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International Conference on Computational Problems of electrical engineering (CPEE)

作者： Nazar Karpiuk Halyna Klym Ivanna Vasylchychyn Department of Specialized Computer Systems Lviv Polytechnic National University Lviv Ukraine Department of Theoretical and General Electrical Engineering Lviv Polytechnic National University Lviv Ukraine

This scientific research is devoted to the analysis and testing of the Haar cascade classifier method for face recognition. The paper also justifies the choice of algorithm and provides a description of the implementation of the system on which the analysis was conducted. During the research, the developed system was tested on a sample of facial photographs that included images at different distances from the camera, different lighting conditions, and with different facial positions in the camera’s plane of view. An analysis of the test results was conducted, and conclusions were drawn regarding the factors that should be considered when designing and using a facial recognition system to achieve high accuracy.

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How to Swarm: Entropy-Based Model Parameter Analysis

How to Swarm: Entropy-Based Model Parameter Analysis

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IEEE Southeastcon

作者： Nathan Rayon Stefan Wheeless Gabriel Broom Hakki Erhan Sevil Department of Computer Science University of West Florida (UWF) Pensacola FL USA Department of Electrical and Computer Engineering University of West Florida (UWF) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida (UWF) Pensacola FL USA

In this study, we implemented our entropy-based swarm model to an autonomous waypoint navigation application for a group of multi-rotor Unmanned Aerial Vehicles (UAVs) through a set course in free space. Multi-UAVs of multiple group sizes were run with variations in parameters, and the path lengths traveled were measured to determine the most efficient configurations, and we investigated the impact of varying parameters on the swarm behavior performance. The simulation of the UAV kinematics and environment was performed in AirSim. The results show that the swarm model with different parameter setup operates successfully and the effects of the parameter selection on our multi-UAV swarm model are discussed.

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A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

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Verifying Nonlinear Neural Feedback systems using Polyhedral Enclosures

arXiv

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot systems in Power Line Inspection Tasks

arXiv

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arXiv 2023年

作者： Silano, Giuseppe Bednar, Jan Nascimento, Tiago Capitan, Jesus Saska, Martin Ollero, Anibal The Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Systems Universidade Federal da Paraiba Brazil The GRVC Robotics Laboratory University of Seville Spain

This paper presents a multi-layer software architecture to perform cooperative missions with a fleet of quad-rotors providing support in electrical power line inspection operations. The proposed software framework guarantees the compliance with safety requirements between drones and human workers while ensuring that the mission is carried out successfully. Besides, cognitive capabilities are integrated in the multi-vehicle system in order to reply to unforeseen events and external disturbances. The feasibility and effectiveness of the proposed architecture are demonstrated by means of realistic simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Software architecture

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Forecasting enhancement of Wind Power Generation using Adversarial Networks: A Data Driven Approach

Forecasting enhancement of Wind Power Generation using Adver...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Swapnil Shelake Rishikesh Kondavathini Mahedin Ansari Punit Sonwadekar Sunny Kumar Prerna Goswami Faruk Kazi Department of Electrical Engineering VJTI Centre Of Excellence Control and Nonlinear Dynamic Systems (CoE CNDS) Mumbai India Department of Electrical Engineering (General Engineering) ICT Mumbai India

Wind energy is major contributor in the power system. However, the unpredictability of wind energy will have a substantial impact on the electrical grid, mostly because of the variable wind speed. Wind energy's consistency and dependability may cause instability, however the issue can be solved by scheduling generation and load. For that economical load dispatch planning is carried out by load dispatch centers. Wind power forecasts can be highly helpful for dispatch planning as well as selling and bidding in the energy market. Prediction can be done using different techniques like Numerical Weather Prediction (NWP), Artificial Intelligence and Machine Learning, time series analysis etc. Prediction using machine learning is incredibly accurate and quick compared to other techniques, particularly when employing Generative Adversarial Network. It is inspired by two-player zero-sum, where the Generator and Discriminator compete against each other. Gated recurrent Unit (GRU) based adversarial networks have fewer errors as compared to others.

关键词： Wind energy Wind speed Machine learning Wind power generation Predictive models Generative adversarial networks Generators

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis 5

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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5th International Conference on System Reliability and Safety engineering, SRSE 2023

作者： Li, Ruiqi Wang, Kai Zeng, Peng Cao, Jiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Harbin Institute of Technology Department of Electrical Engineering China

ISBN: (纸本)9798350305944

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed. © 2023 IEEE.

关键词： Failure analysis

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Vibrational Stabilization of Cluster Synchronization in Oscillator Networks

arXiv

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arXiv 2023年

作者： Qin, Yuzhen Nobili, Alberto Maria Bassett, Danielle S. Pasqualetti, Fabio Department of Mechanical Engineering University of California RiversideCA United States Perceptual Robotics Laboratory at the IIM Institute Department of Excellence in Robotics and A.I. Scuola Superiore Sant’Anna Pisa Italy Department of Bioengineering Department of Electrical & Systems Engineering Department of Physics & Astronomy The Department of Psychiatry Department of Neurology University of Pennsylvania The Santa Fe Institute United States

Cluster synchronization is of paramount importance for the normal functioning of numerous technological and natural systems. Deviations from normal cluster synchronization patterns are closely associated with various malfunctions, such as neurological disorders in the brain. Therefore, it is crucial to restore normal system functions by stabilizing the appropriate cluster synchronization patterns. Most existing studies focus on designing controllers based on state measurements to achieve system stabilization. However, in many real-world scenarios, measuring system states, such as neuronal activity in the brain, poses significant challenges, rendering the stabilization of such systems difficult. To overcome this challenge, in this paper, we employ an open-loop control strategy, vibrational control, which does not requires any state measurements. We establish some sufficient conditions under which vibrational inputs stabilize cluster synchronization. Further, we provide a tractable approach to design vibrational control. Finally, numerical experiments are conducted to demonstrate our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Brain

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

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Iranian Conference on electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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