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Speed Harmonic based Saturation Free Inductance Modeling and Estimation of Interior PMSM Using Measurements Under One Load Condition

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CES Transactions on electrical Machines and Systems 2025年第1期9卷 91-99页

作者： Guodong Feng Yuting Lu Zhe Tong Beichen Ding Guishan Yan Chunyan Lai the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina the School of Advanced Manufacturing Sun Yat-sen University the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.

关键词： PMSM Inductance estimation Speed harmonic High frequency signal injection

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Coded Beam Training for RIS-Assisted Wireless Communications

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3854-3867页

作者： Chen, Yuhao Dai, Linglong Tsinghua University State Key Laboratory of Space Network and Communications Department of Electronic Engineering Beijing100084 China Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios. © 2002-2012 IEEE.

关键词： Error correction

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Effectiveness of Advanced ECG Noise Reduction Filters 22

Effectiveness of Advanced ECG Noise Reduction Filters

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22nd International Learning and Technology Conference, L and T 2025

作者： Salem, Nema Jamjoom, Jude Alzahrani, Maria Qashqari, Maha Effat University Electrical Engineering Research Laboratory Effat College of Engineering Jeddah Saudi Arabia Effat University Effat College of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The study examined various filtering methods applied to ECG signals;the MIT-BIH Arrhythmia dataset included two channels to reduce noise and artifacts while maintaining critical waveform characteristics. Initial filtering included a notch filter and a bandpass filter. Advanced filters such as Savitzky-Golay, Wavelet, Kalman, Wiener, and combined Wiener-Golay were used for noise reduction. Results showed that the Kalman filter performed best with an average SNR of 21.40 dB, while the Wavelet method worked well on channel 2. The Wiener filter was less effective due to its limited adaptability to changing noise. The combined Wiener-Savitzky-Golay filter showed a balanced performance but did not outperform the Kalman filter. Visual inspections of ECG signals from five patients revealed that the Kalman and Wavelet filters successfully maintained crucial morphological characteristics. The Savitzky-Golay filter offered effective smoothing but sometimes dampened the QRS complex, while the Wiener filter introduced high-frequency artifacts. © 2025 IEEE.

关键词： Wiener filtering

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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Plug and Play Detector Design for DC Microgrids With Unknown-Inputs-Based FDI Attack

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IEEE Transactions on Smart Grid 2025年第3期16卷 2052-2064页

作者： Wu, Zhihua Peng, Chen Tian, Engang Zhang, Yajian Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai200444 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

DC microgrids, due to their deep integration of control, computing, communication technologies, and physical equipment, are susceptible to cyber-attacks. Consequently, this paper is dedicated to the development of a novel attack-defense framework for generalized DC microgrids. Firstly, an unknown-inputs-based false data injection (FDI) attack strategy is studied from the adversary’s perspective, unlike traditional stealthy attacks requiring non-minimum phase zeros or unstable poles, which conceals the attack signal as false unknown inputs (FUI) to maliciously disrupt current sharing and voltage balancing. Secondly, a comprehensive analysis of the stealthiness and destructiveness of FUI attack is provided, and a dual-observer-based detector is well constructed to detect the FUI attack and isolate the compromised distributed generation units. Then, structured Lyapunov matrix and semidefinite programming are ingeniously employed to solve the distributed observer gains simultaneously. Moreover, plug and play (PnP) performance is also analyzed to ensure the scalability of proposed FUI attack detector. Finally, the destructiveness and stealthiness of proposed FUI attack, as well as the effectiveness of designed detection scheme are demonstrated through simulations using MATLAB/SimPowerSystems Toolbox. © 2010-2012 IEEE.

