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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...
This paper studies the consensus problem in second-order multi-agent systems. For the first time, an event-triggered impulsive consensus control scheme is proposed to solve this problem. Under the event-triggered impulsive consensus control scheme, a distributed event-condition is defined in advance for each agent and the impulsive control is taken only when such condition satisfies, and no any actions...
This paper presents a neural-network-based approach for the detection of misplaced and missing regions in images. The main objective of this project is to develop an intelligent system that can identify a misplaced or missing region of a tested image. The system can be used to detect misplaced and missing components of printed circuit boards during the manufacturing process. Jigsaw puzzle pieces can...
The economic dispatch problem (EDP) is of vital importance in the operation and planning of power systems. In this paper, we study the EDP with general cost functions, transmission losses and prohibited operating zones. The equality constraint of the EDP is nonlinear due to transmission losses, while the feasible region is discontinuous due to prohibited operating zones, meaning that the EDP is a...
How to develop an intelligent ventilator and control it well to provide a better experience and treatment effect for respiratory patients is still a difficult task needed to be solved. The existing problems focus on the control algorithm and the mechanical structure. Dedicated to these two problems, the paper proposes a design of CPAP ventilator based on the ANN algorithm. Firstly, the paper introduces...
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