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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Yu, Zongyang Yang, Peng Cao, Xianbin Zheng, Dezhi Quek, Tony Q. S. Oliver Wu, Dapeng Beihang University School of Electronic Information Engineering Beijing China Peng Cheng Laboratory Department of Mathematics and Theories Shenzhen China Beijing Institute of Technology Advanced Research Institute of Multidisciplinary Sciences Beijing China Singapore University of Technology and Design Information Systems Technology and Design Singapore City University of Hong Kong Department of Computer Science Kowloon Hong Kong

ISBN: (纸本)9798350324662

Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks. © 2023 IEEE.

关键词： expectation maximization algorithm R-D model rate control video coding Video transmission

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Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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IEEE Conference on Decision and Control

作者： Yorick Broens Hans Butler Roland Tóth Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Systems and Control Laboratory HUNREN Institute for Computer Science and Control Hungary

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype.

关键词： Technological innovation Sensitivity Frequency-domain analysis Stability criteria Prototypes Throughput Frequency response Motion control Optimization MIMO communication

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On Space-Filling Input design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

arXiv

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

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Communication-efficient and Decentralized Federated Minimax Optimization

Communication-efficient and Decentralized Federated Minimax ...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Sajjad Ghiasvand Amirhossein Reisizadeh Mahnoosh Alizadeh Ramtin Pedarsani Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

As distributed learning applications like Federated Learning, the Internet of Things (IoT), and Edge Computing expand, addressing their limitations becomes crucial. We approach decentralized learning across a network of communicating clients or nodes, focusing on two primary challenges: data heterogeneity and adversarial robustness. To address these, we introduce a decentralized minimax optimization method incorporating two key components: local updates and gradient tracking. Minimax optimization serves as a fundamental tool for adversarial training, ensuring robustness. Local updates are vital in Federated Learning (FL) to alleviate the communication bottleneck, while gradient tracking is necessary to demonstrate convergence amid data heterogeneity. Our analysis of the proposed algorithm, Dec-Fed Track, in nonconvex-strongly-concave minimax optimization demonstrates its convergence to a stationary point. Additionally, numerical experiments support our theoretical results.

关键词： Training computer aided instruction Federated learning Distance learning Optimization methods Focusing Robustness Internet of Things Convergence Edge computing

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Disturbance Rejection Control for PMSGs Based on Novel Sliding Mode Control Law and Unknown System Disturbance Estimator

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IEEE Transactions on Energy Conversion 2025年

作者： Wei, Chun Yin, Tianqi Qiao, Wei Zhu, Ying Zhejiang University of Technology College of Information Engineering Zhejiang Hangzhou310023 China University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588 United States Hohai University School of Electrical and Power Engineering Nanjing211100 China

This paper proposes a novel terminal sliding mode control strategy for the speed control of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) to improve wind power generation efficiency. Firstly, a novel terminal sliding mode function based on exponential convergence is constructed, and a controller is designed accordingly, effectively addressing the system's nonlinear characteristics while achieving high precision tracking and chattering suppression with rapid convergence. Secondly, an unknown system dynamic estimator (USDE) is designed to estimate disturbances caused by external environments and inaccurate model parameters, further enhancing the system's disturbance rejection capability. Finally, the proposed USDE-based novel exponential sliding mode control is applied in a 1000 W PMSG-based WECS hardware simulator. Experimental results indicate that compared to the full-order sliding mode controller, the proposed control method exhibits stronger speed tracking, chattering suppression, and disturbance rejection capabilities. © 1986-2012 IEEE.

关键词： Sliding mode control

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Short-Term Prediction of Solar Photovoltaic Power Generation Using a Digital Twin

Short-Term Prediction of Solar Photovoltaic Power Generation...

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Annual North-American Power Symposium

作者： John Yonce Michael Walters Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

Large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants are integrated into the power distribution system due to increased awareness of climate change. These DERs introduce variable and uncertain generation sources due to changing weather conditions. This makes operations and controls challenging and complex. To better understand and manage the dynamic nature of solar PV power plants, digital twins (DTs) will be needed. DTs based on artificial intelligence (AI) methods can be applied to replicate the dynamics of PV plants. This study utilizes a popular paradigm of AI - neural networks to create a variety of data-driven DT (DD-DT) prediction models for a 1 MW solar PV plant located at Clemson University in South Carolina, USA. State-of-the-art internet of things (IoT) based real-time measurements are used to develop the DD-DTs. Typical results for short-term PV power prediction for DTs implemented using multilayer perceptron neural networks (MLPNNs) and Elman recurrent neural networks (ERNNs) are presented in this paper.

