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Effective Probabilistic Neural Networks Model for Model-Based Reinforcement Learning USV

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11625-11641页

作者： Huang, Wenjun Cui, Yunduan Li, Huiyun Wu, Xinyu Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sys Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Shenzhen 518055 Guangdong Peoples R China

Gaussian process (GP) offers a robust solution for modeling the dynamics of unmanned surface vehicles (USV) in model-based reinforcement learning (MBRL). However, the rapidly increasing computational complexity with a large sample capacity of GP limits its application in complex scenarios that require substantial samples to cover the state space. In this article, a novel probabilistic MBRL approach, probabilistic neural networks model predictive control (PNMPC) is proposed to tackle this issue. With an iterative learning framework, PNMPC properly models the USV dynamics using neural networks from a probabilistic perspective to avoid the computational complexity associated with sample capacity. Employing this model to effectively propagate system uncertainties, a model predictive control (MPC) policy is developed to robustly control the USV against external disturbances. Evaluated by position-keeping and multiple targets-tracking scenarios on a real USV data-driven simulation, the proposed method consistently demonstrates its significant superiority in both model accuracy and control performance compared to not only GP model-based approaches but also the probabilistic neural networks-based MBRL baselines, across various scales of external disturbances.

关键词： Computational modeling Probabilistic logic Neural networks Uncertainty Predictive models Vehicle dynamics Training Stability analysis Computational complexity Adaptation models Reinforcement learning (RL) neural networks predictive models marine vehicles

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Monitoring Method of CIPCA Grinding process Based on Multi-modal data Characteristics 35

Monitoring Method of CIPCA Grinding Process Based on Multi-m...

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35th Chinese control and Decision conference (CCDC)

作者： Sun, Shuyan Wang, Shu Zhang, Lin Zhao, Yibo Wang, Tao Qv, Weikai Northeastern Univ Sch Informat Sci & Engn Shenyang 116086 Peoples R China Shandong Gold Min Laizhou Co Ltd Sanshandao Gold Mine Laizhou 261442 Shandong Peoples R China

ISBN: (纸本)9798350334722

In order to solve the problems of multi-work and uncertainty in the grinding process, a process monitoring method called Multimodal Complete Information Principal Component analysis was proposed in this paper. In this method, SDAE-K-Means++ is adopted to carry out modal partitioning of grinding production process data, and online mode recognition is realized by calculating the similarity between mode center and online data. At the same time, considering the uncertainty characteristics of field data due to noise and sensor drift, CIPCA was combined with modal center estimation for monitoring. This method combined interval data processing with interval monitoring method to reduce the impact of uncertain information. By applying to the actual grinding process of a mine to verify the effectiveness of the proposed method.

关键词： Interval principal component analysis process monitoring Grinding process Modal partition Uncertain information monitoring

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A causal based method for denoising non-homologous noises in time series manufacturing monitoring data

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 76卷 92-102页

作者： Liu, Changqing Li, Yingguang Hua, Jiaqi Zhao, Zhiwei Gao, James Nanjing Univ Aeronaut & Astronaut Nanjing 210016 Peoples R China Univ Greenwich Sch Engn Chatham ME4 4TB Kent England

In manufacturing process monitoring, the obtained data is always affected by multi-noise resources with different statistics features, including non-homologous noises, which adversely affect data analysis, especially the performance of data driven models which are increasingly developed in manufacturing applications. Due to the lack of prior knowledge of the noises, traditional denoising methods based on modeling noise distribution with statistical features have major limitations in denoising non-homologous noises. To address this issue, this paper presents a denoising method for monitoring data with non-homologous noises based on causal inference. The causal relationship between raw data from multi sources, and the noises and monitoring data from sensors is modeled. To remove the influence of noises on monitoring data, the instruments variable is introduced into causal model, which forming new back-door paths between non-homologous noises sources. Then the noise could be denoised according to the prior causal knowledge of the causal model. The method is verified in both simulation and actual machining environment, which lays a data foundation for establishing accurate and stable prediction and control models during manufacturing processes.

关键词： data denoising Non-homologous noise Causal-based method Back-door path

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Research on data processing of 3D modeling by images based on helicopter oblique photogrammetry 4

Research on data processing of 3D modeling by images based o...

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2024 4th International conference on Computer, Remote Sensing and Aerospace, CRSA 2024

作者： Mu, Daocheng Guo, Shanchuan Sun, Jinquan Zeng, Lingsha 32023 Troops Dalian116000 China

The use of tilting photogrammetry technology to build a refined urban 3D model has become the industry research hotspot with the rapid development of China's "digital city."To investigate and analyze the methodology of 3D data modeling using helicopter oblique photogrammetry, a 3D model was generated for a study area covering approximately 34 km2. This paper delves into the technical process of 3D modeling data processing utilizing helicopter oblique photogrammetry, and it examines the precision of the model results alongside a comprehensive technical analysis. The results show that the results of the process are consistent with the relevant specifications of the first-order 3D model landscape. Compared with the traditional 3D model, the method has obvious progress in efficiency and has a very wide application prospect. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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Nonlinear data-Driven Moment Matching in Reproducing Kernel Hilbert Spaces*

Nonlinear Data-Driven Moment Matching in Reproducing Kernel ...

