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Detecting Anomalies for Fire Prevention in Distribution Systems: Challenges and Analytical Techniques

IEEE POWER & ENERGY MAGAZINE 2024年第6期22卷 88-90页

作者： Joo, Jhi-Young Annalicia, Christabella Pochiraju, Apoorv Alaca, Ozgur Ekti, Ali Riza Balestrieri, Michael Haghi, Hamed Valizadeh Elandaloussi, Abder Lawrence Livermore Natl Lab Livermore CA 94550 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA Southern Calif Edison Westminster CA 92683 USA Southern Calif Edison Irvine CA 92618 USA

Electric utilities in California have historically been linked to up to 10% of wildfires. To mitigate this risk, Southern California Edison has invested significantly in wildfire prevention strategies, including undergrounding cables and enhancing equipment inspections. This article explores a novel approach to fire prevention by detecting anomalies in the distribution system that may indicate potential fire hazards. The focus is on identifying arcing conditions through high-resolution point-on-wave (POW) measurements. Arcing, a precursor to fires, is challenging to detect due to its subtle transients and complex system topology. The article discusses the use of advanced signal processing and machine learning techniques, such as spectral correlation function and discrete wavelet transform, to extract features from POW data and accurately identify arcing events. The study demonstrates a high accuracy rate in detecting arcing, paving the way for improved fire prevention measures in electric distribution systems.

关键词： Wildfires Accuracy Prevention and mitigation signal processing algorithms Gain measurement signal processing Feature extraction Hazards Discrete wavelet transforms Protection

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Population Monte Carlo With Normalizing Flow

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IEEE signal processing LETTERS 2024年 31卷 16-20页

作者： Pal, Soumyasundar Valkanas, Antonios Coates, Mark Huawei Noahs Arc Lab Montreal PQ H3A 2A7 Canada McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2A7 Canada

Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. Recent work in this area primarily focuses on ameliorating the proposal adaptation procedure for high-dimensional applications. However, most of the AIS algorithms use simple proposal distributions for sampling, which might be inadequate in exploring target distributions with intricate geometries. In this work, we construct expressive proposal distributions in the AIS framework using normalizing flow, an appealing approach for modeling complex distributions. We use an iterative parameter update rule to enhance the approximation of the target distribution. Numerical experiments show that in high-dimensional settings, the proposed algorithm offers significantly improved performance compared to the existing techniques.

关键词： Proposals signal processing algorithms Monte Carlo methods Artificial intelligence Inference algorithms Approximation algorithms Sociology Bayesian inference importance sampling normalizing flow population Monte Carlo

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A Novel Two-Way Pattern Synthesis Method With the Uniform and Thinned Arrays

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第10期23卷 3108-3112页

作者： Wang, Jianbo Wu, Xiaoliang Ye, Jianyu Sun, Jianqing Hua, Guang Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

In this letter, a novel two-way product pattern synthesis method utilizing a uniform transmit array and a thinned receive array is proposed. Specifically, the thinned receive array is an extended coprime array (ECA). Based on the ECA's characteristics, the ECA's array factor is decomposed into the product of the slow-varying array factor and the fast-varying array factor. Subsequently, the positions of nulls and the first sidelobe of the ECA are derived, respectively. It reveals that the fast-varying array factor plays a dominant role in the positions of nulls and the first sidelobe. The ECA architecture is optimized for element number and element spacing using the combinatorial traversal algorithm. It enables the alignment of nulls and the first sidelobe between the uniform array and the ECA. Compared to the traditional uniform transmit-receive method with the same number of elements, a two-way product pattern with a lower peak sidelobe level, a larger aperture, a narrower beamwidth, and a higher resolution can be achieved by the proposed method. The efficiency of the proposed method has been validated by typical simulation results.

