MS, University of Cincinnati, 2018, Engineering and Applied Science: Mechanical Engineering

Dynamic systems incorporating physical plant and control systems should be designed in an integrated way to yield desirable and feasible solutions. Conventionally, these systems are designed in a sequential manner which often fails to produce system-level optimal solutions. However, combined physical and control system design (co-design) methods are able to manage the interactions between the physical artifact and the control part and consequently yield superior optimal solutions. Small-scale to moderate-scale dynamic systems can be addressed by using existing co-design methods effectively; nonetheless, these methods can be impractical and sometimes impossible to apply to large-scale dynamic systems which may hinder us from determining the optimal solution. This work addresses this issue by developing a new algorithm that combines decomposition-based optimization with a co-design method to optimize large-scale dynamic systems. Specifically, the new formulation applies a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) for the co-design of a representative large-scale dynamic system consisting of a plug-in hybrid-electric vehicle (PHEV) powertrain. Moreover, since many of dynamic systems may consist of several time-dependent linking variables among their subsystems, a new consistency measure for the management of such variables has also been proposed. To validate the accuracy of the presented method, the PHEV powertrain co-design problem has been studied with both simultaneous and ATC methods; results from the case studies indicate the new optimization formulation's ability in finding the system-level optimal solution.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Sam Anand Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Ohio University, 2024, Industrial and Systems Engineering (Engineering and Technology)

In response to escalating inventory costs, dynamic purchasing needs, and the demand for rapid operations in the retail sector, both the warehousing and retail industries have accelerated their pace of innovation. Among these advances, the development of automated warehousing and storage systems stands out. However, despite widespread adoption, a comprehensive framework for effectively implementing these systems remains lacking. Hence, this study proposes a systematic approach that provides a foundational blueprint for harnessing vital information from historical sales data in the deployment of intelligent warehouse systems, incorporating a wide array of Automated Storage and Retrieval Systems (AS/RS) technologies. Specifically, it employs unsupervised machine learning for time series clustering to analyze historical sales data, while adapting and modifying the Recency, Frequency, Monetary (RFM) model to optimize the prioritized management of stock-keeping units (SKUs) in periodic segments.

Committee: Tao Yuan (Advisor); Omar Alhawari (Committee Member); Gary Weckman (Committee Member); Ashley Metcalf (Committee Member) Subjects: Engineering; Industrial Engineering; Management; Sustainability; Systems Design; Technology

Doctor of Philosophy (PhD), Ohio University, 2023, Biological Sciences (Arts and Sciences)

This dissertation explores motor systems at both macroscopic and microscopic scales. Chapters 2 and 3 explore the macroscopic perspective, while Chapters 4 and 5 delve into the microscopic level. Chapter 2 introduces an algorithm that identifes a subnetwork within the primate brain's anatomical connectome responsible for visuomotor behavior. The algorithm systematically traces connections from the primary visual cortex to the primary motor cortex, excluding regions unrelated to this task. The intraparietal sulcus, superior parietal cortex, and secondary visual cortex emerge as critical players in this network. A macroscopic visuomotor model is developed, successfully replicating experimental results. Chapter 3 focuses on understanding the neurophysiological responses in brain subnetworks responsible for visuomotor tasks under varying gravity conditions. Hypogravity afects brain regions such as the cingulate cortices, somatosensory cortices, and primary visual cortex, potentially disrupting visuomotor performance during spaceflight. In Chapter 4, the study investigates the slow decline in an outward current in lobster stomatogastric neurons, identifying the involvement of IKCa current and the dynamics of intracellular calcium concentration ([Ca]i). An algorithmically optimized model replicates experimental fndings, suggesting the slow dynamics of [Ca]i in other neuron types. Chapter 5 explores how slow-oscillating pyloric follower neurons couple with a faster pacemaker group. Modeling and experimental data reveal that the hyperpolarization-activated, depolarizing current Ih enables 1:1 coupling with rapid inhibitory inputs. CsCl blocking Ih disrupts this coupling, highlighting the crucial role of Ih in this phenomenon.

