Master of Science in Engineering, University of Akron, 2016, Electrical Engineering

An observer-controller pair was designed to regulate the fuel flow rate and the flue-gas oxygen ratio of a combustion boiler. The structure of the observer was a proportional-integral state estimator. The designed controller was composed of a combination of two common controller structures: state-feedback with reference tracking and proportional-integral-derivative(PID). A discrete-time, linear state-space model of the combustion system was developed such that the linear controller and observer could be designed. This required establishing separate models pertaining to the combustion process, actuators, and sensors. The complete model of the combustion system incorporated all three models. The combustion model, which related the flue-gas oxygen ratio to the fuel and oxygen flow rates, was obtained using the mathematical formulas corresponding to combustion of natural gas. The actuators were modeled using measured fuel and oxygen flow rate data for various actuator signals, and fitting the data to a parametric model. The established nonlinear models for the combustion process and actuators required linearization about a specified operating point. The sensors model was then obtained using the predictive error identification technique based on batch input-output data. For the acquired model of the combustion system, a linear quadratic regulator was used to calculate the optimal state feedback gain. The classical controller gains were determined by tuning the gains and evaluating the simulation of the closed-loop response. Computer-aided simulations provided evidence that the controller and state estimator could regulate the desired set point in the presence of moderate disturbances. The observer-controller pair was implemented and verified on an experimental boiler system by means of an embedded system. Even in the presence of a disturbance resulting from a 50% blockage of the surface area of the air intake duct, the closed-loop system was capable of regulating t (open full item for complete abstract)

Committee: Nathan Ida Dr. (Advisor); Robert Veillette Dr. (Committee Member); Kye-Shin Lee Dr. (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2022, Communication

Minority group members are frequently exposed to identity-threatening stressors, and these stressors can have a negative impact on well-being. Although there are various methods one can undertake to alleviate the negative effects of identity-threatening stressors, social support is of particular importance to the current investigation. Indeed, the positive effects of social support on well-being are well-documented. According to the social identity/self-categorization model of stress (SISCMS), shared social identities are particularly important for alleviating the negative effects associated with identity-threatening stressors. However, the SISCMS is limited in several regards. Current work that tests the model is largely cross-sectional, and many of the current studies only account for personal or achieved social identities rather than ascribed social identities, acute stressors rather than identity-threatening stressors, and perceived social support rather than received social support. Thus, this dissertation employs a 2 (discrimination severity: low vs. high) x 3 (social support: none vs. outgroup vs. ingroup) online experiment to test the overall tenets of the SISCMS among cis-gendered gay men. Primary findings demonstrate that those that received emotional support, regardless of the source, after experiencing an identity-threatening stressor reported lower levels of state anxiety than those that received no emotional support. Further, the perceived quality of social support mediated the relationships between the social support conditions and the outcome variables, state self-esteem and state anxiety. Results also indicated that group identification and the perceived quality of social support were not significant moderators between either the discrimination severity conditions and the outcome variables or the social support conditions and the outcome variables. Overall, the study highlights the importance of receiving emotional support from any source for addres (open full item for complete abstract)

Committee: Jesse Fox (Advisor); Joseph Bayer (Committee Member); Roselyn Lee-Won (Committee Member); Shelly Hovick (Committee Member) Subjects: Communication

Doctor of Philosophy, The Ohio State University, 2023, Social Work

Background: Approximately 50% of individuals seeking treatment for substance use and mental health conditions in behavioral healthcare settings have a lifetime history of TBI affecting their ability to engage in behavioral health treatment. Identifying lifetime history of TBI using validated screening methods can optimize interventions for these individuals, however, TBI screening adoption has failed in these settings. Drawing on the Theory of Planned Behavior and Diffusion of Innovations Theory, this explanatory sequential mixed methods study aimed to improve our understanding about how provider characteristics (attitudes, subjective norms, perceived behavioral control (PBC), intentions), innovation-level factors (acceptability, feasibility, appropriateness), and contextual determinants affect TBI screening adoption in behavioral healthcare settings. Methods: In Phase I, 215 behavioral health providers in the United States completed a training introducing the OSU TBI-ID, followed by a web-based survey assessing attitudes, PBC, subjective norms, and intentions to screen for TBI (Time 1). After one-month, providers completed a second survey assessing the number of TBI screens conducted, and the acceptability, feasibility, and appropriateness of TBI screening (Time 2). Data were analyzed using structural equation modelling with logistic regressions (SEM) and logistic regression with moderation effects. Results informed development of a qualitative interview guide. In Phase II, 20 providers from Phase I participated in interviews to build upon the quantitative results. Data were analyzed thematically and integrated with the quantitative results. Barriers to adoption were also identified and linked to constructs from the Consolidated Framework for Implementation Research (CFIR). Results: Approximately 25% of providers adopted TBI screening, which was driven by motivations to trial the innovation. SEM demonstrated that more favorable attitudes toward TBI screening were (open full item for complete abstract)

