Engineering Commons^™

Bibliometric Analysis Of Particle Swarm Optimization Techniques Used To Enhance Low-Energy Adaptive Clustering Hierarchy Protocol For Wireless Sensor Networks, Anupkumar M. Bongale Dr.

Library Philosophy and Practice (e-journal)

Wireless Sensor Network (WSN) is a network of tiny wireless sensor nodes. The sensor nodes sense information and transmit the sensed information to a data collection point known as Base Station. WSNs have gained massive popularity due to their incredible benefits, and active research is ongoing for the past two decades. The primary concern with WSN is that the sensor operates on a limited power supply. Due to the nature of applications of WSNs and the hostile environment where the sensors are deployed, providing unlimited power or energy supply is not an option. Hence, the research work mainly focuses on …

Open-Source Tig-Based Metal 3d-Printing, Shane Oberloier

Dissertations, Master's Theses and Master's Reports

Metal 3-D printing has been relegated to high-cost proprietary high-resolution systems and low-resolution low-cost metal inert gas (MIG) systems. In order to provide a path to high-resolution, low-cost, metal 3-D printing, this manuscript proposes a new open source metal 3-D printer design based around a low-cost tungsten inert gas (TIG) welder coupled to a commercial open source self replicating rapid prototyper. Optimal printing parameters for the machine are acquired using a novel computational intelligence software. TIG has many advantages over MIG, such as having a low heat input, clean beads, and the potential for both high-resolution prints as well as …

Partial Shading In Photovoltaic Cells Using Fuzzy Logic Tuned Particle Swarm Optimization Method Of Maximum Power Point Tracking Control, Nauman Moiz Mohammed Abdul

Graduate Research Theses & Dissertations

This paper dealt with the study of a newly modified fuzzy adaptive particle swarm

optimization controller and compared it with various existing conventional and advanced

controllers. A traditional PSO controller has the problems of oscillations and slow convergence. It is modified using fuzzy logic to decrease the number of oscillations and optimize

the convergence speed to produce a better efficient output. This controller is built to adapt

under various solar irradiances and partial solar conditions on the solar panels. This controller is then tested along with other various controllers, results recorded and studied. The

simulations are performed on the MatLab …

Machine Learning For Performance Aware Virtual Network Function Placement, Dimitrios Michael Manias

Electronic Thesis and Dissertation Repository

With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization has been identified as a potential solution, several challenges must be addressed to ensure its feasibility. The work presented in this thesis addresses the Virtual Network Function (VNF) placement problem through the development of a machine learning-based Delay-Aware Tree (DAT) which learns from the previous placement of VNF instances forming a Service Function Chain. The DAT is able to predict VNF instance placements …

Optimization For Integration Of Plug-In Hybrid Electric Vehicles Into Distribution Grid, Shuaiyu Bu

Theses and Dissertations

Plug-in hybrid electric vehicles (PHEVs) feature combined electric and gasoline powertrains with internal combustion engine and electric motors powered by battery packs. The battery packs of PHEVs are mostly charged during off-peaks hours at lower prices and meanwhile absorb the excess power from the grid. Similarly, the power stored in the batteries can also flow back to the electric grid in response to ease the peak load demands.

With the increasing penetration and integration of PHEVs, the reliability of PHEVs is essential to overall power system reliability since the charging mechanisms of PHEVs can influence the reliability of power system. …

Smart Rocks For Bridge Scour Monitoring -- Design And Localization Using Electromagnetic Techniques And Embedded Orientation Sensors, Andro Radchenko

Doctoral Dissertations

"River bridge scour is an erosion process in which flowing water removes sediment materials (such as sand, rocks) from a bridge foundation, river beds and banks. As a result, the level of the river bed near a bridge pier is lowering such that the bridge foundation stability can be compromised, and the bridge can collapse. The scour is a dynamic process, which can accelerate rapidly during a flood event. Thus, regular monitoring of the scour progress is necessary to be performed at most river bridges. Present techniques are usually expensive, require large man/hour efforts, and often lack the real-time monitoring …

Evolutionary Algorithms Based Filters For Denoising Signals In Cognitive Radio Systems, Adnan Quadri, Naima Kaabouch