Committee: Scott Hooper (Advisor) Subjects: Applied Mathematics; Artificial Intelligence; Biology; Biomedical Research; Biophysics; Computer Science; Mathematics; Neurobiology; Neurosciences; Physics; Theoretical Mathematics

Master of Science in Industrial and Human Factors Engineering (MSIHE) , Wright State University, 2018, Industrial and Human Factors Engineering

This study examined the effectiveness of a wearable device in delivering various feedback modalities in an attempt to improve performance outcomes in complex systems. Secondarily this study looked at performance when feedback type was matched to preferred learning style according to VARK Learning Styles Inventory results. Participants were required to perform system monitoring and correct for system failures through key presses. Feedback was delivered through a smart watch and was based on response time performance. Feedback modalities included visual, auditory, and haptic feedback. Subjective ratings of situation awareness and mental workload were also examined. Results indicated that auditory feedback condition response times were significantly slower than response times in other feedback condition with the control group having the fastest mean response times. Participants who tested as read write learners were the only learning style group to show higher levels of situation awareness and decreased mental workload when presented with their preferred

Committee: Subhashini Ganapathy Ph.D. (Advisor); Mary E. Fendley Ph.D. (Committee Member); Sansanka V. Prabhala Ph.D. (Committee Member) Subjects: Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2019, Electrical and Computer Engineering

Today's control systems that are implemented on commercial vehicles are designed to operate under nominal conditions with drivers, or pilots, typically handling off-nominal situations and scenarios. In operations requiring high levels of autonomy, which will possibly be the norm in the future, functions that have the capabilities to mitigate off-nominal conditions need to be incorporated in control system designs. Before such complex functions can be integrated into the civilian domain, it is imperative to be able to understand and predict system wide safety concerns by accurately identifying potential incidents or accidents. Such an identification process involves two main challenges. The first is the identification and the ranking of all possible hazards and accidents a system is prone to encounter. The second is the identification of sequences of events, or scenarios leading to the hazards of interest. This dissertation targets the second challenge. A generic Backtracking Process Algorithm (BPA) based on the deductive implementation of Markov Cell-to-Cell Mapping Technique is proposed for risk-informed identification of critical scenarios involving control systems of autonomous vehicles (a class of cyber-physical systems). A hybrid state system structure is used for the representation of autonomous vehicle systems, and a risk assessment framework is proposed for the quantitative and risk-based assurance of autonomous vehicle systems. An Unmanned Aircraft System (UAS) operating under a potential loss of link is used as a case study to demonstrate the capability of BPA in identifying critical scenarios leading to mission failures. An Autonomous Ground Vehicle operating in an urban setting is used as a case study to demonstrate the effectiveness of the proposed risk assessment framework and its usefulness in risk-informed control system design. A Sequential BPA (SBPA) is proposed for the risk assessment of autonomous systems across multiple phases of operation tha (open full item for complete abstract)

Committee: Umit Ozguner (Advisor); Tunc Aldemir (Advisor); Andrea Serrani (Committee Member); Carol Smidts (Committee Member); Keith Redmill (Committee Member); Adrian Lam (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, Case Western Reserve University, 2016, Civil Engineering

This work studies the seismic analysis and design of systems with multiple support motions. Examples of such structures are nuclear safety - related piping, bridges with widely spaced piers, pipelines and tunnels. Dynamic analysis of such systems is more elaborate than analysis of other general structures such as buildings, because it must consider the relationships among various support motions, or explicitly, the correlations between them. Herein, a method that models such correlations is presented. A time domain space state Markov vector approach is used to define a target covariance matrix of a vector random process and this matrix is used as a target for an AR simulation of correlated acceleration time histories. To provide a facility for experimental testing of multiply supported structures, a four-table seismic simulation system was designed, fabricated and tuned to have the ability of executing correlated multiply supported excitations. Features, performance curves, control and implementation details of the system are given as a manual for operation and testing. A review of existing problems and methods for analysis of nuclear piping systems is provided. A set of experiments is performed to evaluate responses of “stiff” and more “flexible” piping designs to correlated, response-spectrum-compatible support motions. These preliminary tests show effects of correlation on piping responses. Guidance is given on more comprehensive experiments needed to evaluate piping designs that are more “flexible”. The system of shaking tables proved to be fully functional. Linearized analytical and numerical models of “stiff” and “flexible” piping are defined. Analytical and numerical eigenvalues are compared. The numerical models are used for predicting responses and comparing these responses to experimental results. Necessary features of a nonlinear model that may be capable of predicting geometrically non-linear behavior of a hanger pipe support (self-weight suppor (open full item for complete abstract)

Committee: Dario Gasparini PhD (Advisor); Brian Metrovich PhD (Committee Member); Wojbor Woyczynski PhD (Committee Member); Michael Pollino PhD (Committee Member) Subjects: Civil Engineering; Mechanical Engineering; Systems Design

