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LBM modeling of three-dimensional mixed convection in CZ crystal growth on curvilinear coordinates system

Results in Physics 2024年 61卷

作者： Zhang, Ni Liu, Ding The Department of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications Xi'an 710121 China The Shaanxi Key Laboratory of Complex System Control and Intelligent of Information Processing Xi'an University of Technology Xi'an 710048 China

A three-dimensional (3D) thermal lattice Boltzmann model of crystal growth is presented to study the magneto-hydrodynamics (MHD) mixed convection in the melt of Czochralski silicon single crystal growth in this paper. In order to solve the problem of lattice Boltzmann method's difficulty in solving the flow of 3D cylindrical cavity, the 3D physical model of crystal growth is transformed from the Cartesian coordinate system to the Curvilinear coordinate system. And the irregular grids in the Cartesian coordinate system are transformed into regular grids in the Curvilinear coordinate system to facilitate the establishment of the Lattice Boltzmann model. Under the above operations, two coupled distribution functions are established to solve the governing equations. Two D3Q19 models are adopted to solve the melt velocity and temperature. And the 3D lattice Boltzmann modeling of crystal growth with cylinder boundary is realized by interpolation in Curvilinear coordinate system. The comparison with the experiment and other references proves that the proposed model and boundary treatment scheme is correct and it proposes a new way for studying the 3D crystal growth or other similar 3D cylindrical cavity simulations. Furthermore, the unstable melt flow for two cases are also simulated in this paper and the critical Reynolds number are also obtained. Results show that the introduction of the transverse magnetic field enhances the stability of the crystal growth system and increases the adjustable range of crystal rotation parameter. The obtained conclusion has an important reference for adjusting the process parameters during the CZ crystal growth. © 2024 The Author(s)

关键词： 3D mixed convection Computer simulation Heat transfer Lattice Boltzmann Method Silicon single crystal growth

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Efficient Fabric Defect Detection Algorithm Based on Improved YOLOv5

Efficient Fabric Defect Detection Algorithm Based on Improve...

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Chinese automation Congress (CAC)

作者： LiHua Tian AoZe Wang ZhiGang Sun Li Xiao dept. School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Huazhong University of Science and Technology Wuhan China

In order to address the issues of real-time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes the ESE_YOLOv5 network based on YOLOv5. Firstly, to address the relative redundancy of the neck detection network feature channels, a relatively lightweight and efficient convolution module is adopted to ensure accuracy while reducing computation and parameter volume. Furthermore, the Efficient Squeeze-Excitation (ESE) module is introduced into the backbone to optimize the dependency of feature channels, which enhances the model's feature extraction capacity and improves detection accuracy. Experimental results show that compared to YOLOv5, the proposed ESE_YOLOv5 model reduces computation and parameter volume while improving accuracy, meeting the needs of fabric defect detection for recognizing fabric defects that have similar characteristics to the background while maintaining real-time performance.

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Data-Driven Safe controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests

arXiv

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arXiv 2023年

作者： Chen, Yu Shang, Chao Huang, Xiaolin Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In this work, we investigate the data-driven safe control synthesis problem for unknown dynamic systems. We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function. To resolve the issue of unknown system dynamic, we follow the existing approach by converting the RCP to a scenario convex program (SCP) by randomly collecting finite samples of system trajectory. However, to improve the sample efficiency to achieve a desired confidence bound, we provide a new posteriori method with validation tests. Specifically, after collecting a set of data for the SCP, we further collect another set of independent validate data as posterior information to test the obtained solution. We derive a new overall confidence bound for the safety of the controller that connects the original sample data, the support constraints, and the validation data. The efficiency of the proposed approach is illustrated by a case study of room temperature control. We show that, compared with existing methods, the proposed approach can significantly reduce the required number of sample data to achieve a desired confidence bound. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Balanced Training for the End-to-End Autonomous Driving Model Based on Kernel Density Estimation

Balanced Training for the End-to-End Autonomous Driving Mode...

