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Chinese control and Decision Conference, CCDC

作者： Hongquan Ren Quan-Yong Fan Xuekui Song Hongxia Li School of Automation Northwestern Polytechnical University Xian China Ansteel Engineering Technology Corporation Limited Anshan China North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9781665478977

In this paper, the problem of intelligent fault detection for switched systems is investigated based on SVM. In order to illustrate the ability of SVM in distinguishing normal and faulty systems, a model-based analysis method is introduced in this paper. Different from the existing methods, this paper considers the influence of disturbance on the output data. The theoretical model of SVM is given, which can realize the fuzzy distinction between normal and fault system data, and describe the indistinguishable region affected by disturbance. Finally, one numerical model of aeroengine is used to illustrate the effectiveness of the proposed analysis.

关键词： Support vector machines Switched systems Analytical models Fault detection Simulation Support vector machine classification Data models

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TSN-GReID: Transformer-based Siamese Network for Group Re-Identification

TSN-GReID: Transformer-based Siamese Network for Group Re-Id...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Xiaoyan Lu Weijie Sheng Xinde Li School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665476881

Group re-identification (GReID) is an important yet less-studied task. GReID focuses on associating the group images across non-overlapping cameras. The key challenges of GReID include layout variation, membership changes, and occlusion. Most existing methods focus on the group variation but ignore the occlusion that frequently occurs in the group. In practical scenarios, so many activities like conversing, queuing, or fighting consist of groups. To this end, we design a novel Transformer-based Siamese Network for GReID (TSN-GReID) for joint learning of classification and correspondence tasks to learn more robust group features for group layout and membership changes. Furthermore, we put forward an original regrouping random patch module(RRPM) which respectively regroups the member patch embedding and member-level local features to generate group features with improved discrimination ability and more diversified coverage to deal with occlusion. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms state-of-the-art methods by 4.6 % Rank-1 on the CUHK-SYSU Group (CSG) dataset and by 7.1% Rank-1 on the DukeMTMC Group dataset.

关键词： automation Layout Transformers Cameras Task analysis Robots

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A LEARNING SYSTEM FOR MOTION PLANNING OF FREE-FLOAT DUAL-ARM SPACE MANIPULATOR TOWARDS NON-COOPERATIVE OBJECT

arXiv

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arXiv 2022年

作者： Wang, Shengjie Cao, Yuxue Zheng, Xiang Zhang, Tao Department of Automation Tsinghua University Beijing China Beijing Institute of Control Engineering Beijing China Department of Computer Science City University of Hong Kong Hong Kong

Recent years have seen the emergence of non-cooperative objects in space, like failed satellites and space junk. These objects are usually operated or collected by free-float dual-arm space manipulators. Thanks to eliminating the difficulties of modeling and manual parameter-tuning, reinforcement learning (RL) methods have shown a more promising sign in the trajectory planning of space manipulators. Although previous studies demonstrate their effectiveness, they cannot be applied in tracking dynamic targets with unknown rotation (non-cooperative objects). In this paper, we proposed a learning system for motion planning of free-float dual-arm space manipulator (FFDASM) towards non-cooperative objects. Specifically, our method consists of two modules. Module I realizes the multi-target trajectory planning for two end-effectors within a large target space. Next, Module II takes as input the point clouds of the non-cooperative object to estimate the motional property, and then can predict the position of target points on an non-cooperative object. We leveraged the combination of Module I and Module II to track target points on a spinning object with unknown regularity successfully. Furthermore, the experiments also demonstrate the scalability and generalization of our learning system. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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A Data-Driven Passive Islanding Detection Scheme

A Data-Driven Passive Islanding Detection Scheme

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IEEE Power India Conference

作者： M. Venkateswara Reddy Sourav De Ranjana Sodhi Grid Automation & Control group National Renewable Energy Laboratory Golden United States Dept. of Electrical Engineering Indian Institute of Technology Ropar Punjab India

ISBN: (纸本)9781665459310

This paper proposes a novel statistical parameters based data driven approach for passive islanding detection. The proposed scheme relies in the voltage data, measured at the point of common coupling, and is comprised of three stages. The Stage-1 quickly detects what is termed as coarse islanding, Stage-2 computes the decaying DC detector (DDCD), and 3) finally, the digital relay logic (DRL) is devised in Stage-3. The proposed scheme is tested as per the IEEE-1547, UL 1741 and IEC-62116 standards. Various tests are investigated on the modified IEEE-13 bus system, modeled in PSCAD/EMTDC, under stringent test scenarios such as power mismatch, induction motor starting effect, capacitor bank switching and various level of loads changes, considered for both single and multiple DG systems. The test results reveal that the proposed scheme can detect islanding in less than 2 cycles, and results in nearly zero non detection zone (NDZ). Also, the scheme can very well be used for single-phase microgrids too as it relies only on the single phase voltage information.

关键词： Islanding Voltage measurement Induction motors Detectors Switches Microgrids Threshold voltage

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AGV Detection in Industrial Environments through Computer Vision

AGV Detection in Industrial Environments through Computer Vi...