Doctor of Engineering, Cleveland State University, 2015, Washkewicz College of Engineering

In this dissertation a three-bus, radial distribution system is proposed to be used as a benchmark for the study of power quality problems (PQPs) and their compensation. To mitigate PQPs in distribution systems, Custom Power Systems (CUPS) devices such as Distribution Static Compensator (D-STATCOM), Dynamic Voltage Restorer (DVR), and Unified Power Quality Conditioner (UPQC) are required. An increasingly popular and practical control technique based on Active Disturbance Rejection Control (ADRC), which is simple and has good disturbance rejection capabilities, is implemented to control CUPS devices. Its performance is compared with the highly dominating Proportional-Integral-Derivative (PID) controller. Modelling of the DVR and the D-STATCOM, together with their respective ADRC and PID controllers, were developed under the MATLAB /Simulink© environment. Simulation results, which included voltage sag/swell, voltage imbalance and current imbalance, proved that the ADRC controllers are attractive for their use with CUPS devices to mitigate the PQPs and can be considered as effective replacements for the PID controller.

Committee: Eugenio F Villaseca PhD (Committee Chair); Alexander Charles PhD (Committee Member); Zhiqiang Gao PhD (Committee Member); Dan Simon PhD (Committee Member); Miron Kaufman PhD (Committee Member); Morinec Allen G. PhD (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy (PhD), Ohio University, 2002, Electrical Engineering & Computer Science (Engineering and Technology)

This thesis addresses problems in the area of certification of real-time performance for software that has to respond to real-world events in a timely manner. Several solutions are presented for software that is under the control of the Solaris operating system scheduler. Experimental results validate the accuracy of the solutions. The new approaches to response time prediction give significant benefits for the quality of service (QoS) management by identifying feasible allocations of real-time software to distributed computing platforms. With these approaches, real-time computing will be possible in the context of general purpose operating systems that have real-time priority class schedulers or time-sharing round robin schedulers. These new appoaches enable resource allocators to manage resources much more effectively than the traditional worst-case approaches.

Committee: Lonnie Welch (Advisor) Subjects:

Master of Science (MS), Ohio University, 2006, Computer Science (Engineering)

The Quality-based Adaptive Resource Management Architecture (QARMA) consists of a framework for describing resource management solutions and a collection of CORBA-based middleware services for the management of distributed, real-time systems. The framework can be used to: (1) characterize existing resource management architectures and tools and (2) assist in integrating existing tools into coherent resource management solutions. The middleware components are an instantiation of a resource management solution based directly on the framework. The main contributions of this thesis include an analysis of the information life-cycle in a resource management system, a collection of algorithmic models that serve as a basis for resource allocation algorithms in the QARMA middleware, and a description of the greedy algorithm used by the Resource Management Service. Experimental results demonstrate that QARMA can control existing application systems and can be integrated with existing management middleware.

Committee: Lonnie Welch (Advisor) Subjects: Computer Science

Master of Science (MS), Ohio University, 2005, Computer Science (Engineering)

Development of real-time resource management system often entails utilizing similar software engineering solutions as other resource managers. However, the developers of these resource managers may reengineer solutions, not knowing that a solution already exists. First time developers of this type of software may not even know what types of problems they will encounter. Each of these situations slows the development process. This thesis examines the challenges in determining what software is needed to build a real-time resource management system. After working with several different types of resource management systems, real-time and otherwise, it is clear that many of these systems use similar approaches in managing their resources. Research in the field of real-time systems unveils that some such approaches are documented as patterns, but many of the existing patterns focus on in depth details about real-time system management, requiring that the reader already have an abundance of knowledge on the topic. The aim of this thesis is to step back and examine the higher-level components of these systems and the interaction of these components and document them as patterns. These patterns allow newcomers to real-time systems to understand the problems incidental the topic. Furthermore, these patterns may aid the expert by presenting the information in a different way. The common components of the systems researched that are found in a number of distributed and real-time systems are essential pieces in building such systems, due to the sheer number of systems in which they are found. Here, they are presented in pattern format.

