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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Fernando J. T. E. Ferreira Dept. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

When an oversized and/or faulty three-phase squirrel-cage induction motor (SCIM) needs to be rewound, there is an opportunity to redesign its stator winding as a function of its actual load cycle, aiming at improving its efficiency, power factor, and reliability. This paper proposes a systematic methodology to optimize the stator winding of in-operation, line-operated, fixed-speed SCIMs, with two goals, namely, (i) optimization of the fundamental component of magnetizing flux to the actual load cycle, reducing the original core saturation level and nominal power of the motor, and (ii) optimization of the pitch and turns of the coils to minimize the winding copper losses and the airgap magnetomotive force harmonic content. Hybrid computing approaches using the first principles and nondeterministic methods can be used for winding optimization. The proposed methodology has been applied to three oversized 5.5 kW, 4-pole SCIMs, driving industrial ceramic paste mixers, and led to a 30-45% reduction in the line current, a 10-25% reduction in the input active power, and a 20-35% increase in the power factor, enabling an upgrade of the efficiency class from "IE0"/IE1 to IE3/IE4. The decrease of the motor line current reduces the Joule losses in the upstream cables and transformers, freeing power transmission capacity in the power supply network. The proposed strategy is a cost-effective alternative to the replacement of old oversized motors with new well-sized motors, avoiding mechanical readaptation of the fixing and coupling systems and allowing for higher efficiency and power factor levels than those of new well-sized motors. It also fosters copper recycling, the electric motor circular economy, and the local economic activity associated with the motor repair/rewinding sector.

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Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation 17

Numerical study on the flow field for deep-sea hydrothermal ...

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17th Asian Congress of Fluid Mechanics, ACFM 2023

作者： Xu, Xue-Han Liu, Jing-Cheng Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin300072 China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k-ϵ turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles. © The Institution of engineering & Technology 2023.

关键词： Flow fields

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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Use of Virtual Reality to Facilitate Engineer Training in the Aerospace Industry

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Machine Graphics and Vision 2023年第2期32卷 19-44页

作者： Paszkiewicz, Andrzej Salach, Mateusz Wydrzyński, Dawid Woźniak, Joanna Budzik, Grzegorz Bolanowski, Marek Ganzha, Maria Paprzycki, Marcin Cierpicki, Norbert Department of Complex Systems Rzeszow University of Technology Rzeszów Poland Department of Machine Design Rzeszow University of Technology Rzeszów Poland Department of Management Systems and Logistics Rzeszow University of Technology Rzeszów Poland Systems Research Institute Polish Academy of Sciences Warszawa Poland The Faculty of Electrical and Computer Engineering Rzeszow University of Technology Rzeszów Poland

This work concerns automation of the training process, using modern information technologies, including virtual reality (VR). The starting point is an observation that automotive and aerospace industries require effective methods of preparation of engineering personnel. In this context, the technological process of preparing operations of a CNC numerical machine has been extracted. On this basis, a dedicated virtual reality environment, simulating manufacturing of a selected aircraft landing gear component, was created. For a comprehensive analysis of the pros and cons of the proposed approach, four forms of training, involving a physical CNC machine, a physical simulator, a software simulator, and the developed VR environment were instantiated. The features of each training form were analysed in terms of their potential for industrial applications. A survey, using the Net Promoter Score method, was also conducted among a target group of engineers, regarding the potential of use of each training form. As a result, the advantages and disadvantages of all four training forms were captured. They can be used as criteria for selecting the most effective training form. © 2023 Faculty of Applied Informatics and Mathematics - WZIM, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Aerospace industry

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Exploring Continuous Awareness Modelling for Improving Worker Safety and Trust*

Exploring Continuous Awareness Modelling for Improving Worke...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Andrey Solovov Mohammad Zarei Vishal Gautam Bruno Ferreira Gustavo Assunção Antonio Marin-Hernandez Paulo Menezes Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal Universidad Veracruzana Xalapa Mexico

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The Industry 5.0 Human-Robot Interaction (HRI) goals have the potential for significantly reducing production costs, but require factory workers to trust that robots will not cause them physical harm when sharing the workspace in order to be achieved. To this end, we propose a model that adjusts the robot’s actions to an estimate of the workers’ awareness of its behavior. Its premise is that mutual awareness should be continuous, and if no recent visual contact has been established, then the robot should adapt its range of motion and speed in order to minimize the intersection between the robot’s space and the worker’s space. On the other hand, if the worker steadily establishes visual contact from times to times, the robot operates under a normal or a faster configuration. After a period of habituation, this awareness model can be further tweaked to continuously improve production times, while improving worker’s safety and trust. We present preliminary results of a pilot study with 32 participants, to asses if the existence of this awareness model impacts the outcomes of the combined task. The obtained tepidly optimistic results are the key to build upon in future designs.

