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In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and...
The synthesis of microstrip antenna(MSA) remains complex and time consuming from convenient design point of view. The Artificial Neural Network (ANN) on the other hand provides quicker and accurate solutions while multiple parameters controlling MSA designs. This paper proposes a new type of square fractal antenna (SFA) structure iterated and optimized by ANN developed using Advanced C and simulated...
Energy demand forecasting is of great importance in the management of power systems. In this paper artificial neural network technique (ANN) and multiple linear regressions method is used for forecasting the load curve. Algorithms using these techniques have been programmed using MATLAB and applied to the case study. The efficiency of both the model is determined from the load curve and the load is...
Image classification is one of the most multifaceted disciplines in image processing. There are quite a few approaches to categorize images and they offer good classification outcome but they not be up to snuff to provide acceptable classification upshots when the image comprises blurry content. The two chief techniques for image classification are supervised and unsupervised classification. Mutually...
Fault Detection and Isolation (FDI) is important in many industries to provide safe operation of a process. To determine the kind, size, location and time of fault, many Fault detection and Identification (FDI) Techniques are proposed. The Characteristic of FDI techniques include robustness, fast detection and isolation of faults. In this paper a comparison of fault diagnosis system based on Artificial...
Artificial neural network (ANN) are a family of models which are having inspiration from the biological neural network. The goal of an ANN system is to develop algorithms that requires machines to perform cognitive task in an efficient manner using the computing power of the Computer Systems. A variety of problems are solved by various types of ANN systems. In ANN system, the problems that are having...
A brain-computer interface (BCI) permits cerebral activity alone to control the external devices for assisting people with neuro muscular impairments. Electroencephalogram (EEG) signals are used for brain computer interaction which is highly non-stationary therefore major challenge is to extract features and classify the signals accurately. In this paper we focused on the extraction of features of...
This paper proposes the use of Artificial Neural Network (ANN) to remove background noise of Batak Toba handwritten script. Several artificial backgrounds are fused with the original script to reconstruct visual perception of manuscript text background. Experiments have been conducted on offline handwritten script cleaning process and show superior impact compared to threshold method and Gaussian...
The solar photo voltaic (PV) systems manifest their utility in a number of ways and have emerged as one of the promising renewable sources of electrical power. Solar PV array has a non-linear characteristic. The voltage across the output terminals of the PV array and its internal resistance vary along with changes in the ambient conditions of temperature and insolation. As irradiation and temperature...
Global Positioning System (GPS) is a satellite based navigation system used worldwide to find the user position on or near the earth. The system provides navigation ability to military, civil, and commercial GPS receivers around the world with good accuracy. However, if the signals transmitted by the GPS satellites are lost temporarily due to varying atmospheric conditions, satellite health problems...
In this paper various types of classifiers for quantitatively identify teletraffic service devices are proposed. The classification method “K — Nearest Neighbors With Defined Cityblock Metric Distance At Three Nearest Neighbors” is selected. A classifier structure is synthesized based on Adaptive Neuro-Fuzzy Interface Systems (ANFIS) in hybrid learning algorithm and Gaussian type membership function...
Implementation of online optimization and control of complex processes near impossible in given time frame owing to large computational time of the models. Fast and efficient surrogate models such as ANN offer a credible solution to this problem. Optimization of the complex model can proceed with a data based surrogate model, thereby making it much faster than the conventional run. However, the process...
Flashover phenomenon in polluted insulators has not yet been described accurately through a mathematical model. The main difficulty lies in the definition of arc constants, which is formed in the dry bands when the voltage exceeds its critical value. We have present an optimization method based on genetic algorithms and Artificial Neural Networks (ANN) experimental data from artificially polluted...
Due to the rising signal speed in today's integrated circuits (ICs), the digital input/output (I/O) device modeling becomes a very serious challenge. However, its nonlinearity issue was even less addressed. But for accurate EMC and EMI characterizations, the I/O nonlinearity could become a source of unexpected EMC and EMI troubles in the high-speed system. In this paper, we analyze the nonlinearity...
Development of Optical Character Recognition (OCR) system for Indian script is an active area of research today. In this paper, we are concerned with the recognition of printed Oriya script a popular Indian script. The development of OCR for this script is challenging as number of identified classes are more than 380 which includes similar looking and compound characters. This paper presents the gradient...
Accuracy in financial forecasting is a key determinant of profits in the financial markets. This paper proposes improvements to existing Artificial Neural Network based forecasting approaches using de-noising in frequency domain and the Hodrick-Prescott Filter. Traditionally used technical indicators are replaced with open, close, high, and low prices only. Forecasts achieved via these improvements...
In this paper, new approach is proposed for stabilization of a cart inverted pendulum system using Linear Quadratic Regulator (LQR) based PID controller and Artificial Neural Network (ANN). The proposed approach is compared with the recently published approach on designing of PID controller using LQR. It is observed that the proposed design shows better performance and disturbance rejection.
In this paper, ANN is trained for determining the length and width of a rectangular patch antenna at a given resonant frequency and height. Training is done through Bayesian Regularization (BR) and Levenberg Marquart (LM) algorithms. After training ANN, the best suitable algorithm is taken and the result from that algorithm obtained are compared with the theoretically obtained value of length and...
We can consider the direct torque control (DTC) as an alternative to conventional methods of control by pulse width modulation (PWM) and by Field oriented control (FOC), the direct torque control DTC found by Takahashi offers high performance in terms of simplicity in control and fast electromagnetic torque response. With dominant characteristics, the direct torque control controlled AC electric motor...
Multilayer perceptron (MLP) based artificial neural network (ANN) equalizers, deploying back propagation (BP) training algorithm, have been profusely used for equalization earlier. However this algorithm suffers from slow convergence rate, depending on the size of network. In this paper, Levenberg-Marquardt and Scaled Conjugate algorithms are proposed to train an MLP based ANN for least square (LS)...
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