Committee: Lonnie Welch (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2012, Management

Over the last two decades, firms have operated in increasingly accelerated ‘high-velocity' dynamic markets, which require them to become agile. During the same time frame, firms have increasingly deployed complex enterprise systems - large-scale packaged software innovations that integrate and automate enterprise-wide organizational processes. While supporting efficiency, literature is divided on whether such innovations promote or hinder organizational agility. Relatively little is known about the effects of enterprise systems on organizational agility along the dimensions of organizational impact, organizational processes and organizational knowledge. These dimensions form the basis for the research in this dissertation: 1) What is the organizational impact of enterprise systems on agility i.e. do enterprise systems promote or hinder agility? 2) What are the organizational processes by which organizations reconcile with enterprise systems' changing business needs driven by organizational agility? 3) What are the effects of organizational knowledge and competencies on the impact of enterprise systems on organizational agility? To address these research questions the dissertation adopts a mixed methods approach. Part 1 proposes a theoretical framework based on innovation assimilation and dynamic capabilities and utilizes a quantitative approach to empirically validate the framework, by measuring the impact of enterprise systems on organizational agility, the effects of systems agility on organizational agility, as well as how systems agility influences enterprise systems' impact on organizational agility. Part 2 employs a qualitative approach to examine a) how organizations reconcile with enterprise systems' changing business needs driven by agility, as well as b) the outcomes of such reconciliation processes. Part 3 uses a quantitative approach to more deeply delve into the critical role of two organizational competencies - business competence in IT (BCIT) and IT (open full item for complete abstract)

Committee: Kalle Lyytinen PhD (Committee Chair); Nick Berente PhD (Committee Member); Bo Carlsson PhD (Committee Member); Varun Grover PhD (Committee Member) Subjects: Business Administration; Information Systems; Information Technology; Management

Master of Science in Engineering, Youngstown State University, 2025, Department of Mechanical, Industrial and Manufacturing Engineering

A continuously variable transmission (CVT) is a type of transmission used commonly in small engine racing such as snowmobiling, go-karting, or in Society of Automotive Engineers (SAE) Baja racing. These transmissions allow for a constantly varying gear ratio while driving, without requiring the driver to shift gears manually. The continually changing ratio adapts well to varying course conditions such as frequent stops and starts, turns, and jumps. CVTs must be properly installed and tuned to reach their highest level of performance, which is a common difficulty for these complex systems. A MATLAB code has been developed that characterizes the torque, horsepower, and shift profile of a Gaged GX9 CVT. This predictive model may be used to select a tune for a vehicle and evaluate its performance without requiring extensive test time on a track. Multiple setups of the primary and secondary were analyzed, including different primary and secondary springs, flyweights, and ramps. The numerical characterization of torque, horsepower, shift curve, and acceleration has been validated experimentally, through the use of an inertia dynamometer, Kohler CH440Pro 14HP engine, and DynoMiteTM analysis software. Theoretical comparison was completed using free body and kinetic diagrams of the forces acting in the system, which were entered into a MATLAB code.   A new inertia dynamometer system has been installed within Youngstown State University's (YSU) engine laboratory, providing a hands-on application of methods learned in the classroom for students. The new installation has been used by several student groups to date. An operator's manual for the system focusing on safety and proper machine operation has been developed to aid in correct usage of the dynamometer. The new installation and numerical modeling completed has also been used to develop a laboratory for mechanical engineering students in the Dynamic Systems Modeling (DSM) class. Within the lab students will learn t (open full item for complete abstract)

Committee: C. Virgil Solomon PhD (Advisor); Hazel Marie PhD (Committee Member); Fred Persi PhD (Committee Member) Subjects: Applied Mathematics; Automotive Engineering; Mechanical Engineering

Doctor of Organization Development & Change (D.O.D.C.), Bowling Green State University, 2025, Organization Development

This study investigates the mechanisms that shape interprofessional team learning in complex healthcare environments. Qualitative analysis revealed five key mechanisms central to the team learning process: communication, interactions, decision-making, leadership, and coaching. These themes build upon foundational constructs from the literature, including systems thinking, growth mindset, situated learning, sensemaking, diversity, and power dynamics. Findings from this research informed the development of the DYNAMIC Teaming model, a cohesive framework for understanding how interprofessional teams learn, adapt, and perform. This model highlights how dialogue and decision-making foster shared understanding, which yields growth and networked knowledge. These networks, grounded in accountability and supported by modeling coaching skillsets, create a foundation for impactful inclusive participation and collaborative interprofessional team learning. By integrating theoretical constructs with grounded insights, this study offers practical implications for advancing interprofessional education and improving team effectiveness in complex healthcare settings (Barr et al., 2008).

