Wireless video sensor networks (WVSNs) feature the characteristics of easy deployment and low maintenance cost and therefore are suitable for a wide range of important applications, including remote surveillance, heal...
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ISBN:
(纸本)9781849192408
Wireless video sensor networks (WVSNs) feature the characteristics of easy deployment and low maintenance cost and therefore are suitable for a wide range of important applications, including remote surveillance, health care delivers and traffic control systems Compared to traditional communication systems, Sensor nodes in WVSN operate under resource constrains such as power supply and transmission bandwidth The main object of this paper as to studs the resource allocation of a wireless sensor node and improve the system performance, especial the video distortion lore specifically, by taking into account the relations between separated modules on a sensor node, we build a comprehensive power-rate-distortion (Prd) framework for WVSN optimization Based on this framework, we address the problem of power allocation between video encoder module and wireless transmission module on the node to achieve the optimal reconstructed video quality under power and rate constraints
The approach described in this paper is part of the German national research project VOGUE. VOGUE leverages trusted network connect concepts as a key to implement/design a holistic and vendor neutral network access sy...
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Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as...
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Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure. (C) 2010 Elsevier Ltd. All rights reserved.
We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy nu...
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We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using alpha-cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers, we present a dynamic programming method for finding a shortest path in the network. Examples are worked out to illustrate the applicability of the proposed model. (C) 2010 Elsevier Ltd. All rights reserved.
This paper describes the implementation of a genetic algorithm to evolve the population of weight matrices for storing and recalling the patterns in a Hopfield type neural network model. In the Hopfield type neural ne...
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This paper describes the implementation of a genetic algorithm to evolve the population of weight matrices for storing and recalling the patterns in a Hopfield type neural network model. In the Hopfield type neural network of associative memory, the appropriate arrangement of synaptic weights provides an associative function in the network. The energy function associated with the stable state of this model represents the appropriate storage of the input patterns. The aim is to obtain the optimal weight matrix for efficient recall of any prototype input pattern. For this, we explore the population generation technique (mutation and elitism), crossover and the fitness evaluation function for generating the new population of the weight matrices. This process continues until the selection of the last weight matrix or matrices has been performed. The experiments incorporate a neural network trained with multiple numbers of patterns using the Hebbian learning rule. In most cases, the recalling of patterns using a genetic algorithm seems to give better results than the conventional recalling with the Hebbian rule. The simulated results suggest that the genetic algorithm is the better searching technique for recalling noisy prototype input patterns. (C) 2010 Elsevier Ltd. All rights reserved.
To meet the requirements of the tactical targets, in the progress of the missiles39; flight, we often give the related parameters a range to make the targets to extreme. In this paper, we use variable Calculus to le...
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Directed Acyclic Graph (DAG) is a nature way of workflow description. One of the most important and challenging problems in the DAG workflow field is the QoS-constrained scheduling problem with the aim to minimize the...
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Taking advantage of the feature that the energy of the image would gather and spread on four components (LL2, LH2, HL2 and HH2) in the subimage after first-order CArdBAL2 multi-wavelet transform, propose an Informatio...
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Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as...
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Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure. (C) 2010 Elsevier Ltd. All rights reserved.
This paper is devoted to the static friction torque of electromagnetic clutch. The torque maximization is also investigated by optimizing the geometrical shape of armature. For the purpose of designing and optimizing ...
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ISBN:
(纸本)087849300X
This paper is devoted to the static friction torque of electromagnetic clutch. The torque maximization is also investigated by optimizing the geometrical shape of armature. For the purpose of designing and optimizing electromagnetic clutch, torque prediction is a very important factor. We construct an axi-symmetric FEM model to analyze static friction torque and use a torque tester to evaluate real torque. In this work, analytically predicted torque is compared with the experimental one to discuss the rationality of numerical process. The analytical result agrees well with experimental data, which proves the validity of the mathematical process. Through optimization of the shape of armature, we also improve the static torque of electromagnetic clutch about 30%.
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