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Robust Islanding Detection in Microgrids Employing Rate of Change of Kinetic Energy Over Reactive Power

IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 505-515页

作者： Khosravi, Hasan Samet, Haidar Tajdinian, Mohsen Shiraz Univ Sch Elect & Comp Engn Shiraz *** Iran Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands

This study tries to outline a comprehensive approach established on the kinetic energy of the distributed generations (DGs) for islanding mode detection in micro-grids. More specifically, the proposed index calculates the rate of change of kinetic energy over reactive power (ROCOKORP) for each DG at the point of common coupling (PCC). Despite the islanding detection being the most challenging issue during complete match between the load and generation of DGs, the proposed method can swiftly deal with such an issue, therefore confirming its superiority. Other challenging conditions where islanding detection can either be difficult or confused with other events such as loads inside the non-detection zone (NDZ), various fault types, and loads at various power factors have also been considered to investigate the effectivity of the proposed approach. The proposed method's performance has been verified and evaluated in comparison to the state-of-the-art via a simulation testbed.

关键词： Islanding Kinetic energy Indexes Reactive power signal processing algorithms Rotors Reliability Renewable energy resources islanding distributed generation kinetic energy

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Large Direction-of-Arrival Mismatch Correction for Adaptive Beamforming

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IEEE ACCESS 2022年 10卷 61201-61212页

作者： Chang, Lin Yang, Hua Gulliver, T. Aaron Tan, Shizhe Wang, Yan Ocean Univ China Dept Elect Engn Qingdao 266100 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada Taishan Univ Dept Phys & Elect Engn Tai An 271000 Shandong Peoples R China

Direction-of-arrival (DOA) mismatch can degrade the performance of adaptive beamforming algorithms. Thus, a projection method is proposed to correct this mismatch. In a beamforming algorithm, the DOA error is usually regarded as a steering vector error which is corrected using a steering vector optimization algorithm. This approach can provide an optimal steering vector but ignores the actual DOA estimate. The proposed algorithm provides correction after DOA estimation but before beamforming to improve both the DOA estimation accuracy and beamforming gain. First, the signal-to-noise ratio (SNR) of the signal is estimated and used to regularize the covariance matrix. Then, an estimated steering vector with DOA close to the true value is determined based on a minimum number of projections. Numerical results are presented to verify the effectiveness of the proposed method for DOA estimation correction. In most cases, this method improves the performance of the beamforming algorithms without changing them.

关键词： Direction-of-arrival estimation Array signal processing signal to noise ratio Estimation Covariance matrices Interference signal processing algorithms Direction-of-arrival (DOA) mismatch correction adaptive beamforming covariance matrix regularization projection method

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Autonomous Driving Using Fluid Flow Simulation

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IEEE ACCESS 2022年 10卷 33349-33361页

作者： Sulkowski, Tomasz Izydorczyk, Jacek Szelest, Marcin Silesian Tech Univ Dept Automat Control PL-44100 Gliwice Poland APTIV Serv Poland SA PL-30707 Krakow Poland

This paper describes a fluid simulation application for the generation of driving trajectories in a simulated environment. The proposed method translates a segmented view from a front-facing camera into a top-down view of a road network, which is used as a river system where fluid flow is being simulated. Inspired by the natural phenomenon of water particles navigating complex riverbed obstacles, the driving trajectory is planned by tracing the simulated fluid flow. To demonstrate the feasibility of the proposed solution, the method was tested in several driving scenarios proposed in state-of-the-art papers, including navigating traffic and obstacles in city and highway environments. This solution shows that even very complex tasks can be solved using elegant nature-inspired approaches.

关键词： Roads Trajectory Mathematical models Automobiles Neural networks Autonomous vehicles Lattices Automotive applications autonomous systems vehicle driving signal processing algorithms

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Imaginary Control of a Mobile Vehicle Using Deep Learning Algorithm: A Brain Computer Interface Study

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IEEE ACCESS 2022年 10卷 20043-20052页

作者： Hekmatmanesh, Amin Azni, Hamed Mohammadi Wu, Huapeng Afsharchi, Mohsen Li, Ming Handroos, Heikki LUT Univ Lab Intelligent Machines Lappeenranta 53850 Finland Univ Zanjan Dept Comp Engn Zanjan *** Iran

Controlling a remote mobile vehicle using electroencephalograph (EEG) signals is still a challenge specially achieving a high degree of accuracy and precision. In the present study, the focus is on implementing an efficient feature space in a deep-based learning (DL) algorithm for a single trial application. More specifically, a boosting feature algorithm by means of long short-term memory (LSTM) networks are implemented in a deep auto-encoder (DAE) algorithm for producing an effective feature space tp identify event related desynchronization/event related synchronization (ERD/ERS) patters in EEG signals. For this purpose, three different DL-based algorithms are implemented that the models are based on a convolutional neural network (CNN), DAE, and LSTM networks to extract and boost the main features. In addition, our previous improved support vector machine (SVM)-based algorithm is employed to consider the potential of SVM and implemented DL-based algorithms for a two classes identification. To consider the efficiency of our implemented methods, algorithms are employed for control of a remote mobile vehicle in an imaginary right-hand opening and making a right-hand fist task. In our experiment, eleven subjects participated in an imaginary movement task. In the experiment, the displayed movement pictures were colored in yellow and red colors for stimulating brain to generate stronger ERD/ERS patterns. Results showed that the proposed algorithm by using the boosting technique significantly increased the accuracy with a higher precision of 73.31% +/- 0.03. The proposed method enabled the DL algorithm to be used in single trial experiments.

