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Flux-weakening control of PMSM Based on Model Predictive control

Flux-weakening Control of PMSM Based on Model Predictive Con...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Wenjing Wang Chun Xiao Yan Liu Yu Wan Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Wuhan University of Technology Wuhan China Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Wuhan University of Technology Foshan China

In this study, an effective flux-weakening control for permanent magnet synchronous motor (PMSM) is proposed, which combines model predictive control (MPC) with vector control. On the one hand, in order to achieve full-speed domain control, the optimal working point of the motor running is tracked and the reference current is calculated according to the feedback information in the speed control loop. On the other hand, MPC replaces the three PI controllers of the speed loop and the current loop in the traditional vector control, which not only does not need to set the PI parameters, but also improves the rapidness and accuracy of the speed control system. The results show that, compared with traditional vector control, the proposed control method has a great dynamic response and disturbance rejection performance in the full-speed domain.

关键词： Tracking loops PI control Torque Velocity control Predictive models Permanent magnet motors Synchronous motors

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A grid-operated NPC inverter control using voltage and current control loops

A grid-operated NPC inverter control using voltage and curre...

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Khlid Ben Hamad Doudou N. Luta Department of Electrical and Electronic Engineering Faculty of Engineering Fezzan University Fezzan Lybia Mechanical and Mechatronic Engineering Departement Faculty of Engineering Cape Peninsula University of Technology Bellville South Africa

The goal of this work is to model a dual loop controlled 3-level (3-L) neutral point clamped (NPC) inverter that is operating in grid-tied mode. The adopted control strategy includes voltage and current regulation in addition to a phase-locked-loop for synchronizing the grid's phase and frequency with that of the NPC inverter. The power source is configured to generate 1.3 MW AC power at 600 V from a 1.54 MW proton exchange membrane (PEM) fuel cell (FC) stack that runs at 1400 V. The NPC inverter is fitted with an LCL filter that was positioned between the grid and the inverter to ensure that the amount of system harmonics is decreased. The arrangement also ensures that the inverter meets the required standards, such as the IEEE 519 & IEC 61000-3-6. Matlab (Simelectrical) is utilized for the modeling and simulation. In overall, the designed system performed excellently based on the observed simulation results.

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Economic Optimal Scheduling of Microgrids Based on Model Predictive control

Economic Optimal Scheduling of Microgrids Based on Model Pre...

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Automation, Robotics and computer Engineering (ICARCE), international conference on

作者： Shang Gao Chi Zhang Yifan Wu State Grid Beijing Electric Power Company Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Microgrids, as independent and controllable power systems, are key to integrating renewable energy sources. However, the stochastic nature of renewables and load fluctuations complicates dispatch modeling. This study addresses system uncertainties by developing a two-level stochastic programming model with compensation and proposes a model predictive control (MPC)-based algorithm to solve it. Simulation results show that the proposed method reduces operating costs by 10.9% compared to traditional deterministic models, demonstrating improved operational economy and practical application potential.

关键词： Renewable energy sources Uncertainty Fluctuations Stochastic processes Microgrids Predictive models Programming Prediction algorithms Load modeling Predictive control

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6th IFIP international conference on Internet of Things, IFIP IoT 2023

6th IFIP International Conference on Internet of Things, IFI...

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6th IFIP international conference on Internet of Things, IFIP IoT 2023

