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arXiv 2023年

作者： Salomone, Robert Yu, Xuejun Nott, David J. Kohn, Robert Centre for Data Science Queensland University of Technology Australia Department of Statistics and Data Science National University of Singapore Singapore Department of Statistics and Data Science National University of Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Australia

Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This paper considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. Different parametrizations are explored for this variational family, and the speed of convergence and quality of the approximation can depend on the parametrization used. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. Our parametrization of the implicit copula approximation is novel, even in the special case of a Gaussian approximation. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models, and we conclude that our copula approaches are most accurate, but that the SDGM methods are often nearly as good and have lower computational demands. © 2023, CC BY.

关键词： Importance sampling

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Optimizing User Interaction with Combined Avatars in BIM Systems

Optimizing User Interaction with Combined Avatars in BIM Sys...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Ildikó Horváth Borbála Berki Péter Baranyi Corvinus Institute for Advanced Studies Institute of Data Analytics and Information Systems Corvinus University of Budapest Budapest Hungary Hungarian Research Network HUN-REN Budapest Hungary Faculty of Engineering and Information Technology University of Pécs Pécs Hungary

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Integrating virtual reality with Building Information Modeling (BIM) has been recognized for its numerous benefits in enhancing visualization, spatial recognition, distance and real-time collaboration, and efficiency in construction projects. However, there is a growing interest in exploring the cognitive aspects associated with using different types of avatars in these systems. This paper explores the cognitive implications of avatars in BIM systems, examining how they influence user experience, perception, decision-making, and overall cognitive load.

关键词： Visualization Solid modeling Uncertainty Avatars Decision making Collaboration Cognitive load Software User experience Real-time systems

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Unified Gas-Kinetic Wave-Particle Methods VI:Disperse Dilute Gas-Particle Multiphase Flow

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Communications in Computational Physics 2022年第3期31卷 669-706页

作者： Xiaojian Yang Chang Liu Xing Ji Wei Shyy Kun Xu Department of Mechanical and Aerospace Engineering Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Institute of Applied Physics and Computational Mathematics BeijingChina Department of Mathematics Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Hong Kong University of Science and TechnologyHong KongChina Shenzhen Research Institute Hong Kong University of Science and TechnologyShenzhenChina

A coupled gas-kinetic scheme(GKS)and unified gas-kinetic wave-particle(UGKWP)method for the disperse dilute gas-particle multiphaseflow is *** the two-phaseflow,the gas phase is always in the hydrodynamic regime and is fol-lowed by GKS for the Navier-Stokes *** particle phase is solved by UGKWP in all regimes from particle trajectory crossing to the hydrodynamic wave interac-tion with the variation of particle’s Knudsen *** the intensive particle colli-sion regime,the UGKWP gives a hydrodynamic wave representation for the particle phase and the GKS-UGKWP for the two-phaseflow reduces to the two-fluid Eulerian-Eulerian(EE)*** the rarefied regime,the UGKWP tracks individual particle and the GKS-UGKWP goes back to the Eulerian-Lagrangian(EL)*** the tran-sition regime for the solid particle,the GKS-UGKWP takes an optimal choice for the wave and particle decomposition for the solid particle phase and connects the EE and EL methods *** GKS-UGKWP method will be tested in allflow regimes with a large variation of Knudsen number for the solid particle transport and Stokes number for the two-phase *** is confirmed that GKS-UGKWP is an efficient and accurate multiscale method for the gas-particle two-phaseflow.

关键词： Unified gas-kinetic wave-particle method gas-kinetic scheme disperse gas-particle two-phaseflow.

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A New Approach to the Position of Acupoints on the Back of Humans

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Recent Patents on Mechanical Engineering 2023年第2期16卷 150-162页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Song, Fengfeng Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Hangzhou Qiandaohu Ruichun Robot Research Institute Co. Ltd Zhejiang Hangzhou310018 China Zhejiang Shusi Robot Co. Ltd Zhejiang Huzhou313000 China

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effec-tiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high. © 2023 Bentham Science Publishers.

关键词： Computer vision

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A Meta-learning System for Automatic Objects Tracking

A Meta-learning System for Automatic Objects Tracking

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International Conference on Applied Machine Learning (ICAML)

作者： Qiaolin Li Wenfeng Wang Jingjing Zhang Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of Technology Shanghai China Research Center of Artificial Intelligence and Robotics Interscience Institute of Management and Technology Bhubaneswar India

Recent years, reinforcement learning has been developed in many area and achieve high performance in many application. However, for huge amount data and training will make hard leaning for the agent of reinforcement learning, in the project, the agent of DDQN Network was integrated with LSTM meta learner for learning the game called Cartpole and LunarLander. It get a good performance for large train.

