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Accelerating Two-Phase Multiobjective Evolutionary Algorithm for Electric Location-Routing Problems

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9121-9136页

作者： Wang, Chao Liu, Rongrong Su, Yansen Zhang, Xingyi Anhui Univ Minist Educ Sch Artificial Intelligence Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Sch Artificial Intelligence Minist Educ Hefei 230601 Peoples R China Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

Electric location-routing problem is a challenging problem consisting of the optimization of electric vehicle routing and charging facility location, simultaneously. Existing algorithms generally adopt the two-phase search strategy to alternately optimize the routing and the location. However, they are usually criticized for the inefficiency as the problem scale increases. In order to improve the search efficiency in each phase, we propose an accelerating two-phase multiobjective evolutionary algorithm, where the learning method is used to mine the useful information from the historical search process to generate the high-quality routing and location offspring. To be specific, in the routing optimization phase, an interpolation method is developed to extract the frequent visiting orders existing in the historical best solutions. These frequent visiting orders are used to create potential routing offspring that can accelerate the convergence toward the optimal solutions. In the location optimization phase, a surrogate model is used to approximatively represent the relationship from routing to location, which can directly output a promising location scheme for a given routing offspring and thus reduce the optimization time. Experimental results on different scales of test instances demonstrate the competitiveness of the proposed algorithm in comparison with several state of-the-art algorithms, including four widely used heuristic algorithms and two multiobjective evolutionary algorithms.

关键词： Routing Search problems Optimization Heuristic algorithms Evolutionary computation Batteries signal processing algorithms Electric location-routing problem multiobjective optimization evolutionary algorithm interpolation surrogate

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Batch processing algorithms for blind equalization using higher-order statistics

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IEEE signal processing MAGAZINE 2003年第1期20卷 25-49页

作者： Chi, CY Chen, CY Chen, CH Natl Tsing Hua Univ Inst Commun Engn Hsinchu 30043 Taiwan ADMtek Inc Hsinchu Taiwan Ind Technol Res Inst Comp & Commun Res Labs Transmiss Technol Dept Digital Video & Opt Commun Technol Div Hsinchu 31015 Taiwan

Statistical signal processing has been one of the key technologies in the development of wireless communication systems, especially for broadband multiuser communication systems which severely suffer from intersymbol interference (ISI) and multiple access interference (MAI). This article reviews batch processing algorithms for blind equalization using higher-order statistics for mitigation of the ISI induced by single-input, single-output channels as well as of both the ISI and MAI induced by multiple-input, multiple-output channels. In particular, this article reviews the typical inverse filter criteria (IFC) based algorithm, super-exponential algorithm, and constant modulus algorithm along with their relations, performance, and improvements. Several advanced applications of these algorithms are illustrated, including blind channel estimation, simultaneous estimation of multiple time delays, signal-to-noise ratio (SNR) boost by blind maximum ratio combining, blind beamforming for source separation in multipath, and multiuser detection for direct sequence/code division multiple access (DS/CDMA) systems in multipath.

关键词： Blind equalizers Higher order statistics signal processing algorithms Intersymbol interference Multiple access interference Multiaccess communication Wireless communication Broadband communication Filters Delay estimation

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Direct-Optimization-Based DC Dictionary Learning With the MCP Regularizer

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第7期34卷 3568-3579页

作者： Li, Zhenni Yang, Zuyuan Zhao, Haoli Xie, Shengli Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China Guangdong Key Lab IoT Informat Technol GDUT Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Minist Educ Key Lab Intelligent Informat Proc & Syst Integrat Guangzhou 510006 Peoples R China

Direct-optimization-based dictionary learning has attracted increasing attention for improving computational efficiency. However, the existing direct optimization scheme can only be applied to limited dictionary learning problems, and it remains an open problem to prove that the whole sequence obtained by the algorithm converges to a critical point of the objective function. In this article, we propose a novel direct-optimization-based dictionary learning algorithm using the minimax concave penalty (MCP) as a sparsity regularizer that can enforce strong sparsity and obtain accurate estimation. For solving the corresponding optimization problem, we first decompose the nonconvex MCP into two convex components. Then, we employ the difference of the convex functions algorithm and the nonconvex proximal-splitting algorithm to process the resulting subproblems. Thus, the direct optimization approach can be extended to a broader class of dictionary learning problems, even if the sparsity regularizer is nonconvex. In addition, the convergence guarantee for the proposed algorithm can be theoretically proven. Our numerical simulations demonstrate that the proposed algorithm has good convergence performances in different cases and robust dictionary-recovery capabilities. When applied to sparse approximations, the proposed approach can obtain sparser and less error estimation than the different sparsity regularizers in existing methods. In addition, the proposed algorithm has robustness in image denoising and key-frame extraction.

关键词： Machine learning Optimization Dictionaries Convergence Convex functions signal processing algorithms Approximation algorithms Convergence analysis dictionary learning direct optimization minimax concave penalty (MCP) regularizer

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Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

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IEEE ACCESS 2022年 10卷 17430-17440页

作者： Favato, Andrea Carlet, Paolo Gherardo Toso, Francesco Torchio, Riccardo Ortombina, Ludovico Bruschetta, Mattia Carli, Ruggero Alotto, Piergiorgio Bolognani, Silverio Rodriguez, Jose Univ Padua Dept Ind Engn I-35151 Padua Italy Univ Padua Dept Informat Engn I-35151 Padua Italy Univ Andres Bello Fac Engn Santiago 8370146 Chile

This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications. A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.