关键词： MATLAB

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Fast Demodulation of OFDR-Based Distributed Sensing Based on Enhanced Buneman Frequency Estimation

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Journal of Lightwave Technology 2025年第9期43卷 4283-4289页

作者： Zhang, Zhaopeng Liu, Bo Liu, Xiao Li, Caiyun Alsalman, Osamah Zhu, Chen Zhejiang Laboratory Research Center for Novel Computing Sensing and Intelligent Processing Hangzhou311121 China King Saud University Department of Electrical Engineering College of Engineering Riyadh11421 Saudi Arabia Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process—typically required for a finer spectral sampling interval but at the cost of demodulation efficiency—can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional algorithms in terms of underlying physical principles and resultant demodulation noise distribution, establishing a strong foundation for adopting the BFE algorithm to improve demodulation efficiency without sacrificing performance metrics. Demonstration experiments using identical hardware configurations show that the proposed BFE method enhances demodulation efficiency by over 17 times without sacrificing other performance metrics, confirming its effectiveness and practicality. This advancement holds promise for improving the practical application of OFDR-based distributed sensing systems. © 1983-2012 IEEE.

关键词： Frequency domain analysis

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CATPlan: Loss-based Collision Prediction in End-to-End Autonomous Driving

arXiv

引用

arXiv 2025年

作者： Xiong, Ziliang Liu, Shipeng Helgesen, Nathaniel Johnander, Joakim Forssén, Per-Erik Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden Zenseact Sweden

In recent years, there has been increased interest in the design, training, and evaluation of end-to-end autonomous driving (AD) systems. One often overlooked aspect is the uncertainty of planned trajectories predicted by these systems, despite awareness of their own uncertainty being key to achieve safety and robustness. We propose to estimate this uncertainty by adapting loss prediction from the uncertainty quantification literature. To this end, we introduce a novel light-weight module, dubbed CATPlan, that is trained to decode motion and planning embeddings into estimates of the collision loss used to partially supervise end-to-end AD systems. During inference, these estimates are interpreted as collision risk. We evaluate CATPlan on the safety-critical, nerf-based, closed-loop benchmark NeuroNCAP and find that it manages to detect collisions with a 54.8% relative improvement to average precision over a GMM-based baseline in which the predicted trajectory is compared to the forecasted trajectories of other road users. Our findings indicate that the addition of CATPlan can lead to safer end-to-end AD systems and hope that our work will spark increased interest in uncertainty quantification for such systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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Stable Stochastic MPC for Uncertain Wind Energy Conversion System Over Whole Operating Regions

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IEEE Transactions on Energy Conversion 2025年第1期40卷 515-528页

作者： Liu, Xiangjie Guo, Shifan Ma, Lele Kong, Xiaobing Lee, Kwang Y. North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China Baylor University Department of Electrical and Computer Engineering WacoTX76798-7356 United States

The stochastic uncertainty of wind speed presents a great challenge for achieving reliable power control in wind energy conversion system (WECS). Due to the excellence in handling the uncertainties based on probabilistic descriptions, stochastic model predictive control (SMPC) has been regarded as a promising strategy for WECS. However, owing to fluctuation characteristic of wind speed, operating points commonly change in WECS. Thus, an important limitation of applying SMPC to WECS stems from the fact that feasibility and stability of the traditional SMPC can only be ensured at one single predesigned operating point. Regarding this issue, a stable SMPC strategy for uncertain WECS is proposed in this paper to ensure the feasibility and stability of WECS under changing operating points over the whole operating regions. A Luenberger observer is employed to estimate model-plant mismatch introduced by linearization process, while a tube-based control framework is deployed to cope with stochastic wind speed disturbance. The feasibility of SMPC is ensured by incorporating artificial steady targets as optimization variables, while the stability of WECS is guaranteed by modifying the cost function and extending the terminal constraint. The effectiveness of the proposed strategy is validated through simulations and experiments under different scenarios. © 1986-2012 IEEE.

关键词： Stochastic systems

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Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

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IEEE Transactions on Automatic Control 2025年第5期70卷 3296-3302页

作者： Yue, Hao Xu, Yakun Hu, Hesuan Wu, Weimin Li, Lingxi College of Computer Science and Technology Qingdao266580 China Xidian University School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation. © 1963-2012 IEEE.

关键词： Discrete event simulation

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