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Torque ripple minimization in an 8/6 switched reluctance motor drive: optimization using the AZOA method

Australian Journal of electrical and Electronics engineering 2025年第1期22卷 27-44页

作者： Chaple, Megha D. Deotale, Nitin T. Deotale, Nilesh T. Department of Electrical Engineering Lokmanya Tilak College of Engineering Maharashtra Navi Mumbai India Department of Electronics and Telecommunication Engineering Lokmanya Tilak College of Engineering Maharashtra Navi Mumbai India Department of Computer Engineering St. John College of Engineering and Management Palghar India

In this manuscript, an American Zebra Optimization Algorithm (AZOA) is proposed for minimising torque-ripple in an 8/6 switched reluctance motor (SRM) drive. The major objective of the proposed technique is to improve efficiency, steady state performance, and minimise the torque ripple. The objective is achieved by applying the AZOA to determine the optimal converter topology. The switched reluctance motor is fed by three converters with three legs. It consists of freewheeling diodes, IGBTs, capacitors, and resistors. By then, the proposed technique is executed in the MATLAB software and its performance is evaluated with various existing approaches. The simulation results of the SRM drive with different converter typologies are presented to analyse its performance with torque and speed characteristics. The proposed method demonstrates superior performance across all approaches, including the heap-based optimiser (HBO), salp swarm algorithm (SSA), and cuckoo search algorithm (CSA), yielding better results. From the result, it is concluded that the proposed technique displays a high efficiency of 95% and a low cost of 1.5$ compared with other existing methods. ©, Engineers Australia.

关键词： MATLAB

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Adaptive Pure Pursuit with Deviation Model Regulation for Trajectory Tracking in Small-Scale Racecars

Adaptive Pure Pursuit with Deviation Model Regulation for Tr...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Al Fata, Ralph Daher, Naseem American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Department of Electrical and Computer Engineering Department of Mechanical Engineering Beirut Lebanon

ISBN: (纸本)9798331529604

Designing motion controllers for autonomous racecars allows researchers to study the safety and computational efficiency of their algorithms under extreme conditions, while also serving as a benchmark for conventional autonomous driving technologies. Small-scale autonomous racecars compel developers to use feeble processors or microcontrollers that are incapable of executing computationally-expensive control algorithms, like Model Predictive Control (MPC), in real time. On the other hand, geometric controllers are derived from kinematic models, where the steering angle is computed based on simple trigonometric equations, which significantly simplifies their implementation. While geometric controllers achieve acceptable tracking performance at low-to-medium velocities, their accuracy decreases as the vehicle velocity increases, which is due to ignored dynamics, large tire slip, and unmodeled nonlinearities that dominate at high speeds. In this work, we propose an adaptive pure pursuit controller that calculates a variable look-ahead distance based on curvature in a feedforward fashion, complemented by a regulated deviation (error-based) model in the feedback loop, which generates a corrective steering angle that accounts for nonlinearities at higher speeds. A comparative simulation study is presented to demonstrate the superiority of the proposed controller over the traditional pure pursuit controller, which is evidenced by 85.69% improvement in lateral position tracking, 19.75% improvement in yaw angle tracking, and 8% improvement in lap time. © 2025 IEEE.

关键词： Steering

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Optimization of 4D Splines for Unmanned Aerial System Trajectories under Kinematic, Obstacle, and Time Constraints

