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A Kind of Electro-hydraulic Servo System Cooperative Control Simulation: An Experimental Research

RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2022年第4期15卷 309-322页

作者： Gao, Bingwei Shen, Wei Dai, Ye Wang, Wei Harbin Univ Sci & Technol Coll Mech & Power Engn Xuefu Rd Harbin 15080 Heilongjiang Peoples R China

Background: The single position control or force control of the electro-hydraulic servo system has long been unable to meet the actual needs of production and life. To achieve a good control effect and solve various problems in real life, people no longer only consider a single position control or force control. Objective: The proposed method provides technical support for multi-objective collaborative control of the electro-hydraulic servo system, completes the requirements of multitasking operation, improves the positioning accuracy and response velocity of the electro-hydraulic servo system, and realizes the synergy between the position and force. Methods: A control method of the outer loop control of force as feedforward compensation is adopted to realize the coordinated control of force and position based on position control. The position control part adopts the PID control algorithm to ensure real-time response accuracy and positioning accuracy. The outer force loop uses an adaptive fuzzy neural network control algorithm to reduce vibration and shock caused by system instability and nonlinear factors during the force control process. Results: The control effect of the designed position/force collaborative controller is verified by simulation and experiment. It can effectively reduce the vibration and impact caused by the contact force during the working process of the system, at the same time greatly improving the accuracy of position control and improving the stability of the system. Conclusion: The designed force/position collaborative control system has a good overall control effect.

关键词： Electro-hydraulic servo systems PID control collaborative control adaptive fuzzy neural network control sensor fusion signal processing algorithms

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One-Step Calculation Circuit of FFT and Its Application

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第7期69卷 2781-2793页

作者： Liu, Yiyang Wang, Chunhua Sun, Jingru Du, Sichun Hong, Qinghui Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) are core components in the field of signal processing. However, in the existing research, there is no fully analog circuit that can realize the one-step calculation of FFT. Therefore, in this paper, an analog circuit that can calculate FFT and its inverse transform IFFT in one-step is proposed. First, a circuit that can realize complex number operations is designed. On the basis of this structure, a fully analog circuit that can realize fast and efficient computing of FFT and IFFT in one-step is proposed. In addition, different coefficient matching can be obtained to achieve arbitrary points of FFT and IFFT by adjusting the resistance value of the memristor, which has good programmability. Specific examples are given in the paper to evaluate the proposed method. The PSPICE simulation results show that the average accuracy is above 99.98%. More importantly, the calculation speed has been greatly improved compared with MATLAB simulation. Finally, the proposed circuit can be used to quickly solve convolution operation, and the average accuracy can reach 99.95%.

关键词： signal processing algorithms Discrete Fourier transforms Memristors Computer architecture Analog circuits Integrated circuit modeling Hardware FFT IFFT circuit design memristor complex number operation

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Distributed Support Vector Machines Over Dynamic Balanced Directed Networks

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 758-763页

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Charalambous, Themistoklis Khan, Usman A. Aalto Univ Sch Elect Engn Espoo 02150 Finland Semnan Univ Fac Mech Engn Semnan *** Iran Georgia State Univ Robinson Coll Business Atlanta GA 30309 USA Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA

In this letter, we consider the binary classification problem via distributed Support Vector Machines (SVMs), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database. Agents only share processed information regarding the classifier parameters and the gradient of the local loss functions instead of their raw data. In contrast to the existing work, we propose a continuous-time algorithm that incorporates network topology changes in discrete jumps. This hybrid nature allows us to remove chattering that arises because of the discretization of the underlying CT process. We show that the proposed algorithm converges to the SVM classifier over time-varying weight balanced directed graphs by using arguments from the matrix perturbation theory.

关键词： Support vector machines Manganese Heuristic algorithms Distributed databases Radio frequency Switches signal processing algorithms Support vector machines distributed optimization matrix perturbation theory

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CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 196-211页

作者： Janjusevic, Nikola Khalilian-Gourtani, Amirhossein Wang, Yao NYU Tandon Sch Engn Elect & Comp Engn Dept Brooklyn NY 11201 USA

Deep learning based methods hold state-of-the-art results in low-level image processing tasks, but remain difficult to interpret due to their black-box construction. Unrolled optimization networks present an interpretable alternative to constructing deep neural networks by deriving their architecture from classical iterative optimization methods without use of tricks from the standard deep learning tool-box. So far, such methods have demonstrated performance close to that of state-of-the-art models while using their interpretable construction to achieve a comparably low learned parameter count. In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising and joint denoising and demosaicing (JDD) performance both in low and high parameter count regimes. Specifically, we show that the proposed model outperforms state-of-the-art fully convolutional denoising and JDD models when scaled to a similar parameter count. In addition, we leverage the model's interpretable construction to propose a noise-adaptive parameterization of thresholds in the network that enables state-of-the-art blind denoising performance, and near-perfect generalization on noise-levels unseen during training. Furthermore, we show that such performance extends to the JDD task and unsupervised learning.