关键词： Mathematical models Linear systems Reluctance motors signal processing algorithms Prediction algorithms Licenses Computational modeling Electric drives model predictive control (MPC) permanent magnet synchronous motor (PMSM) synchronous reluctance motor (SyRM) quadratic programming (QP)

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Achieving Online Regression Performance of LSTMs With Simple RNNs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7632-7643页

作者： Vural, N. Mert Ilhan, Fatih Yilmaz, Selim F. Ergut, Salih Kozat, Suleyman Serdar Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Univ Toronto Dept Comp Sci Toronto ON M5S Canada DataBoss AS TR-06800 Ankara Turkey Rumeli Univ Dept Comp Engn TR-34570 Istanbul Turkey

Recurrent neural networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies. As an RNN model, long short-term memory networks (LSTMs) are commonly preferred in practice, as these networks are capable of learning long-term dependencies while avoiding the vanishing gradient problem. However, due to their large number of parameters, training LSTMs requires considerably longer training time compared to simple RNNs (SRNNs). In this article, we achieve the online regression performance of LSTMs with SRNNs efficiently. To this end, we introduce a first-order training algorithm with a linear time complexity in the number of parameters. We show that when SRNNs are trained with our algorithm, they provide very similar regression performance with the LSTMs in two to three times shorter training time. We provide strong theoretical analysis to support our experimental results by providing regret bounds on the convergence rate of our algorithm. Through an extensive set of experiments, we verify our theoretical work and demonstrate significant performance improvements of our algorithm with respect to LSTMs and the other state-of-the-art learning models.

关键词： Training signal processing algorithms Recurrent neural networks Logic gates Convergence Approximation algorithms Time complexity Neural network training online gradient descent online learning recurrent neural networks (RNNs) regression

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Robust CFAR Detector Based on CLEAN for Sidelobe Suppression

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IEEE SENSORS JOURNAL 2024年第9期24卷 14930-14942页

作者： Zhu, Jiachen Huang, Xiaohong Liu, Jiayu Deng, Zhenmiao North China Univ Sci & Technol Sch Artificial Intelligence Tangshan 063210 Peoples R China Hebei Key Lab Ind Intelligent Percept Tangshan 063210 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China

In response to challenges posed by abnormal interference, such as target sidelobes within the reference window, this article proposes a sidelobe suppression-constant false alarm rate (SS-CFAR) detector. Drawing on the CLEAN concept, the SS-CFAR is designed for target detection in indoor environments with multiple targets using millimeter (mm)-wave radar. It uses second-order difference statistics combined with the Monte Carlo (MC) algorithm and iterative censoring (IC) to separate target signals from background clutter in radar echoes. In addition, sidelobe suppression (SS) for the target is achieved by reconstructing the target's echo information and the CLEAN concept. The simulation and actual experimental results demonstrate that SS-CFAR delivers superior detection performance, obviating the need for prior knowledge of anomalies or manual setting of parameters such as the number of reference and guard cells, which traditionally depend on experiential adjustments. Compared with mean class and ordered statistics (OS) class CFAR detectors, SS-CFAR transcends reference window constraints. It accurately estimates background clutter, enhancing detection capabilities in multitarget environments. This innovative approach not only showcases superior detection performance and adaptability but also paves the way for the broader application of mm-wave radar in complex indoor scenarios.

关键词： Clutter signal processing algorithms Object detection Radar detection Detectors Millimeter wave radar Sensors CLEAN constant false alarm rate (CFAR) interfering targets millimeter (mm)-wave radar reference window sidelobe suppression (SS) target detection

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APPLICATION OF KALMAN FILTERING TO REAL-TIME PREprocessing OF GEOPHYSICAL-DATA

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1992年第5期30卷 897-910页

作者： NORIEGA, G PASUPATHY, S UNIV TORONTO DEPT ELECT ENGN TORONTO M5S 1A1 ONTARIO CANADA

Data recorded by digital data acquisition systems is often not suitable for direct computer processing, and a data "editing" or preprocessing stage is usually necessary;this is a slow and typically only semiautomatic process. In this paper we propose an algorithm for automatic prepocessing of multiple data sequences in real-time. Based on a fixed-lag Kalman filter approach proposed by J. Halpenny (1984), it models the signal using a state vector that consists of the signal, its first three differences, and a special variable used to implement data editing functions. The smoothed output lessens some of the typical noise problems encountered in practice, and the method also provides mechanisms for identification and removal of spikes, identification and measurement of steps, and filling of data gaps. Two versions of the algorithm are developed, the first based on the conventional form of the Kalman filter, and the second one using a "sequential processing" technique. The computational requirements of each are analyzed and compared. In this regard, we also consider an alternate approach for fixed-lag smoothing, proposed by R.G. Brown (1983), and based on a one-step forward predictor and an L-step backward sweep, with L being the fixed lag. It is shown that despite the greater complexity of the model used in the algorithms proposed, for L>30 the computational requirements are very similar to those of the alternate method. The implementation of the algorithms on AT&T's DSP32 family of digital signal processors is analyzed, including the practical computational requirements. Extensive simulations with synthetic and real data demonstrate the performance of the algorithms. When compared to the alternate method proposed by R.G. Brown (1983), the algorithms developed produced estimates roughly 2.5 times more accurate.

