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This article shows the possibility of using renewable energy sources in order to improve energy efficiency, reduce greenhouse gas emissions and therefore prevent climate change. This article proposes a remote monitoring and control system with interfaces and data collectors. In addition to research, development, testing and use of renewable energies, it is also necessary to measure and track online,...
This paper's purpose is describing and highlighting of how our system is intended to work and how it can help us in everyday life, as well as making the Earth a safer and cleaner place, by not even noticing it's presence. Our system is always monitoring the traffic of every major city, analyzing it and making decisions on how people should drive, in a manner that the roads are safer, the fuel consumption...
In this paper we show the possibility to use feed forward neural networks for failure rate prediction, and this can be used for improving predictive maintenance. We use a series of real values that represent the failure rate of a radio-reception system, and describe a Java application that we developed, that simulates a feed forward neural network which is trained to predict, based on the available...
Feed forward neural networks have an intrinsic fault tolerance to the faults of neurons from the hidden layer. In this paper is presented a simulation program that analyses the behaviour of a feed forward neural network, with a single hidden layer, in the presence of faults. The neural network is used to classify a binary image in four classes. The fault that is analysed is the short circuit of the...
In this paper we describe the way in which we select the weights set for a two hidden layers feed forward neural network. The weights are selected based on the fault tolerance of the neural network. We developed a Java application for training the network that generates more valid weights files and then the application selects the file that offers the maximum fault tolerance to the faults of the hidden...
In this paper is presented a solution based on a bi-dimensional cellular automata (CA) for image density classification task (DCT). The two necessary properties: density preserving and translation are combined together to obtain the DCT solution. These two properties are achieved using a combination of nine fundamental 2D-CA rules and the proposed solution for DCT has two phases: preprocessing phase...
In this paper is described a method for weights set selection for a feed forward neural network, based on the fault tolerance analysis of the network. For a certain neural network used in a specific problem, one can obtain many weight sets, as a result of backpropagation training algorithm, due to the fact that this algorithm initializes the weights with random numbers. Each time we repeat the training,...
This paper presents how an improvement can be realized by using prediction in Process Failure Mode and Effects Analyze (PFMEA) with the neural networks approach to determine the fault occurrence. Neural networks have the ability to time-series data prediction, in our case series containing all values of the failures of the items. The improvement in prediction of PFMEA has followed continuously data...
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