Committee: Alicia Bunger (Advisor); Alan Davis (Committee Member); Jennifer Bogner (Committee Member) Subjects: Behavioral Sciences; Health Care; Public Health; Social Research; Social Work

PhD, University of Cincinnati, 2022, Engineering and Applied Science: Aerospace Engineering

In this dissertation, the main focus is on, resilience and enhancement in the performance of autonomous multi-agent vehicles and transportation systems using machine learning techniques. Initially, we consider a vehicle platooning problem with a bi-directional controller. Here we study the effects of control based attack on string stability and solutions to detect, identify and mitigate the attack. Using Deep Convolutional Neural Networks (DCNN) with Gramian Angular Fields as pre-processing technique we detect and identify the ad- versarial vehicle in the platoon with only local information from the vehicles. Also, we provide mitigation strategy using Routh Hurwitz stability criterion. Next, we examine multiple ground robots performing cooperative localization using an Extended Infor- mation Filter (EIF). In this problem, we investigate the effects of Stealthy False Data Injection (SFDI) attack on the state estimator. Here, we provide various distributed deep learning strategies and specially One-Shot learning to detect these SFDI attacks and generalize it to n number of robots in the environment. In the next problem we solve a problem related to shoulder drop-offs on highways. Since shoulder drop-offs are one of the main reasons for accidents they have to be repaired and maintained by Department of Transportation which they currently to it manually and it is very challenging for visual eye. So, we automate this process using a low cost LIDAR called Livox and a camera to predict these drop-off levels using state of the art deep learning methods like PointNet, Vision Transformers and ConvNets. In the end we have solved problems related to the enhancement of state estimators and control algorithms for multi-agent vehicles. In the first problem, we have two Unmanned Aerial Vehicles (UAV) geo-localizing multiple ground targets using gimbal camera. The main focus here is to reduce the uncertainty and es (open full item for complete abstract)

Committee: Rajnikant Sharma Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member); Boyang Wang (Committee Member); Ali Minai Ph.D. (Committee Member); Ou Ma Ph.D. (Committee Member) Subjects: Artificial Intelligence

Doctor of Philosophy, The Ohio State University, 2022, Computer Science and Engineering

With the continuous increase of time series data, more and more research is focused on using these data to improve people's lives. On the one hand, the Markov Decision Process (MDP) is used widely in decision-making. An agent can decide the best action based on its current state. When the agent is applied to time series data, the model will help people make more informed decisions. However, state identification, which is very important in obtaining an optimal decision, has received less attention. On the other hand, with the development of deep learning, identifying the category of a time series has become more and more precise. As a result, the recognition of complex time series sequences has become the hub of public attention. In this dissertation, we focus on developing an automatic state selection using MDP and investigate the application of deep learning in recognizing time series data. We propose a method that combines decision-tree modeling and MDP to permit automatic state identification in a way that offers desirable trade-offs between simplicity and Markovian behavior. We first create a simplified definition of the host state, which becomes the response measure in our decision-tree model. Then, we fit the model in a way that weighs accuracy and interpretability. The leaves of the resulting decision-tree model become the system states. This follows, intuitively, because these are the groupings needed to predict (approximately) the system evolution. Then, we generate and apply an MDP control policy. Our motivating example is cyber vulnerability maintenance. Using the proposed methods, we predict that a Midwest university could save more than four million dollars compared to the current policy. Prechtl's general movements assessment (GMA) allows visual recognition of movement patterns in infants that, when abnormal (cramped synchronized, or CS), have very high specificity in predicting later neuromotor disorders. However, training req (open full item for complete abstract)

Committee: Rajiv Ramnath (Advisor); Ping Zhang (Committee Member); Theodore Allen (Advisor) Subjects: Artificial Intelligence; Bioinformatics; Business Costs; Computer Engineering; Computer Science