关键词： Kalman filters Filtering signal processing algorithms Crosstalk Data preprocessing Smoothing methods Circuit noise Working environment noise Data acquisition Geophysics computing

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Robust Iterative Sparse ML-Based Approaches for CES Data Model: Application to DOA Estimation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 8635-8650页

作者： Abeida, Habti Glentis, George-Othon Alsharef, Mohammad Li, Jian Taif Univ Coll Engn Dept Elect Engn Taif 21944 Saudi Arabia Univ Peloponnese Dept Informat & Telecommun Tripolis 22100 Greece Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

SCM is widely used in the design of array signal processing algorithms that perform well for CCG distributed samples. However, SCM-based DOA estimation algorithms are inaccurate for CES data models. This article aims to extend the conventional SCM-based iterative sparse SMLA for DOA estimation to encompass more general circular CES distributed observations. These extended RSMLA use robust CES-based ML-estimators of the CM instead of SCM, or more generally M-estimators. Joint and interleaved computation of M-estimators and the RSMLA are also proposed [denoted as IRSMLA algorithms] when the size of the observed CES vector, M, is of the same order as the samples size, N, or when N < M. To improve DOA estimation accuracy and eliminate grid mismatch, refined approaches (referred to as RSMLA-RML) that combines the power-based RSMLA and robust ML (RML) methods are also proposed, in which an alternative RML cost function is minimized over a scalar DOA parameter. Performance comparisons are made between the developed approaches and existing DOA estimation algorithms as well as CRB in terms of sample size, SNR, DOA separation, signal source correlation parameters, and computational cost, demonstrating superior performance over existing schemes for heavy-tailed CES distributed observations. Moreover, it is experimentally demonstrated that the proposed algorithms perform effectively even when the encountered source signals are coherent, despite being derived under the assumption of uncorrelated sources.

关键词： Direction-of-arrival estimation signal processing algorithms Accuracy Maximum likelihood estimation signal to noise ratio Multiple signal classification Vectors Circular complex elliptically symmetric (CES) distributions direction of arrival (DOA) estimation M-estimators stochastic maximum likelihood approaches (SMLA) algorithms sparse representation

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The 8051 microcontroller application for complex signal generating in model-based signal processing

The 8051 microcontroller application for complex signal gene...

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International Conference on CAD Systems in Microelectronics (CADSM)

作者： Volodymyr Shvaichenko Dimitry Titkov Sergy Kroshko Electronic Department National Technical University of Ukraine Kyiv Polytechnic Institute Kyiv Ukraine

In this paper methods of generating sigma-delta modulator output impulse sequence using 8051 microcontroller are given. By applying the microcontroller based system, model-based signal processing can reduce time requi... 详细信息

ISBN: (纸本)9789531841306

关键词： signal processing Microcontrollers signal generators Digital signal processing signal design Circuits Delta-sigma modulation Microprocessors signal analysis signal processing algorithms

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Evaluation measures for adaptive PLI filters in ECG signal processing

Evaluation measures for adaptive PLI filters in ECG signal p...

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Computers in Cardiology (CinC)

作者： F.C. Chang C.K. Chang K.Y. Chi Y.D. Lin Graduate Institute of Electrical and Communications Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan

Many studies have been devoted to the adaptive power-line interference (PLI) filter design for ECG signal processing. However, almost all existing PLI suppression filters are developed for applications in which the presence of PLI noise is assumed a priori. Indiscriminate application of PLI suppression over an ECG signal that is free of PLI noise may deform ECG morphology, and even cause degraded performance of subsequent processing. To date, little work has been done on the possibility of ECG signal degradation by such filtering operation and the impact on further processing. In order to evaluate the difference between the original and the filtered pattern, this study proposes quantitative evaluation measures. The assessments include convergence time, the frequency tracking efficiency, the execution time and the relative statistics in time and frequency domain. Extensive experiments have been done with artificially and practically corrupted ECG signals for four existing PLI adaptive filtering techniques (Ahlstrom and Tompkinspsila, Pei and Tsengpsilas, Sopsilas and Ziarani and Konradpsilas algorithm). The results reveal that ECG signal distortion resulted from these existing adaptive filters. None of the existing algorithms outperform the others in all assessments. The proposed evaluation measures can also be used for the performance evaluation of the other types of artifact suppression, such as the baseline wander and EMG corruption contaminated in ECG, after minor modification. The proposed measures also make the optimal filter design under different constraints possible for ECG signal processing.

关键词： Adaptive filters Electrocardiography Adaptive signal processing Pollution measurement signal processing signal design Degradation signal processing algorithms Interference Morphology

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