ISBN: (纸本)9783031458774

The proceedings contain 63 papers. The special focus in this conference is on Internet of Things. The topics include: Blockchain-Based Secure Noninvasive Glucometer and Automatic Insulin Delivery System for Diabetes Management;an Efficient and Secure Mechanism for Ubiquitous Sustainable Computing System;understanding Security Challenges and Defending Access control Models for Cloud-Based Internet of Things Network;fog Computing in the Internet of Things: Challenges and Opportunities;A 2-Colorable DODAG Structured Hybrid Mode of Operations Architecture for RPL Protocol to Reduce Communication Overhead;role-Based Access control in Private Blockchain for IoT Integrated Smart Contract;VXorPUF: A Vedic Principles - Based Hybrid XOR Arbiter PUF for Robust Security in IoMT;Easy-Sec: PUF-Based Rapid and Robust Authentication Framework for the Internet of Vehicles;fortiRx: Distributed Ledger Based Verifiable and Trustworthy Electronic Prescription Sharing;a Survey of Pedestrian to Infrastructure Communication System for Pedestrian Safety: System Components and Design Challenges;survival: A Smart Way to Locate Help;federated Edge-Cloud Framework for Heart Disease Risk Prediction Using Blockchain;understanding Cybersecurity Challenges and Detection Algorithms for False Data Injection Attacks in Smart Grids;comprehensive Survey of Machine Learning Techniques for Detecting and Preventing Network Layer DoS Attacks;power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques;Lite-Agro: Exploring Light-Duty Computing Platforms for IoAT-Edge AI in Plant Disease Identification;farmIns: Blockchain Leveraged Secure and Reliable Crop Insurance Management System;PTSD Detection Using Physiological Markers;a Signal Conditioning Circuit with Integrated Bandgap Reference for Glucose Concentration Measurement;detection of Aircraft in Satellite Images using Multilayer Convolution Neural Network;computer vision Based 3D Model Floor Construction

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An Automated Detection of Amyotrophic Lateral Sclerosis from Resting-State MEG Data Using 3D Deep Convolutional Neural Network

An Automated Detection of Amyotrophic Lateral Sclerosis from...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Samanta, Kaniska Roy, Sujit Marchand-Pauvert, Veronique Dora, Shirin Duguez, Stephanie Singh, Muskaan Prasad, Girijesh Bhattacharyya, Saugat Ulster University Intelligent System Research Center United Kingdom Brainalive Research Pvt. Ltd. Kanpur208001 India Sorbonne Universités Upmc Univ Paris 06 Cnrs Inserm Laboratoire d'Imagerie Biomédicale France Loughborough University Dept. of Computer Science United Kingdom School of Biomedical Sciences Ulster University United Kingdom

ISBN: (纸本)9798350337020

A novel 3D deep convolutional neural network (3D-CNN) model called MEGNet3D has been proposed in the paper. MEGNet3D is designed to differentiate between amyotrophic lateral sclerosis (ALS) and healthy individuals from their resting state (eyes open and eyes closed condition) sensor-level magnetoencephalography (MEG) data. The raw MEG data is initially transformed into their time-frequency representation, which are then used as inputs to MEGNet3D. Both magnetometer and gradiometer recordings have been investigated separately. The proposed model exhibits an accuracy of over 75% for most classification conditions. Thus, MEGNet3D is capable of handling high subject variability and shows that spectral-temporal representation of resting-state MEG data yields relevant neural markers related to the existence of ALS. Furthermore, it has also been observed resting state with eyes closed yields better classification accuracy as compared to the resting state with eyes open condition. © 2023 IEEE.

关键词： Amyotrophic lateral sclerosis (ALS) deep learning magneto encephalography (MEG) vision transformer

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Design of Interactive Teaching System Based on computer vision Technology

Design of Interactive Teaching System Based on Computer Visi...

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computer Technology and Information Science (ISCTIS), international Symposium on

作者： Yanan Chen Fengying Huang School of Information Science & Technology Xiamen University Tan Kah Kee College Zhangzhou China

With the development of computer technology, visual algorithms and camera technology, non-contact human-computer interaction have gradually become the mainstream of people's daily life, and the campus should also follow the trend of the times to establish an epoch-making and more interactive modern teaching environment. This paper proposes a highly convenient and innovative interactive teaching system that integrates various cutting-edge technologies including gesture recognition technology, face recognition technology based on HOG feature computing, and Internet of Things technology. Teachers can use gestures to control computers after logging in through face recognition. Using subconscious and natural gestures to teach lesson can simplify computer-side operation and improve teachers' lecture efficiency.

