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International Conference on electrical Machines, ICEM

作者： André M. Silva Miguel Aires Filipe Correia Carlos Henggeler Antunes Fernando J. T. E. Ferreira Continental Engineering Services Portugal Unipessoal Lda. Porto Portugal University of Coimbra Institute for Systems Engineering and Computers (INESC) Coimbra Portugal University of Coimbra Institute of Systems and Robotics (ISR-UC) Coimbra Portugal

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

In this paper, a comprehensive, multi-variable, multi-objective, optimization method is proposed for the design of radial-flux synchronous reluctance machines with high efficiency and power density. In the constrained multi-objective optimization problem, a relatively large number of critical parameters of the stator core, rotor core, and stator winding are simultaneously controlled through an evolutionary algorithm. A novel flux barrier shape and position mechanism, based on polynomial functions, is proposed to evaluate a greater number of cross-sectional geometries. The electromagnetic performance of the solutions is assessed with a non-parametric magnetic equivalent circuit method, which allows for fast and accurate calculations. A design optimization example is provided for validation of the proposed method. The selected design has a peak power density of 6.5 kW/kg at 5000 r/min and a peak torque density of 12.4 N. m/kg. The weight of the active parts is 32.8 kg (winding and electrical steel cores) and the volume is 8.1 liters, which can be considered within the published state-of-the-art.

关键词： Stator cores Power system measurements Torque Density measurement Magnetic cores Windings Stator windings Rotors Steel Electromagnetics

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Costmap-based Local Motion Planning using Deep Reinforcement Learning

Costmap-based Local Motion Planning using Deep Reinforcement...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Luís Garrote João Perdiz Urbano J. Nunes Department of Electrical and Computer Engineering and the Institute of Systems and Robotics University of Coimbra Portugal

Local motion planning is an essential component of autonomous robot navigation systems as it involves generating collision-free trajectories for the robot in real-time, given its current position, the map of the environment and a goal. Considering an a priori goal path, computed by a global planner or as the output of a mission planning approach, this paper proposes a Two-Stream Deep Reinforcement Learning strategy for local motion planning that takes as inputs a local costmap representing the robot’s surrounding obstacles and a local costmap representing the nearest goal path. The proposed approach uses a Double Dueling Deep Q-Network and a new reward model to avoid obstacles while trying to maintain the lateral error between the robot and the goal path close to zero. Our approach enables the robot to navigate through complex environments, including cluttered spaces and narrow passages, while avoiding collisions with obstacles. Evaluation of the proposed approach was carried out in an in-house simulation environment, in five scenarios. Double and Double Dueling architectures were evaluated; the presented results show that the proposed strategy can correctly follow the desired goal path and, when needed, avoid obstacles ahead and recover back to following the goal path.

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Enhancing Impulse Response-Based SSVEP Decoding Method by Incorporating Temporal Local Correlation 7

Enhancing Impulse Response-Based SSVEP Decoding Method by In...

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7th International Conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Huang, Jiayang Wang, Jiaxi Yang, Pengfei Xiong, Bang Zhang, Zhi-Qiang School of Computer Science and Technology The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xidian University China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds United Kingdom

ISBN: (纸本)9783800762910

Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of SSVEPs, leading to decreased classification performance. Therefore, we propose an SSVEP decoding method that calculates template signals and spatial filters using the subject's impulse responses and periodic impulses, incorporating the temporally local correlation coefficient in the SSVEP decoding process. Then, we employ a combined correlation coefficient for SSVEP classification. The proposed method addresses the temporal instability of SSVEPs, with template signals that exhibit strong individual characteristics. Utilizing a public benchmark dataset, we achieved a classification accuracy of 87.43% and an ITR of 256.1 bits/min at 1s time window, demonstrating the performance of this method. Compared to other classical spatial filtering methods, the proposed method demonstrates superior classification performance, which can enhance the practical usability of SSVEP-BCI systems. © VDE VERLAG GMBH.

关键词： Wiener filtering

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A Pneumatic Anchoring Mechanism for Improved Stability in Soft Magnetic Manipulators for Surgery*

A Pneumatic Anchoring Mechanism for Improved Stability in So...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Zaneta Koszowska Michael Brockdorff Peter Lloyd James H. Chandler Pietro Valdastri STORM Lab Institute of Robotics Autonomous Systems and Sensing School of Electronic and Electrical Engineering University of Leeds Leeds UK

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

This paper presents a synergistic soft robotic actuation approach, whereby pneumatically induced anchoring is applied to a Soft Magnetic Manipulator (SMM) to enable improved post navigation stability and tip manipulation accuracy. A SMM is equipped with an integrated pneumatic expansion chamber (anchor) and tool channel to enhance stability during interventional procedures. The pneumatic anchor is tested for its position-locking effectiveness during SMM tip loading. Additionally, tip manipulation stability with and without the anchoring system activated is assessed by measurement of the SMM’s ability to trace a parametric circle using an integrated laser fiber. The anchoring mechanism showed a mean improvement in tip actuation accuracy of 73.26%. The system is further demonstrated in the case study of a brain phantom, with the prototype equipped with a laser fiber. The SMM was navigated through a brain fold through application of external magnetic fields exploiting the dual External Permanent Magnet (dEPM) platform. Upon arrival at target site, the anchor was deployed to stabilize the SMM’s tip while laser targeting was performed, demonstrating the potential of this approach for procedures requiring high precision.