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European control conference (ECC)

作者： Moreschini, Alessio Scandella, Matteo Parisini, Thomas Imperial Coll London Dept Elect & Elect Engn London England Univ Bergamo Dept Management Informat & Prod Engn Via Marconi 5 I-24044 Dalmine BG Italy Univ Trieste Dept Engn & Architecture I-34127 Trieste Italy Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-1678 Nicosia Cyprus

ISBN: (纸本)9798331540920;9783907144107

The continuously increasing amount of noisy data demands the development of accurate and efficient models for analysis, modeling, and control. In this article, we propose a novel data-driven moment matching method which employs Tikhonov regularization in the Reproducing Kernel Hilbert Spaces (RKHSs). Specifically, considering a realistic scenario in which the system's plant is unknown and only noisy measured data are available, we provide an estimation of the moment of the unknown plant by solving a regularized optimization problem on RKHS. For, we first demonstrate that the estimation of the moment can be improved via tuning the regularization term, and further, we show under which condition the effect of the transient improves the performance of the estimation. Then, we construct a parameterized model characterized by a kernel-based output mapping. Finally, the proposed data-driven approach is validated and discussed by means of a DC-to-DC C ' uk converter driven by a Van der Pol oscillator.

关键词： Model reduction data-driven moment matching Kernel-based modeling Nonlinear system identification

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analysis Software Development for Optical Critical Dimension based on RCWA and 3D modeling 38

Analysis Software Development for Optical Critical Dimension...

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conference on Metrology, Inspection, and process control XXXVIII

作者： Lee, Jiwon Lee, Minhyeok Ryu, Shinyoung Kim, Kwangwoo Kim, Jongjeong Kang, Tae Dong AUROS Technol Inc Hwaseong 18487 Gyeonggi Do South Korea Haedosa Inc Hwaseong Gyeonggi Do South Korea

ISBN: (纸本)9781510672178;9781510672161

This paper introduces our development of software designed for OCD (Optical Critical Dimension) modeling, utilizing 3D graphics design functionality. In the OCD metrology, the role of analysis software is crucial for accurately and precisely extracting CD parameters from intricate device structures. Our software incorporates calculation engines grounded in Physics and Machine Learning - RCWA (Rigorous Coupled Wave analysis) and DL (Deep Learning). The software's advanced 3D modeling engine supports complex structure manipulation and precise adjustments of a broad range of parameters, including optical properties. This facilitates detailed device geometry exploration through a cohesive interface. The DL algorithm has been developed ensuring consistency between RCWA and DL predictions, essential for accurate and rapid OCD metrology. We have conducted a comprehensive evaluation process to assess the consistency between RCWA calculations and 3D representations, encompassing both 2D and 3D structures. Further evaluation is planned, specifically focusing on real patterned wafers.

关键词： OCD RCWA 3D model optical critical dimension software development

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Integration of process modeling and Six Sigma for defect reduction: A case study in a wind blade factory 5

Integration of Process Modeling and Six Sigma for defect red...

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5th International conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Oliveira, Daniela Teixeira, Leonor Alvelos, Helena Univ Aveiro Dept Econ Management Ind Engn & Tourism DEGEIT P-3810193 Aveiro Portugal Univ Aveiro Inst Elect & Informat Engn Aveiro IEETA Lab Associado Sistemas Inteligentes LASI Dept Econ Management Ind Engn & Tourism P-3810193 Aveiro Portugal Univ Aveiro Ctr Invest & Desenvolvimento Matemat & Aplicacoes Dept Econ Management Ind Engn & Tourism P-3810193 Aveiro Portugal

The energetic and environmental crisis currently being experienced around the world has brought an exponential growth on the demand for wind energy, and, consequently, real challenges for companies in this sector. Since this industry is relatively new, its production processes require a significant amount of human intervention and have a high variability in the process and high lead times. Therefore, there is a need to focus on the quality of the processes and their improvement. This paper presents the use of the Six Sigma DMAIC approach integrated with Business process Management, to reduce the number of defects and the repair time in a leading wind blade manufacturing company in Portugal. During the measurement and analysis phases, data was collected to understand the performance of the process and to identify the main problems and their causes. The data was analysed through several graphical and statistical techniques. In the analysis phase, process modeling was also utilized, using Business process Model and Notation, to identify the critical steps that are responsible for producing the majority of defects. One month after beginning to use this approach, it was possible to notice a reduction in the number of defects by more than 30%, in the repair time by 14%, and an increase in the process sigma level by more than 100%. Thus, this hybrid approach has allowed the company to become more competitive and responsive to the constant growth in demand. (c) 2023 The Authors. Published by ELSEVIER B.V.