关键词： Training Games Reinforcement learning Market research Object tracking

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Predictability of weakly turbulent systems from spatially sparse observations using data assimilation and machine learning

arXiv

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arXiv 2024年

作者： Gupta, Vikrant Chen, Yuanqing Wan, Minping Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering 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 Jiaxing Research Institute Southern University of Science and Technology Jiaxing314031 China

We apply two data assimilation (DA) methods, a smoother and a filter, and a model-free machine learning (ML) shallow network to forecast two weakly turbulent systems. We analyse the effect of the spatial sparsity of observations on accuracy of the predictions obtained from these data-driven methods. Based on the results, we divide the spatial sparsity levels in three zones. First is the good-predictions zone in which both DA and ML methods work. We find that in the good-predictions zone the observations remain dense enough to accurately capture the fractal manifold of the system's dynamics, which is measured using the correlation dimension. The accuracy of the DA methods in this zone remains almost as good as for full-resolution observations. Second is the reasonable-predictions zone in which the DA methods still work but at reduced prediction accuracy. Third is the bad-predictions zone in which even the DA methods fail. We find that the sparsity level up to which the DA methods work is almost the same up to which chaos synchronisation of these systems can be achieved. The main implications of these results are that they (i) firmly establish the spatial resolution up to which the data-driven methods can be utilised, (ii) provide measures to determine if adding more sensors will improve the predictions, and (iii) quantify the advantage (in terms of the required measurement resolution) of using the governing equations within data-driven methods. We also discuss the applicability of these results to fully developed turbulence. © 2024, CC BY.

关键词： Forecasting

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Three-Dimensional Spatiotemporal Wind Field Reconstruction Based on Lidar and Multi-Scale Pinn

SSRN

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SSRN 2024年

作者： Chen, Yuanqing Wang, Ding Feng, Dachuan Tian, Geng Gupta, Vikrant Cao, Renjing Wan, Minping Chen, Shiyi Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering 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 Eastern Institute for Advanced Study Eastern Institute of Technology Zhejiang315200 China

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmosoheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimising a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalised errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices. © 2024, The Authors. All rights reserved.

关键词： Large eddy simulation

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Transfer Learning Based Rolling Bearing Fault Diagnosis 10

Transfer Learning Based Rolling Bearing Fault Diagnosis

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10th IEEE data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Yang, Zhengni Wang, Xuying Yang, Rui School of Advanced Technology Xi'an Liaotong-Liverpool University Suzhou215123 China Research Institute of Big Data Analytics Xi'an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9781665424233

In recent years, transfer learning has been an important method to address the problem that labeled data are rarely in the real world. In many industry scenarios, collected labeled sample signals are usually not in the same data distribution. A major assumption for traditional learning and deep learning based bearing fault diagnosis methods is the training data and testing data must follow the same data distribution. However, this assumption may not hold in reality. To address the different distribution problem, this paper proposed an unsupervised approach for bearing fault diagnosis based on transfer learning. The correlation alignment (CORAL) algorithm is used to align data distribution of domains in the proposed approach, then the proposed approach applies statistical algorithms to extract shallow features and wavelet scattering network to extract deep features. The 1 nearest neighbors (1-NN) classifier is trained with the feature vector matrix of source domain, which is able to classify the unlabeled samples of target domain presenting the effectiveness of the proposed approach. Different experiments were carried out to demonstrate the performance of the proposed approach. The experiment results show that the proposed model is superior to other bearing fault diagnosis methods. © 2021 IEEE.

关键词： Fault detection

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High-Order Unified Gas-kinetic Scheme

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Communications in Computational Physics 2022年第9期32卷 951-979页

作者： Gyuha Lim Yajun Zhu Kun Xu Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Department of Mathematics The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Hong Kong University of Science and TechnologyHong KongChina Shenzhen Research Institute Hong Kong University of Science and TechnologyShenzhenChina

In this paper,we present a high-order unified gas-kinetic scheme(UGKS)using the weighted essentially non-oscillatory with adaptive-order(WENO-AO)method for spatial reconstruction and the two-stage fourth-order scheme for time *** the UGKS updates both the macroscopic flow variables and microscopic distribution function,and provides an adaptive flux function by combining the equilibrium and non-equilibrium parts,it is possible to take separate treatment of the equilibrium and non-equilibrium calculation in the UGKS for the development of highorder *** the fact that high-order techniques are commonly applied in the continuum flow simulation with complex structures,and that the rarefied flow structure is usually smooth in the physical space,we apply the high-order techniques in the equilibrium part of the UGKS for the capturing of macroscopic flow evolution,and retain the calculation of distribution function as a second-order method,so that a balance of computational cost and numerical accuracy could be well *** HUGKS has been validated by several numerical test cases,including sine-wave accuracy test,Sod-shock tube,Couette,oscillating Couette,lid-driven cavity and oscillating cavity *** is shown that the current method preserves the multiscale property of the original UGKS and obtains accurate solutions in the near continuum regimes.

关键词： High-order reconstruction two-stage fourth-order scheme WENO-AO micro flow

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research on recognition algorithm for gesture page turning based on wireless sensing

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Intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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