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PCA Method in Facial Features Extraction for Attendance Tracking Prototype 3

PCA Method in Facial Features Extraction for Attendance Trac...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Nazri, Farhan Najmi Mohd Kamarudin, Siti Nur Kamaliah Shamsuddin, Mohd Razif Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Selangor Shah Alam Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Facial recognition has long become a way to identify an individual-it represents an identity of a person. For this study, an automated students' attendance tracking prototype based on facial recognition was proposed, and later developed. Facial recognition systems can be extremely helpful in real-world applications, particularly in security control systems. For this study, firstly video framing was prepared by activating the camera via a user-friendly interface. Using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm, the face Region of Interest (ROI) was then identified, and segmented from the obtained video frames. In order to minimize loss of information, the images were resized, cropped, and RGB-greyed at the pre-processing step. Principal Component Analysis (PCA) was then used to extract the characteristics from facial images during the face recognition stage. The extracted features from the test images were then compared to the extracted features from the training images. The facial images were then categorised, and identified using the PCA algorithm. Finally, the identified student's attendance will be recorded for tracking purposes, and stored in a local database. One of the main advantages of the prototype is the automated registration of new students where Adhoc registration are allowed using the prototype. The study managed to achieve the best recognition accuracy of 98.7%. For future works, it is suggested that a higher number of images per individual to be used, with different pre-processing techniques. © 2022 IEEE.

关键词： Face recognition

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Navigating the Road: A Thorough Analysis of Vehicle Detection Methods in Current computer vision

Navigating the Road: A Thorough Analysis of Vehicle Detectio...

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Delhi Section conference (DELCON), IEEE

作者： Danish Garg Akash Kumar Krish Chaudhary Arshdeep Kour Bhavya Mishra Manni Kumar Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Vehicle detection, a fundamental problem in computer vision, still faces great challenges and has wide applications such as traffic surveillance system, autonomous driving vehicle and safe city etc. In the past few years many methods are proposed to improve accuracy and efficiency of vehicle detection in images, videos. The review paper provides a comprehensive review of these methods, and their development from traditional techniques like background subtraction to more complex deep learning approaches such as Single Shot Multibox Detector (SSD), You Only Look Once (YOLO), Histograms of Oriented Gradient(HOG)-based algorithms, Haar Cascade Classifiers, Faster R-CNN. We examine these methods across a wide array of datasets, characterizing the relative strengths and weaknesses in an effort to provide our views on what is likely optimal today. Additionally, we present recommendations for future research directions in vehicle detection, hoping to help both industry experts and university academics. All things considered, this study is a great tool for learning about and developing the area of vehicle detection.

关键词： YOLO computer vision Accuracy Reviews Vehicle detection Urban areas Traffic control Real-time systems Automobiles Videos

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Detection of Breast Cancer by Leveraging vision Transformer Model

Detection of Breast Cancer by Leveraging Vision Transformer ...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Sowjanya. Y Sudheer Reddy. K Department of Information Technology Anurag University Hyderabad India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The most common and deadliest cancer in women is breast cancer. It ranks first among all diseases that affect women in terms of fatality and morbidity. Every year, the number of new cases has been increasing by nearly 30%. In the previous studies, convolutional neural networks (CNNs) have emerged as the preferred technique for vision applications. Though it is excelled in capturing the local spatial structures, vision Transformers (ViT) are more advantageous in cases where contextual learning and global dependencies are critical. Due to the self-attention mechanism, it identifies the relationships among the various parts of the image. However, in order to attain the better performance, ViT need more training data as well as pre-trained model. Therefore, the authors analyzed ViT and pre-trained ViT. They conducted the experiment on the BreakHis dataset and achieved better results of 96.73%, 97.82%, and 96.8% for batch sizes of 64, 32, and 16 respectively.

关键词： Training computer vision Renewable energy sources Accuracy Computational modeling Training data Predictive models Transformers Breast cancer Convolutional neural networks

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ITCC 2021 - 2021 3rd international conference on Information Technology and computer Communications

ITCC 2021 - 2021 3rd International Conference on Information...

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3rd international conference on Information Technology and computer Communications, ITCC 2021

ISBN: (纸本)9781450389884

The proceedings contain 20 papers. The topics discussed include: capturing a clear image on computer screen or LCD screen directly using smartphone camera;static MRI reconstruction based on K-space and image-domain information;UAVs target detection and tracking algorithm based on multi-feature fusion;research on inspection method of pipeline weld image quality;multi-exposure remote sensing image HDR synthesis technology based on spaceborne DSP;kinect-based gait assessment method for hemiplegic patients;a multi-tenant access control method based on environmental attributes and security labels;differential leave-one-out cross-validation for feature selection in generalized linear dependence models;and the mobile payment acceptance model of Chinese residents.

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