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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

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Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

UniQuad: A Unified and Versatile Quadrotor Platform Series for UAV Research and Application

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arXiv 2024年

作者： Zhang, Yichen Chen, Xinyi Liu, Peize Wang, Junzhe Zou, Hetai Pan, Neng Gao, Fei Shen, Shaojie The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

As quadrotors take on an increasingly diverse range of roles, researchers often need to develop new hardware platforms tailored for specific tasks, introducing significant engineering overhead. In this article, we introduce the UniQuad series, a unified and versatile quadrotor platform series that offers high flexibility to adapt to a wide range of common tasks, excellent customizability for advanced demands, and easy maintenance in case of crashes. This project is fully open-source at https://***/UniQuad. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Real Time Sound Source Localization Using von-Mises ResNet

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Real Time Sound Source Localization Using von-Mises ResNet

2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

A Duality-Based Approach to Inverse Kalman Filtering

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IFAC-PapersOnLine 2023年第2期56卷 10258-10263页

作者： Yibei Li Bo Wahlberg Lihua Xie Xiaoming Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Optimization and Systems Theory Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.

关键词： Inverse filtering Kalman filters duality covariance estimation identifiability

Delay-agnostic Asynchronous Coordinate Update Algorithm

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arXiv 2023年

作者： Wu, Xuyang Liu, Changxin Magnússon, Sindri Johansson, Mikael Division of Decision and Control Systems Eecs Kth Royal Institute of Technology Stockholm Sweden Department of Computer and System Science Stockholm University Stockholm Sweden

We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and block-coordinate descent as special cases. We prove that DEGAS converges under both bounded and unbounded delays under delay-free parameter conditions. We also validate by theory and experiments that DEGAS adapts well to the actual delays. The effectiveness of DEGAS is demonstrated by numerical experiments on classification problems. © 2023, CC BY.

关键词： Computation theory

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING

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arXiv 2022年

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2022, CC BY.

关键词： Cost functions

Knowledge Management Subsystem of the Intellectual Situational Center

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Knowledge Management Subsystem of the Intellectual Situation...

2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2022

作者： Simankov, Vladimir S. Buchatskiy, Pavel Yu. Teploukhov, Semen V. Buchatskaya, Victoria V. Kuban State Technological University Department of Computer Technologies and Information Security Krasnodar Russia Adyghe State University Department of Automated Systems of Processing Information and Control Maykop Russia Adyghe State University Department of Applied Mathematics Information Technology and Information Security Maykop Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

Within the framework of the situational center, a knowledge management subsystem is identified and considered, which solves the following tasks: identification and accounting for the uncertainty of the initial information, automated selection of mathematical methods for describing systems, assessment and modeling of the situation, taking into account information about the structure of the control object and its functioning at a particular point in time, provides interaction with experts and extracts knowledge from the Internet. The solution of these problems is achieved using separate interconnected modules, which makes it possible to use the situational center in various subject areas, manage complex hierarchical systems, and also make an automated choice of mathematical methods for processing information. © 2022 IEEE.

关键词： Knowledge management

Enhanced Model-Free Predictor for Latency Compensation in Remote Driving systems

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Enhanced Model-Free Predictor for Latency Compensation in Re...

IEEE Symposium on Intelligent Vehicle

作者： Lin Zhao Mikael Nybacka Malte Rothhämel Jonas Mårtensson Department of Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Remote driving plays a vital role in coordinating automated vehicles in challenging situations. Data transmission latency, however, can cause several problems in remote driving. Firstly, it can degrade the performance of remote-controlled vehicles, evident in issues like lane-following deviation and vehicle stability. Additionally, the remote control tower’s driving feedback is affected by delayed vehicle signals, leading to delayed driving experience. To address this, a model-free-based predictor is employed to compensate for the delay in remote driving. This approach does not require any dynamic model of the system and only needs tuning of two parameters to reduce communication delay. This study enhances the previous work by mitigating the amplitude of overshoot around peak points. It leverages the principle of the second-order derivative to predict the signal’s peak time and uses it to address the predictor’s overshoot issue. The effectiveness of the proposed method is validated using real car data from multiple participants in two scenarios, including Slalom and lane-following. Simulation results indicate that the proposed method can reduce prediction error by nearly 25% compared to previous works. Moreover, the solutions in this study are capable of managing not only delays in remote driving vehicles but also in traditional mechanical systems, such as CAN bus delays in conventional cars.

关键词： Intelligent vehicles Simulation Predictive models Stability analysis Delays Automobiles Mechanical systems

Differentially Private Online Federated Learning with Correlated Noise

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (ϵ, δ)DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning