Fault is an undesired and unexpected event that changes the system behaviour resulting in performance degradation or even instability, so how to detect and diagnose fault become a great deal in engineering community. ...
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Fault is an undesired and unexpected event that changes the system behaviour resulting in performance degradation or even instability, so how to detect and diagnose fault become a great deal in engineering community. In this study, an adaptive fuzzy wavelet network-based fault detection and diagnosis (AFWN-FDD) scheme is proposed for non-linear systems subject to unstructured uncertainty. The proposed scheme is composed of a diagnostic estimator and an adaptive fuzzy wavelet network (AFWN). Diagnostic estimator is designed for residual generation and fault detection and AFWN based on multi-resolution analysis of wavelet transform and fuzzy concept is proposed to approximate the model of fault. learning algorithm of the proposed AFWN-FDD scheme is derived in the Lyapunov stability sense. The proposed scheme can simultaneously detect and estimate multiple incipient and abrupt faults in the presence of uncertainty. Stability analysis for the presented fault detection and diagnosis (FDD) scheme is provided. Furthermore, an extension of the proposed scheme for a class of non-linear systems with unmeasured states is presented. The efficiency and performance of the proposed scheme is evaluated through simulations that are performed for two well-known case studies. Comparison results highlight the superiority and capability of the proposed scheme.
Nonlinear equalisers based on minimum BER are proposed for the equalisation of nonlinear time-varying channels. To train the equalisers online, a sliding-window-based hybrid quasi-Newton algorithm is proposed. Switchi...
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Nonlinear equalisers based on minimum BER are proposed for the equalisation of nonlinear time-varying channels. To train the equalisers online, a sliding-window-based hybrid quasi-Newton algorithm is proposed. Switching between sliding-window stochastic gradient algorithm and sliding-window quasi-Newton algorithm makes the new algorithm significantly stabler with a fast convergence rate. Results from extensive simulation tests show that performance of nonlinear equalisers based on minimum BER is better than the equaliser based on minimum mean square error. The proposed algorithm demonstrates high efficiency as well. Copyright (c) 2013 John Wiley & Sons, Ltd.
This article investigates the issue of joint relay selection and channel allocation in LTE relay networks, where the available channels of each node are restricted and heterogeneous. We decompose the issue into two su...
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ISBN:
(纸本)9781467376884
This article investigates the issue of joint relay selection and channel allocation in LTE relay networks, where the available channels of each node are restricted and heterogeneous. We decompose the issue into two subproblems: one is the channel selection of relay to mitigate the interference among relay nodes and another is joint relay and channel selection of source node to maximize the total capacity. The first subproblem is formulated as a global interaction game and a learning algorithm is proposed to achieve the Nash equilibrium. Based on the result of the first subproblem, the second one is formulated as a congestion game with player-specific payoff function. We propose a distributed solution to maximize the total capacity. Simulation results show that our approach can obtain a large total capacity and a high fairness index.
In this paper a polynomial fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. Within this paper the fuzzy neural network model is used to obtain an estimate for the fuzzy parame...
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In this paper a polynomial fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. Within this paper the fuzzy neural network model is used to obtain an estimate for the fuzzy parameters in a statistical sense. Based on the extension principle, a simple algorithm from the cost function of the fuzzy neural network is proposed, in order to find the approximate parameters. Finally, we illustrate our approach by some numerical examples. (C) 2014 Elsevier B.V. All rights reserved.
Ice accretion on power transmission lines is one of the major causes for cable failure in Zhaotong area, Yunnan Province, South China. This study proposes a method to predict the remaining-dangerous time (RDT) of the ...