关键词： Accuracy Navigation Measurement by laser beam Surgery Laser stability Soft robotics Manipulators Soft magnetic materials Thermal stability Fiber lasers

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Multi-Source Renewable Energy Forecasting Using CNN-LSTM Hybrid Model

Multi-Source Renewable Energy Forecasting Using CNN-LSTM Hyb...

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International Middle-East Power System Conference, MEPCON

作者： Asmaa Mohamed El-Saieed Hanan A. Mosallam Dept. of Computers and Systems Mansoura High Institute of Engineering and Technology Mansoura Egypt Department of Electrical Engineering Mansoura Higher Institute of Engineering and Technology Mansoura Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Accurate forecasting of renewable energy output is vital for enhancing the efficiency and stability of integrated power systems. This study introduces a novel deep learning approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to predict energy production from solar, wind and hydropower sources. The hybrid CNN-LSTM model utilizes historical meteorological data, including solar irradiance, cloud cover, temperature, wind speed, and precipitation, to forecast future energy generation with high precision. The CNN component extracts spatial features from the weather data, while the LSTM component captures temporal dependencies and long-term trends. For solar energy forecasting, the model estimates solar radiation based on factors like temperature and humidity, while wind energy predictions rely on wind speed variations, and hydropower output is forecasted using rainfall and watershed information. This comprehensive approach enables accurate and reliable predictions of renewable energy production. The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R 2 ), etc. Results demonstrate that the hybrid model significantly improves the accuracy and reliability of renewable energy forecasts. This advanced tool provides valuable insights for energy managers, utility companies, and policymakers, aiding in optimizing resource allocation and enhancing the sustainability and resilience of energy grids.

关键词： Accuracy Hydroelectric power generation Predictive models Power system stability Hybrid power systems Data models Power system reliability Convolutional neural networks Wind forecasting Forecasting

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Forward-looking Sonar Based Autonomous Aquaculture Inspection

Forward-looking Sonar Based Autonomous Aquaculture Inspectio...

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OCEANS

作者： Daniel Rosa David Cabecinhas Fausto Ferreira Institute for Systems and Robotics Instituto Superior Técnico Lisbon LABUST Faculty of Electrical Engineering and Computing Zagreb

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

In recent years, there has been a rising interest in aquaculture development due to the increasing demand for food driven by population's growth. Aquaculture infrastructures require regular monitoring to assure their preservation and food safety, traditionally conducted by slow and risky manual methods involving professional divers. To solve this issue, an autonomous aquaculture net inspection using an Autonomous Underwater Vehicle (AUV) is proposed. The AUV is equipped with a Forward-Looking Sonar (FLS), which provides acoustic images of the environment. By using image processing techniques to remove noise, the vehicle's distance and orientation relative to the net cage are measured. The net cage's center is estimated, using a circle regression, to compute the relative orientation. Then, velocity and orientation references are generated, so the AUV can cover the net whole perimeter, while staying at a desired distance. Finally, many tests are performed to analyze the vehicle's behavior while encircling the net.

关键词： Autonomous underwater vehicles Current measurement Sonar measurements Measurement uncertainty Sonar Sea measurements Inspection

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED 14

A High-Precision Edge Detection Algorithm Based on Improved ...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Yuan, Yeborui Chen, Wenyuan Tang, Jie Yang, Jia School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics 110016 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9798331506056

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology. © 2024 IEEE.

关键词： Contrastive Learning

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

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Asian Journal of Control 2022年第1期24卷 388-400页

作者： Moaveni, Bijan Rashidi Fathabadi, Fatemeh Molavi, Ali Center of excellence for Modelling and Control of Complex Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Western Michigan University MI United States Institute of Robotics and Industrial Informatics Polytechnical University of Catalonia Barcelona Spain

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Traction motors

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Trajectory Control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive Control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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2024 China Automation Congress, CAC 2024

作者： Wang, Yiqing Xia, Changqing Jin, Xi Xu, Chi Zhang, Yanzhu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368604

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive Control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive Control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities. © 2024 IEEE.

关键词： Extended Kalman filters

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Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

arXiv

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arXiv 2023年

作者： Almeida, Eurico Silva, Bruno Batista, Jorge The Institute of Systems and Robotics University of Coimbra Coimbra3030-219 Portugal The Department of Electrical and Computers Engineering Faculty of Science and Technology University of Coimbra Coimbra3030-290 Portugal

This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle re-identification (V-ReID). While most V-ReID work uses a combination of complex multi-branch architectures to extract robust and diversified embeddings towards re-identification, we advocate that simple and lightweight architectures can be designed to fulfill the Re-ID task without compromising performance. We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability. We combine a ResNet50 global branch architecture with a BotNet self-attention branch architecture, both designed within a Loss-Branch-Split (LBS) strategy. We argue that specialized loss-branch-splitting helps to improve re-identification tasks by generating specialized re-identification features. A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size. In addition, we designed an improved solution to leverage additional metadata, such as camera ID and pose information, that uses 97% less parameters, further improving re-identification performance. In comparison to state-of-the-art (SoTA) methods, our approach outperforms competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1. Overall, our work provides important insights into improving vehicle re-identification and presents a strong basis for other retrieval tasks. Our code is available at the link. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

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