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2022 International Conference on Electrical Machines, ICEM 2022

作者： Garcia-Calva, T.A. Gyftakis, K.N. Skarmoutsos, G.A. Mueller, M. Morinigo-Sotelo, D. Romero-Troncoso, R. De J. University of Guanajuato Research Group HSPDigital Department of Electronic Engineering SalamancaGTO 36700 Mexico Technical University of Crete School of Electrical and Computer Engineering 73100 Greece University of Edinburgh School of Engineering and the Institute for Energy Systems EdinburghEH9 3FB United Kingdom Universidad de Valladolid Research Group HSPDigital-ADIRE ITAP ValladolidES VLL 47001 Spain Autonomous University of Queretaro Research Group HSPDigital Department of Mechatronics San Juan del RioQRO 76806 Mexico

ISBN: (数字)9781665414326

ISBN: (纸本)9781665414326

In this paper, demagnetization detection in permanent magnet generators is analyzed using advanced signal processing techniques. The merits of these methods are thoroughly compared against the commonly adopted short-time Fourier transform analysis. Moreover, traditional signal processing techniques may not be suitable for fault detection when the machine operates at variable speed. Under such condition, interference between the fundamental frequency of the machine and the fault harmonic can lead to false alarms. Simulation results demonstrate effective performance and feasibility of the proposed methods for demagnetization detection of permanent generators operating under non-stationary conditions. © 2022 IEEE.

关键词： Spectrum analysis

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The Tale of Errors in Microservices: Extended Abstract 25

The Tale of Errors in Microservices: Extended Abstract

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Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of computer systems

作者： I-Ting Angelina Lee Zhizhou Zhang Abhishek Parwal Milind Chabbi Department of Computer Science & Engineering Washington University in St. Louis St. Louis USA Programming Systems Group Uber Technologies Inc. Sunnyvale USA Marketplace Configurations Uber Technologies Inc. Sunnyvale USA

ISBN: (纸本)9798400715938

Microservice architectures have become the de facto paradigm for building scalable, service-oriented systems. Although their decentralized design promotes resilience and rapid development, the inherent complexity leads to subtle performance challenges. In particular, non-fatal errors - internal failures of remote procedure calls that do not cause top-level request failures - can accumulate along the critical path, inflating latency and wasting *** this work, we analyze over 11 billion RPCs across more than 6,000 microservices at Uber. Our study shows that nearly 29% of successful requests experience non-fatal errors that remain hidden in traditional monitoring. We propose a novel latency-reduction estimator (LR estimator) to quantify the potential benefit of eliminating these errors. Our contributions include a systematic study of RPC error patterns, a methodology to estimate latency reductions, and case studies demonstrating up to a 30% reduction in tail latency.

关键词： RPC errors

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Performance Evaluation of YOLO Models in Varying Conditions: A Study on Object Detection and Tracking

Performance Evaluation of YOLO Models in Varying Conditions:...

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International Conference on computer and Applications (ICCA)

作者： Ahmed S. Mekled Slim Abdennadher Omar M. Shehata Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University Cairo Egypt Faculty of Engineering German University Cairo Egypt Computer Science Faculty of Informatics German International University Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Object detection and tracking are essential components in autonomous systems, enabling accurate environmental perception and real-time decision-making. Despite the widespread use of advanced frameworks such as YOLO, RCNN, SSD, FPN, and RetinaNet in applications like autonomous driving and surveillance, these systems often experience degraded performance in images with poor lighting conditions caused by adverse weather and other external factors. Recent developments have introduced various deep-learning and image-processing techniques to address these challenges. This paper provides an extensive evaluation of three state-of-the-art object detection models-YOLOv5, YOLOv8, and YOLOv9 under diverse experimental conditions, including varying confidence thresholds (50%, 60%, 70%, and 80%), different object poses, lighting conditions (day and night), and camera configurations (static and dynamic). The evaluation is conducted using key performance metrics such as mean Average Precision (µAP), Intersection over Union (IoU), Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), Identity F1 score (IDF1), precision, and recall. The study offers a comprehensive analysis of the models' performance and suitability for different real-world applications, delivering practical insights for model selection and deployment. Additionally, it highlights potential areas for future research aimed at enhancing model robustness and effectiveness in challenging environments.

