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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Milapji Singh Gill Jingxi Zhang Andreas Wortmann Alexander Fay Institute of Automation Technology Helmut Schmidt University Hamburg Germany Institute of Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Germany Chair of Automation Ruhr University Bochum Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Cyber-Physical Production Systems necessitate efficient configuration and continuous reconfiguration to adapt to evolving requirements and shifting goals. This process involves integrating various system structures to derive new functions and behaviors. The concept of the Digital Twin, which allows for effective integration and testing, is instrumental in facilitating this task. However, a fundamental prerequisite is that Digital Twins of individual system components must be composed efficiently and in accordance with dynamic System-of-Systems. We address this challenge by introducing an approach for the automated horizontal and vertical composition of Digital Twins, aimed at minimizing manual intervention and enhancing adaptability. Therefore, a pipeline specifically designed for this goal is proposed, which includes the generation of new functions and behaviors. This approach is intended to provide a foundation for future research.

关键词： Production systems Instruments Pipelines Digital twins Manufacturing automation Testing

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High-Precision Dimensional Measurement for Digital Crown Part of Smartwatch from Microscopic Image

High-Precision Dimensional Measurement for Digital Crown Par...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Hongqiang Lyu Yuhang Zhang Liang Bai Shengjun Xu Huangwei Zhong Yuan Liu Faculty of Electronic and Information Engineering School of Automation Science and Engineering Xi'an Jiao tong University Shaanxi China School of Information and Control Engineering Xi'an University of Architecture and Technology Shaanxi China

Digital crown is a precision part used for human-computer interaction in the smartwatch. The diameter of the digital crown determines its grade. Currently, mainly used Coordinate Measuring Machine(CMM) to measure the diameter of digital crown, but the measurement method has the problem of high cost and low efficiency. To address the problem, we propose a high-precision dimensional measurement system for digital crown part of smartwatch. The system consists of four modules: microscopic image acquisition module, central host, machine vision software and PLC control module. The image of digital crown is captured by the microscopic image acquisition module and sent to the software through the central host. In the machine vision software, we propose a defect detection algorithm using ResNet to filter defective digital crown in real time, and we design a circle detection algorithm based on edge point tracking to measure the diameter of digital crown. The measurement result is sent to the PLC control module through wireless transmission and control the mechanical suction cups to place the part. Finally, we create a dataset to test the performance of the system, the defect detection model achieves an accuracy of 97.33% and a runtime of 47ms, the dimensional measurement algorithm achieves a maximum error of 0.003878mm, a grading accuracy of 95.29%, and a measurement time of 465ms.

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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Automating RTGC with PID control: Utilizing Camera-Based Image Processing and Object Detection

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IFAC-PapersOnLine 2024年第7期58卷 299-304页

作者： Steven Bandong Yul Y. Nazaruddin Augie Widyotriatmo Muhammad R. Miransyahputra Yan Setiaji Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

The high traffic in the container yard requires effective management, and one method to address this is through automation. The Rubber Tyred Gantry Crane (RTGC) plays a crucial role in container yards. Automating the RTGC involves determining the container’s location and sway angle to provide feedback for the control system. The advancements in computer vision technology offer a unique solution to tackle these challenges. This paper proposes image processing as a means to sense the sway angle and MobileNet SSD to detect the container’s location. The proposed method yields accurate measurement results and is integrated with optimized PID-PD for position and sway angle control in RTGC. The effectiveness of the proposed method is demonstrated through successful performance in both simulation and experiments conducted on a laboratory-scale RTGC prototype.

关键词： port automation RTGC control system computer vision optimization camera based sensor

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Digital CEOs in Digital Enterprises:Automating,Augmenting,and Parallel in Metaverse/CPSS/TAOs

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 820-823页

作者： Juanjuan Li Rui Qin Sangtian Guan Xiao Xue Peng Zhu Fei-Yue Wang with State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Economics and Management Nanjing University of Science and TechnologyNanjing 210094China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan... 详细信息

关键词： exceptional Auto advancing

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Enhancing Wind Power Profitability Through Integrated Clusters in the Electricity Market

Enhancing Wind Power Profitability Through Integrated Cluste...

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2023 IEEE Asia Meeting on Environment and Electrical engineering, EEE-AM 2023

作者： Dinh, Ngoc Sang Nguyen, Tung Linh Truong, Viet Anh University of Architecture Hochiminh City Hochiminh City University of Technology and Education Hochiminh City Viet Nam Faculty of Control and Automation Hanoi City Viet Nam Hochiminh City University of Technology and Education Faculty of Electrical and Electronics Engineering Hochiminh City Viet Nam

ISBN: (纸本)9798350381061

In the context of wind power sources participating in the electricity market, they frequently encounter significant challenges arising from their inherent uncertainties. Compared to the initial projections, deviations in their power output could potentially lead to decreased revenue for investors. This necessitates compensation or penalties due to shortfalls in wind power output. This study thoroughly investigates and assesses proposed integration scenarios to mitigate the risk of such penalties or compensation requirements. These scenarios involve the integration of multiple wind turbines and wind farms, aiming to enable mutual offsetting of electricity output power in cases of emergency power shortages. Through simulation-based experiments on the IEEE 30-bus system, the findings of this study unequivocally demonstrate that the financial viability of wind power plants within integrated clusters exceeds that of their standalone economic operations. © 2023 IEEE.