ADNAN QUADRI

A 3-Dof Stewart Platform For Trenchless Pipeline Rehabilitation, Derek K. Brecht

Electronic Thesis and Dissertation Repository

A major component of the infrastructure of any modern city is a network of underground pipes that transport drinking water, storm water and sewage. Most of the pipes currently being used are made out of concrete or various plastics. As with any material, they have an expected lifespan after which deterioration begins to occur. This can result in cracks, and in some cases, even large holes in the pipe which can cause a complete loss of function of the pipe. These defects invariably lead to water losses that necessitate the repair of the pipeline, which is an expensive undertaking.

The …

Evolutionary Optimization Algorithms For Nonlinear Systems, Ashish Raj

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In the real world, we encounter a number of problems which require iterative methods rather than heuristic approaches to solve them. Not every problem can be solved with a definitive method. Optimization algorithms come to the aid of such instances. These algorithms carry out multiple iterations or generations to try to achieve the lowest value of a cost function. The demand for fast and robust stochastic algorithms to cater to the optimization needs is very high. The faster the convergence to a low value of the cost function, the better the algorithm is. This is attained in greedy criterion approaches, …

Intelligent Data Fusion For Applied Decision Support, Xiang Ye

Electrical Engineering and Computer Science - Dissertations

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications.

The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main …

Pso Tuned Flatness Based Control Of A Magnetic Levitation System, Ganesh K. Venayagamoorthy, E. C. Anene

Electrical and Computer Engineering Faculty Research & Creative Works

Investigation on the application of flatness-based feedback linearization to the magnetic levitation model of INTECOTm Maglev system is presented in this paper. The MAGLEV system dynamics studied consists of a set of third order nonlinear differential equations. Using computational techniques proposed by Levine, it is verified that the ball position is the flat output. The derived flat output is applied in the construction of a nonlinear control law used to control the levitation to a set point as well as tracking a sine function trajectory. The controller gains are obtained and optimized using particle swarm optimization. The simulation results compared …

Particle Swarm Optimization Tuned Flatness-Based Generator Excitation Controller, Ganesh K. Venayagamoorthy, E. C. Anene, U. O. Aliyu

Electrical and Computer Engineering Faculty Research & Creative Works

An optimal transient controller for a synchronous generator in a multi-machine power system is designed using the concept of flatness-based feedback linearization in this paper. The computation of the flat output and corresponding controller for reduced order model of the synchronous generator is presented. The required feedback gains used to close the linearization loop is optimized using particle swarm optimization for maximum damping. Typical results obtained for transient disturbances on a two-area, four-generator power system equipped with the proposed controller on one generator and conventional power system stabilizers on the remaining generators are presented. The effectiveness of the flatness-based controller …

Learning Functions Generated By Randomly Initialized Mlps And Srns, R. Cleaver, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) and two benchmark functions are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - PSO-QI is introduced. PSO-QI is a standard particle swarm optimization (PSO) algorithm with the addition of a quantum step utilizing the probability density property of a quantum particle. The results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs …

Real Time Implementation Of An Artificial Immune System Based Controller For A Dstatcom In An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

A new adaptive control strategy based on artificial immune system (AIS) for a DSTATCOM in an electric ship power system is presented in this paper. DSTATCOM is a shunt compensation device, which can be used to improve the power quality during the pulse power requirements in a naval shipboard system. The role of DSTATCOM controller is very important to meet this objective. In this paper, the DSTATCOM controller parameters are first tuned by particle swarm optimization (PSO) technique, so that it can provide innate immunity to common system disturbances. Then, these optimum parameters are modified online by an artificial immune …

Real-Time Implementation Of Intelligent Modeling And Control Techniques On A Plc Platform, Curtis Alan Parrott, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Programmable logic controllers (PLCs) have been used for many decades for standard control in industrial and factory environments. Over the years, PLCs have become computational efficient and powerful, and a robust platform with applications beyond the standard control and factory automation. Due to the new advanced PLC's features and computational power, they are ideal platforms for exploring advanced modeling and control methods, including computational intelligence based techniques such as neural networks, particle swarm optimization (PSO) and many others. Some of these techniques require fast floating-point calculations that are now possible in real-time on the PLC. This paper focuses on the …

