The paper presents a practical artificial neural network (ANN) based relay algorithm for electric distribution high impedance fault detection. The scheme utilizes the characteristics of high impedance faults (HIFs) in...
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The paper presents a practical artificial neural network (ANN) based relay algorithm for electric distribution high impedance fault detection. The scheme utilizes the characteristics of high impedance faults (HIFs) in the resulting waveforms of the three phase residual current, voltage, admittance and power. By using Fourier analysis, their low order harmonic vectors were worked out which were then fed to a neural network. The network was based on either perceptron or feed forward algorithm. The trained network was verified using other distribution systems.
Batch processes may constantly be changing, sometimes in unpredictable ways. For this reason, the operators need to keep a close watch on things. Batch-plant control stations have traditionally been located very close...
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Batch processes may constantly be changing, sometimes in unpredictable ways. For this reason, the operators need to keep a close watch on things. Batch-plant control stations have traditionally been located very close to the process, because operators can do a better job of sensing abnormalities if they can see, hear and smell what is going on. The reaction process can be considered as unstable, the instability being understood as the characteristic making the self abandoned system evolve towards unpermitted temperature values with the associated danger of accidents. The temperature also plays a decisive role in the final product quality, each time the reactor changes from the desired temperature the quality diminishes, the appearance of undesired species increases, and the overall performance of the reaction decreases which directly affects the economic performance.< >
Non-linear techniques have been developed for the purpose of filtering navigational data and control of ships. Recently, research at the Institute of Marine Studies into ship control employed artificial intelligence i...
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Non-linear techniques have been developed for the purpose of filtering navigational data and control of ships. Recently, research at the Institute of Marine Studies into ship control employed artificial intelligence in the form of neuralnetworks. Mathematical and scale models of the controlsystems are developed to provide an intelligent autopilot ship control capable of emulating the human operator.
Many authors have shown by simulated studies, that a great number of non-linear dynamical systems could be identified and controlled by using neural network models. The authors applied these results to a real process ...
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Many authors have shown by simulated studies, that a great number of non-linear dynamical systems could be identified and controlled by using neural network models. The authors applied these results to a real process : an oven with two inputs, one for heating and one for cooling. The output to be controlled is the temperature inside the oven. The choice of the control strategy on the one hand and the choice of the neural network architecture for the oven identification on the other hand had to satisfy two main objectives. First, the control strategy should be quite insensible to random disturbances (air leaks, door openings, ...) and the neural model should be able to fit any modification of the plant dynamics during its lifetime (modification of the internal load alteration of the heating system ...). The authors chose to use the internal model control as their control strategy. They also used a radial basis function network to identify the plant. The process identification is composed of two phases, an off-line one and an online one. The off-line part consists first in training the network while determining its internal structure using an initial training set. The on-line phase is the adaptive part of the control scheme.< >
Traditionally, power system control and management functions have been performed in centralised locations, with unprocessed data being collected from several measuring points throughout the power system and returned t...
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Traditionally, power system control and management functions have been performed in centralised locations, with unprocessed data being collected from several measuring points throughout the power system and returned to the central point for analysis. Whilst many technology advances have been made in schemes such as this, including the introduction of expert systems, neuralnetworks, and parallel computing methods at power system control centres, the potential now exists to consider distribution centres, the potential now exists to consider distributing this information technology throughout the power system by realising the concept of intelligent substations. This paper will explore the possibilities for a distributed control and management system, with major transmission substations performing tasks such as alarm processing, fault diagnosis and conditions monitoring on a local basis;that is, accepting data from local and adjacent substations, processing this data, and sending concise and summarised messages to the control centre (with the potential for localised executive action in some instances). The advantages of such an arrangement will be demonstrated, including: reduction in SCADA system load, especially during critical periods;increased local autonomy, thus facilitating substation automation;faster response times due to the distributed nature of the processing throughout the power system. Examples of ongoing research into the realisation of such a system will be given, showing test results from package already developed, using data provided by utilities and manufactures. Problems with the implementation of such systems will also be covered, and ideas for the future solutions to these problems will be suggested.
The principal constituents of soft computing are the fuzzy logic (FL), artificial neuralnetworks (NN) and probabilistic reasoning (PR). It is generally regarded that FL primarily deals with imprecision, NN with learn...
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The principal constituents of soft computing are the fuzzy logic (FL), artificial neuralnetworks (NN) and probabilistic reasoning (PR). It is generally regarded that FL primarily deals with imprecision, NN with learning, and PR with uncertainty. They have, however, overlapping boundaries and are known to be complementary rather than competitive to each other in many applications. Here, two control algorithms, one implemented by fuzzy logic and the other by a neural network, are used as the basis to highlight salient features of soft computing. A DC motor servo system with the proposed soft computing based algorithms is discussed. The fuzzy logic control employs the principles of fuzzy logic to calculate an optimal output action based on input conditions, and a knowledge base expressed in linguistic forms, thereby performing a parallel operation to control the output with a high degree of robustness against parameter change. In the neural network control, focus is on how neuralnetworks can overcome deadzone-plus-saturation nonlinearity commonly found in the power driver of a DC servo motor. Simulation results have been performed to establish the validity of these control algorithms.< >
Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known in the past that a number of infectious or metabo...
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ISBN:
(纸本)0780376013
Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections, and in vivo urine samples from patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK Health Laboratory environment. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on a neuralnetworks, genetic algorithms, and multivariate techniques such as principal components analysis and discriminant function analysis-cross validation. The experimental results confirm the validity of the presented methods.
One of the important problems to be solved for neural network applications is to find a suitable network structure solving the given task. To reduce the engineering efforts for the architecture design a data driven al...
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One of the important problems to be solved for neural network applications is to find a suitable network structure solving the given task. To reduce the engineering efforts for the architecture design a data driven algorithm is desirable which constructs a network structure during the learning process. There are different approaches for structure adaptation with evolutionary algorithms, growth algorithms and others. To solve large problems successfully it is necessary to divide the problem into subproblems and to solve them separately by experts. This is a fundamental principle of nature. To implement this principle in artificial neuralnetworks there are different approaches, but these algorithms yield fixed network structures. The authors propose a learning architecture for growing complex artificial neuralnetworks which tries to include both sides of the coin, structure adaptation and task decomposition. The growing process is controlled by self-observation or reflexion. The algorithm generates a feedforward network bottom up by cyclically inserting cascaded hidden layers. Inputs of a hidden layer unit are locally restricted with respect to the input space by using a new kind of activation function, combining the local characteristics of radial basis function units with sigmoid units. Contrary to the cascade-correlation learning architecture the authors introduce different correlation measures to train the network units featuring different goals. The task decomposition between subnetworks is done by maximizing the anticorrelation between the hidden layer units output and a connection routing algorithm which only connects cooperative units of different layers. These features resemble the TACOMA (TAsk decomposition, COrrelation Measures and local Attention neurons) learning architecture. Self-observation is done by transforming the errors and the network structure to the input space. So it is possible to infer from errors to structure and reverse.< >
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