关键词： Noise reduction Dictionaries Convolutional codes Convolution Training Task analysis signal processing algorithms Interpretable deep learning unrolled networks blind denoising joint demosaicing and denoising dictionary learning sparse coding

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Optimal Diffusion Learning Over Networks-Part I: Single-Task algorithms

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 107-127页

作者： Merched, Ricardo Univ Fed Rio de Janeiro Dept Elect & Comp Engn BR-21941901 Rio De Janeiro Brazil

We revisit the theory of distributed networks of cooperative agents under a broader perspective of diffusion adaptation, by exploiting proximity concepts. This leads to two main families of algorithms with enhanced convergence rate and mean-square-error performance. Part I of this work considers mainly single-task scenarios, which are based on formulating optimal learning and fusion steps via an adaptive network penalty function. The main recursions, which we refer to as Adapt-and-Fuse (AAF) diffusion, are reminiscent of a reweighted network regularized algorithm, usually seen in standalone formulations. This is in line with early approaches that promote proximity among agents in cooperative networks. The AAF strategy employs exact fusion in the least-squares sense, and outperforms the exact global least-squares solution that ignores the topology of the network. It also suggests simplified LMS-complexity algorithms, and motivates us to develop a normalized version of the relative variance diffusion algorithm, which also learns combination weights. It is verified that even when agents do not share estimates, but only their uncertainties, the simplified AAF improves accuracy over the NLMS-RV algorithm in the presence of intruders, and becomes more robust to noisy links. In order to cope with the computational burden associated with long parameter vectors and correlated inputs, an overlapped block multidelay adaptive frequency-domain (FD) version of each new algorithm is derived. It turns out that for correlated inputs, these FD-LMS versions outperform the exact fullband RLS solutions. In the accompanying Part II of this work, we pursue extensions to the multitask scenario. Extensive simulations illustrate the superiority of the new approaches.

关键词： signal processing algorithms Uncertainty Convergence Noise measurement signal processing Data models Covariance matrices Adaptation combination weights diffusion networks fusion least-squares single-task

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Infection Analysis on Irregular Networks Through Graph signal processing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 1939-1952页

作者： Hosseinalipour, Seyyedali Wang, Jie Tian, Yuanzhe Dai, Huaiyu North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA

In a networked system, functionality can be seriously endangered when nodes are infected, due to either internal random failures or a contagious virus that develops into an epidemic. Given a snapshot of the network representing the nodes' states (infected or healthy), infection analysis refers to distinguishing an epidemic from random failures and gathering information for effective countermeasure design. This analysis is challenging due to irregular network structure, heterogeneous epidemic spreading, and noisy observations. This article treats a network snapshot as a graph signal, and develops effective approaches for infection analysis based on graph signal processing. For the macro (network-level) analysis aiming to distinguish an epidemic from random failures, i) multiple detection metrics are defined based on the graph Fourier transform (GFT) and neighborhood characteristics of the graph signal;ii) a new class of graph wavelets, distance-based graph wavelets (DBGWs), are developed;and iii) a machine learning-based framework is designed employing either the GFT spectrum or the graph wavelet coefficients as features for infection analysis. DBGWs also enable the micro (node-level) infection analysis, through which the performance of epidemic countermeasures can be improved. Extensive simulations are conducted to demonstrate the effectiveness of all the proposed algorithms in various network settings.

