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A Comparison of Induction Motor Fault Detection Using Single and Double-Channel Phase Currents

A Comparison of Induction Motor Fault Detection Using Single...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Robert Wright Poria Fajri Xingang Fu Arash Asrari Department of Electrical and Biomedical Engineering University of Nevada Reno Reno USA Department of Electrical and Computer Engineering Purdue University Northwest Hammond USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Rotating machines, such as Induction Motors (IMs), play critical functions for industrial equipment, owing to their reliability and robustness. However, costly downtime occurs when these motors sustain faults, such as damaged bearings. Condition-Based Monitoring (CBM) methods and early fault detection are paramount in safeguarding machinery from unexpected damage and downtime. With the rapid development of artificial intelligence, Machine Learning (ML) techniques have emerged at the forefront of monitoring the health of IMs. This paper contributes a study comparing single and double-channel phase current approaches towards Convolutional Neural Networks (CNN) employing Short-Time Fourier Transform (STFT) based spectrograms. Motor current signals can be readily and non-invasively collected from the motor compared to other signals. Regarding classification accuracy, the double-channel method is an effective approach for bearing fault classification using motor phase current signals.

关键词： Time-frequency analysis Accuracy Induction motors Fourier transforms Tensors Fault detection Robustness Convolutional neural networks Monitoring Spectrogram

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A Gradient Boosting Algorithm to Predict Energy Consumption for Home Applications 2

A Gradient Boosting Algorithm to Predict Energy Consumption ...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivakumar, V.G. Arunfred, N. Anusha, N. Balakrishnan, C. Meenakshi, B. Sujatha, S. Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chengalpattu Tamil Nadu Chennai India Vellore Institute of Technology School School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Energy-efficient solutions are in high demand in today's fast-developing technological environment, necessitating the use of sophisticated predictive modeling methods. Conventional forecasting systems have problems given the particular aspects related to home demand for energy. As a consequence, new strategies are constantly being created for more accurately foreseeing future occurrences. Weather, user habits, and advancements in technology interfere with precise forecasts even more. This study provides a novel application of the Gradient Boosting approach to solve such obstacles and enhance energy use prediction for residential applications. Through utilising the electrical energy of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), the suggested method takes into account prior use patterns, appliance-specific data, and external factors like as climate. The model's response to changes in the actual world provides exact estimates, providing homeowners with the information they require to make energy-saving decisions. This method also investigates the comprehending of Gradient Boosting models, showing key characteristics that affect energy usage patterns. Stakeholders focus heavily on interpretability to make informed decisions with regard to ways to save energy and resource allocation. © 2024 IEEE.

关键词： Machine learning

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Bidirectional Performance-considered 11 kW Integrated Wired/Wireless EV Charging System with Effective Operation Strategy

Bidirectional Performance-considered 11 kW Integrated Wired/...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Ju-A Lee Dong Hyeon Sim Hyeonu Jo Chae-Lyn Kim Dong-Wook Yoo Byoung Kuk Lee Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Department of Electrical and Biomedical Engineering Hanyang University Seoul South Korea

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

In this paper, a circuit configuration of a bidirectional integrated wired/wireless charging system is proposed for electric vehicle (EV). In the conventionally proposed integrated charging system, a structural limitation is identified. Through consideration of the structural limitation, an enhanced integrated charging system is proposed to address the fundamental problems of the conventional system. As a result, the control strategy for the effective operation of the proposed system is explained in wired/wireless operation modes. Additionally, superior performance is demonstrated through experimental validation considering battery charging and discharging situations.

关键词： Electric vehicle charging Batteries

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IoT-Embedded Traffic Cones with CNN-based Object Detection to Roadwork Safety 2

IoT-Embedded Traffic Cones with CNN-based Object Detection t...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saillaja, V. Rehaman Pasha, Mohammed Krishnaveni, S. Ravinder, Bathini Srinivasan, S. Nandagopal, V. SRM Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India S.A.Engineering College Department of Information Technology Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India School of Engineering Mohan Babu University Department of Electrical and Electornics Engineering Andrapradesh Tirupathi India

ISBN: (纸本)9798350327533

Motorists and construction workers both need to use extreme caution while passing through roadwork zones. This research provides a novel strategy for increasing the security of roadwork by combining Internet of Things (IoT) devices with object recognition through Convolutional Neural Networks (CNNs). To improve communication and data sharing with a centralized control center, this technology equips conventional traffic cones with integrated IoT devices. Traffic cones with IoT sensors track things like their location, the brightness of the surrounding area, and the volume of vehicles on the road. Useful information about the roadwork environment is supplied wirelessly from the cones to the control center. Live video feeds from the IoT-enabled traffic cones are analyzed using a CNN-based object identification algorithm. The model can view and follow vehicles, pedestrians, and other possible threats in a road construction zone. Construction workers and traffic management officials are immediately notified of potential safety hazards through real-time object detection warnings. In terms of roadwork safety, this integrated system has several advantages, such as enhanced knowledge of the current situation, faster identification of potential dangers, and more effective traffic management. Roadwork zones may be made safer for employees and the public using IoT technology and cutting-edge computer vision methods. © 2024 IEEE.

关键词： Hazard Detection Object Detection Roadwork Environment Safety Alerts Traffic Cones

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Modeling of hydrodynamics and mechanical aspects of a Wave Energy Converter (WEC) to understand the main principles

Modeling of hydrodynamics and mechanical aspects of a Wave E...