Doctor of Philosophy, University of Akron, 2021, Mechanical Engineering

Lithium-based batteries are widely used in many battery based-power devices such as cell phones, laptops, electric vehicles and renewable energy systems. Due to their high energy density, they are becoming applicable as one of the main storage systems of Electric Aircraft (EA) and Hybrid Electric Aircraft (HEA). Since safety is the most important element for EA and HEA, using a battery management system (BMS) is vital to prevent batteries from possible damages, catching fire or explosion. The main functions of BMS includes fault diagnosis and isolation, state of charge (SOC) and state of health (SOH) estimation, cells monitoring, cell balancing, charge and discharge process control, and even thermal management of the cells. In this proposal, at the first stage, a comparative investigation on BMS structure and its main functions such as SOC estimation, SOH estimation and fault detection mechanism in battery packs of EA and HEA during flight is done. Then, a novel experimental setup for batteries is built to test the proposed BMS related functions and battery models developed in this study. The hardware of this intelligent BMS besides micro controller unites contains some printed circuit boards (PCBs) – including cell monitoring board, main board, cell balancing board, charge control board and discharge control board – that were designed using Altium Designer. For developing a model-based fault diagnosis algorithm, after reviewing different faults and their causes, battery cells were modeled numerically in MATLAB/SIMULINK Software based on a second order electric circuit model. To improve the accuracy of the developed models, a real-time parameter estimator is proposed. Comparative verification experiments show good accuracy and robustness of the proposed parameter estimator led to an accurate battery model with an average error less than 0.4%. Then this battery model was utilized to develop a model-based fault diagnosis s (open full item for complete abstract)

Committee: Siamak Farhad (Advisor); Ajay Mohan Mahajan (Advisor); Zhenmeng Peng (Committee Member); Yalin Dong (Committee Member); Truyen Nguyen (Committee Member); Alper Buldum (Committee Member) Subjects: Electrical Engineering; Mechanical Engineering

MS, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

Experimental modal analysis (EMA), which is an integral part of vibration analysis deals with finding the dynamic characteristics of a system namely the natural frequencies, damping and modal scaling. This information is crucial to the design of any structure as they would help predict the system response in its operating conditions. EMA is usually performed on an input output data model that is acquired from a structure. There are several methods which operate in the time and frequency domain to evaluate the modal parameters from a meaningful set of experimental data. The traditional polynomial based approaches use least squares methods to arrive at a good estimate of the numerator and the denominator matrix polynomials that can represent the frequency response functions. The modal parameters are then obtained from this mathematical fit of the experimental data. Another approach to this problem is the use of state space models used in controls domain. An nth order linear differential equation can be represented in the state space form with the defining system matrices A, B, C and D. The problem statement here is to fit the experimental data into its defining state space matrices of a suitable order since they would contain all the modal information in them. Numerical simulations for subspace identification (N4SID), developed by Van Overschee and de Moore, is one algorithm that can be used to build a state space model from measured input output data. This thesis work attempts to compare the above mentioned traditional polynomial based approaches to modal analysis with a state space based system identification approach using N4SID. Through these comparisons, the similarities in the description of a transfer function by these two methods are described. It also would serve as a starting point for its reader to compare more state space approaches with the traditional Unified Matrix Polynomial Approach (UMPA).

Committee: Randall Allemang Ph.D. (Committee Chair); Michael Mains M.S. (Committee Member); Allyn Phillips Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Wright State University, 2013, Human Factors and Industrial/Organizational Psychology MS

Objective: Our purpose was to discuss alternative principles of design (emergent features and perceptual objects) for analogical visual displays, to evaluate the utility of four different displays for a system state identification task, and to compare outcomes to predictions derived from the design principles. Background: An interpretation of previous empirical findings for three displays (bar graph, polar graphic, alpha-numeric) is provided from an emergent features perspective. A fourth display (configural coordinate) was designed to leverage powerful perception-action skills using principles of cognitive systems engineering / ecological interface design (i.e., direct perception). Methods: An experiment was conducted to evaluate these four displays. Primary dependent variables were accuracy and latency. Results: Numerous significant effects were obtained and a clear rank ordering of performance emerged (from best to worst): configural coordinate, bar graph, alpha-numeric, polar graphic. Conclusions: The findings are difficult to reconcile with principles of design based on perceptual objects but perfectly consistent with principles based on emergent features. Limitations of the most effective configural coordinate display are discussed and a redesign is provided to address them. Applications: The principles of ecological interface design that are described here (i.e., the quality of very specific mappings between domain, display, and observer constraints) are applicable to the design of all forms of displays for all work domains.

Committee: Kevin Bennett Ph.D. (Advisor); John Flach Ph.D. (Committee Member); Herb Colle Ph.D. (Committee Member) Subjects: Cognitive Psychology; Psychology

Doctor of Philosophy, The Ohio State University, 2010, Electrical and Computer Engineering