关键词： Apertures Phased arrays Array signal processing Radar signal processing algorithms Radar antennas Arithmetic Extended coprime array (ECA) linear arrays peak sidelobe (PSL) level reduction phased array radar two-way pattern

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Enhanced Radar Anti-Jamming With Multi-Agent Reinforcement Learning

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IEEE signal processing LETTERS 2024年 31卷 3114-3118页

作者： Peng, Wenshen He, Chuan Cao, Fei Hu, Changhua Jiao, Licheng Rocket Force Univ Engn Xian 710025 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China

As jamming systems continue to evolve, jammers are becoming increasingly complex. They modify their strategies in response to changes in radar status, thereby creating a dynamic confrontation with the radar. Currently, deep reinforcement learning (DRL) methods are widely employed in the decision-making process for radar anti-jamming strategies;however, most of these methods overlook the learning capabilities of the jammer. This letter proposes a novel multi-agent deep reinforcement learning (MADRL) method that leverages the robust capabilities of the neural fictitious self-play (NFSP) method and extensive-form game (EFG) frameworks to address multi-agent interactions and partial information observability challenges. The letter primarily discusses the model design, which includes the construction of the EFG framework, hierarchical optimization of the action space, and the design of the reward function. Comparative results with other state-of-the-art methods demonstrate that this approach achieves significant accuracy and rapid convergence in anti-jamming decision-making, thereby effectively enhancing the intelligent anti-jamming capabilities of radar systems.

关键词： Jamming Radar Games Decision making Electronic countermeasures Convergence Spaceborne radar Radar countermeasures Indexes signal processing algorithms Multi-agent deep reinforcement learning (MADRL) neural fictitious self-play (NFSP) radar anti-jamming extensive-form game (EFG) decision making

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Efficient Complex Immittance Spectral Frequency With the Perceptual-Metric-Based Codebook Search

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IEEE signal processing LETTERS 2024年 31卷 2720-2724页

作者： Jo, Byeongho Beack, Seungkwon ETRI Daejeon 34129 South Korea

Complex-valued frequency-domain linear predictive coding (CLPC) has been developed for audio coding. Recently, representations for efficiently quantizing CLPC coefficients have been proposed, including the complex immittance spectral frequency (CISF). The CISF has limitations in that it requires signalling the sequential information to eliminate ambiguity and the highest-order coefficient (HOC) for reconstructing the CLPC coefficients. This study developed a modified CISF-based method that eliminates the need for additional information by utilizing intermediate complex polynomial properties. Furthermore, a perceptual-metric-based codebook search was proposed to improve quantization efficiency. The experimental results show robust quantization performance, while listening tests demonstrate superior audio quality compared to MPEG-D USAC long TCX at 12 kbps.

关键词： Polynomials Quantization (signal) signal processing algorithms Noise Image reconstruction Decoding Vectors Immittance spectral frequency complex immittance spectral frequency linear predictive coding speech coding audio coding

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Graphon Filters: Graph signal processing in the Limit

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IEEE TRANSACTIONS ON signal processing 2021年 69卷 1740-1754页

作者： Morency, Matthew W. Leus, Geert Delft Univ Technol EWI NL-2628 CD Delft Netherlands

Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about which we only know a model. To do this we leverage the theory of graphons proposed by L. Lovasz and B. Szegedy. We make three key contributions to the emerging field of graph signal processing. We show first that filters defined over the scaled adjacency matrix of a random graph drawn from a graphon converge to filters defined over the Fredholm integral operator with the graphon as its kernel. Second, leveraging classical findings from the theory of the numerical solution of Fredholm integral equations, we define the Fourier-Galerkin shift operator. Lastly, using the Fourier-Galerkin shift operator, we derive a graph filter design algorithm which only depends on the graphon, and thus depends only on the probabilistic structure of the graph instead of the particular graph itself. The derived graphon filtering algorithm is verified through simulations on a variety of random graph models.

关键词： signal processing Kernel signal processing algorithms Eigenvalues and eigenfunctions Fourier transforms Convergence Laplace equations Graph signal processing graph filter design graphons random graphs

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Generalized Unitary Joint Diagonalization Algorithm Based on Approximate Givens Rotations

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IEEE signal processing LETTERS 2024年 31卷 101-105页

作者： Azzouz, Malika Mesloub, Ammar Abed-Meraim, Karim Belouchrani, Adel Ecole Mil Polytech Lab Traitement Signal Algiers 16111 Algeria Univ Orleans F-45100 Orleans France Ecole Natl Polytech Algiers 16200 Algeria

Like the Joint Diagonalization of a unique set of matrices, Generalized Joint Diagonalization is an algebraic problem encountered in different applications such as data fusion and blind source separation. This letter proposes a new generalized unitary joint diagonalization approach based on the Jacobi iterative scheme using Givens rotations and simplified criterion, introducing three approximations. These approximations allowed us to reach a simultaneous estimation of different parameters. The first appears in the simplified criterion composed of entries doubly affected by the Givens rotations. The second approximation is in the Givens parameter using a small amplitude angle. The last approximation resides in keeping only the first order of transformed entries. Numerical experiments, including examples of joint blind audio source separation, are provided. The results show the effectiveness of the developed algorithm as compared to existing ones. The simultaneous estimation of different Givens rotations improves the algorithm's computation complexity and convergence rate.