关键词： Epidemics signal processing algorithms Wavelet transforms signal processing Viruses (medical) Wavelet analysis Social networking (online) Epidemic spreading graph fourier transform graph signal processing graph wavelets infection analysis

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Conjugate Gradient Iterative Hard Thresholding for Structured Sparsity

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 387-402页

作者： Blanchard, Jeffrey D. Grinnell Coll Dept Math & Stat Grinnell IA 50112 USA

Greedy sparse recovery algorithms are studied in the structured sparsity (sparsity in levels) framework. Recovery guarantees are provided for Normalized Iterative Hard Thresholding and Conjugate Gradient Iterative Hard Thresholding in the form of restricted isometry properties for sparsity in levels. Empirical results indicate that CGIHT is comparable to CoSaMP in recovery capability in the structured setting, while maintaining the computational complexity of NIHT. While exploiting structured sparsity improves recovery performance, pessimistic theoretical guarantees mask when practitioners should use these algorithms;the empirical results offer guidance for using the original greedy algorithms over CGIHT in Levels.

关键词： Compressed sensing signal processing algorithms Indexes Sparse representation Iterative algorithms Standards Sensors Compressed sensing conjugate gradient iterative hard thresholding greedy algorithms model-based compressed sensing structured sparsity sparsity in levels sparse approximation

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Quantum Resources Required to Block-Encode a Matrix of Classical Data

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2022年第1期3卷 1页

作者： Clader, B. David Dalzell, Alexander M. Stamatopoulos, Nikitas Salton, Grant Berta, Mario Zeng, William J. Goldman Sachs & Co New York NY 10282 USA AWS Ctr Quantum Comp Pasadena CA 91125 USA CALTECH Pasadena CA 91125 USA Amazon Quantum Solut Lab Seattle WA 98170 USA Imperial Coll London Dept Comp London SW7 2BX England Rhein Westfal TH Aachen Inst Quantum Informat D-52062 Aachen Germany

We provide modular circuit-level implementations and resource estimates for several methods of block-encoding a dense NxN matrix of classical data to precision & varepsilon;the minimal-depth method achieves a T-depth of O(log(N/& varepsilon;)), while the minimal-count method achieves a T-count of O(Nlog(1/& varepsilon;)). We examine resource tradeoffs between the different approaches, and we explore implementations of two separate models of quantum random access memory (QRAM). As part of this analysis, we provide a novel state preparation routine with T-depth O(log(N/& varepsilon;)), improving on previous constructions with scaling O(log(2)(N/& varepsilon;)). Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.

关键词： Qubit Logic gates Costs signal processing algorithms Registers Quantum circuit Integrated circuit modeling Block encoding quantum circuit synthesis quantum circuits quantum computing quantum random access memory (QRAM) quantum resources

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Dif-MAML: Decentralized Multi-Agent Meta-Learning

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 71-93页

作者： Kayaalp, Mert Vlaski, Stefan Sayed, Ali Ecole Polytech Fed Lausanne Adapt Syst Lab CH-1015 Lausanne Switzerland Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited for this decentralized setting, where the learner benefits from information and computational power spread across the agents. Motivated by this observation, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, robustness, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting.

关键词： Task analysis Adaptation models Data models signal processing algorithms signal processing Optimization Biological system modeling Decentralized optimization diffusion algorithm distributed learning learning to learn meta-learning multi-agent systems networked agents

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Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control algorithms

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 1615-1629页

作者： Ito, Masaaki Kanno, Issei Yamazaki, Kosuke Kishi, Yoji Chen, Wei-Yu Choi, Thomas Molisch, Andreas F. KDDI Res Inc Wireless Technol Div Saitama 3568502 Japan Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G (B5G). CF-mMIMO systems with multiple-antenna APs have been investigated from various viewpoints recently. However, no comprehensive analysis of the impact of antenna distribution on CF-mMIMO system performance has been done so far, which is important for practical deployment. Besides spectral efficiency, in B5G, energy efficiency of user equipments is one of the key indicators because various kinds of battery-limited devices connect to the network. Thus, this paper provides a comprehensive performance analysis of the impact of antenna distribution on the performance indicators, while considering several combining/precoding schemes and transmit power control algorithms. For uplink maximal-ratio combining, the concentrated deployment has the best performance thanks to the channel hardening and favorable propagation phenomena. On the other hand, the concentrated deployment prominently suffers from shadowing effects. For uplink/downlink minimum mean-square error combining with transmit power control, the semi-distributed deployments show the best performance, and it implies that we can reduce the number of APs to 1/4 for uplink and to 1/2 for downlink while keeping the same performance as the fully-distributed deployment.

关键词： Antennas Power control Uplink signal processing algorithms Transmitting antennas Shadow mapping Minimax techniques Antenna distribution battery lifetime prolongation cell-free massive MIMO energy efficiency spectral efficiency transmit power control

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