Enhanced Particle Swarm Optimizer For Power System Applications, Yamille Del Valle, M. Digman, A. Gray, J. Perkel, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Power system networks are complex systems that are highly nonlinear and non-stationary, and therefore, their performance is difficult to optimize using traditional optimization techniques. This paper presents an enhanced particle swarm optimizer for solving constrained optimization problems for power system applications, in particular, the optimal allocation of multiple STATCOM units. The study focuses on the capability of the algorithm to find feasible solutions in a highly restricted hyperspace. The performance of the enhanced particle swarm optimizer is compared with the classical particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and bacterial foraging algorithm (BFA). Results show that the enhanced PSO …

Economic Load Dispatch Using Bacterial Foraging Technique With Particle Swarm Optimization Biased Evolution, Ahmed Yousuf Saber, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other …

A Dstatcom Controller Tuned By Particle Swarm Optimization For An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

In an all-electric ship power system, the power quality problems mainly arise due to the pulsed loads, which cause the degradation of the overall system performance. The paper proposes the application of DSTATCOM to improve these power quality problems of an electric ship. DSTATCOM is a shunt compensation device, which regulates the bus voltage by injecting reactive power during the pulsed load operations. The control strategy of DSTATCOM plays an important role to meet the objectives. The paper proposes a controller design strategy which is based on particle swarm optimization (PSO). PSO, an algorithm that falls into swarm intelligence family, …

Mimo Beam-Forming With Neural Network Channel Prediction Trained By A Novel Pso-Ea-Depso Algorithm, Chris G. Potter, Ganesh K. Venayagamoorthy, Kurt Louis Kosbar

Electrical and Computer Engineering Faculty Research & Creative Works

A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received signal-to-noise ratio is derived for un-coded and beam-forming MIMO systems to see how the RNN error affects the performance. This error is shown to deteriorate the accuracy of the weak singular modes, making beam-forming more desirable. Bit error rate simulations are performed to validate these …

Design And Optimization Of Nanostructured Optical Filters, Jeremiah Brown

Electronic Theses and Dissertations

Optical filters encompass a vast array of devices and structures for a wide variety of applications. Generally speaking, an optical filter is some structure that applies a designed amplitude and phase transform to an incident signal. Different classes of filters have vastly divergent characteristics, and one of the challenges in the optical design process is identifying the ideal filter for a given application and optimizing it to obtain a specific response. In particular, it is highly advantageous to obtain a filter that can be seamlessly integrated into an overall device package without requiring exotic fabrication steps, extremely sensitive alignments, or …

Comparative Application Of Differential Evolution And Particle Swarm Techniques To Reactive Power And Voltage Control, G. A. Bakare, G. Krost, Ganesh K. Venayagamoorthy, U. O. Aliyu

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the comparative application of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) to the solution of the reactive power and voltage control problem. Efficient distribution of reactive power in an electric network leads to minimization of the system losses and improvement of the system voltage profile. It can be achieved by varying the excitation of generators or the on-load tap changer positions of transformers as well as by switching of discrete portions of inductors or capacitors etc. This constitutes a typical mixed integer non-linear optimization problem for the solution of which metaheuristic techniques have …

Inference Of Genetic Regulatory Networks With Recurrent Neural Network Models Using Particle Swarm Optimization, Rui Xu, Donald C. Wunsch, Ronald L. Frank

Electrical and Computer Engineering Faculty Research & Creative Works

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene …

Miso Damping Controller Design For A Tcsc Using Particle Swarm, Swakshar Ray, Ganesh K. Venayagamoorthy, Balarko Chaudhuri, Rajat Majumder

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new approach for designing multi-input-single-output (MISO) damping controller for a TCSC in a multi-machine power system. The damping controller design uses particle swarm optimization (PSO) to determine the coefficients of single or multi-stage lead-lag compensators. The classical technique works well in the design of lead-lag compensators for SISO controllers. But, there is no proper step-by-step procedure to achieve the desired performance characteristics for a MISO controller. Hence, in this paper, a computational optimization tool has been used to determine the optimal gains and time constants of a linear MISO damping controller. The damping controller is implemented …

