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adaptive Ensemble Fault Diagnosis Based on Online Learning of Personalized Decision Parameters

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第11期65卷 8882-8894页

作者： Hua, Zhongsheng Yu, Hongtao Hua, Ye Zhejiang Univ Sch Management Hangzhou 310058 Zhejiang Peoples R China

Data-driven fault diagnosis is increasingly prevalent in recent years for complex systems, but still suffers from two practical difficulties: one is the lack of enough historical examples of fault equipment and the other is the absence of personalized characteristics of the equipment to be diagnosed. This paper proposes an adaptive ensemble fault diagnosis method to resolve these two difficulties. The proposed method first extracts fault and normal patterns by constructing hidden Markov models (HMMs), respectively, from historical fault and normal examples. Then fault diagnosis decisions made by the supervised and unsupervised methods are intrinsically integrated through the hidden states identified by HMMs to overcome the difficulty of lacking enough historical fault examples. Finally, a learning process is designed and embedded in the proposed method to describe the impacts of personalized characteristics of the equipment on its fault diagnosis. Theoretical and experimental results both verify the effectiveness of the proposed method.

关键词： adaptive algorithm fault diagnosis hidden Markov models (HMMs) pattern recognition probability reliability supervised learning unsupervised learning

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adaptive Consensus-Based Robust Strategy for Economic Dispatch of Smart Grids Subject to Communication Uncertainties

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第6期14卷 2484-2496页

作者： Wen, Guanghui Yu, Xinghuo Liu, Zhi-Wei Yu, Wenwu Southeast Univ Sch Math Nanjing 210096 Jiangsu Peoples R China RMIT Univ Sch Engn Melbourne Vic 3001 Australia Huazhong Univ Sci & Technol Coll Automat Wuhan 430074 Hubei Peoples R China

The economic dispatch problem is investigated in this paper for a class of smart grids subject to unknown communication uncertainties. Compared with existing works related to economic dispatch where the dispatch algorithms are carried out by a centralized controller, a new kind of distributed dispatch algorithms are developed to achieve optimal dispatch of electric power by appropriately sharing the load among different generating units while guaranteeing consensus among incremental costs. An adaptive weight-adjustment technique is suggested that enables the dispatch algorithms to choose the communication weights among neighboring generating units which yield consensus of incremental costs under both cases with or without capacity limitations. The achievement of such a consensus leads to optimal dispatch of electronic power and secures the system performance against unknown communication uncertainties. Meanwhile, it is proved that the power demand and supply of the considered smart grids will be kept in a balanced state during the dispatch process. The interesting issue of how to assign the power outputs among generating units to balance the power demand and supply of the considered smart grids is also addressed. Finally, the numerical results of several case studies have been provided to verify the effectiveness of the proposed algorithms.

关键词： adaptive algorithm distributed control economic dispatch leader-following consensus smart grid

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New Scheduling algorithm for Mobile Teaching Cloud Resource Push

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2018年第7期13卷 17-29页

作者： Wan, Qing Yang, Xueping Chen, Guanghai Chongqing Vocat Inst Engn Chongqing Peoples R China

In order to solve the drawbacks of the traditional message push mechanism in terms of diversity, power consumption, traffic consumption, and message real-time performance, an adaptive scheduling algorithm was designed. Based on the algorithm, an adaptive message push strategy that can dynamically allocate message push mode for the terminal was proposed. The experimental results showed that this strategy could reduce the power consumption of mobile terminals and save network traffic while adapting to the diversity of terminals. Therefore, it can ensure the real-time performance of messages.

关键词： adaptive algorithm mobile teaching cloud resources push

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An adaptive C^0IPG method for the Helmholtz transmission eigenvalue problem

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Science China Mathematics 2018年第8期61卷 1519-1542页

作者： Hao Li Yidu Yang School of Mathematical Sciences Guizhou Normal University

The interior penalty methods using C^0 Lagrange elements(C^0 IPG) developed in the recent decade for the fourth order problems are an interesting topic at present. In this paper, we discuss the adaptive proporty of C^0 IPG method for the Helmholtz transmission eigenvalue problem. We give the a posteriori error indicators for primal and dual eigenfunctions, and prove their reliability and efficiency. We also give the a posteriori error indicator for eigenvalues and design a C^0 IPG adaptive algorithm. Numerical experiments show that this algorithm is efficient and can get the optimal convergence rate.