关键词： YOLO Performance evaluation Analytical models Technological innovation Accuracy Surveillance Lighting Real-time systems Robustness Object tracking

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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Clustering graph data: the roadmap to spectral techniques

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Discover Artificial Intelligence 2024年第1期4卷 7页

作者： Mondal, Rahul Ignatova, Evelina Walke, Daniel Broneske, David Saake, Gunter Heyer, Robert Faculty of Computer Science Otto-von-Guericke-University Universitätsplatz 2 Saxony-Anhalt Magdeburg39106 Germany Lange Laube 12 Lower Saxony Hannover30159 Germany Faculty of Technology Bielefeld University Universitätsstraße 25 North Rhine-Westphalia Bielefeld33615 Germany Faculty of Process and Systems Engineering Otto-von-Guericke-University Universitätsplatz 2 Saxony-Anhalt Magdeburg39106 Germany Multidimensional Omics Analyses Group Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Bunsen-Kirchhoff-Straße 11 North Rhine-Westphalia Dortmund44139 Germany Faculty of Computer Science Technische Hochschule Ingolstadt Esplanade 10 Bavaria Ingolstadt85049 Germany

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, leading to the conception of spectral clustering algorithms. Spectral clustering algorithms have been one of the most effective in grouping similar data points in graph data models. In this paper, we have compiled 16 spectral clustering algorithms and compared their computational complexities, after an overview of graph data models and graph database models. Furthermore, we provided a broad taxonomy to classify most existing clustering algorithms and discussed the taxonomy in detail. © The Author(s) 2024.

关键词： Digital storage

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Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded systems with GPUs

arXiv

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arXiv 2023年

作者： Eisoldt, Marc Mock, Alexander Porrmann, Mario Wiemann, Thomas Computer Engineering Group Osnabrück University Osnabrück Germany Knowledge-based Systems Group Osnabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany DFKI Niedersachsen Plan-based Robot Control Osnabrück Germany

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in literature that is able to solve it in real-time. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation. Copyright © 2023, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded systems with GPUs

Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded System...

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IEEE International Conference on Robotic Computing (IRC)

作者： Marc Eisoldt Alexander Mock Mario Porrmann Thomas Wiemann Computer Engineering Group Osnabrück University Osnabrück Germany Knowledge-based Systems Group Osnabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany DFKI Niedersachsen Plan-based Robot Control Osnabrück Germany

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in the literature that is able to solve it in realtime. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation.

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

Model predictive control for tracking using artificial refer...

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IEEE Conference on Decision and Control

作者： P. Krupa J. Köhler A. Ferramosca I. Alvarado M.N. Zeilinger T. Alamo D. Limon Computer Science Group Gran Sasso Science Institute (GSSI) L’Aquila Italy ETH Zürich Institute for Dynamic Systems and Control Zürich Switzerland Department of Management Information and Production Engineering University of Bergamo Via Marconi 5 Dalmine (BG) Italy Department of Systems Engineering and Automation Universidad de Sevilla Seville Spain

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.

关键词： Surveys Asymptotic stability Costs Tutorials Optimization Predictive control

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Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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A Dual Cavity Pleated Structures Soft Pneumatic Actuator for Soft Robotic Applications

A Dual Cavity Pleated Structures Soft Pneumatic Actuator for...

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AFRICON Conference

作者： Shakiru Olajide Kassim Krisjanis Visnevskis Vahid Vaziri Sumeet S. Aphale Department of Computer Engineering Technology Artificial Intelligence Robotics and Mechatronic Systems Group (ARMS) The Federal Polytechnic Damaturu Yobe State of Nigeria Centre for Advanced Dynamic Research (CADR) School of Engineering University of Aberdeen Aberdeen UK

The overwhelming popularity and growth in evolution of soft robotics is largely credited to the ever-increasing design and capability portfolio of soft pneumatic actuators (SPAs). Despite the numerous challenges that complicate their modeling and control, viz: geometrical and material nonlinearities, intrinsic dynamic behaviour and diverse control requirements, they have found more application in flexible robots design. To this end, this work presents a design of a novel dual cavity pleated structure soft pneumatic actuator (DCPS-SPA) targeted towards providing enhanced dexterous applications. In order to achieve this, the finite-element model (FEM) of the DCPS-SPA is presented to describe its dynamic behaviour using the Ogden hyperelasticity model parameters. The bending angle and contact force are experimentally validated and compared with the FEM results and a single cavity type SPA, respectively. Experimental results obtained show a promising possibility of utilizing the proposed DCPS-SPA for enhanced dexterous manipulation applications.

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