引用

2022 International Symposium on Power Electronics, electrical Drives, Automation and Motion, SPEEDAM 2022

作者： Delmiglio, Francesco Frosini, Lucia Anglani, Norma University of Pavia Dept. of Civil Engineering and Architecture Pavia Italy University of Pavia Department of Electrical Computer Biomedical Engineering Pavia Italy

ISBN: (数字)9781665484596

ISBN: (纸本)9781665484596

This work aims at illustrating a first-approach modeling of an all-purpose wave energy converter (WEC), and reproducing its allegedly behavior, once there is a need to forecast its production from a location, where all the monitoring data are available, to another location. A few commercial software provide such a feature but, to proficiently use them, several practical parameters are needed and this makes the learning quite hard. In the proposed model, some parameters are offered and can be tuned according with the device to reproduce, and its known data (e.g. power matrix). The Simulink/Matlab model needs the couple (H,T) (wave height and period) as input, and it provides mechanic, and then, electric power as output: using a time series of (Hi,Ti), it is able to produce a suitable power profile, useful to be used-for instance-in feasibility studies. © 2022 IEEE.

关键词： Simulink

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Digital Historical Pavia: 3D Modeling in Educational Context for Cultural Heritage Promotion 3rd

Digital Historical Pavia: 3D Modeling in Educational Contex...

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3rd Florence Heri-Tech International Conference, Florence Heri-Tech 2022

作者： Cantoni, Virginio Dondi, Piercarlo Gullotti, Alessio Lombardi, Luca Mosconi, Mauro Nour, Roberto Setti, Alessandra Department of Electrical Computer and Biomedical Engineering University of Pavia Via A. Ferrata 5 Pavia27100 Italy

ISBN: (纸本)9783031203015

The "Digital Historical Pavia" project aims to prove the value of digital media technologies for enhancing promotion and accessibility of Cultural Heritage, while teaching students the basics of computer graphics. It integrates technology into attractive and meaningful presentations of local cultural assets of the 14 th – 16 th century when the city of Pavia experienced several transformations. This paper summarizes the main results of the project showing the outcomes of three case studies: (i) the setup of an educational and experiential room for the exhibition "1525–2015. Pavia, the Battle, the Future. Nothing was the same again";(ii) the virtual reconstruction of the city of Pavia in the Renaissance, including a timelapse of the urban evolution of the city during the 16 th century;(iii) the 3D modeling of the Ark of St. Augustine, a complex funerary monument of the second half of the 14 th century, and the development of a related online serious game. All the 3D models were created by students of the master’s degree in computer engineering of the University of Pavia, showing the potentiality of an experiential learning approach for teaching computer science and computer graphics concepts. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： 3D modeling

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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High Coupling Efficiency Grating Couplers for Silicon Photonics

High Coupling Efficiency Grating Couplers for Silicon Photon...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Petropoulos, P. Vitali, V. Marchetti, R. Bucio, T. Dominguez Gardes, F.Y. Lacava, C. Optoelectronics Research Centre University of Southampton Southampton United Kingdom Optoelectronics Res. Ctr. Univ. of Southampton Southampton U. Kingdom and Elec. Univ. of Pavia Computer and Biomedical Engineering Department Pavia Italy University of Pavia Electrical Computer and Biomedical Engineering Department Pavia Italy

We present techniques for optimizing the coupling efficiency between silicon photonic and optical fibre systems based on the adoption of back-end-of-line CMOS-compatible waveguide grating couplers. © 2023 IEEE.

ISBN: (纸本)9798350312614

关键词： grating couplers silicon photonics

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers 5

Observing Generator Performance by Finite Element Analysis C...

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5th International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2023

作者： Habibi, Muhammad Afnan Ali Irfani, Mochamad Farhan Gumilar, Langlang Kusumawardana, Arya Aripriharta Baskoro, Angger Universitas Negeri Malang Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia National Yang Ming Chiao Tung University Dept. Electrical Engineering and Computer Sciences Hsinchu Taiwan

ISBN: (纸本)9798350369717

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies. © 2023 IEEE.

关键词： Finite element method

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Enhancing Wildlife Protection: Poacher Detection Using Machine Learning Models

Enhancing Wildlife Protection: Poacher Detection Using Machi...

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International Conference on Developments in eSystems engineering (DESE)

作者： Omar Mohamed Gad Hagar Elbatanouny Eqab Almajali Jawad Yousaf Abir Hussain Department of Electrical Engineering University of Sharjah Sharjah UAE Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

Wildlife conservation is a pressing global concern, with illegal poaching posing a severe threat to many endangered species. In recent years, advanced technologies like machine learning and digital signal processing have shown significant potential in supporting conservation efforts by detecting and preventing illegal poaching. This work explores the integration of these technologies into wildlife conservation strategies, focusing on their role in identifying and combating poachers. Two datasets are utilized: ‘Poacher Detection 3 Classes” and “Illegal Poacher Detection”. Several machine learning models including Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Convolutional Neural Networks (CNN) are applied to detect illegal activities in these datasets. For the first dataset, the SVM model achieved the best accuracy of ${9 3 \%}$, while the CNN model performed best on the second dataset, achieving accuracy of $75 \%$. In the final phase, both datasets were combined, and augmented images were introduced to increase data diversity. On the combined dataset, the Random Forest model achieved the highest accuracy of ${8 3 \%}$. These results demonstrate the effectiveness of machine learning in improving wildlife conservation efforts.

关键词： Support vector machines Radio frequency Accuracy Wildlife Digital signal processing Pressing Convolutional neural networks Decision trees Protection Random forests

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