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Different ANN architectures using back propagation can forecast the electricity demand at half-hourly intervals for up to 24 hours ahead with various degrees of success that is highly dependent on mainly trial and error heuristic tailoring of the architecture and the various learning parameters to cover the solution space. This paper presents the reults of an investigation of an approach in the neuro-evolution...
According to signal characteristic of Unified S-band (USB) measurement and control system, a method of spectrum sensing based on artificial neural network (ANN) is introduced in this paper. Spectrum sensing classifier is designed by the independent learning ability of ANN. The information collected by departed classifier is studied and stored to accurately judge that whether the master signal exists...
Forecasting of water demand is becoming an essential tool for the design, management and modernization of water supply and distribution systems. In this paper, the hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EMD) and several single forecasting methods such as Autoregressive Integrated Moving Average (ARIMA) and artificial neural networks (ANN) models are proposed...
This paper deals with a 6-bar mechanism, which! finds its application in a precision deep drawing press. The approach for the kinematic simulation is based on loop closure analysis, which has been performed to derive expressions for slider displacement. The results are consolidated using Artificial Neural Network (ANN). Genetic Algorithm (GA) is used for optimizing the dimensions of the mechanism,...
A method, based on ADALINE Artificial Neural Networks (ANNs), for spectrum analysis and fundamental frequency estimation of asynchronously sampled signals is compared with standard multiharmonics four parameter sine fit algorithm (4PSF). The performance of the method is demonstrated on real sinusoidal and real harmonically distorted sampled signals. The method resolves convergence problems of standard...
The general process of oil spill detection from SAR image with artificial neural network (ANN) classifier briefly includes five steps, target extraction, feature extraction, feature selection, ANN training and ANN classification. Feature extraction and feature selection are concerned in this paper. Firstly, 68 features are calculated for each target. By cross-correlation analysis, 24 features are...
This research proposes the use of Artificial Neural Networks to diagnose industrial networks communication via Profibus DP Protocol. These diagnostics are based on information provided by the Physical Layer from the Profibus DP Protocol. In order to analyze the physical layer, an Artificial Neural Network first analyzes signal samples transmitted through the industrial network. In case these signals...
Watching of comedy movies has been reported to alter the cardiac autonomic modulation. The study has been designed to investigate the autonomic modulation using heart rate variability (HRV) parameters. An attempt was made to understand whether the normal state can be classified from the post-stimulus state using artificial neural network (ANN) classification. Further, time domain and wavelet processed...
This paper investigates the relationship between rhythm metrics and the ability to classify speakers depending on gender and/or social environments that may have been affected by factors such as second language effects and ways of living as expressed through speech. The BBN/AUB (BBN Technologies and American University of Beirut) corpus was used; it contains four subsets of native Levantine dialect...
Detection of Power Quality (PQ) is an essential service which many utilities perform for their industrial and large commercial customers. Poor PQ affect the load connected to the supply. It shortens the life of load and can damage the load. It is a difficult task to detect and classify electrical problems which can cause PQ problems. Various types of PQ disturbances are defined in IEEE standards 1159-2009...
Modern technologies such as DNA microarray have been developed to study the transcriptome of cancer cells. It has been used in many studies for tumor classification and of identification of marker genes associated with cancer. However, this technique often suffers the ‘curse of dimensionality’. A general approach to overcome this setback is to perform feature selection technique prior to classification...
With the growth of technology and increasing interest in communicating widely, the concept of trust become more significant. Trust in a mutual relationship refers to the subjective belief of one agent about another. In each experiment, the trusting agent assigns a value to trustee agent which represents the satisfaction level of the agent. The time between two experiments is called the time slot,...
As an important component of the future HumanComputer Interface, Automatic Speech Recognition is designed for the purpose of realizing identification recognition and natural language comprehension by means of human voice. Speech recognition technology has acquired significant achievements with some successful popularity and applications. IBM's ViaVoice system, for instance, has good performances when...
In this era of flexible manufacturing systems, increase in demand of automatic and unattended machining process is very high. Thus arise the need for proper online tool condition monitoring methods, in order to minimize error and waste of work-material. In this study, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Bayes classifier are used to develop such a system for automatic...
Distributed Generation Power Packs with a combustion engine prime mover are still widely used to supply electric power in a variety of applications. These applications range from backup power supply systems to providing power in places where grid connection is either technically impractical or financially uneconomic. Due to the ever increasing cost of diesel fuel and the environmental issues associated...
In this paper we report on a system design for automatic classification of surface Electromyography (EMG) signals using Artificial Neural Network as a classifier. Key requirements to the system components are shortly described together with the main features and challenges in the field. The system comprise of wireless measurement system to measure, record and transfer EMG signal to signal processing...
Direct Torque Control (DTC) is known to produce quick and robust response in AC drives. However, during steady state, torque, flux and current ripple occur. An improvement of the electric drive can be obtained using a DTC scheme based on the Space Vector Modulation (SVM) which reduces the torque and flux ripple. The proposed control scheme considers the rotor resistance variation. This paper also...
This paper presents an artificial neural network (ANN) technique for tuning of the proportional and integral (PI) gains of the static synchronous compensator which is used as a reactive power compensator in a wind diesel hybrid power system. The gains are optimized for typical values of the load voltage characteristics (nq) by conventional techniques. The method of multilayer feed forward ANN with...
A Dynamic Energy Management (DEM) controller which is capable of taking decisions based on the status of the grid-connected smart microgrid has been developed using Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The proposed control strategy involves the decisions for the dynamic charge-discharge transactions in the energy storage systems like battery and pumped hydro (PH) units...
The integration of photovoltaic (PV), intermittent and uncontrollable power, into the electrical grid has become one of the major challenges for power system operators. Therefore the PV power forecasting can be beneficial in system planning and balancing energies. In this paper the PV power forecasting of a real generator [1] is presented. Different Artificial Neural Networks (ANN) strategies are...
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