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Ice accretion on power transmission lines is one of the major causes for cable failure in Zhaotong area, Yunnan Province, South China. This study proposes a method to predict the remaining-dangerous time (RDT) of the icing load accretion on an interval of the power transmission lines with modified hidden semi-Markov model (HSMM). Based on the predicted RDT of the cables during ice accretion, the appropriate preventative measures can be scheduled in advance by electric power companies. The estimation model with the learning algorithm of support vector machine for icing load accretion is built through historical icing load accretion data and meteorological conditions first. Then, the estimated icing load accretion sequence can be obtained through the estimation model by forecasting the meteorological conditions. The modified HSMM method can eliminate the possible underflow issue during computation, and be used to build the RDT prognosis model. With the estimated icing load accretion sequence and RDT prognosis model, the authors can predict RDT of the icing load accretion on an interval of the power transmission lines. The developed prognosis algorithm is verified through collected meteorological conditions and icing load accretion data on the Dazheng 73# power transmission line in Zhaotong area, Yunnan Province, South China.
In May 2014, the authors of the top 26 papers from the IEEE International Conference on Multimedia & Expo (ICME) 2014 were invited to submit extended versions of their papers to this fast track special issue. Afte...
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In May 2014, the authors of the top 26 papers from the IEEE International Conference on Multimedia & Expo (ICME) 2014 were invited to submit extended versions of their papers to this fast track special issue. After a rigorous peer-review process, eight of those submissions were accepted for this special issue, now titled "Hot Topics in Multimedia Research." This is just the beginning of a close collaboration between MM and major multimedia conferences.
In this paper a new discrete perceptron model is introduced. The model forms a cascade structure and it is capable of realizing an arbitrary classification task designed by a constructive learning algorithm. The main ...
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In this paper a new discrete perceptron model is introduced. The model forms a cascade structure and it is capable of realizing an arbitrary classification task designed by a constructive learning algorithm. The main idea is to copy a discrete perceptron neuron's output to have a complementary dual output for the neuron, and then to select, by using a multiplexer, the true output, which might be 0 or 1 depending on the given input. Hence, the problem of realization of the desired output is transformed into the realization of the selector signal of the multiplexer. In the next step, the selector signal is taken as the desired output signal for the remaining part of the network. The repeated applications of the procedure render the problem into a linearly separable one and eliminate the necessity of using the selector signal in the last step of the algorithm. The proposed modification to the discrete perceptron brings universality with the expense of getting just a slight modification in hardware implementation.
In spite of advanced electro-mechanical technology on passenger or load elevators, elevator accidents still occur. Therefore, it is necessary to analyze vibrations of elevators with and without load for predicting som...
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In spite of advanced electro-mechanical technology on passenger or load elevators, elevator accidents still occur. Therefore, it is necessary to analyze vibrations of elevators with and without load for predicting some possible faults on their mechanical parts. This study proposes an adaptive neural network predictor to estimate and evaluate the vibrations on elevator systems. For this purpose, elevator vibrations are measured from two points of the elevator system for different working conditions, and different types of neural network analyzers are employed to evaluate the system vibrations. Simulation results show that neural networks can be used as an adaptive analyzer for such systems in the experimental applications.
In this paper, a novel algorithm named DNA-like learning algorithm is proposed. This algorithm is enable to quickly train the CNN template implementing LSBF, and has many advantages including without the need to consi...
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In this paper, a novel algorithm named DNA-like learning algorithm is proposed. This algorithm is enable to quickly train the CNN template implementing LSBF, and has many advantages including without the need to consider its convergence property, in particular faster running speed and better robustness. The important problem of implementing non-LSBF will be further discussed in the future study.
On the web, we can find dictionaries for viewing a sign of French Sign Language (FSL), from a word. However, finding a word from a sign is much more complicated. For this purpose, we propose to design a web applicatio...
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ISBN:
(纸本)9783319085999;9783319085982
On the web, we can find dictionaries for viewing a sign of French Sign Language (FSL), from a word. However, finding a word from a sign is much more complicated. For this purpose, we propose to design a web application to find the meaning of a FSL sign in the French language from the sign's features. In order to do this, we have developed an intelligent system capable of learning and self-improving by feeding off the information presented to it during its use. We have managed to find a middle ground between the reliability of the results and the ergonomics of Human-Machine Interfaces (HMI).
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