关键词： Feature extraction Electroencephalography signal processing algorithms Support vector machines Task analysis Optimization Neurons Brain-computer interface EEG signal processing event related desynchronization deep learning

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Gohberg-Semencul Factorization-Based Fast Implementation of Sparse Bayesian Learning With a Fourier Dictionary

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Dai, Fengzhou Wang, Yuanyuan Hong, Ling Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Shaanxi Normal Univ Key Lab Modern Teaching Technol Minist Educ Xian 710119 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Sparse Bayesian learning (SBL) is a popular and robust algorithm for sparse signal reconstruction (SSR). Unfortunately, the SBL algorithm suffers from heavy computational complexity when it is implemented directly since the inversion and multiplying operations of the estimation of the covariance matrix are involved in each iteration, which is proportional to the cube of the observed data length and thus prevents it solving large-scale problems. In many applications, such as radar imaging and array signal processing, the signal to be recovered is sparse in the Fourier dictionary. In this article, we propose an efficient implementation method for the Fourier dictionary-based SBL (FD-SBL). In the case that the Fourier dictionary is adopted, the estimation of the covariance matrix is a Toeplitz matrix for 1-D data or a Toeplitz-block-Toeplitz (TBT) matrix for 2-D data during the FD-SBL iterations. By utilizing this property, we employ the Gohberg-Semencul (G-S)-type factorization to accelerate the implementation of FD-SBL. To be noted, there is no approximation in our proposed method, and the computational cost is reduced by several orders of magnitude compared with the direct implementation of SBL. Finally, the experimental results verify the effectiveness of the proposed method.

关键词： Dictionaries signal processing algorithms Sparse matrices Bayes methods Estimation Covariance matrices Two dimensional displays Fast algorithm Fourier dictionary Gohberg-Semencul factorization sparse Bayesian learning

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3-D Array Image Data Completion by Tensor Decomposition and Nonconvex Regularization Approach

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4291-4304页

作者： Yang, Ming Luo, Qilun Li, Wen Xiao, Mingqing Southern Illinois Univ Carbondale Dept Comp Sci Carbondale IL 62901 USA Southern Illinois Univ Carbondale Dept Math Carbondale IL 62901 USA South China Normal Univ Sch Math Sci Guangzhou 510631 Guangdong Peoples R China

Various image datasets appear naturally in the form of multi-dimensional arrays (hypermatrices), called tensors. Image with incomplete entries, which often can be formulated as the low-rank tensor completion problem, is practically important in order to process large size 3D array images both efficiently and effectively. To make the low-rank approximation being tractable, most current approaches make use of the lower convex envelope of tensor multi-rank function for the approximation. Due to the gap between the rank function and its lower convex envelope, the adoption of tensor nuclear norm may lead to the approximation of the corresponding tensor tubal-rank being insufficient. In this paper, we introduce a new nonconvex regularization approach, which can better capture the low-rank characteristics than the convex approach. By transforming the original problem to the Fourier domain, we formulate an equivalent optimization problem with more transparent tensor rank characteristics, whose explicit solution can be obtained under our framework. A minimization algorithm, associated with the augmented Lagrangian multipliers and the nonconvex regularizer, is established and is shown to be feasible. The constructed sequence converges to the desirable Karush-Kuhn-Tucker point, which is mathematically validated in detail. Extensive experimental results demonstrate that our proposed approach outperforms the existing state-of-the-art convex approaches consistently.

关键词： Tensors Optimization Convergence Approximation algorithms signal processing algorithms Minimization Indexes Compressive sensing tensor completion low rank approximation non-convex regularization

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2D-DOA Estimation in Arc-Array With a DNN Based Covariance Matrix Completion Strategy

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IEEE ACCESS 2022年 10卷 57608-57620页

作者： Tian, Ye Mei, Ruru Huang, Yonghui Tang, Xiaogang Cui, Tianshu Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Space Engn Univ Sch Aerosp Informat Beijing 101416 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Two-dimensional direction of arrival (2D-DOA) estimation, or estimating the azimuth and elevation angles of sources simultaneously, is an increasingly important area in array signal processing. This paper presents a 2D-DOA algorithm together with a novel structure of array named Arc-Array (ArcA). The ArcA is enlarged to a virtual Uniform Circular Array (UCA) through a Deep-Neural-Network (DNN) based covariance matrix completion strategy;afterward, the MUSIC algorithm is performed with the completed covariance matrix. The proposed method is named as ArcA-DNN, and the performance of ArcA-DNN is evaluated by computer simulations. The simulation results indicate that the performance of 2D-DOA estimation in ArcA is able to approach that of a complete UCA;meanwhile, the number of physical elements is substantially reduced compared to the UCA. Moreover, the proposed ArcA-DNN algorithm gives access to implementing underdetermined 2D-DOA estimation with reasonable results.

