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A Novel Zero Common-Mode Voltage Modulation Strategy for Dual Three-Phase PWM Inverter Based on Unified Voltage Modulation

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A Novel Zero Common-Mode Voltage Modulation Strategy for Dua...

Energy, Power and Electrical engineering (EPEE), International Conference on

作者： Jinpeng Kan Yongqing Meng Changyi Li Wenjie Chen Fengjiao Li Yixin Shi School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operation of the system. This article proposes a zero common-mode voltage modulation strategy based on unified voltage modulation for a 60° Y-connected six-phase permanent magnet synchronous motor system driven by a dual three-phase inverter. The theoretical basis and zero common-mode implementation scheme of unified modulation are analyzed, and the effectiveness of the proposed zero common-mode voltage modulation strategy is finally verified through simulation.

关键词： Pulse width modulation inverters Performance evaluation Simulation Modulation Voltage Switches Permanent magnet motors Inverters Permanent magnets Switching circuits

Risk-Averse Learning with Non-Stationary Distributions

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

Predicting Concentration Levels of Air Pollutants by Transfer Learning and Recurrent Neural Network

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arXiv 2025年

作者： Fong, Iat Hang Li, Tengyue Fong, Simon Wong, Raymond K. Tallón-Ballesteros, Antonio J. Department of Computer and Information Science University of Macau Taipa China Faculty of Engineering University of New South Wales Sydney Australia Department of Languages and Computer Systems University of Seville Seville Spain

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy;it incurred shorter training time than randomly initialized recurrent neural networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Long short-term memory

FLOOR EXTRACTION AND DOOR DETECTION FOR VISUALLY IMPAIRED GUIDANCE

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arXiv 2024年

作者： Berenguel-Baeta, Bruno Guerrero-Viu, Manuel de Nova, Alejandro Bermudez-Cameo, Jesus Perez-Yus, Alejandro Guerrero, Jose J. Instituto de Investigacion en Ingenieria de Aragon Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. The combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. This combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. The proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance. © 2024, CC BY.

关键词： Floors

A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation

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A Novel Random Pulse Width Modulation for PMSM Drive System ...

Energy, Power and Grid (ICEPG), International Conference on

作者： Jinpeng Kan Yongqing Meng Yixin Shi Fengjiao Li Wenjie Chen Changyi Li School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation.

关键词： Motor drives Electromagnetic interference Voltage Switches Pulse width modulation Permanent magnet motors Power electronics Electromagnetics

Residual Autoencoder Deep Neural Network for Electrical Capacitance Tomography

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computers, Materials & Continua 2022年第12期73卷 6307-6326页

作者： Wael Deabes Kheir Eddine Bouazza Department of Computer Science in Jamoum Umm Al-Qura UniversityMakkah25371Saudi Arabia Computers and Systems Engineering Department Mansoura UniversityMansoura35516Egypt Laboratoire D’Informatique et des Technologies de L’Information D’Oran(LITIO) University of Oran31000OranAlgeria

Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography(ECT)image ***,there is still a need to make these image reconstruction results faster and of better ***,Deep Learning(DL)is flourishing and is adopted in many *** DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks(ANNs).An ECT image reconstruction model using DNN is proposed in this *** proposed model mainly uses Residual Autoencoder called(ECT_ResAE).Alarge-scale dataset of 320 k instances have been generated to train and test the proposed ECT_ResAE *** instance contains two vectors;a distinct permittivity distribution and its corresponding capacitance *** capacitance vector has been modulated to generate a 66×66 image,and represented to the ECT_ResAE as an *** scalability and practicability of the ECT_ResAE network are tested using noisy data,new samples,and experimental *** experimental results show that the proposed ECT_ResAE image reconstruction model provides accurate reconstructed *** achieved an average image Correlation Coefficient(CC)of more than 99%and an averageRelative ImageError(IE)around 8.5%.

关键词： ECT image reconstruction deep learning residual autoencoder IE CC

Machine Learning Based Intelligent Routing for VDTNs

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Machine Learning Based Intelligent Routing for VDTNs

International Conference on computer Communications and Networks (ICCCN)

作者： Shiyi Liu Haiying Shen Brian L. Smith Volker Fessmann Department of Computer Science University of Virginia Department of Engineering Systems and Environment University of Virginia Federal Highway Administration

Delay Tolerant Networks (DTNs) are wireless mobile networks resilient to disruptions, significant latency, and incomplete paths between source and destination (S-D). Vehicular Delay Tolerant Network (VDTNs) is a subclass of DTNs. Most existing routing algorithms for VDTNs rely on predetermined rules that are inflexible to changes in node features (such as location, contact history, and node relationships). Moreover, given the extremely dynamic network architecture of the VDTN, the routing protocols must be flexible to the varying node features. Thus, the purpose of this work is to develop an intelligent routing system capable of determining the optimal routing method given a pair of S-D nodes. In this study, a novel self-adaptive routing method for VDTNs based on machine learning (ML) is proposed. Using real-time features of S-D nodes, the proposed method selects the optimal routing method. Using the Opportunistic Network Environment (ONE) simulator based on a real-world taxi trace, we evaluated the performance of our ML-based self-adaptive routing method employing various ML models. Evaluation results show that our method achieves up to 34.34% greater success rate and up to 23.75% lower average message delivery delay than existing methods.

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Advanced Channel Coding Designs for Index-Modulated Fluid Antenna systems

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arXiv 2024年

作者： Faddoul, Elio Kraidy, Ghassan M. Psomas, Constantinos Krikidis, Ioannis The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus The Department of Electronic Systems Norwegian University of Science and Technology Gjøvik Norway

The concept of fluid antennas (FAs) has emerged as a promising solution to enhance the spectral efficiency of wireless networks, achieved by introducing additional degrees of freedom, including reconfigurability and flexibility. In this paper, we investigate the use of index-modulated (IM) transmissions within the framework of FA systems, where an FA position is activated during each transmission interval. This approach is motivated by the common characteristics exhibited by FAs and IM transmissions, which entails the use of a single radio-frequency chain. From this perspective, we derive a closed-form expression for the bit error rate of IM-FAs considering spatial correlation, demonstrating superior performance compared to conventional IM systems. To enhance the performance of IM-FAs in correlated conditions, channel coding techniques are applied. We first analyze a set partition coding (SPC) scheme for IM-FAs to spatially separate the FA ports, and provide a tight performance bound over correlated channels. Furthermore, the spatial SPC scheme is extended to turbo-coded modulation where the performance is analyzed for low and high signal-to-noise ratios. Our results reveal that through the implementation of channel coding techniques designed for FAs and IM transmission, the performance of coded IM-FAs exhibits notable enhancements, particularly in high correlation scenarios. © 2024, CC BY-NC-SA.

关键词： Signal to noise ratio

Trust Establishment for the Role-Based Collaborative Multi-Robot systems

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Trust Establishment for the Role-Based Collaborative Multi-R...

IEEE International Conference on systems, Man and Cybernetics

作者： Behzad Akbari Haibin Zhu Ya-Jun Pan Department of Mechanical Engineering Advanced Control and Mechatronics Laboratory Dalhousie University Halifax NS Canada Department of Computer Science and Mathematics Collaborative Systems Laboratory Nipissing University NorthBay ON Canada

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways.

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