Committee: Deborah O'Neil Ph.D. (Committee Chair); Clare Barratt Ph.D. (Other); James Stoller MD (Committee Member); Margaret Brooks Ph.D. (Committee Member) Subjects: Adult Education; Curriculum Development; Design; Education; Health Care; Health Care Management; Higher Education; Instructional Design; Management; Operations Research; Organizational Behavior; Systems Design

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The development of dynamic systems (both physical plant and control systems) in a sequential manner often results in sub-optimal solutions. However, solutions obtained using combined physical and control system design methodologies have been observed to yield optimal solutions. The overarching interest in obtaining closed-loop solutions with decent computational cost requirements brings about the topic of interest - a comparison of two of the most popular methods employed to cater for this: Model Predictive Control and Dynamic Programming. If the primary requirement is real-time control with a need to handle constraints dynamically, Model Predictive Control (MPC) is the more practical choice. If the problem allows for offline computation and requires globally optimal solutions, and the state and action spaces are not extremely large, Dynamic Programming (DP) may be more practical. This work studies both methods with respect to accuracy, type of closed-loop feedback solutions, and computational efficiency. Both methods are incorporated within a nested control co-design formulation. To validate the accuracy of both techniques, their practical application is demonstrated through case studies involving a single link manipulator, a single pendulum-type crane, and a quarter car suspension system. Each case study includes a model description, problem formulation, and results obtained using both MPC and DP techniques. The findings highlight the effectiveness of nested formulations with feedback methods in achieving optimal control co-design, with comprehensive assessments of each approach.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Energy storage is widely used to respond to the uncertain balance of electricity supply and demand and prepare for the contingency. Among many purposes of energy storage, this dissertation will focus on arbitrage trade, peak load shift, and frequency regulation. For the first part, a two-stage stochastic programming model is introduced to schedule energy storage devices and maximize arbitrage profits for the storage operator. In addition, the model considers adjustments depending on the uncertain price of the real-time electricity market when the decision in the day-ahead market is made. Then, value of stochastic solution is computed to see effect of the stochastic programming. Furthermore, several interesting cases are observed and illustrated, such as simultaneous charging and discharging. These are considered as an sub-optimal solution in general, but this occurs in specific conditions. Second, when storage is used for peak load shift, it improves resource adequacy of the power systems by contribution of the power from energy storage. In this chapter, a non-performance penalty is imposed to ensure that energy storage operators reserve energy for such shortages. A stochastic dynamic programming model is used to obtain optimal decision policy for the storage device. Using this model, case studies are conducted for the two different systems. System load of these systems are peaked in the summer and winter, so these are analyzed and compared. In the third part, energy storage capacity value and expected profits are estimated when it provides energy, capacity, and frequency regulation services. To estimate capacity value, three steps approach is adopted. First, discretized stochastic dynamic programming is used to obtain decisions policies for the discretized states. These decision policies are used to get actual decisions by solving mixed-integer optimization in a rolling-horizon fashion. Then, capacity value of energy storage is estimated using simulation. A case (open full item for complete abstract)

Committee: Chen Chen (Advisor); Ramteen Sioshansi (Committee Member); Antonio Conejo (Committee Member); Matthew Pratola (Committee Member) Subjects: Energy; Industrial Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

Due to the complexity of the operational design domain of Automated Driving Systems, the industry is trending towards the use of simulation-based methods for their verification and validation (V\&V), which rely on the use of models of the vehicles, sensors, vehicle environments, etc. Depending on the testing requirements, computational capabilities, modeling effort, and other such factors, these models can vary in fidelity. However, this variation in fidelity has an effect on the excitation of the control systems under test, and can therefore affect the results of the tests themselves. Moreover, since every model is an approximation of the actual physical system it represents, there is uncertainty associated with its output. Therefore, we need to be able to compare uncertain system models in order to understand the effect of model fidelity variation on test accuracy. The existing metrics such as Hankel Singular Values compare asymptotic behavior of the models, whereas simulation studies are over finite time. Although some of these metrics may be applied over finite time, they rely on hyper-parameters like weights on time or frequency. In this study, we propose an approach for computing a (pseudo)metric based on the literature for comparing the predictive performance of two models, called finite-time Kullback-Leibler (KL) rate. For any two general state space models with a general additive uncertainty, we first discuss an approach for propagating a general additive uncertainty (represented as a Gaussian Mixture Model (GMM) ) through a linear time-invariant system. We then apply this propagation approach to linear representations of nonlinear systems obtained through Dynamic Mode Decomposition (DMD). We illustrate this combined approach for the comparison of two lateral vehicle dynamics models over an obstacle avoidance maneuver to measure the effect of fidelity on the predictive performance of each model. We also apply this to a \vv problem, wherein we compare (open full item for complete abstract)