关键词： transmission eigenvalues interior penalty Galerkin method Lagrange elements a posteriori error estimates adaptive algorithm

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Optimizing computing time in the wave iterative method by RLS and SFTF algorithms

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2019年第6期32卷

作者： Hafedh, Hrizi Hamdi, Noureddine Sboui, Noureddine Univ El Manar Natl Enginneering Sch Tunis ENIT Commun Syst Lab SysCom Tunis Tunisia Univ El Manar Fac Sci Tunis Phys Lab LP3ME Tunis Tunisia Univ Carthage Higher Inst Appl Sci & Technol Mateur Tunis Tunisia

In this research paper, adaptive filtering techniques are used to improve performance of the wave iterative method. This method is used for the modeling and the study of the planar microwave electronic circuits which are the most used today in the new technologies of radio frequency communication. In order to improve the convergence speed of the wave iterative method, we use the recursive least square algorithm and the stabilized fast transversal filter. A significant gain in computation time is obtained with these new techniques with a good accuracy in the found results.

关键词： adaptive algorithm convergence computing time recursive least square (RLS) stabilized fast transversal filter (SFTF) wave concept iterative procedure (WCIP)

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Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2019年第2-4期80卷 176-208页

作者： Panagant, Natee Pholdee, Nantiwat Wansasueb, Kittinan Bureerat, Sujin Yildiz, Ali R. Sait, Sadiq M. Khon Kaen Univ Fac Engn Sustainable Infrastruct Res & Dev Ctr Dept Mech Engn Khon Kaen 40002 Thailand Bursa Uludag Univ Dept Automot Engn TR-16059 Bursa Turkey King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran 31261 Saudi Arabia

In this paper, an approach called real-code population-based incremental learning hybridised with adaptive differential evolution (RPBILADE) is proposed for solving many-objective automotive floor-frame optimisation problems. adaptive strategies are developed and integrated into the algorithm. The purpose of these strategies is to select suitable control parameters for each stage of an optimisation run, in order to improve the search performance and consistency of the algorithm. The automotive floor-frame structures are considered as frame structures that can be analysed with finite element analysis. The design variables of the problems include topology, shape, and size. Ten optimisation runs using various optimisers are carried out on two many-objective automotive floor-frame optimisation problems. Twelve additional benchmark tests against all competitors are also performed to demonstrate the search performance of the proposed algorithm. RPBILADE provided better results than other recent optimisers for both the automotive floor-frame optimisation and benchmark problems.

关键词： automotive floor-frame design many-objective optimisation population-based incremental learning differential evolution adaptive algorithm

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An adaptive approach for prediction of propellant feedline dynamics in fluid network

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INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW 2018年第6期28卷 1335-1351页

作者： Ravindran, Sivaguru S. Majumdar, Alok K. Univ Alabama Dept Math Sci Huntsville AL 35899 USA NASA Dept Prop Syst Marshall Space Flight Ctr Huntsville AL USA

Purpose This paper aims to propose an adaptive unstructured finite volume procedure for efficient prediction of propellant feedline dynamics in fluid network. Design/methodology/approach The adaptive strategy is based on feedback control of errors defined by changes in key variables in two subsequent time steps. Findings As an evaluation of the proposed approach, two feedline dynamics problems are formulated and solved. First problem involves prediction of pressure surges in a pipeline that has entrapped air and the second is a conjugate heat transfer problem involving prediction of chill down of cryogenic transfer line. Numerical predictions with the adaptive strategy are compared with available experimental data and are found to be in good agreement. The adaptive strategy is found to be efficient and robust for predicting feedline dynamics in flow network at reduced CPU time. Originality/value This study uses an adaptive reduced-order network modeling approach for fluid network.

