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A new training scheme for neural-network-based controller for power electronics systems is proposed. It utilizes the circuit model of the power conversion stage (PCS) in the training process. The training algorithm is a distributed form of evolutionary computation, being able to run on a computer cluster equipped with multiple graphics processing units (GPUs). As a design example, a boost converter...
For automotive applications, accurate torque production capability and high efficiency of the traction motor is very important. However, the performance of widely used interior permanent magnet (IPM) machine is influenced by temperature variation. In this paper, a control algorithm is proposed to compensate the performance variations in IPM machines due to temperature change utilizing current pulse...
Deep Learning is appealing for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from big data. Deep learning, by its simplest definition, is expressed as the application of machine learning methods to the big data. In this study, it was investigated how to apply hierarchical deep learning models for the problems...
This paper discusses the elimination of C.I. Acid Yellow 23 (AY23) using UV/Ag-TiO2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO2 nanoparticles processed under desired circumstances, two computational techniques namely neural network (NN) and particle swarm optimization (PSO) modeling are developed. A summed up of 100 data are used to establish the models,...
For processing purposes of silver colloidal suspensions in view of specific applications, this study evaluates the suitability of using alginate/lignosulfonate mixtures as an efficient dispersion matrix for the silver nanoparticles. The rheological behavior of the in situ obtained silver nanoparticle suspensions was investigated by rotational measurements performed using cone-plate geometry, considering...
In this paper, an Artificial Neural Network (ANN) is employed for the estimation of LaTeral Misalignment (LTM) as well as compensation of its effect on Dynamic Wireless Power Transfer (DWPT) systems for Electric Vehicles (EVs) charging. In a DWPT system, energy efficiency and energy transfer capability are significantly affected by the degree of LTM. Therefore, the real-time estimation of LTM, followed...
Millions of people around the world suffer from hearing disability. This large number demonstrates the importance of developing a sign language recognition system converting sign language to text for sign language to become clearer to understand without a translator. In this paper, a sign language recognition system using Backpropagation Neural Network Algorithm is proposed based on American Sign...
Artificial neural networks (ANN) are among the nonlinear prediction techniques popular in the last two decades. Recent studies show that ANN can be modeled with different training techniques. ANN is usually trained by the backpropagation method (BP). In this study, ANN structures were trained by using artificial bee colony algorithm (ABC) and, weight and bias values were tried to be determined. ABC...
Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart...
In this paper, we apply outer synchronization to identify unknown parameters existing in the node dynamics between two interacted networks. According to the coupling and interacted matrices, we design the corresponding adaptive control schemes and updating laws to achieve the outer synchronization. By the Lyapunov functional theory, we derive two theorems of the appearance of outer synchronization...
The interactive dynamic influence diagrams provides a way to model and solve multi-agent decision-making problems from the perspective of the subject agent. The subject agent usually optimizes its own decisions by predicting the behavior of other agents. The exponential increase in the model of other agents over time bring great difficulties to the decision. In this paper, we propose a learning algorithm...
This study involves the evaluation of the second implementation of a Research Experiences for Teacher Advancement In Nanotechnology (RETAIN) program offered at Indiana University-Purdue University Indianapolis (IUPUI). RETAIN represents a professional development model for providing high school teachers with laboratory research experiences in nanoscience and related content areas. In this intensive...
In this paper, a neural network based adaptive state feedback control scheme is proposed to solve the problem of trajectory tracking in the joint space for robotic manipulators with the presence of high uncertainty in the system parameters. First nonlinear behavior of the robot is approximately eliminated by applying a linearizing control, the closed loop dynamics is stabilized using static compensator...
Although there are widely used methods as Genetic Algorithms, Fuzzy Logic and Artificial Neural Network, the Optimization Based Tools are considered the future of the systems of information. This issue is about Artificial Neural Network (ANN) used in Short Term Load Forecast (STLF). It proposes that the method is valid to predict STLF and how important it is on demand scheduling, contingency analysis,...
Analysis of electromyography (EMG) signals of normal physical actions have found to be important in order to detect certain abnormalities of the musculoskeletal system and diagnose abnormalities in patient behavior. This paper presents the results of the development of an Artificial Neural Network (ANN) for classification of EMG signals, according to the type of human behavior. The developed ANN is...
Currently deployed wireless and cellular positioning techniques are optimized for outdoor operation and cannot provide highly accurate location information in indoor environments. Meanwhile, new applications and services for mobile devices, including the recent Enhanced 911 (E911), require accurate indoor location information up to the room/suite level. In this work, a new system for improving indoor...
Energy forecast is essential for a good planning of the electricity consumption as well as for the implementation of decision support systems which can lead the decision making process of energy system. Energy consumption time series prediction problems represent a difficult type of predictive modelling problem due to the existence of complex linear and non-linear patterns. This paper presents two...
The study of network features is an important analysis method for the social networks, and prediction of network features is a research problem with many applications, particularly in decision making. In this paper, we propose a novel feature prediction method for temporal social networks, which estimates network measurements in the future based on a small window of measurements in the past. We utilized...
This paper presents the FPGA implementation of neuron block units based on a sigmoid activation function for artificial neural networks (ANNs) applications. The Coordinate Rotation Digital Computer (CORDIC) algorithm has been employed for the approximation of sigmoid activation function. The proposed design was simulated using ModelSim XE II and synthesized using Altera's Quartus II with a Cyclone...
The fundamental roots of micro-grids are different types of renewable energy sources. There are two broad and distinctive control set ups for power systems. They are centralized and decentralized (hierarchical) controls. In market models of micro-grids there are normally groups of electricity sources and loads that operate in synch with a centralized grid or macro-grid. This paper studies the functionality...
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