In this dissertation, the identification and state estimation for linear parameter varying (LPV) systems as well as their applications to battery management system design are investigated. First, two complete LPV system identification procedures are described. One procedure uses a layered optimization process while the other uses a subspace based method. Both methods provide theoretically sound and practical processes under which realistic LPV system identification problems can be solved. Secondly, controller and observer design techniques for LPV systems are examined. In particular, the stability conditions in the form of parameter dependent linear matrix inequalities (LMI) that result from the applications of the standard Lyapunov stability theory and other advanced techniques such as L2 and H∞ to LPV systems are discussed in detail. Also discussed are the some of the techniques for solving these LMIs. Lastly, because real systems often contain parametric uncertainty, the use of input to state stability to characterize closed loop performance of controllers or state estimator under such condition is also reviewed. The tools developed for LPV systems are then applied to solve the problems of model identification and state of charge (SoC) estimation for battery cells. The model identification problem is tackled using both identification schemes so that differences in performance and effectiveness between the methods can be compared and contrasted. A SoC estimator based on LPV system state estimation techniques is then designed using the model identified. Because parametric uncertainty is inherent in the estimator designed, the stability and performance of the estimator is analyzed using the notion of input to state stability. Experimental data is then used to illustrate the efficacy of this method. The goal of these applications is to show the relevance of the LPV structure and techniques to problems in battery management system design, so that research will be (open full item for complete abstract)

Committee: Steve Yurkovich PhD (Advisor); Giorgio Rizzoni PhD (Committee Member); Yann Guzennec PhD (Committee Member) Subjects: Automotive Materials; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2008, Industrial and Systems Engineering

This dissertation proposes methods for steady state linear system identification for both forward cases in which prediction of outputs for new inputs are desired and also inverse prediction of which inputs fostered measured outputs are needed. Special attention is given to genetic network modeling applications. Inverse prediction matters here because then one can predict the effective genetic perturbation associated with a new target drug compound or therapy. The primary application addressed in this dissertation is motivated by our on-going contributions related to Down syndrome which affects approximately 1 out of every 800 children. First, single shot experimentation and analysis to develop network models is considered. The discussion focuses on linear models because of the relevance of equilibrium conditions and the typical scarcity of perturbation data. Yet, deviations from linear systems modeling assumptions are also considered. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. In addition, topics for further research are suggested including the need to develop more numerically stable analysis methods, improved diagnostic procedures, sequential design and analysis procedures. Next, multi-stage design and analysis procedures are proposed for experimentation in which both forward and inverse predictions are relevant. Methods are proposed to derive desirable experimental plans for the next batch of tests based on both space filling and D-optimality. The space filling designs are intended to support both linea (open full item for complete abstract)

Committee: Theodore Allen PhD (Committee Chair); Mario Lauria PhD (Committee Co-Chair); Clark Mount-Campbell PhD (Committee Member); Hakan Ferhatosmanoglu PhD (Committee Member) Subjects: Bioinformatics; Biostatistics; Engineering; Operations Research; Statistics

Doctor of Philosophy, The Ohio State University, 2007, Communication

Previous research has shown video games afford learning experiences, thus what occurs within the gaming realm is applicable to the real-world and vice-versa. Therefore, this study extends the video game effects literature by exposing the complexity of competitive gaming situations. In that spirit, this study incorporated a college football game to enact identification processes and direct competition to determine how player membership, opponent membership, and competition outcomes impact media effects variables such as enjoyment, presence, and state hostility. Two-hundred ninety four subjects participated in the 3 (opponent membership—main rival, conference opponent, other opponent) x 2 (player membership—identifier, non-identifier) x 2 (competitive outcome—win, loss) design. Overall, competition outcome significantly predicts levels of enjoyment and state hostility. Moreover, who the gamer plays as and against also influences these responses. Beating an emotionally relevant opponent solicited greater enjoyment than an irrelevant team. Further, losing while playing as an emotionally relevant team produced greater state hostility levels than losing as an emotionally irrelevant team. Similarly, losing to an emotionally relevant opponent generated higher state hostility levels than losing to an emotionally irrelevant team.

Committee: Matthew Eastin (Advisor) Subjects: Mass Communications

Master of Science in Mechanical Engineering, Cleveland State University, 2008, Fenn College of Engineering

Road surface condition is greatly dependent on the surface's friction coefficient. The abrupt change of the coefficient results in variation of wheel slip which likely leads to vehicle instability. Vehicle steering model and the dynamic equations for four-wheel drive vehicle is developed. A new observer, called Extended State Observer (ESO) is used to estimate the longitudinal velocity, lateral velocity and yaw rate, and more importantly an additional quantity known as system dynamics. A trained neural network was employed to help determine the friction coefficient. Fuzzy logic was employed to quickly detect the change of road surface condition and further classify the surface condition. The presented methods were simulated with a vehicle encountering a significant change from a uniform-μ (i.e. uniform friction coefficient) surface to a split-μ surface (i.e. different friction coefficient on each side of the wheels) during cornering. The results this obtained show that the developed techniques could effectively detect and identify the road surface condition. Further more, a new anti-skid controller by means of Active Disturbance Rejection Controller (ADRC) and the ESO is proposed. The simulation results show that the controller can effectively control the vehicle's yaw rate while cornering.

Committee: Paul Lin PhD (Committee Chair); Zhiqiang Gao PhD (Committee Member); Majid Rashidi PhD (Committee Member) Subjects: Mechanical Engineering