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IEEE Latin American Robotics Symposium, LARS

作者： Wilson Eduardo Barioni Igor Pardal Latini André Lazzaretti Marco Teixeira Flavio Neves Lucia Valeria Ramos De Arruda Automation and Advanced Control System Laboratory (LASCA) Graduate Program in Electrical and Computer Engineering (CPGEI) Universidade Tecnologica Federal do Parana (UTFPR) Curitiba Parana Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Automated Guided Vehicles (AGVs) are usual in industrial settings, with an increasing economic impact on processes. They move through numerous environments inside factories, commonly navigating long distances, while performing several activities. The constant detection of such vehicles, especially in cases of maintenance and safety, is a main issue in an industry setting. Usually, this information could be provided by a supervisory system, but many applications are not so large as to make such a system viable. Thus, a solution via machine learning and computer vision is developed in this work, by using simple cameras, such as the factories' security cameras. As the industrial environment is a scenario with a lot of variation and noise, the Transfer Learning technique is used to improve the training step of the developed AGV detection system. Finally, a database with 1067 images is used to build and validate the model, achieving a result greater than 80% of F1-score for various confidence values.

关键词： Training Computer vision Remotely guided vehicles Computational modeling Transfer learning Cameras Production facilities

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Data-Based Efficient Off-Policy Stabilizing Optimal control Algorithms for Discrete-Time Linear Systems via Damping Coefficients

arXiv

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arXiv 2024年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy Q-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear Positive Semi-Markov Jump Systems with Time-varying Delays

Asynchronous $\mathcal{L}_{1}$-gain Filtering of Nonlinear P...

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Chinese control Conference (CCC)

作者： Chao Ma Hang Fu Yidao Ji Wei Wu School of Mechanical Engineering University of Science and Technology Beijing China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper, the $\mathcal{L}_{1}$ -gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design approach. More precisely, the mode-dependent filters are designed in terms of practical observed modes instead of true system modes, such that less conservatism can be achieved. In addition, the effect of time-varying delays is taken into account for more robustness and applicability. By selecting suitable stochastic Lyapunov-Krasovskii functions and applying the linear programming method, sufficient conditions are established to fulfill the desired $\mathcal{L}_{1}$ -gain performance. Eventually, the illustrative simulation is performed to verify the effectiveness of our developed control scheme.

关键词： Maximum likelihood detection Sufficient conditions Filtering Hidden Markov models Stochastic processes Nonlinear filters Linear programming

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Validation of Moment Monitoring Results in Rolling Mill Stand Spindles

Validation of Moment Monitoring Results in Rolling Mill Stan...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Olga A. Gasiyarova Boris M. Loginov Andrey A. Radionov Vadim R. Gasiyarov Vadim R. Khramshin Department of Material Science LLC NPP Uchtekh-Profi Chelyabinsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia Department of Automation and Control Moscow Polytechnic University Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

The authors highlight the need for the development and industrial deployment of online monitoring systems for the elastic moment on the rolling mill stand spindles. An example of a 5000 plate mill was used to demonstrate that these systems are made due to the need for a reliable moment assessment in dynamic modes. This problem is relevant for the development of control systems limiting the maximum (amplitude) values of moments when the workpieces enter the stand. The shock loading causes a multifold increase of the dynamic moment over the steady rolling moment. This results in the fatigue breakdown of spindle connections., as well as mill failures and emergency stops. The authors provide information on a telemetric elastic moment monitoring system installed on a 5000 mill. The data were validated in the industrial context. To achieve this, the authors propose a procedure where experimental research is carried out in two stages. During the first stage, the electromagnetic moment of the motor is compared to the spindle moment in the quasi-steady rolling mode when the elastic properties of the spindle are not manifested. During the second stage, the elastic moments of the upper and lower roll motors are assessed during 9 passes of reverse rolling. Some conclusions are made about the proportion of roll motor loads, spindle moment amplitudes, and the relevance of aligning rolling moments through the improvement of electric drive speed control algorithms.

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Condensed Representation of Machine Learning Data

arXiv

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arXiv 2022年

作者： Zengin, Rahman Salim Sezer, Volkan Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Autonomous Mobility Group Electrical and Electronics Engineering Faculty Istanbul Technical University Istanbul Turkey

Training of a Machine Learning model requires sufficient data. The sufficiency of the data is not always about the quantity, but about the relevancy and reduced redundancy. Data-generating processes create massive amounts of data. When used raw, such big data is causing much computational resource utilization. Instead of using the raw data, a proper Condensed Representation can be used instead. Combining K-means, a well-known clustering method, with some correction and refinement facilities a novel Condensed Representation method for Machine Learning applications is introduced. To present the novel method meaningfully and visually, synthetically generated data is employed. It has been shown that by using the condensed representation, instead of the raw data, acceptably accurate model training is possible. Copyright © 2022, The Authors. All rights reserved.

关键词： K-means clustering

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection

A Multi-Kernel Principal Component Analysis Method for Quali...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Lingxia Mu Biyu Lei Ding Liu School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665465373

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction.

关键词： Correlation automation Fault detection Feature extraction Eigenvalues and eigenfunctions Reliability Matrix decomposition

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