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Kalman Filtering With adaptive Step Size Using a Covariance-Based Criterion

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-10页

作者： Or, Barak Bobrovsky, Ben-Zion Klein, Itzik Univ Haifa Leon H Charney Sch Marine Sci Dept Marine Technol IL-3498838 Haifa Israel Tel Aviv Univ Elect Engn Sch IL-69978 Tel Aviv Israel

In Kalman filtering (KF), a tradeoff exists when selecting the filter step size. Generally, a smaller step size improves the estimation accuracy, yet with the cost of a high computational load. To mitigate this tradeoff influence on performance, a criterion that acts as a guideline for a reasonable choice of the step size is proposed. This criterion is based on the predictor-corrector error covariance matrices of the discrete KF. In addition, this criterion is elaborated to an adaptive algorithm, for the case of the time-varying measurement noise covariance. Two simulation examples and a field experiment using a quadcopter are presented and analyzed to show the benefits of' the proposed approach.

关键词： adaptive algorithm drones Kalman filter (KF) step size vehicle tracking

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Jointly Optimizing Throughput and Content Delivery Cost Over Lossy Cache Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第6期69卷 3846-3863页

作者： Chu, Weibo Yu, Zhiwen Lui, John C. S. Lin, Yi Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric - the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential-based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.

关键词： Routing Packet loss Throughput Measurement Servers Optimization Peer-to-peer computing Cache optimization throughput routing cost adaptive algorithm

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Fast multiline spectral reshaping algorithm for active vibration control

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2021年第1期40卷 481-496页

作者： Liu, Jinxin Xu, Maojun Zhang, Xingwu Yan, Ruqiang Chen, Xuefeng State Key Lab Mfg Syst Engn Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian Shaanxi Peoples R China

Multiline spectral vibration (or noise) is very common in rotating machinery like motor, gearbox, compressor, propeller, etc. Active reshaping of these vibrations is one of the most important branches for active vibration control, which may find application in fields of sound quality control, psychoacoustics, military camouflage, etc. The traditional filtered-reference LMS is the most popular structure for multiline spectral reshaping control. However, with the increase of spectral lines, vibration sources and observation points (feedback channels), the traditional structure will have extremely heavy computational complexity, especially for the system whose dynamic of secondary path is complex, since it requires two long filters for every single spectral line and secondary path to adjust the reference signals. In this study, we propose a fast-multiline spectrum reshaping algorithm for active vibration control. We first construct an equivalent system of the traditional multiline spectrum reshaping algorithm by introducing two extra control branches, which is able to improve the convergence property of the system. Then, we generate a group of pre-phase-scheduled reference signals using the phase information at certain frequencies of the secondary paths instead of using filters. Afterwards, we conduct a theoretical analysis of computational complexity of multiline spectrum reshaping and fast multiline spectrum reshaping algorithms. Finally, we carried out two case studies based on the FEMilS test-bed to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm performs an accurate residual vibration control with uniformed convergence speed and reduced computational complexity.

关键词： Active vibration reshaping multiline spectrum adaptive algorithm FEMilS

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A Novel Transmission Line Defect Detection Method Based on adaptive Federated Learning

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷

作者： Deng, Fangming Zeng, Ziqi Mao, Wei Wei, Baoquan Li, Zewen East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China

The existing transmission line detection technologies based on cloud computing are faced with the problems such as low response speed, high communication cost, and difficulty in obtaining scattered data. This article proposes a transmission line defect detection method based on adaptive federated learning (FL), which can adaptively change the data volume of the next training round according to the training energy consumption of each round. As a result, the proposed method can achieve the optimal number of communication between local training cost and global communication efficiency, which greatly improves the training speed and reduces the communication cost. Furthermore, a lightweight Mask region-convolutional neural network (R-CNN) that adopts the MobileNet architecture to replace the original network is designed for the edge, which reduces the required number of parameters and improves the detection speed. The experimental results show that the model training efficiency of the proposed adaptive FL is 70% higher than the centralized cloud computing, and the computing cost is saved by about 40%. Compared with the original Mask R-CNN, the proposed lightweight Mask R-CNN model can achieve 1.1% higher precision, 10% shorter training time, and 40% faster detection speed.