Optimization of 4D Splines for Unmanned Aerial System Trajec...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Osburn, Matthew D. Peterson, Cameron K. Salmon, John L. Department of Electrical and Computer Engineering United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Unmanned aerial systems (UAS) operating in dynamic environments must ensure safety for the duration of their flight. This paper presents an optimization method for planning safe, kinematically constrained, and time-constrained UAS trajectories in the presence of static and dynamic obstacles. Vehicle constraints are applied to a novel 4D 5th-order polynomial spline formulation and the results are presented to show meaningful relationships between constraints, optimality, and feasibility. This 4D 5th-order polynomial spline is shown to be a special subset of the Bézier spline, which ensures that our approach inherits the advantages and convex hull properties of Bézier splines. The connection with Bézier splines will allow algorithms designed for Bézier splines to be used with 4D-polynomial splines in future work. Numerical experiments show the success of this approach in providing optimal trajectories for UAS navigating dynamic environments. The experiments further illustrate when gradient descent optimization will result in feasible trajectories, sub-optimal solutions, or distributions of optimal trajectories. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Constrained optimization

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Early Diagnosis of Osteoporosis: An Artificial Intelligence-Based Framework 7

Early Diagnosis of Osteoporosis: An Artificial Intelligence-...

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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Zaylaa, Amira J. Daher, Alaa Ayache, Mohammad Omeirat, Niveen Mahmoud, Razane Hawili, Bader Abou Beirut Arab University Biomedical Engineering Laboratory Biomedical Engineering Program Electrical and Computer Engineering Faculty of Engineering Lebanon

ISBN: (纸本)9798350325850

Osteoporosis is a common disease characterized by low bone density and structural deterioration of bone tissue. For a successful course of treatment and fracture avoidance, early diagnosis of this disease is essential. The aim was to provide a novel method for osteoporosis prediction using Artificial Intelligence (AI)-based framework. The purpose was to predict the likelihood of osteoporosis based on the Bone Mineral Density (BMD), along with other characteristics such as age, weight, height, gender, and Body Mass Index (BMI) extracted from medical reports and images collected from a comprehensive medical center in Lebanon. Three machine-learning algorithms were implemented and tested, Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT). Variety of quantitative statistical metrics were used to evaluate the performance of our framework, upon training and testing our algorithms. The metrics that were employed to evaluate our results included accuracy, precision, sensitivity, and F-score, in addition to the Receiver Operating Characteristic (ROC) Curve and the Area Under the Curve (AUC). Experimental Results demonstrated that both the SVM and LR algorithms achieved the highest accuracy of detection of osteoporosis as compared to existing algorithms applied in this field, with an accuracy of 89%. The sensitivity of diagnosis obtained was 98% by LR and 97% by SVM and surpassed the sensitivity obtained by DT. As such LR showed the best performance. The output of the algorithms could help medical doctors assess patients automatically. These findings demonstrated the potential of AI in osteoporosis prediction and thus prevention, highlighting the significance of early diagnosis. Thereby as a future prospect, choosing carefully the framework is crucial and additional algorithms have to be considered and tested. © 2023 IEEE.

关键词： Decision trees

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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Path and Trajectory Planning in Unknown Tracks Using Pixy2.1 Camera for Autonomous Racecars

Path and Trajectory Planning in Unknown Tracks Using Pixy2.1...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Kassem, Mostafa Abedulhadi, Ghadi Azar, Joe Daher, Naseem American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Department of Electrical and Computer Engineering Department of Mechanical Engineering Beirut Lebanon

ISBN: (纸本)9798331529604

Maintaining maximum allowable speeds throughout a racing track, especially at corners, is essential for competitive performance in autonomous racing. This research integrates computationally-efficient perception and path- and trajectory-planning algorithms, which are suitable for real-time deployment on limited hardware. The algorithms aim is to achieve the global-minimum lap time on an unknown race track by minimizing the summation of locally-minimum times at each track section using a Pixy2.1 camera, which is a fast vision sensor. By processing the vector outputs of the Pixy2.1 camera, smooth track right and left lanes are generated and an optimal race line that minimizes curvature is generated, and an adaptive velocity profile is developed to reach maximum allowable speed at each waypoint along the race line. Experimental testing and validation were conducted on the MR-BUGGY3-KIT car hardware equipped with an RDDRONE-FMUK66 NXP microcontroller. Testing demonstrated the system's ability to generate smooth lane boundaries from the Pixy2.1 camera vectors. Furthermore, the curvature profiles effectively captured the geometric characteristics of the track, facilitating efficient path and trajectory planning while minimizing curvature. © 2025 IEEE.