关键词： Six Sigma DMAIC BPM process Capability analysis

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control of technological parameters in the process of ribbed panel forging: use of measuring equipment and mathematical modeling methods

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MEASUREMENT TECHNIQUES 2024年第10期66卷 776-784页

作者： Zharov, M. V. Preobrazhenskii, E. V. Natl Res Univ Moscow Aviat Inst Moscow Russia

This article discusses the aspects of controlling technological parameters for the isothermal forging of cross-ribbed panels, associated with strict adherence to temperature and rate conditions of deformation. However, to produce defect-free panels, because of errors and external disturbances, the control of technological parameters based only on the measurement results of temperature field sensors is often insufficient. The metal temperature at the deformation site can only be estimated indirectly. By analogy with the Kalman filter, a method is proposed for monitoring the technological parameters of the process of isothermal forging of ribbed panels based on a set of results obtained from sensors and calculated by the finite element method. The accuracy and speed of calculating a finite element forging model in four widely used specialized software products, DeForm, QForm, Forge NxT, and Simufact Forming, were studied. Comparing the data obtained during the analysis confirmed the high degree of reliability of the modeling results and demonstrated the potential possibility of controlling the technological parameters for the production of defect-free products using the proposed method. The finite element method in a two-dimensional formulation of the problem provided an acceptable velocity for numerical simulation while monitoring the progress of operations in real time. The results obtained are relevant for metallurgical enterprises producing critical parts for the aircraft and space industries. Therefore, there are increased requirements for production processes to comply with the range of permissible changes in technological parameters.

关键词： Isothermal forging Cross-ribbed panels QForm Finite element method Finite volume method Extended Kalman filter MATLAB

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data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results

Data-based modeling and control of nonlinear process systems...

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2023 AIChE Annual Meeting

作者： Abdullah, Fahim Christofides, Panagiotis D. Department of Chemical and Biomolecular Engineering University of California Los AngelesCA90095-1592 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095-1592 United States

This paper discusses recent developments in the data-based modeling and control of nonlinear chemical process systems using sparse identification of nonlinear dynamics (SINDy). SINDy is a recent nonlinear system identification technique that uses only measurement data to identify model dynamical systems in the form of first-order nonlinear differential equations. In this work, the challenges of handling time-scale multiplicities and noisy sensor data when using SINDy are addressed. Specifically, a brief overview of novel methods devised to overcome these challenges are described, along with modeling guidelines for using the proposed techniques for process systems. When applied to two-time-scale systems, to overcome model stiffness, which leads to ill-conditioned controllers, a reduced-order modeling approach is proposed where SINDy is used to model the slow dynamics, and nonlinear principal component analysis is used to algebraically ''slave'' the fast states to the slow states. The resulting model can then be used in a Lyapunov-based model predictive controller with guaranteed closed-loop stability provided the separation of fast and slow dynamics is sufficiently large. To handle high levels of sensor noise, SINDy is combined with subsampling and co-teaching to improve modeling accuracy. The challenges of modeling and controlling large-scale systems using noisy industrial data are then addressed by using ensemble learning with SINDy. After summarizing the advances, a nonlinear chemical process is used to provide an end-to-end demonstration of process modeling using sparse identification with guidelines for chemical engineering practitioners. Finally, several future research directions for the incorporation of SINDy into process systems engineering are proposed. © 2023 Elsevier Ltd. All rights reserved.

关键词： Dynamics

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Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor analysis via Diversified PID controllers

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Li, Jinli Yuan, Ye Luo, Xin Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

In Big data-based applications, high-dimensional and incomplete (HDI) data are frequently used to represent the complicated interactions among numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can process such data efficiently. Unfortunately, a standard SGD algorithm trains a single latent factor relying on the stochastic gradient related to the current learning error only, leading to a slow convergence rate. To break through this bottleneck, this study establishes an SGD-based LFA model as the backbone, and proposes six proportional-integral-derivative (PID)-incorporated LFA models with diversified PID-controllers with the following two-fold ideas: a) refining the instant learning error in stochastic gradient by the principle of six PID-variants, i.e., a standard PID, an integral separated PID, a gearshift integral PID, a dead zone PID, an anti-windup PID, and an incomplete differential PID, to assimilate historical update information into the learning scheme in an efficient way;b) making the hyper-parameters adaptation by utilizing the mechanism of particle swarm optimization for acquiring high practicality. In addition, considering the diversified PID-variants, an effective ensemble is implemented for the six PID-incorporated LFA models. Experimental results on industrial HDI datasets illustrate that in comparison with state-of-the-art models, the proposed models obtain superior computational efficiency while maintaining competitive accuracy in predicting missing data within an HDI matrix. Moreover, their ensemble further improves performance in terms of prediction accuracy.

关键词： Computational modeling Accuracy Stochastic processes Predictive models Optimization Standards Feedback control Adaptation models Prediction algorithms Machine learning algorithms High-dimensional and incomplete data latent factor analysis proportional integral derivative particle swarm optimization parameter adaptation ensemble

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