关键词： Computational modeling Image edge detection Adaptation models Training Performance evaluation Edge computing Data models adaptive algorithm deep learning defect detection edge computing federated learning (FL)

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A RUL prediction of bearing using fusion network through feature cross weighting

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第10期34卷 105908-105908页

作者： Wang, Zhijian Li, Yajing Dong, Lei Li, Yanfeng Du, Wenhua North Univ China Sch Mech Engn Taiyuan 030051 Shanxi Peoples R China

Nowadays, the methods of remaining useful life (RUL) prediction based on deep learning only use single model, or a simple superposition of two models, which makes it difficult for to maintain good generalization performance in various prediction scenarios, and ignores the dynamic sensitivity of features in the prediction, limiting the accuracy. This paper proposes a method of RUL prediction of bearing using fusion network through two-feature cross weighting (FNT-F). First, a fusion network with two subnets is proposed in this paper to adapt to the prediction problem in different scenarios. Meanwhile, a method of cross weighted joint analysis of the two features is proposed to make up for the shortcomings of feature analysis and achieve complementarity between time-domain and time-frequency features.

关键词： fusion network deep learning cross weighting remaining useful life prediction adaptive algorithm

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Essays on Computation and Empirical Macroeconomics

Essays on Computation and Empirical Macroeconomics

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作者： Kang, Seok Il Indiana University

学位级别：Ph.D., Doctor of Philosophy

The first chapter of the dissertation introduces a simulation-based adaptive algorithm from the computer engineering literature and applies it to models in economics. The algorithm aims to solve a general Markov decision process with a relatively large state space. The strategy is to construct a finite policy space of heuristic policies and adapt policies through simulated state variables. By updating and evaluating the performance of each policy, the algorithm provides convergence to the optimal value function and stationary distribution over policy space from the theoretical perspective. Application to solve the simple two-period Real Business Cycle model shows the performance is desirable within the feasible number of iterations and size of restricted policy space. The second chapter of the dissertation studies whether the data could reveal the necessity of the fiscal policy response for successful inflation-targeting monetary policy. Such necessity comes from the government debt valuation equation in which the market value of the debt equals the present value of primary surpluses in equilibrium. The Dynamic Stochastic General Equilibrium(DSGE) model estimation with the government debt valuation reports that in response to a monetary contraction, the market value of government debt falls, discount rates fall even more, while primary surpluses increase to compensate. This result suggests that the data reveals the necessity of fiscal backing. The third chapter of the dissertation revisits the same topic of the second chapter, using a VectorAutoRegression(VAR) analysis to examine how closely the data conforms to the theory. The VAR estimation shows that the data accounts for around 90% of the primary surplus adjustment dictated by theory, suggesting fiscal responses to monetary policy are sufficient to ensure successful inflation targeting by the central bank. Therefore, the result provides evidence that observed fiscal adjustments are sufficient for successful infl

关键词： Computation Empirical macroeconomics adaptive algorithm Vector auto regression

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Scale-up prediction of supercritical CO2 circulating fluidized bed boiler based on adaptive PSO-SVM

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POWDER TECHNOLOGY 2023年第1期419卷

作者： Cui, Ying Zou, Ye Jiang, Shujun Zhong, Wenqi Wuxi Inst Technol Sch Automot & Transportat Wuxi 214000 Jiangsu Peoples R China Southeast Univ Sch Energy & Environm Key Lab energy convers & Proc measurement & contr Minist Educ Nanjing 210096 Jiangsu Peoples R China Monash Univ ARC Res Hub Computat Particle Technol Dept Chem Engn Clayton Vic 3800 Australia Monash Univ Southeast Univ Ctr Simulat & Modelling Particulate Syst Joint Res Inst Suzhou 215000 Peoples R China Gaolang West Rd 1600 Wuxi 214000 Jiangsu Peoples R China Sipailou 2 Nanjing 210096 Jiangsu Peoples R China

Supercritical CO2 (S-CO2) circulating fluidized bed (CFB) boiler has broad prospects in the field of coal-fired power generation because of its high combustion efficiency, compact structure, and low pollution emission. Scale-up regularity research based on combustion characteristics of S-CO2 CFB boiler is the key to make it a wide range of industrial applications. In order to simplify the complicated workload and save the huge time cost of numerical simulations, it is of great significance to accurately make the scale-up prediction according to the operating performance of the S-CO2 CFB boiler. This study proposed a scale-up prediction model corresponding to the combustion characteristics of S-CO2 CFB boiler based on adaptive particle swarm optimization support vector machine (APSO-SVM). The parameters of the particle swarm algorithm were processed adaptively firstly combined with the boiler characteristics, and then the adaptive particle swarm algorithm was integrated with the support vector machine to solve the precocious convergence problem of particles being easy to gather in a certain position in the process of pattern recognition. The novel method effectively predicts the boiler in the scaling process from the aspect of boiler capacity, optimizes the scale-up regularity expression by numerical simulations, greatly saves time cost and applicability of enlarge design by altering complex numerical simulations, and lays the application foundation of S-CO2 CFB boiler in the industrial field with acceptable operation accuracy.

