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A New Resilient Configuration of Wound Rotor Resolver in Mechanical Fault Conditions

IEEE Sensors Journal 2025年第11期25卷 19069-19076页

作者： Zare, F. Tootoonchian, F. Nasiri-Gheidari, Z. Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran Iran University of Science and Technology Electrical Engineering Department Tehran Iran Sharif University of Technology Electrical Engineering Department Tehran Iran

Resolvers are widely employed as position sensors to detect both rotational and linear displacement. Among the various types, wound rotor resolvers offer superior accuracy in absolute measurements, especially under mechanical fault conditions, but they present significant manufacturing challenges. This paper introduces a novel winding arrangement designed to simplify the manufacturing process of wound rotor resolvers and enhance the accuracy of the sensor under mechanical fault conditions. The performance of the resolver with this new winding configuration is analyzed, with a particular focus on its resilience to eccentricity faults. To validate the high fault tolerance of the proposed design, a prototype of the optimized configuration was developed and tested. The performance of the prototype under these conditions was comprehensively evaluated through experimental measurements. © 2001-2012 IEEE.

关键词： Failure (mechanical)

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Analysis of the effect of different scattering mechanisms on the excess noise behavior of Sb-based avalanche photodiodes

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Optics Express 2025年第4期33卷 7337-7344页

作者： Karimi, Hannaneh Herrera, Daniel J. Dadey, Adam A. Wei, Dongxia Mcarthur, Andrew Bank, Seth R. Campbell, Joe. C. Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Carlo (MC) model described in this work reveals the effect of high alloy scattering on the excess noise factor in these material systems. A comparison between the excess noise factor of different combinations of scattering rates is investigated. Furthermore, the ionization path length probability distribution is presented for AlInAsSb APDs with different scattering mechanisms. Good agreement is achieved between simulation results and experimental measurements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Meta-Learning Online Control for Linear Dynamical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4163-4169页

作者： Muthirayan, Deepan Kalathil, Dileep Khargonekar, Pramod P. University of California Irvine Department of Electrical Engineering and Computer Sciences IrvineCA United States Texas A&M University Department of Electrical and Computer Engineering United States

In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a linear dynamical system for a finite horizon of T time steps. The cost function and system noise at each time step are adversarial and unknown to the controller before taking the control action. Meta-learning is a broad approach where the goal is to prescribe an online policy for any new unseen task exploiting the information from other tasks and the similarity between the tasks. We propose a meta-learning online control algorithm for the control setting and characterize its performance by meta-regret, the average cumulative regret across the tasks. We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor D/D∗ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where D is a problem constant and D∗ is a scalar that decreases with increase in the similarity between tasks. Thus, when the sequence of tasks are similar the regret of the proposed meta-learning online control is significantly lower than that of the naive approaches without meta-learning. We also present experiment results to demonstrate the superior performance achieved by our meta-learning algorithm. © 1963-2012 IEEE.

关键词： Cost functions

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A High Power Density Active-Clamp Forward-Flyback Converter with Three Planar Transformer for xEV Low Voltage DC-DC Converter

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Transactions of the Korean Institute of electrical Engineers 2025年第3期74卷 468-476页

作者： Kwon, Gyeonghyun Lee, Dongin Jeong, Seongwook Jeong, Yeonho Youn, Hanshin Dept. of Electrical Engineering Incheon National University Korea Republic of Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI United States

This paper proposes an Active-Clamp Forward-Flyback (ACFF) converter with three planar transformers to achieve 4 kW output power. As autonomous driving technology rapidly advances, the demand for higher output power from low-voltage DC-DC converters (LDC) in electric vehicles has significantly increased. Conventionally, two 2 kW ACFF converters with four magnetic cores connected in series-parallel have been used to meet 4 kW power requirement, but this configuration has proven inefficient in terms of size, power density, and cost. The proposed converter improves power density by integrating the four magnetic cores of the conventional 4 kW ACFF converter into three, reducing the overall magnetic volume and allowing for a more compact design. Furthermore, the use of planar magnetic components enables the winding to be embedded in a 4-layer PCB. Copyright © The Korean Institute of electrical Engineers.

关键词： High frequency transformers

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Multi-stage Segmentation of Diabetic Foot Ulcers Using Self-supervised Learning 4th

Multi-stage Segmentation of Diabetic Foot Ulcers Using Self...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Gupta, Neetika Nangia, Ayush Khan, Naimul Mefraz Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9783031808708

Diabetic Foot Ulcers (DFUs) pose a significant healthcare challenge due to their potential to lead to severe complications, including infections and ischemia, often necessitating limb amputation or causing mortality. Accurate diagnosis of ulcer regions, and prompt response can prevent amputations and fatalities through early and reliable DFU detection. However, manual examination of DFUs is labor-intensive and prone to variability. In response to these challenges, we propose DFUC_CAM, a novel method for instance segmentation of DFUs using self-supervised learning (SSL). The proposed approach leverages a multi-stage methodology. Initially, a convolutional neural network (CNN) trained for classification is utilized to generate Class Activation Maps (CAMs), which localize and highlight discriminative regions within DFU images. These CAMs serve as pseudo-labels to train a segmentation network using a SSL framework. For the MICCAI 2024 Diabetic Foot Ulcer Challenge (DFUC 2024), we trained DFUC_CAM using the DFUC 2024 and FUSeg datasets. The FUSeg dataset primarily serves for the quantitative evaluation of our method. During the validation phase of DFUC 2024, our team, ***, achieved a mean Dice score of 0.342, and in the final testing phase, we improved this to 0.385. These results demonstrate the potential of our approach for enhancing DFU detection and segmentation in clinical settings. The code is available at: https://***/RyersonMultimediaLab/DFUC_CAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Maximally Flat Negative Group Delay Prototype Filter Based on Capped Reciprocal Transfer Function of Classical Bessel Filter

