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In Electrode Polarization Impedance type Flow meter the relationship between the conductivity coefficient and the voltage output of the flow meter is nonlinear. This paper mainly focusses on nonlinear calibration of electrode polarization impedance type flow meter using Artificial Neural Network. The operation and the method of calibration of electrode polarization impedance type flow meter are discussed...
In this paper, artificial neural network (ANN) based fuzzy filter is proposed for removal of impulse noise from gray images. ANN is used for classification of noisy and non-noisy pixels from the image corrupted by impulse noise. Based on the classification, fuzzy filtering is done adjusting the corrupted and non-corrupted pixels. In this method, feature set comprises of predicted error, absolute difference...
There are several current systems developed to identify common skin lesions such as eczema that utilize image processing and most of these apply feature extraction techniques and machine learning algorithms. These systems extract the features from pre-processed images and use them for identifying the skin lesions through machine learning as the core. This paper presents the design and evaluation of...
This paper presents an effective supervised learning approach for static security assessment. The approach proposed in this paper employs Least Square Support Vector Machine (LS-SVM) to rank the contingencies and predict the severity level for a standard IEEE −39 Bus power system. SVM works in two stage, in stage 1st estimation of a standard index line MVA Performance Index PIMVA is carried out under...
Identifying and treating the tumor at its early stages has become one of the major challenges faced in the area of breast imaging field since the number of women diagnosed with breast cancer has gradually increase over the years. Breast thermography has distinguished itself as a promising adjunctive imaging modality to the current breast imaging standard for early detection of breast cancer. It provides...
Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC)...
This paper presents comparison analysis of artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) artificial intelligence (AI) based maximum power point tracking (MPPT) techniques for tracking maximum power from the Photovoltaic (PV) array. These algorithms are essential since PV arrays have non-linear characteristics with its firm dependence on changing solar irradiation...
The use of Artificial Neural Networks (ANN) by power distribution companies has gained a wide reception due to its ability to predict close to accurate forecasted electric load consumption. A local power utility company in the Philippines has existing data of its electric load consumption however there is no ANN model that can process this data to produce close to accurate forecasted load which is...
There is growing interest in unmanned aerial vehicles (UAVs) such as quadrotors over the past several years. Cooperation among multiple quadrotors is one of the areas of focus. This paper proposes a neural network form of control for a cooperative task done by four quadrotors and will be tested through simulations. The task at hand is a ball and plate balancing problem during flight of multiple quadrotors...
The Traffic matrix Estimation of IP networks has become a research topic in this later 10 years, where several methods have been used to resolve this ill posed problem. This paper deals with the later and presents a comparison study on training algorithms in Artificial Neural Networks (ANN) method, namely the BFGS Quasi-Newton; the Levenberg-Marquardt and Bayesian Regularization algorithms, which...
This paper aims to forecast the photovoltaic power, which is an important and challenging function of energy management system for grid planning, scheduling, maintenance and improving stability. Forecasting of photovoltaic power using Artificial Neural Network (ANN) is the main focus of this paper. The training algorithm used for ANN is Extreme Learning Machine (ELM). Accurate forecast of Renewable...
This paper describes pattern recognition of electromyography (EMG) signal during load lifting using Artificial Neural Network (ANN). EMG is a method to measure and record the muscle activity when individuals perform certain operation and actions. This research will classify the EMG signal based on force apply to the arm due to the gravity act on it during load lifting. Recognizing pattern based on...
This paper deals with design of software for partial discharge (PD) pattern recognition and signal assessment on power generator using artificial neural network (ANN). The identified PD signal was the simulation of PD measurement result on high voltage generator. The simulated signal contains background noise (BGN) and phase-resolved PD pattern (PRPD pattern) data. The characteristics of information...
Price forecasting has become essential tool in deregulated electricity market. It is used by utility operators for bidding in the competitive market to increase their profits and services. The models for electricity price forecasting can be mainly categorized into (i) Statistical models, (ii) Artificial Intelligence models & (iii) Hybrid models. AI based models, i.e., ANN have gained popularity...
Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict significant quality of refined palm oil which is Free Fatty Acid (FFA) content. The variables; FFA content, Iodine Value (IV), moisture content, bleaching earth and citric acid dosage as well as the pressure and temperature of the deodorizer is used to build the ANN prediction model. A feed forward...
Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient...
When implementing Artificial Neural Networks with imperative programing languages, the resulting programs are usually highly coupled. This problem usually hampers distribution over multiple processors, especially when the ANN executes on general-purpose processors. An emerging technique called Notification Oriented Paradigm (NOP) facilitates the development of distributed and decoupled systems, and...
State estimation is a vital apparatus in observing the power electric grids. As the measure of the electric power grid keeps on growing, a state estimator must be all the more computationally effective and robust. This paper presents a real time state estimation using a new methodology of multilayer neural networks exhibited in composite topologies, hybrid Cascade and hybrid Parallel topologies in...
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