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IEEE Symposium on Intelligent Vehicle

作者： Tong Yao Wei Yuan Songan Zhang Chunxiang Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

End-to-end autonomous driving models are widely used currently to avoid error transmission. The training of these models is affected by the imbalance of the training labels, such as imbalanced distributions of trajectory waypoint, steering, throttle, and brake labels. Therefore, the models cannot be trained enough on low-frequency labels. To mitigate this problem, a balanced training method is proposed in this paper. First, a general kernel density estimation method is used to estimate the probability function of labels. Then, to conduct balanced training, we design a cost-sensitive loss function that assigns different weights to each training data according to the probability of the labels. Comparative experiments on the Longest6 Benchmark show that the balanced training method proposed in this article improves the baseline significantly. We also conduct ablation studies to discuss the balanced training among different tasks in multi-task learning, as well as between two output forms: waypoint and action. Due to the generalization of kernel density estimation, the balanced training method proposed in this paper can be extended to any label distribution of two output forms in the end-to-end autonomous driving models.

关键词： Training Computational modeling Estimation Training data Benchmark testing Data models Trajectory

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Dynamic Modeling and control for an Offshore Semisubmersible Floating Wind Turbine

arXiv

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arXiv 2024年

作者： Gong, Yingjie Yang, Qinmin Geng, Hua Meng, Wenchao Wang, Lin The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering The Huzhou Institute Zhejiang University Hangzhou310027 China The Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The Zhejiang Windey Co. Ltd. Hangzhou310027 China

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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ICP algorithm point cloud registration method based on a new improved Harris Hawk algorithm

ICP algorithm point cloud registration method based on a new...

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2021 China automation Congress, CAC 2021

作者： Wang, Chen Jiao, Shangbin Gao, Rui Li, Yuxing School of Automation and Information Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of Electronic and Electrical Engineering Baoji University of Arts and Sciences Baoji China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Baoji China

ISBN: (纸本)9781665426473

To address shortcomings of ICP algorithm, which is sensitive to the initial position and easy to fall into local optimum, we propose a Harris Hawks Optimizer (HHO) point cloud registration method combining PCA preprocessing and parallel population initialization. The PCA method is used to align the point clouds with the main axes and perform the main axes correction. Since the six input variables in the population initialization stage of HHO point cloud registration are different and independent, the initial population of HHO algorithm is initialized by the parallel initialization method. The results show that the improved algorithm is able to successfully overwhelm the fault of ICP algorithm, and its global seek performance and optimization accuracy are more advantageous, and the algorithm operates efficiently, with high registration accuracy and robustness. © 2021 IEEE

关键词： PCA HHO Parallel Computing ICP

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Discrete-time inverse linear quadratic optimal control over fnite time-horizon under noisy output measurements

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control Theory and technology 2021年第4期19卷 563-572页

作者： Han Zhang Yibei Li Xiaoming Hu Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Mathematics KTH Royal Institute of Technology10044 StockholmSweden

In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is *** goal is to recover the corresponding quadratic objective function using noisy ***,the identifability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally ***,we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown,yet the exact observations on the initial states are *** formulate the problem as a risk minimization problem and approximate the problem using empirical *** is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative *** then study the case where the exact observations on the initial states are not available,yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian(with unknown mean and covariance).EM-algorithm is used to estimate the parameters in the objective *** efectiveness of our results are demonstrated by numerical examples.

关键词： Inverse optimal control Linear quadratic regulator Statistical consistency EM-algorithm

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior

arXiv

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arXiv 2025年

作者： Li, Mingrui Liu, Shuhong Deng, Tianchen Wang, Hongyu the Department of Computer Science Dalian University of Technology Dalian China the Department of Mechano-informatics Information Science and Technology The University of Tokyo Tokyo Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education China

Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. © 2025, CC BY.

关键词： Real time systems

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