关键词： Visualization Adaptation models Costs Service robots Human-robot interaction Production facilities Safety Motion control Fifth Industrial Revolution

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WildGaussians: 3D Gaussian Splatting in the Wild 38

WildGaussians: 3D Gaussian Splatting in the Wild

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Kulhanek, Jonas Peng, Songyou Kukelova, Zuzana Pollefeys, Marc Sattler, Torsten Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Science ETH Zurich Switzerland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework. © 2024 Neural information processing systems foundation. All rights reserved.

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A Bi-Level Optimization Approach to Joint Trajectory Optimization for Redundant Manipulators

arXiv

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arXiv 2024年

作者： Fried, Jonathan Paternain, Santiago The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States

In this work, we present an approach to minimizing the time necessary for the end-effector of a redundant robot manipulator to traverse a Cartesian path by optimizing the trajectory of its joints. Each joint has limits in the ranges of position, velocity and acceleration, the latter making jerks in joint space undesirable. The proposed approach takes this nonlinear optimization problem whose variables are path speed and joint trajectory and reformulates it into a bi-level problem. The lower-level formulation is a convex subproblem that considers a fixed joint trajectory and maximizes path speed while considering all joint velocity and acceleration constraints. Under particular conditions, this subproblem has a closed-form solution. Then, we solve a higher-level subproblem by leveraging the directional derivative of the lower-level value with respect to the joint trajectory parameters. In particular, we use this direction to implement a Primal-Dual method that considers the path accuracy and joint position constraints. We show the efficacy of our proposed approach with simulations and experimental results. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Big Data Bot with a Special Reference to Bioinformatics

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computers, Materials & Continua 2023年第5期75卷 4155-4173页

作者： Ahmad M.Al-Omari Shefa M.Tawalbeh Yazan H.Akkam Mohammad Al-Tawalbeh Shima’a Younis Abdullah A.Mustafa Jonathan Arnold Biomedical Systems and Informatics Engineering Department Yarmouk UniversityIrbid21163Jordan Department of Medicinal Chemistry and Pharmacognosy Yarmouk UniversityIrbid21163Jordan Department of Electrical Computer and Software EngineeringUniversity of Ontario Institute of TechnologyOshawaL1H7K4Canada Department of Mechanical Engineering University of MosulMosul41001Iraq Genetics Department University of GeorgiaAthens30602GAUSA

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential *** scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough *** data should go through several processes,including mining,data processing,analysis,and *** study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human *** software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line *** software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel ***,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data *** a result,data collection from the web will become faster and less expensive,with no need for manual data *** software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

关键词： Bioinformatics big data data extraction bot drug design

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PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture

arXiv

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arXiv 2024年

作者： Barros, T. Garrote, L. Conde, P. Coombes, M.J. Liu, C. Premebida, C. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Dept. of Aeronautical and Automotive Engineering LUCAS Lab Loughborough University United Kingdom

3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. This characteristic often results in highly similar LiDAR scans from adjacent rows, leading to descriptor ambiguity and, consequently, compromised retrieval performance. In this work, we address the challenges of 3D LiDAR place recognition in horticultural environments, particularly focusing on inter-row ambiguity by introducing three key contributions: (i) a novel model, PointNetPGAP, which combines the outputs of two statistically-inspired aggregators into a single descriptor;(ii) a Segment-Level Consistency (SLC) model, used exclusively during training to enhance descriptor robustness;and (iii) the HORTO-3DLM dataset, comprising LiDAR sequences from orchards and strawberry fields. Experimental evaluations conducted on the HORTO-3DLM and KITTI Odometry datasets demonstrate that PointNetPGAP outperforms state-of-the-art models, including OverlapTransformer and PointNetVLAD, particularly when the SLC model is applied. These results underscore the model’s superiority, especially in horticultural environments, by significantly improving retrieval performance in segments with higher ambiguity. The dataset and the code will be made publicly available at https://***/Cybonic/*** © 2024, CC BY.

关键词： Deep learning

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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