关键词： Jacobian matrices Mathematical models Covariance matrices Approximation algorithms Convergence signal processing algorithms Estimation Generalized unitary joint diagonalization (GUJD) Givens rotations Jacobi iterative algorithm joint blind source separation (JBSS)

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Mixture to Mixture: Leveraging Close-Talk Mixtures as Weak-Supervision for Speech Separation

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IEEE signal processing LETTERS 2024年 31卷 1715-1719页

作者： Wang, Zhong-Qiu Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

We propose mixture to mixture (M2M) training, a weakly-supervised neural speech separation algorithm that leverages close-talk mixtures as a weak supervision for training discriminative models to separate far-field mixtures. Our idea is that, for a target speaker, its close-talk mixture has a much higher signal-to-noise ratio (SNR) of the target speaker than any far-field mixtures, and hence could be utilized to design a weak supervision for separation. To realize this, at each training step we feed a far-field mixture to a deep neural network (DNN) to produce an intermediate estimate for each speaker, and, for each of considered close-talk and far-field microphones, we linearly filter the DNN estimates and optimize a loss so that the filtered estimates of all the speakers can sum up to the mixture captured by each of the considered microphones. Evaluation results on a 2-speaker separation task in simulated reverberant conditions show that M2M can effectively leverage close-talk mixtures as a weak supervision for separating far-field mixtures.

关键词： Microphones Machine-to-machine communications Training signal processing algorithms Microwave integrated circuits signal to noise ratio Indexes Weakly-supervised neural speech separation

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Optimal Parametric Estimation of Biased Sinusoidal signals Using DREM

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IEEE signal processing LETTERS 2024年 31卷 1049-1053页

作者： Gao, Dongxu Liu, Lijun Yu, Zhen Liu, Shihan Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Xiamen Univ Shenzhen Res Institude Shenzhen 518000 Peoples R China

This brief presents a novel optimal parametric estimation method of improving the reconstructing accuracy for biased sinusoidal signals. First, a second-order generalized integrator (SOGI) is used to construct a linear regression equation, whose unknown coefficient vector is a combination with the bias and frequency of the input signal. Next, the dynamic regression extension and mixing technique is employed to estimate the unknown parameters by solving this equation. Then, the particle swarm optimization algorithm simultaneously optimizes the parameters of SOGI and adaptive control gains. Finally, a comparison of the accuracy of frequency estimation and reconstruction ability illustrates the superiority of the proposed strategy.

关键词： Phase locked loops Frequency estimation Vectors Mathematical models Convergence signal reconstruction signal processing algorithms Adaptive control dynamic regressor extension and mixing frequency estimation parameter optimization

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Real-Time Light Field signal processing Using 4D/5D Linear Digital Filter FPGA Circuits

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第7期68卷 2735-2741页

作者： Edussooriya, C. U. S. Wijenayake, C. Madanayake, A. Liyanage, N. Premaratne, S. Vorhies, J. T. Dansereau, D. G. Agathoklis, P. Bruton, L. T. Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa 10400 Sri Lanka Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4072 Australia Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA Univ Sydney Sydney Inst Robot & Intelligent Syst Camperdown NSW 2006 Australia Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Light fields (LFs) and light field videos (LFVs) capture both angular and spatial variation of light rays emanating from scenes. This richness of information leads to novel applications such as post-capture refocusing, depth estimation and depth-velocity filtering which are not possible with images and videos. These capabilities come, however, with a significant increase in data to be processed. In order to fully exploit opportunities provided by LFs and LFVs, low-complexity signal processing algorithms that process LF and LFV data in real-time are required. In this brief, we survey such state-of-the-art algorithms, in particular for depth filtering, refocusing and denoising of LFs and depth-velcoty filtering for LFVs, and future directions for these real-time LF an LFV processing algorithms.

关键词： Real-time systems Finite impulse response filters Light fields Field programmable gate arrays Videos Manganese signal processing algorithms Light fields light field videos multi-dimensional filters real-time processing FPGA

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