关键词： adaptive algorithm Network fluid system Thermo fluid analysis Unstructured finite volume method

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adaptiveWAVELET algorithm FOR SOLVING NONLINEAR INITIAL-BOUNDARY VALUE PROBLEMS WITH ERROR CONTROL

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2018年第1期16卷 19-43页

作者： Harnish, C. Matous, K. Livescu, D. Univ Notre Dame Ctr Shock Wave Proc Adv React Mat Dept Aerosp & Mech Engn Notre Dame IN 46556 USA Los Alamos Natl Lab Comp & Computat Sci Div Los Alamos NM 87545 USA

We present a numerical method which exploits the biorthogonal interpolating wavelet family, and second-generation wavelets, to solve initial-boundary value problems on finite domains. Our predictor-corrector algorithm constructs a dynamically adaptive computational grid with significant data compression, and provides explicit error control. Error estimates are provided for the wavelet representation of functions, their derivatives, and the nonlinear product of functions. The method is verified on traditional nonlinear problems such as Burgers' equation and the Sod shock tube. Numerical analysis shows polynomial convergence with negligible global energy dissipation.

关键词： multiresolution analysis wavelets adaptive algorithm nonlinear PDEs data compression

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Sensorless Control for IPMSM Based on adaptive Super-Twisting Sliding-Mode Observer and Improved Phase-Locked Loop

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ENERGIES 2019年第7期12卷 1225-1225页

作者： Chen, Shuo Zhang, Xiao Wu, Xiang Tan, Guojun Chen, Xianchao China Univ Min & Technol Sch Elect & Power Engn Xuzhou 221116 Jiangsu Peoples R China Xuzhou Yirui Construct Machinery Co Ltd Xuzhou 221000 Jiangsu Peoples R China

In traditional sensorless control of the interior permanent magnet synchronous motors (IPMSMs) for medium and high speed domains, a control strategy based on a sliding-mode observer (SMO) and phase-locked loop (PLL) is widely applied. A new strategy for IPMSM sensorless control based on an adaptive super-twisting sliding-mode observer and improved phase-locked loop is proposed in this paper. A super-twisting sliding-mode observer (STO) can eliminate the chattering problem without low-pass filters (LPFs), which is an effective method to obtain the estimated back electromotive forces (EMFs). However, the constant sliding-mode gains in STO may cause instability in the high speed domain and chattering in the low speed domain. The speed-related adaptive gains are proposed to achieve the accurate estimation of the observer in wide speed range and the corresponding stability is proved. When the speed of IPMSM is reversed, the traditional PLL will lose its accuracy, resulting in a position estimation error of 180 degrees. The improved PLL based on a simple strategy for signal reconstruction of back EMF is proposed to ensure that the motor can realize the direction switching of speed stably. The proposed strategy is verified by experimental testing with a 60-kW IPMSM sensorless drive.

关键词： interior permanent magnet synchronous motor (IPMSM) sensorless control adaptive algorithm super-twisting sliding mode observer (STO) phase-locked loop (PLL)

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adaptive Importance Sampling Unscented Kalman Filter based SAR image super resolution

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COMPUTERS & GEOSCIENCES 2019年 133卷 104310-000页

作者： Kanakaraj, Sithara Nair, Madhu S. Kalady, Saidalavi Natl Inst Technol Dept Comp Sci & Engn Calicut 673601 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi 682022 Kerala India

Enhancing Synthetic Aperture Radar (SAR) images for sharp and detailed images through super-resolution (SR) is of great significance in many remote sensing applications. Due to the inherent resolution limitations and the cost incurred for further development of sensor devices, image enhancements through image processing techniques have become more popular. However, in these techniques, the soft edges present in the images are not reconstructed completely, thereby marring the clarity of the generated image. This paper presents a method to restore more details in the image by adaptively adjusting the measurement noise covariance and process noise covariance into the intensity estimation framework for SAR image super-resolution. For this, an adaptive Importance Sampling Unscented Kalman Filter (ISUKF) framework is implemented using the covariance matching technique. Experimental results indicate that the super-resolution using ISUKF framework performs better, regarding denoising and feature preservation, when accounted for the measurement and process noise covariance. It also outperforms the other recent and standard SR techniques covered in the literature.

关键词： Super resolution Synthetic Aperture Radar image Importance sampling Unscented Kalman Filter adaptive algorithm Noise covariance

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