Electrical and Computer Engineering Commons^™

Event-Based Optimal Regulator Design For Nonlinear Networked Control Systems, Avimanyu Sahoo, Hao Xu, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel stochastic event-based near optimal control strategy to regulate a networked control system (NCS) represented as an uncertain nonlinear continuous time system. An online stochastic actor-critic neural network (NN) based approach is utilized to achieve the near optimal regulation in the presence of network constraints, such as, network induced time-varying delays and random packet losses under event-based transmission of the feedback signals. The transformed nonlinear NCS in discrete-time after the incorporation the delays and packet losses are utilized for the actor-critic NN based controller design. To relax the knowledge of the control coefficient matrix, a NN …

Neural Network-Based Adaptive Optimal Consensus Control Of Leaderless Networked Mobile Robots, Haci Mehmet Guzey, Hao Xu, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN)-based optimal adaptive consensus control scheme is introduced in this paper for networked mobile robots in the presence of unknown robot dynamics. Throughout the paper, two NNs are used. The unknown formation dynamics of each robot is identified by using the first NN. The second NN is utilized to approximate a novel value function derived in this paper as a function of augmented error vector, which is comprised of the regulation and consensus-based formation errors of each robot. A novel near optimal controller is developed by using approximated value function and identified formation dynamics. The Lyapunov …

Removing Random-Valued Impulse Noise In Images Using A Neural Network Detector, İlke Türkmen

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms …

An Online Outlier Identification And Removal Scheme For Improving Fault Detection Performance, Hasan Ferdowsi, Sarangapani Jagannathan, Maciej Jan Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be …

New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan

Open Access Dissertations

When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …

Role Of Energy Management In Hybrid Renewable Energy Systems: Case Study-Based Analysis Considering Varying Seasonal Conditions, Recep Yumurtaci

Turkish Journal of Electrical Engineering and Computer Sciences

The recent popularity of alternative energy technologies is mainly promoted by the increasing awareness of environmental concerns as well as the economic impacts of the depleting fossil fuel reserves. Among several alternative technologies, wind- and solar-based energy have been given specific importance with government-based support for providing a cost-effective structure to realize better penetration of such environmentally friendly sources in the energy market. Even these sources are advantageous over the conventional means of energy production from many aspects, a main drawback being the total dependence on the meteorological conditions (wind speed, solar radiation, temperature, etc.) of the wind and solar …

Prediction Of Emissions And Exhaust Temperature For Direct Injection Diesel Engine With Emulsified Fuel Using Ann, Görkem Kökkülünk, Erhan Akdoğan, Vezi̇r Ayhan

Turkish Journal of Electrical Engineering and Computer Sciences

Exhaust gases have many effects on human beings and the environment. Therefore, they must be kept under control. The International Convention for the Prevention of Pollution from Ships (MARPOL), which is concerned with the prevention of marine pollution, limits the emissions according to the regulations. In Emission Control Area (ECA) regions, which are determined by MARPOL as ECAs, the emission rates should be controlled. Direct injection (DI) diesel engines are commonly used as a propulsion system on ships. The prediction and control of diesel engine emission rates is not an easy task in real time. Therefore, in this study, an …

Information Measures For Statistical Orbit Determination, Alinda Kenyana Mashiku

Open Access Dissertations

The current Situational Space Awareness (SSA) is faced with a huge task of tracking the increasing number of space objects. The tracking of space objects requires frequent and accurate monitoring for orbit maintenance and collision avoidance using methods for statistical orbit determination. Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty given by the probability density function (PDF). As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full PDF of the random orbit state. Through representing the full PDF …

Dismount Threat Recognition Through Automatic Pose Identification, Andrew M. Freeman

Theses and Dissertations

The U.S. military has an increased need to rapidly identify nonconventional adversaries. Dismount detection systems are being developed to provide more information on and identify any potential threats. Current work in this area utilizes multispectral imagery to exploit the spectral properties of exposed skin and clothing. These methods are useful in the location and tracking of dismounts, but they do not directly discern a dismount's level of threat. Analyzing the actions that precede hostile events yields information about how the event occurred and uncovers warning signs that are useful in the prediction and prevention of future events. A dismount's posturing, …

Forecasting Natural Gas Consumption In İstanbul Using Neural Networks And Multivariate Time Series Methods, Ömer Fahretti̇n Demi̇rel, Seli̇m Zai̇m, Ahmet Çalişkan, Pinar Özuyar

Turkish Journal of Electrical Engineering and Computer Sciences

The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a …

Decentralized Optimal Control Of A Class Of Interconnected Nonlinear Discrete-Time Systems By Using Online Hamilton-Jacobi-Bellman Formulation, Shahab Mehraeen, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the direct neural dynamic programming technique is utilized to solve the Hamilton-Jacobi-Bellman equation forward-in-time for the decentralized near optimal regulation of a class of nonlinear interconnected discrete-time systems with unknown internal subsystem and interconnection dynamics, while the input gain matrix is considered known. Even though the unknown interconnection terms are considered weak and functions of the entire state vector, the decentralized control is attempted under the assumption that only the local state vector is measurable. the decentralized nearly optimal controller design for each subsystem consists of two neural networks (NNs), an action NN that is aimed to …