关键词： Supercritical CO 2 boiler Circulating fluidized bed Combustion characteristics Scale-up prediction adaptive algorithm

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Computation-efficient solution for fully-connected active noise control window: Analysis and implementation of multichannel adjoint least mean square algorithm

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期199卷

作者： Shi, Dongyuan Lam, Bhan Ji, Junwei Shen, Xiaoyi Lai, Chung Kwan Gan, Woon-Seng Nanyang Technol Univ Sch Elect & Elect Engn Digital Signal Proc Lab Singapore Singapore

The multichannel active noise control (MCANC) system, in which multiple reference sensors and actuators are used to enlarge the noise-cancellation zone, is widely utilized in complex acoustic environments. However, as the number of channels increases, the practicality decreases due to the exponential rise in computational complexity. This paper, therefore, revisits the adjoint least mean square (ALMS) algorithm and its multichannel applications. The computational analysis reveals that the multichannel adjoint least mean square (McALMS) algorithm1 has a significantly lower computation cost when implementing the fully connected active noise control (ANC) structure. In addition to this advantage, the theoretical analysis presented in this paper demonstrates that the McALMS algorithm can achieve the same optimal solution as the standard adaptive algorithm without the assumptions of input independence and white Gaussian noise. In addition, a practical step-size estimation strategy based on the Golden-section search (GSS) method is proposed to predict the fast step size of the McALMS algorithm. The numerical simulations in a multichannel ANC system demonstrate the effectiveness of the McALMS algorithm and validate the derived theoretical analysis. Furthermore, the McALMS algorithm with proposed step-size approach is used to implement a multichannel noise cancellation window that achieves satisfactory global noise reduction performance for tonal, broadband, and even real-world noises.

关键词： Multichannel active noise control adaptive algorithm Adjoint least mean square algorithm

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Learning-Based Neural adaptive Anti-Coupling Control for a Class of Robots Under Input and Structural Coupled Uncertainties

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IEEE ACCESS 2021年 9卷 32149-32160页

作者： Niu, Junlong Qin, Xiansheng Wang, Zheng Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Res Ctr Unmanned Syst Strategy Dev Xian 710072 Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China Northwestern Polytech Univ Natl Key Lab Aerosp Flight Dynam Xian 710072 Peoples R China

This paper investigates the learning - based adaptive anti-coupling control issue for the robots under input and structural coupled uncertainties. In this paper, the input and structural coupled uncertainties are modeled and transformed into a system state related term, an internal state related term and a system input related term. With the aid of the actual exponential input-state stability and the dynamic auxiliary signal, the internal state related uncertainties can be suppressed. By utilizing the properties of the robot dynamics and several special nonlinear functions, the system state related uncertainties can be handled. Moreover, to overcome the system input related uncertainties, an indirect control law and the adaptive boundary estimation law have been designed. To simplify the control structure, the neural networks have been introduced as online approximators. Finally, a novel learning-based intelligent adaptive anti-coupling control structure has been established for the robots. The simulation results revealed the satisfactory control performance of the proposed anti-coupling control algorithm.

关键词： Robots Uncertainty Couplings Legged locomotion Nonlinear dynamical systems Disturbance observers Aerodynamics adaptive control adaptive algorithm uncertain systems nonlinear dynamical systems robot control

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adaptive Short-Time Fractional Fourier Transform Based on Minimum Information Entropy

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Journal of Beijing Institute of Technology 2021年第3期30卷 265-273页

作者： Bing Deng Dan Jin Junbao Luan Naval Aviation University Yantai 264001ShandongChina

Traditional short-time fractional Fourier transform(STFrFT)has a single and fixed window function,which can not be adjusted adaptively according to the characteristics of fre-quency and frequency change *** order to overcome the shortcomings,the STFrFT method with adaptive window function is *** this method,the window function of STFrFT is ad-aptively adjusted by establishing a library containing multiple window functions and taking the minimum information entropy as the criterion,so as to obtain a time-frequency distribution that better matches the desired *** method takes into account the time-frequency resolution characteristics of STFrFT and the excellent characteristics of adaptive adjustment to window func-tion,improves the time-frequency aggregation on the basis of eliminating cross term interference,and provides a new tool for improving the time-frequency analysis ability of complex modulated sig-nals.

关键词： short-time fractional Fourier transform(STFrFT) adaptive algorithm minimum in-formation entropy

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