Adaptive Neural Network Based Stabilizing Controller Design For Single Machine Infinite Bus Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani, Mariesa Crow

Engineering Management and Systems Engineering Faculty Research & Creative Works

Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. In power system control literature, the performances of the proposed controllers were mostly demonstrated using simulation results without any rigorous stability analysis. This paper proposes a stabilizing neural network (NN) controller based on a sixth order single machine infinite bus power system model. The NN is used to approximate the complex nonlinear dynamics of power system. Unlike the other indirect adaptive NN control schemes, there is no offline training process and the NN can be directly …

Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of a companding nonuniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs and particle swarm optimization, aiming to maximize the signal-to-noise ratio. The comparison of these optimal quantizer designs over a bit-rate range of 3-6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality standard

Multiclass Cancer Classification Using Semisupervised Ellipsoid Artmap And Particle Swarm Optimization With Gene Expression Data, Georgios C. Anagnostopoulos, Donald C. Wunsch, Rui Xu

Electrical and Computer Engineering Faculty Research & Creative Works

It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. with the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to research on binary classification such as normal versus tumor samples, which attracts numerous efforts from a variety of disciplines, the discrimination of multiple tumor types is also important. Meanwhile, the selection of genes which are relevant to a certain cancer type not only improves the performance of the classifiers, but also provides …

Bio-Inspired Algorithms For The Design Of Multiple Optimal Power System Stabilizers: Sppso And Bfa, Tridib Kumar Das, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Power System Stabilizers (PSSs) provide stabilizing control signals to excitation systems to damp out inter-area and intra-area oscillations. The PSS must be optimally tuned to accommodate the variations in the system dynamics. Designing multiple optimal PSSs is a challenging task for researchers. This paper presents the comparison between two bio-inspired algorithms: a Small Population based Particle Swarm Optimization (SPPSO) and the Bacterial Foraging Algorithm (BFA) for the simultaneous tuning of a number of PSSs in a multi-machine power system. The cost function to be optimized by both algorithms takes into consideration the time domain transient responses. The effectiveness of the …

Optimal Allocation Of A Statcom In A 45 Bus Section Of The Brazilian Power System Using Particle Swarm Optimization, J. C. Hernandez, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces the application of Particle Swarm Optimization (PSO) to solve the optimal allocation of a STATCOM in a 45 bus system which is part of the Brazilian power network. The criterion used in finding the optimal location is based on the voltage profile of the system, i.e. the voltage deviation at each bus, with respect to its optimum value, is minimized. In order to test the performance of the PSO algorithm in this particular application, different approaches for inertia weight are investigated; also different values of acceleration constants, number of iterations and maximum velocity are considered. A sensitivity …

Comparison Of Pso And Ga For K-Node Set Reliability Optimization Of A Distributed System, G. A. Bakare, I. N. Chiroma, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Particle Swarm Optimization (PSO), as a novel evolutionary computing technique, has succeeded in many continuous problems, but quite a little research on discrete problem especially combinatorial optimization problem has been reported. In this paper, a discrete PSO algorithm is proposed to solve a typical combinatorial optimization problem: K-Node Set Reliability (KNR) optimization of a distributed computing system (DCS) which is a well-known NP-hard problem is presented. It computes the reliability of a subset of network nodes of a DCS such that the reliability is maximized and specified capacity constraint is satisfied. The feasibility of the proposed algorithm is demonstrated on …

Density Estimation Using A Generalized Neuron, R. Kiran, Ganesh K. Venayagamoorthy, M. Palaniswami

Electrical and Computer Engineering Faculty Research & Creative Works

Neural networks have been shown to be useful tools for density estimation. However, the training of neural network structures is time consuming and requires fast processors for practical applications. A new method with a generalized neuron (GN) for density estimation is presented in this paper. The GN is trained with the particle swarm optimization algorithm which is known to have fast convergence than the standard backpropagation algorithm. Results are presented to show that the GN can estimate the density functions for distribution functions with different means and variances. This density estimation method can also be applied to the multi-sensor data …