing research, the richer the feature lexicon of IoT devices, the finer the granularity of device recognition will be. The paper is based on word2vec technology to expand the IoT device recognition feature vocabulary. Firstly, the keywords carried by the device information are used as the input of the search engine, and the corpus of keywords is constructed. Secondly, we use word2vec to model the corpus, and then extract the known IoT device feature words related to keywords from the model. Finally, based on the binary correlation judgment algorithm proposed in this paper, we judge the correlation between keywords and feature words in turn. If the correlation is consistent, keywords will be added to the corresponding feature lexicon. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Random verifiable multi-server searchable encryption scheme

Random verifiable multi-server searchable encryption scheme

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： He, Yu Tian, Youliang Xu, Hua State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Institute of Cryptography and Data Security Guizhou University Guiyang550025 China

ISBN: (纸本)9781665441582

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time. © 2021 IEEE.

关键词： Game theory

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CAGroup3D: class-aware grouping for 3D object detection on point clouds 22

CAGroup3D: class-aware grouping for 3D object detection on p...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Haiyang Wang Lihe Ding Shaocong Dong Shaoshuai Shi Aoxue Li Jianan Li Zhenguo Li Liwei Wang Center for Data Science Peking University and Peng Cheng Laboratory and Pazhou Laboratory (Huangpu) Max Planck Institute for Informatics Huawei Noah's Ark Lab China Beijing Institute of Technology Center for Data Science Peking University and National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University

ISBN: (纸本)9781713871088

We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consistency and diverse locality abandoned in previous bottom-up approaches. Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module to directly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific features of each 3D proposal. Our model achieves state-of-the-art 3D detection performance with remarkable gains of +3.6% on ScanNet V2 and +2.6% on SUN RGB-D in term of [email protected].

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Quantized Tensor Neural Network

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ACM/IMS Transactions on data science 2021年第4期2卷 1–18页

作者： Gao, Yuan Yang, Laurence T. Zheng, Dehua Yang, Jing Zhao, Yaliang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology and Department of Computer Science St. Francis Xavier University AntigonishNSB2G2W5 Canada Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University and with Department of Computer Science St. Francis Xavier University Antigonish Canada

Tensor network as an effective computing framework for efficient processing and analysis of high-dimensional data has been successfully applied in many fields. However, the performance of traditional tensor networks still cannot match the strong fitting ability of neural networks, so some data processing algorithms based on tensor networks cannot achieve the same excellent performance as deep learning models. To further improve the learning ability of tensor network, we propose a quantized tensor neural network in this article (QTNN), which integrates the advantages of neural networks and tensor networks, namely, the powerful learning ability of neural networks and the simplicity of tensor networks. The QTNN model can be further regarded as a generalized multilayer nonlinear tensor network, which can efficiently extract low-dimensional features of the data while maintaining the original structure information. In addition, to more effectively represent the local information of data, we introduce multiple convolution layers in QTNN to extract the local features. We also develop a high-order back-propagation algorithm for training the parameters of QTNN. We conducted classification experiments on multiple representative datasets to further evaluate the performance of proposed models, and the experimental results show that QTNN is simpler and more efficient while compared to the classic deep learning models. © 2022 Association for Computing Machinery.

关键词： Tensors

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Influence of lattice mismatch of nano-holed substrate on the crystallization of Lennard-Jones liquids

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Journal of Molecular Liquids 2025年 430卷

作者： Su, Xiaodong Zhang, Huijun Peng, Shuming Zhou, Xiaosong Sui, Tingting Wan, Chubin Ju, Xin School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China State Key Laboratory for Mechanical Behavior of Materials Shaanxi International Research Center for Soft Matter School of Materials Science and Engineering Xi'an Jiaotong University Xi'an710049 China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang621900 China

Crystallization often occurs on the substrate and is influenced by the morphology of the surface. This study investigates the process of crystallization in Lennard-Jones liquids on lattice-mismatched substrates with a cylindrical hole, utilizing molecular dynamics simulation. By decreasing the value of δ, representing the difference in lattice constant between the substrate and the bulk crystal, various crystallization behaviors are observed and studied. More precisely, as δ drops progressively, the crystallization can be shown to have three distinct stages: I at −0.03 © 2025

关键词： Lattice mismatch

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