关键词： Covariance matrices Estimation Antenna arrays Array signal processing signal processing algorithms Direction-of-arrival estimation Neurons 2D-DOA estimation UCA ArcA covariance matrix completion deep learning

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Double Nonstationarity: Blind Extraction of Independent Nonstationary Vector/Component From Nonstationary Mixtures-algorithms

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5102-5116页

作者： Koldovsky, Zbynek Kautsky, Vaclav Tichavsky, Petr Tech Univ Liberec Fac Mechatron Proc GrpInformat Acoust Signal AnalInterdisciplinary Studies Liberec 46117 Czech Republic Czech Acad Sci Inst Informat Theory & Automat Prague 18208 Czech Republic

In this article, nonstationary mixing and source models are combined for developing new fast and accurate algorithms for Independent Component or Vector Extraction (ICE/IVE), one of which stands for a new extension of the well-known FastICA. This model allows for a moving source-of-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian source model assuming tridiagonal covariance matrix structures is proposed. It is shown to be beneficial in the frequency-domain speaker extraction problem. The algorithms are verified in simulations. In comparison to the state-of-the-art algorithms, they show superior performance in terms of convergence speed and extraction accuracy.

关键词： signal processing algorithms Mathematical models Probability density function Covariance matrices Computational modeling Blind source separation Sensors Blind source extraction blind source separation dynamic models independent component analysis independent vector analysis moving sources

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signal processing with Spiking Neuron Model

Signal Processing with Spiking Neuron Model

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Digital signal processing and its Applications (DSPA), International Conference on

作者： Vladimir Bondarev Institute of Information Technologies Sevastopol State University Sevastopol Russian Federation

The article deals with signal processing models that use the representation of signals by spike trains. The “input-output” model of an integrate and fire spiking neuron is considered. The possibilities of using this model for spectral analysis and adaptive filtering are investigated. To solve the problem of adaptive processing of spike sequences, a generalized multi-input vector-matrix model of a spiking neuron is introduced. Based on this model, we derive supervised learning rules to adapt the dynamics of the spiking neuron. Examples of adaptive signal recovery from spike trains, additive noise suppression, and adaptive synthesis of band-pass filters are demonstrated.

关键词： Adaptation models Deconvolution Filtering Computational modeling Neurons Supervised learning signal processing algorithms

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Sky Imager Data Reduction Using Autoencoder and Internet of Things Computing

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IEEE ACCESS 2022年 10卷 111232-111240页

作者： Alotaibi, Ziyad N. Khouli, Saeed A. Ibrahim, Ahmed B. Alrabeiah, Muhammad Ragheb, Amr M. Almaiman, Ahmed S. Alshebeili, Saleh A. King Saud Univ Elect Engn Dept Riyadh 11421 Saudi Arabia

Forecasting the output power of renewable energy sources is of an utmost importance for maintaining the stability of the electricity grid and ensuring uninterrupted service. Internet-of-Things (IoT) devices equipped with sky imagers are often used to estimate solar irradiance whose value is essential for forecasting solar energy. This paper considers distributed sky imagers whose data are transmitted through the Internet to a cloud for possible storage, forecasting, and/or performing other signal processing operations. The data comprise a collection of images, and this makes their streaming to the cloud a burden, i.e., they require a large bandwidth. In an attempt to deal with that, an IoT system is proposed in this paper such that it collects, compresses, transmits, and reconstructs those images with as little bandwidth as possible. More specifically, an IoT device multiplexes the collected images and compresses them using a deep learning algorithm, the encoder of a convolutional autoencoder (CAE) in particular. It, then, transmits the compressed images to a cloud server where they could be decompressed with high-fidelity using the decoder of the CAE. As the core to the proposed system, the performance of the CAE algorithm is evaluated using a publicly available dataset of sky images. Results indicate that the images can be compressed to 2% of their original size (i.e., compression ratio of 98%) and reconstructed again with high fidelity, i.e., average structural similarity index measure (SSIM) of 99%. Hardware implementation of the CAE algorithm has also been attempted using a Raspberry Pi platform representing the IoT device with an average processing speed of 6.6681 seconds per frame. This speed well meets practical requirements given the low frame-rate of sky imagers.

关键词： Image coding Forecasting Internet of Things Cloud computing signal processing algorithms Codecs Image reconstruction Convolutional neural networks Convolutional auto-encoder image compression sky imager raspberry pi Internet of Things

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