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Progress in Electromagnetics Research B 2025年 110卷 91-105页

作者： Kandic, Miodrag Bridges, Greg E. Department of Electrical and Computer Engineering University of Manitoba WinnipegMB Canada

A prototype filter design exhibiting Negative Group Delay (NGD) is presented, based on the ratio of two low-pass classical Bessel filter transfer functions of the same order, but with different 3 dB-bandwidths. The resulting design is a reciprocal-Bessel filter transfer function, capped at a finite out-of-band gain. The proposed capped reciprocal-Bessel design is based on a similar concept applied to previously reported capped reciprocal-Butterworth and reciprocal-Chebyshev NGD designs, which use ratios of corresponding classical low-pass filter transfer functions. It is shown that within the in-band frequency range, the synthesized NGD transfer function exhibits a maximally flat group delay characteristic (Bessel-like property). Due to its near-flat in-band group delay characteristic, the design is suitable for constant phase shifter applications. For high design orders, it is shown that the achieved NGD-bandwidth product has an upper asymptotic limit, given by the square root of the out-of-band gain in decibels. When the prototype baseband transfer function is translated to a non-zero center frequency, it is demonstrated that resonator-based implementations are feasible via Sallen-Key, as well as all-passive ladder topologies. A combined in-band magnitude/phase distortion metric is evaluated for selected design examples and applied Gaussian and sinc input waveforms, and it is shown to be proportional to the design order and out-of-band gain. The proposed design's distortion metric is also shown to be generally lower than the previously reported capped reciprocal-Butterworth and reciprocal-Chebyshev designs. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Group delay

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Knowledge distillation on spatial-temporal graph convolutional network for traffic prediction

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International Journal of computers and Applications 2025年第1期47卷 45-56页

作者： Izadi, Mohammad Safayani, Mehran Mirzaei, Abdolreza Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only (Formula presented.) of network parameters. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cost functions

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Predicting Distant Metastatic Sites in Cancer Using miRNA and mRNA Expression Data

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Current Bioinformatics 2025年第5期20卷 473-485页

作者： Mamatkarimov, Dostonjon Kang, Jiahui Han, Kyungsook Department of Electrical and Computer Engineering Inha University Incheon Korea Republic of

Background: Cancer patients with metastasis face a much lower survival rate and a higher risk of recurrence than those without metastasis. So far, several learning methods have been proposed to predict cancer metastasis, but most of these methods are intended to predict lymph node metastasis rather than distant metastasis. Distant metastasis is more difficult to predict than lymph node metastasis because distant metastasis is detected after a comprehensive examination of the entire body, and there are not enough publicly available tumor samples with distant metastasis that can be used for training learning methods. Predicting distant metastatic sites is even more challenging than predicting whether distant metastasis will occur or not. Methods: The problem of predicting distant metastatic sites is a multi‐class and multi‐label classification problem;there are more than two classes for distant metastatic sites (bone, brain, liver, lung, and other organs), and a single sample can have multiple labels for multiple metastatic sites. We transformed the multi‐label and multi‐class problem into multiple single‐label binary problems. For each metastatic site, we built a random forest model that deals with binary classification and linked the models along a chain. Results: Testing the model on miRNA and mRNA expression datasets of several cancer types showed a high performance in all performance measures. In the comparison of our model with other methods, our method outperformed the others. Conclusion: We developed a new method for predicting multiple metastatic sites using miRNA and mRNA expression data. The technique will be useful in predicting distant metastatic sites before distant metastasis occurs, which in turn will help clinicians determine treatment options for cancer patients. © 2025 Bentham Science Publishers.

关键词： Diseases

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mmWave Radar-Based Non-Invasive Vehicle Occupancy Detection with Insights from Vision, RF, and Physical Sensors: A Review

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IEEE Sensors Journal 2025年第11期25卷 18643-18661页

作者： Yousaf, Jawad Palmer, Alyssa Shams, Maitha Wasma, Zayna Qasymeh, Montasir Hassan, Taimur Arora, Deepak Ghazal, Mohammed Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Liwa College Faculty of Engineering and Computing Abu Dhabi United Arab Emirates

The potential uses of human occupancy detection (HOD) in vehicles are crucial for handling resources, passenger safety, and privacy-preserving technology. In this study, the usage of various sensor systems for non-invasive vehicle occupant detection is examined, including mmWave radar, vision-based, and physical sensors. These technologies undergo a thorough review analysis that looks at methods, performance indicators, and use cases before being assessed for robustness, accuracy, real-time capabilities, scalability, and cost-effectiveness. The impact of clutter, scalability, and privacy in car interiors on HOD is also thoroughly discussed. Based on its ability to balance cost, accuracy, and adaptability, frequency-modulated continuous wave (FMCW) radar is termed the best option for vehicle occupant identification. The study discussed the possible challenges and future direction, as well as the conclusion provides research opportunities in hybrid sensing systems and advanced machine learning integration to overcome existing constraints. © 2001-2012 IEEE.

关键词： Frequency shift keying

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Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

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IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

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