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Sparkling hot spots in perovskite solar cells under reverse bias

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ChemPhysMater 2022年第1期1卷 71-76页

作者： Weiqi Li Keqing Huang Jianhui Chang Caiqi Hu Caoyu Long Hai Zhang Xavier Maldague Biao Liu Jianqiao Meng Yuxia Duan Junliang Yang School of Physics and Electronics Central South UniversityChangsha 410083China Computer Vision and Systems Laboratory Department of Electrical and Computer EngineeringUniversitéLaval1065av.de la MédecineQuébec(QC)Canada G1V 0A6

Perovskite solar cells(PSCs)are attracting much attention and are on the way to ***,some modules are subject to reverse bias in actual fields,so it is meaningful to investigate the reverse-bias behav-ior of ***,an in-situ temperature and current measurement technique was ***,some hot spots were observed and then quickly disappeared in the reverse biased PSCs,along with the simultaneous increase in the current and local ***,the potential mechanism has been revealed and *** abnormal bulge in the perovskite film was found at the hot ***,the appearance and disappearance of hot spots were perfectly explained by band bending and tunneling current caused by ion ***-tionally,statistical analysis suggested that sparkling hot spots were related to reverse voltage and efficiencies of *** research provides a great significance for the study of PSCs under reverse bias.

关键词： Perovskite solar cells Reverse bias Hot spots Stability

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Training Graph Neural Networks on Growing Stochastic Graphs

Training Graph Neural Networks on Growing Stochastic Graphs

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Juan Cerviño Luana Ruiz Alejandro Ribeiro Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA Computer Science & Artificial Intelligence Laboratory MIT Cambridge USA

Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational costs leading to scalability limitations in practice. To overcome these limitations, proposed methods rely on training GNNs in smaller number of nodes, and then transferring the GNN to larger graphs. Even though these methods are able to bound the difference between the output of the GNN with different number of nodes, they do not provide guarantees against the optimal GNN on the very large graph. In this paper, we propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon. We propose to grow the size of the graph as we train, and we show that our proposed methodology – learning by transference – converges to a neighborhood of a first order stationary point on the graphon data. A numerical experiment validates our proposed approach.

关键词： Training Convolution Scalability Signal processing algorithms Graph neural networks Acoustics Computational efficiency

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Recognition of Greek Orthodox Hymns Using Audio Fingerprint Techniques 8

Recognition of Greek Orthodox Hymns Using Audio Fingerprint ...

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8th South-East Europe design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Karasavvidis, Konstantinos Kampelopoulos, Dimitris Moysis, Lazaros Boursianis, Achilles D. Nikolaidis, Spiridon Sarigiannidis, Panagiotis Goudos, Sotirios K. School of Physics Aristotle University of Thessaloniki ELEDIA@AUTH Thessaloniki54124 Greece School of Physics Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits & Complexity Thessaloniki54124 Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece

ISBN: (纸本)9798350383676

Audio fingerprinting was originally developed for music song identification, and over the years has been used for many more cases. With fingerprinting, an equivalent signature of the audio signal is saved in a simple form, so that it can be easily compared with another fingerprint. In this work, an audio fingerprinting algorithm is developed for the recognition of Greek Orthodox hymns. The main difference between music songs and Greek Orthodox hymns is the absence of music instruments, as there is only the chanter's voice. The test dataset consists of 10 hymns, and for each hymn there are 5 different performances, creating a dataset of 50 performances. Several tunable parameters of the fingerprinting technique are tested, like the parameters of the Hamming window, the frequency spectrum, the sample duration, and the sample noise, to find the differences in Greek Orthodox hymns. The recognition results for all cases are positive. The algorithm, in most cases, had a high recognition rate. even with noisy samples. © 2023 IEEE.

关键词： Statistical tests

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