Low Complexity Feature Extraction For Classification Of Harmonic Signals, Peter William

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain.The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a stand-alone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics’ amplitudes of …

Solving The Vehicle Re-Identification Problem By Using Neural Networks, Tanweer Rashid

Computational Modeling & Simulation Engineering Theses & Dissertations

Vehicle re-identification is the process by which vehicle attributes measured at one point on a road network are compared to vehicle attributes measured at another point in an effort to match vehicles without using any unique identifiers such as license plate numbers. A match is made if the two measurements are estimated to belong to the same vehicle. Vehicle attributes can be sensor readings such as loop induction signatures, or they can also be actual vehicle characteristics such as length, weight, number of axles, etc. This research makes use of vehicle length, travel time, axle spacing and axle weights for …

A Delay-Dependent Approach To Robust Stability For Uncertain Stochastic Neural Networks With Time-Varying Delay, Chien-Yu Lu, Chin-Wen Liao, Koan-Yuh Chang, Wen-Jer Chang

Journal of Marine Science and Technology–Taiwan

This paper investigates the global delay-dependent robust stability in the mean square for uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on a linear matrix inequality approach, globally delay-dependent robust stability criterion is derived by introducing some relaxation matrices which, when chosen properly, lead to a less conservative result. Two numerical examples are given to illustrate the effectiveness of the method.

Zero-Sum Two-Player Game Theoretic Formulation Of Affine Nonlinear Discrete-Time Systems Using Neural Networks, S. Mehraeen, T. Dierks, S. Jagannathan, M. L. Crow

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. the approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control input and disturbance for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle-point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of …

Neural Network Control Of Quadrotor Uav Formations, Travis Dierks, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel framework for leader-follower formation control is developed for the control of multiple quadrotors unmanned aerial vehicles (UAVs) based on spherical coordinates. the control objective for the follower UAV is to track its leader at a desired- separation, angle of incidence, and a bearing by using an auxiliary velocity control. Then, a novel neural network (NN) control law for the dynamical system is introduced to learn the complete dynamics of the UAV including unmodeled dynamics like aerodynamic friction. Additionally, the interconnection dynamic errors between the leader and its followers are explicitly considered, and the stability of …

Decentralized Control Of Large Scale Interconnected Systems Using Adaptive Neural Network-Based Dynamic Surface Control, Shahab Mehraeen, Sarangapani Jagannathan, Mariesa L. Crow

Electrical and Computer Engineering Faculty Research & Creative Works

A novel decentralized controller using the dynamic surface control (DSC) is proposed for a class of uncertain large scale interconnected nonlinear systems in strict feedback form while relaxing the "explosion of complexity" problem which is observed in the typical backstepping approach. the matching condition is not assumed when dealing with the interconnection terms. Neural networks (NNs) are utilized to approximate the uncertainties in both subsystem and interconnected terms. by using novel NN weight update laws, it is demonstrated using Lyapunov stability that the closed-loop signals are asymptotically stable in the presence of NN approximation errors in contrast with the uniform …

Analysis Of Electroencephalogram Signals For The Identification Of Mental Tasks, My Thy Thi Tran

Electrical & Computer Engineering Theses & Dissertations

Electroencephalogram (EEG) signals can be used for implicit communication such as to control robots or medical equipment by brain activity or to detect an individual's intentions of committing premeditated crimes. An EEG based brain-computer interface allows paralyzed patients to express their thoughts. However, biological and technical artifacts heavily interfered with EEG signals due to blinking of the eyes, muscle activities and line noise. Sometimes the noise interference due to signal artifacts becomes more prominent than the information content. This thesis investigates novel feature extraction methodologies in EEG signals to represent different thought processes and employs neural network-based pattern classification techniques …

Architecture For Intelligent Power Systems Management, Optimization, And Storage., J. Chris Foreman

Electronic Theses and Dissertations

The management of power and the optimization of systems generating and using power are critical technologies. A new architecture is developed to advance the current state of the art by providing an intelligent and autonomous solution for power systems management. The architecture is two-layered and implements a decentralized approach by defining software objects, similar to software agents, which provide for local optimization of power devices such as power generating, storage, and load devices. These software device objects also provide an interface to a higher level of optimization. This higher level of optimization implements the second layer in a centralized approach …

Prediction Of Interference Pathloss Inside Commercial Aircraft Using Modulated Fuzzy Logic And Neural Networks, Madiha Jamil Jafri

