Owing to advances in many technologies, the high-speed flywheel energy storage system (FESS), flywheel battery, has become a viable alternative to electrochemical batteries and attracted much research attention in rec...
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Owing to advances in many technologies, the high-speed flywheel energy storage system (FESS), flywheel battery, has become a viable alternative to electrochemical batteries and attracted much research attention in recent years. A self-organising fuzzy neural network controller is presented for FESS to improve transient stability and increase transfer capability of power systems. The main difference from a traditional control approach ties in the model-free description of the control system and parallel computing capability. Simulation results from the Taiwan power system (Taipower) show that FESS with the proposed controller has produced significant improvement in power system performance.
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.
On-line dynamic security analysis has now become realistic due to advances in computer technology and algorithms for security assessment. Details of pattern recognition based electro, mechanical stability screens whic...
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On-line dynamic security analysis has now become realistic due to advances in computer technology and algorithms for security assessment. Details of pattern recognition based electro, mechanical stability screens which have been implemented within a dynamic security assessor are presented. Use of statistical functions of features is shown to overcome the dimensionality problem of applying pattern recognition techniques to large power systems. The low computational cost of this approach coupled with efficient operation has resulted in a significant step towards achieving full online dynamic security assessment.
The proceedings contains 17 papers from the advances in neuralnetworks for control and systems symposium held at the systems Technology Research Centre, Daimler-Benz AG, Berlin from May 25 to 27, 1994. Topics discuss...
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The proceedings contains 17 papers from the advances in neuralnetworks for control and systems symposium held at the systems Technology Research Centre, Daimler-Benz AG, Berlin from May 25 to 27, 1994. Topics discussed include constructive learning algorithms;learning systems;semi-empirical modelling of nonlinear dynamical systems;neuralnetworks for industrial process control;Kohonen feature maps;batch reactor temperature control;interpolation memories;gradient-based training algorithm;stability theory;adaptive neurofuzzy systems;constructive learning and nonlinear adaptive clustering.
The general principles of neural and hybrid architectures for multimedia in general are discussed. From the perspective of knowledge engineering, hybrid symbolic/neural agents are advantageous since different mutually...
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The general principles of neural and hybrid architectures for multimedia in general are discussed. From the perspective of knowledge engineering, hybrid symbolic/neural agents are advantageous since different mutually complementary properties can be combined. Symbolic representations have advantages with respect to easy interpretation, explicit control, fast initial coding, dynamic variable binding and knowledge abstraction. neural agents show advantages for gradual analog plausibility, learning, robust fault-tolerant processing, and generalization to similar input. Since these advantages are mutually complementary, a hybrid symbolic neural architecture can be useful if different processing strategies have to be supported.
The proceedings contains 73 papers from the Fourth International Conference on advances in Power System control, Operation and Management. Topics discussed include: operation development;power research;intelligent con...
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The proceedings contains 73 papers from the Fourth International Conference on advances in Power System control, Operation and Management. Topics discussed include: operation development;power research;intelligent controlsystems;change management;flexible alternating current transmission systems;power system planning;neuralnetworks;integrated fuzzy logic generator controller;adaptive variable window algorithm;digital distance protection;high impedance fault protection;power frequency model;voltage support devices;power system voltage stability;electric load forecasting;and distributed feeder expansion planning method.
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.
proceedings of the colloquium on Computing and control Division 'neural and fuzzy systems: design, hardware and applications' (9 May 1997, Savoy Place, London) are presented. Nine reports were discussed. The m...
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proceedings of the colloquium on Computing and control Division 'neural and fuzzy systems: design, hardware and applications' (9 May 1997, Savoy Place, London) are presented. Nine reports were discussed. The main topics were the following ones: neural network and fuzzy system hardware implementation, multilayer feedforward neuralnetworks, hardware implementation of neuro-fuzzy systems;neuralnetworks with intrinsic learning behaviours;neuro-fuzzy network applications.
Applications of neuro-fuzzy systems have provided advances in the ability to deal with abrupt fault conditions and incorporate multiple fault conditions. The support for ambiguities or approximate information implicit...
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Applications of neuro-fuzzy systems have provided advances in the ability to deal with abrupt fault conditions and incorporate multiple fault conditions. The support for ambiguities or approximate information implicit in fuzzy systems modelling provides the basis for teleological modelling. Finally, we draw attention to recent advances to the integration of fuzzy systems and Dempster-Shafer belief structures. The ensuing fuzzy system is better able to explicitly represent the characteristics of noise sources, therefore improving false alarm rates.
Instead of modeling complicated processes by mathematical formulas, neuralnetworks learn this tasks autonomously and is suited for modeling, optimization, forecasting, and control of multidimensional nonlinear system...
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Instead of modeling complicated processes by mathematical formulas, neuralnetworks learn this tasks autonomously and is suited for modeling, optimization, forecasting, and control of multidimensional nonlinear systems and processes. This high potential for applications of neural nets are realized in the corporate research divisions of Siemens in Munich (ZFE) and Princeton (SCR). The broad range of applications on which the central research division cooperates with the business units are presented. These include ideas for steel solutions for metals, neuralnetworks in water business, smoke detection, Simulation Environment for neural nets (SEneuralnetworks) and weather forecasting.
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