关键词： Power markets

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Model Reduction of Cooperative Systems Using Separable Energy Functions

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IFAC-PapersOnLine 2024年第5期58卷 96-101页

作者： Arijit Sarkar Yu Kawano Jacquelien M.A. Scherpen Jan C Willems Center for Systems and Control Discrete Technology and Production Automation ENTEG Faculty of Science and Engineering University of Groningen Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan

In this paper, we develop a model reduction method for cooperative nonlinear systems via their variational systems. Inspired by scalable stability analysis of cooperative systems based on separable functions, we provide novel L1 differential observability and L∞ differential reachability functions and study them by using vector-valued functions. These vector-valued functions characterize not-so-important state variables. By truncating them, model reduction is performed, which preserves cooperativity and stability properties.

关键词： Nonlinear systems monotone systems model reduction balanced realization contraction

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A Dual-Channel Nonlinear Method for Crude Copper Grade Prediction Based on LSTM-Attention and GWO-RF

A Dual-Channel Nonlinear Method for Crude Copper Grade Predi...

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第43届中国控制会议

作者： Qinghang Guan Chunxi Yang Xiufeng Zhang Jin Zhang Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan International Joint Laboratory of Inteligent Control and Application of Advanced Equipment Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Converter blowing is the key process method for refined copper extraction, characterized as a large-hysteresis, weakly robust, nonlinear system. The grade of crude copper directly impacts the quality and technological level of refined copper,making it essential to establish a predictive model for crude copper grade. In this paper, a new two-channel nonlinear method LAGR-ELM is proposed. Initially, wavelet denoising method is used to improve the data quality. Subsequently, the first channel is based on LSTM combined with Attention mechanism to predict the crude copper grade by using its ability to capture the hidden temporal information of the data, and the second channel employs the Grey Wolf Optimizer to optimize the Random Forest algorithm. Then, a nonlinear aggregation mechanism based on ELM is used to address the inherent shortcomings of single methods and linear combinations. The effectiveness is validated using real data from a copper smelting plant in *** comparing with eight prediction methods, the simulation results show that LAGR-ELM can achieve better prediction results.

关键词： Wavelet denoising LSTM Random forests Extreme learning machines Nonlinear combination Two-channel prediction Crude copper grade prediction

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Transfer-Learning of a LiDAR Detection Algorithm Pre-Trained on Autonomous Driving Data for Roadside Infrastructure Application

Transfer-Learning of a LiDAR Detection Algorithm Pre-Trained...

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International Conference on Intelligent Transportation

作者： Daniel Lengerer Mathias Pechinger Carsten Markgraf Faculty of Electrical Engineering Technical University of Applied Sciences Augsburg Germany Chair of Traffic Engineering and Control Technical University of Munich Germany

This study investigates the optimization of Light Detection and Ranging (LiDAR) object detection on roadside data for automated driving through transfer-learning. A State-of-the-art object detection algorithm, trained on an extensive dataset from Automated Vehicles (AVs), is evaluated for its applicability to roadside sensors. To address the scarcity of roadside data, a novel sensor setup was introduced, enabling simultaneous data collection from AVs and roadside poles. The performance of a pre-trained model on both AV and roadside data demonstrated a significant decline, with the roadside data yielding only 82% of the performance achieved on AV data. However, by fine-tuning the pre-trained model with a small subset of roadside data, a remarkable improvement to 94% of the AV data based performance was achieved. These findings highlight the necessity of transfer-learning and emphasize its potential to enhance object detection accuracy in roadside scenarios. This study contributes to the advancement of automated driving technology, paving the way for more safe and efficient transportation systems.

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Efficient Homography Estimation Using a Recursive Algorithm with Mixture Weighted Gaussian Kernels

SSRN

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SSRN 2023年

作者： Hashemzadeh, Farzad Kumbasar, Tufan Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Control and Automation Engineering tufan Istanbul Technical University Istanbul Turkey

In this study, we propose an efficient recursive algorithm, named GK-RLS, defined with a mixture of weighted Gaussian kernels for efficient homography estimation. By defining the homography estimation problem as a least square problem and optimizing the estimation parameters based on minimizing expected estimation errors, GK-RLS offers efficient incremental processing of feature points, rather than processing them as a batch like RANSAC, resulting in reduced computation time (CT) when handling a large number of paired features. To address real-world challenges such as noise and outliers commonly encountered in feature extraction and pairing, GK-RLS incorporates a small-pass filter defined with Gaussian kernels to effectively attenuate their resulting large prediction errors, thus reducing outlier drawbacks. The algorithm's effective stopping criteria are established based on a concept akin to RANSAC, with termination occurring when the estimated homography matrix yields low geometric error for a predefined portion of paired feature points. Leveraging its iterative structure and effective stopping criteria, GK-RLS estimates the homography matrix using only a limited number of feature points, resulting in a smaller CT compared to RANSAC while having a similar estimation performance. Extensive evaluations, including sensitivity analysis, a drone simulation, and experimental implementation, demonstrate the superiority of GK-RLS over RANSAC, especially concerning the required CT. Overall, GK-RLS presents a promising solution for robust and efficient homography matrix estimation in various real-world scenarios that require process data in high sampling frequencies. © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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