Electrical & Computer Engineering Theses & Dissertations

Although several modeling techniques have been used to model indoor radio wave propagation and coupling patterns, to date no efficient model exists that calculates indoor-outdoor radio wave propagations on commercial aircraft. Due to the complexity of an aircraft structure, with the additive introduction of creeping wave phenomenon and unknown back-door propagation values from the exterior aircraft antenna to the avionics bay, numerical modeling approaches using Method of Moments (MoM) or Finite Difference Time Domain (FDTD) prove too complex with limitations. This dissertation presents an expert neuro-fuzzy (NF) model for Interference pathloss (IPL) predictions inside an Airbus 320 (A320) airplane, for …

Gaussian Mixture Models And Neural Networks For Automatic Speaker Identification, Usha Gayatri Chalkapally

Electrical & Computer Engineering Theses & Dissertations

Automatic Speaker Recognition is the process of automatically recognizing who is speaking on the basis of individual information contained in speech signals. This technique of Automatic Speaker Recognition makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

In this thesis, the techniques of Gaussian Mixture Models and Neural Networks for Automatic Speaker Identification are presented. Algorithms for Speaker Identification using Gaussian Mixture Models were developed, …

Learning As A Nonlinear Line Of Attraction For Pattern Association, Classification And Recognition, Ming-Jung Seow

Electrical & Computer Engineering Theses & Dissertations

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns.

It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter …

Whole Word Phonetic Displays For Speech Articulation Training, Fansheng Meng

Electrical & Computer Engineering Theses & Dissertations

The main objective of this dissertation is to investigate and develop speech recognition technologies for speech training for people with hearing impairments. During the course of this work, a computer aided speech training system for articulation speech training was also designed and implemented. The speech training system places emphasis on displays to improve children's pronunciation of isolated Consonant-Vowel-Consonant (CVC) words, with displays at both the phonetic level and whole word level. This dissertation presents two hybrid methods for combining Hidden Markov Models (HMMs) and Neural Networks (NNs) for speech recognition. The first method uses NN outputs as posterior probability estimators …

Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …

Intelligent Control Of Nonlinear Systems With Actuator Saturation Using Neural Networks, Wenzhi Gao

Doctoral Dissertations

Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the …

An Embedded Real-Time Neuro-Fuzzy Controller For Mobile Robot Navigation, Nian Zhang, Daryl G. Beetner, Donald C. Wunsch, B. Hemmelman, Ahmad Hasan

Electrical and Computer Engineering Faculty Research & Creative Works

A reactive fuzzy logic based control strategy was developed for mobile robot navigation. To decrease the number of fuzzy rules and related processing, a RAM-based neural network was combined with the fuzzy logic strategy. The fuzzy rules are used to interpret sensor information. The neural network uses results from the fuzzy logic as well as environmental information to make navigation decisions. The feasibility of this neuro-fuzzy approach was demonstrated on a mobile robot using a simple, 8-bit microcontroller. Experiments show the approach works well, as the robot was able to successfully avoid objects while seeking a goal in real-time. The …

Autonomous Control Of A Scale Model Of A Trailer-Truck Using An Obstacle-Avoidance Path-Planning Hierarchy, Robert S. Woodley, Levent Acar

Electrical and Computer Engineering Faculty Research & Creative Works

A scale model of a tractor-trailer truck was developed as a testbed for control algorithms. The truck operates in autonomous or semi-autonomous modes. An on-board Pentium computer with a PC104 bus performs the computations and data collection. Various sensors and a wireless transceiver are on-board the truck. Our research focus has been in the autonomous control of vehicles using intelligent systems. For this document we have employed a multi-resolutional hierarchy to plan a path for the tractor-trailer truck. The hierarchy starts with a simple path then warps it around obstacles. The modular construction of the hierarchy allows more intelligent agents …

Statistical Anomaly Denial Of Service And Reconnaissance Intrusion Detection, Zheng Zhang

Dissertations

This dissertation presents the architecture, methods and results of the Hierarchical Intrusion Detection Engine (HIDE) and the Reconnaissance Intrusion Detection System (RIDS); the former is denial-of-service (DoS) attack detector while the latter is a scan and probe (P&S) reconnaissance detector; both are statistical anomaly systems.

The HIDE is a packet-oriented, observation-window using, hierarchical, multi-tier, anomaly based network intrusion detection system, which monitors several network traffic parameters simultaneously, constructs a 64-bin probability density function (PDF) for each, statistically compares it to a reference PDF of normal behavior using a similarity metric, then combines the results into an anomaly status vector that …

A Neuromorphic Controller For A Distillation Column, Xiao-Hua Yu

Electrical Engineering

This paper investigates the design of a neural network based controller to control the concentration of the overhead and bottom product in the model of a distillation column. Satisfactory computer simulation results of this approach are obtained.

Tcm Decoding Using Neural Networks, Edit J. Kaminsky, Nikhil Deshpande

Electrical Engineering Faculty Publications

This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude imbalance are analyzed.