关键词： Ability testing

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A Cyclic Vernier Digital-to-Time Converter for Time-Mode Successive Approximation TDC 66

A Cyclic Vernier Digital-to-Time Converter for Time-Mode Suc...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Lee, Daniel Junehee Yuan, Fei Zhou, Yushi Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Lakehead University Department of Electrical and Computer Engineering Thunder BayON Canada

ISBN: (纸本)9798350302103

This paper presents an 8-bit cyclic Vernier digital-to-time converter (DTC) for time-mode successive approximation register time-to-digital converters (SAR TDCs). The DTC offers a low degree of mismatch-induced nonlinearity hence a small lower bound of the dynamic range, a virtually unlimited upper bound of the dynamic range, and a small silicon area. The principle, operation, design, and calibration of the DTC are presented. The DTC is designed in TSMC 130 nm 1.2 V technology and analyzed using Spectre with BSIM3v3 device models. Simulation results show that the DTC achieves a dynamic range of 2.31 591.4 ps, a DNL of 0.37 LSB, an INL of 0.66 LSB, and consumes 1.49 mW. © 2023 IEEE.

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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PROPOSED CONVOLUTION NEURAL NETWORK MODEL FOR FACE RECOGNITION 19th

PROPOSED CONVOLUTION NEURAL NETWORK MODEL FOR FACE RECOGNITI...

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19th International Middle Eastern Simulation and Modelling Conference, MESM 2024

作者： Atta, Rand A. Zghair, Noor A. Fayad, Dhiaa M. Zamil, Asmaa S. Hasan, Hussein M. Biomedical Engineering Department University of Technology Iraq Computer Engineering Department University of Technology Iraq

ISBN: (纸本)9789492859341

Face recognition is a form of biometric method that relates to the automatic recognition of faces by computerized systems through observation of the face. It is a popular feature in biometrics, digital cameras, and social tagging. Face recognition has received increased research focus in recent years. In this paper, a model of the CNN algorithm for face recognition was proposed, where a study was made on the weaknesses of previous models and concluded that if the number of images used for training is small, large models cannot learn. That is why a model was built that balances datasets with large and small sizes. In this paper, two datasets was used (Avengers Faces Dataset) this dataset contains a small number of images, and (Famous People Face Recognition) this dataset contains a large number of images. The accuracy of the proposed model of CNN in classification reached 0.9795% for the small dataset and 0.9778% for the large dataset. The tests conducted on the developed facial recognition system have demonstrated that the proposed algorithms and the developed small and big datasets of facial recognition system yield satisfactory results. © 2025 EUROSIS-ETI.

关键词： Neural network models

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Exploring Biological Features of Electroencephalography Signals for Explanation-Guided Learning

Exploring Biological Features of Electroencephalography Sign...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Joo, Hyosung Kim, Dongseok Trang, Le Thi Quan, Luong Do Anh Woo, Jihwan University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of University of Ulsan Department of Biomedical Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350371888

Explanation-guided learning (EGL) has gained prominence for improving both the explainability and performance of deep neural networks by integrating additional supervision signals based on the elucidation of the model. However, its applicability in analyzing electroencephalography (EEG) signals remains underexplored. This study attempted to identify and evaluate biological features for EGL-based EEG analysis using compact convolutional neural networks. The identified features included anatomical location, dipolarity, relative peak power, and event-related desynchronization of the cortical source. The feasibility of the features was evaluated with performance prediction tests, which predict model performance based on the interpreted features. All features except dipolarity possessed a significant relationship with model performance. These findings indicate the possibility of utilizing these features as biological supervision signals in future EGL-based EEG analysis. © 2024 IEEE.

关键词： Electroencephalography

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