Committee: Punit Tulpule (Advisor); Shawn Midlam-Mohler (Advisor); Marcello Canova (Committee Member) Subjects: Mechanical Engineering

Master of Science, University of Toledo, 2022, Exercise Science

Context: Anterior cruciate ligament (ACL) injuries continue to plague athletes of all levels at a high rate. After ACL injury athletes often complete a test battery of assessments. These assessments historically are highly controlled and uniplanar in nature; often failing to be replicative of the “chaotic' demands of sport. Considering complex dynamic systems, re-creating the chaos of sport may aide clinicians in return to sport assessments to improve ACL outcomes. The objective of this study was to investigate the interpersonal dynamics between women's soccer athletes as they completed an agility-based collision avoidance task. More specifically, we explored how an external perturbation influences task stability and sought to determine if levels of fear or readiness influenced movement pattern success or leader-follower status throughout the task. Methods: Dyads of women's soccer players (healthy and with a history of lower extremity injury) simultaneously completed a collision-avoidance agility task under 3D motion capture. Each trial contained an external perturbation which signaled a change of direction. Measures of interpersonal dynamics were assessed using cross recurrent quantification analysis. Results: All 9 dyads (18 participants) of women's collegiate soccer players demonstrated high values of determinism throughout all trials. Leader-follower status was variable across trials. A windowed analysis of the task revealed leader-follower status changed through the trials. During the period of external perturbation, stability was disrupted for both participants. However, when the participant with the history of LE injury was the follower, they were unable to recover stability after perturbation. Further, leader-follower status was not predictable using self-reported measures of function. Conclusion: Women's soccer players successfully coordinate behavior during a collision-avoidance agility task; however, the stability of their coordination was negativel (open full item for complete abstract)

Committee: Grant Norte (Advisor) Subjects: Kinesiology; Sports Medicine

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

As passenger vehicle technologies have advanced, so have their capabilities to avoid obstacles, especially with developments in tires, suspensions, steering, as well as safety technologies like ABS, ESC, and more recently, ADAS systems; however, environments around passenger vehicles have also become more complex, and dangerous. As autonomous road vehicle (ARV) development aims to address these complex environments, one area that is still new and open is ARV emergency obstacle avoidance at highway speeds (55-165 km/h) and on slippery road surfaces. When introducing obstacle avoidance capabilities into an ARV, it is important to target performance that meets or exceeds that of human drivers. This dissertation highlights subsystems within an entire ARV, which are crucial for the completion of a highly functional emergency obstacle avoidance maneuver (EOAM), and combines them in a novel framework while considering the nuances of traveling at highway speeds and/or slippery road surfaces. The primary subsystems developed and tested in this research include the synthesis of ARV sensing, perception, decision making, control, and actuation. These subsystems are introduced with some novelties to the current state-of-the-art as well as the holistic ARV EOAM Framework, designed to handle highway speeds and slippery surfaces, as a novelty. Lastly, a newly considered testing and validation methodology for ARV EOAM performance and validation is presented. This general obstacle avoidance capability assessment (GOACA) has implications for adoption by national or even global regulation bodies, regarding ARV EOAM safety performance while requiring all the core ARV systems to perform well, and in harmony, to achieve top marks

Committee: Levent Güvenç (Advisor); Ayonga Hereid (Committee Member); Mrinal Kumar (Committee Member); Bilin Aksun-Güvenç (Committee Member) Subjects: Automotive Engineering; Computer Science; Engineering; Mechanical Engineering; Physics; Robotics; Transportation

Doctor of Philosophy, The Ohio State University, 2021, Computer Science and Engineering

Sustainable computing, dark silicon and approximate computing have ushered a new era in which some processing capacity is available only as ephemeral bursts, a technique called computational sprinting. Computational sprinting speeds up query execution by increasing power usage, dropping tasks, precision scaling, and etc. for short bursts. Sprinting policy decides when and how long to sprint. Poor policies inflate response time significantly. However, sprinting alters query executions at runtime, creating a complex dependency between queuing and processing time. Sprinting can speed up query processing and reduce queuing delay, but it is challenging to set efficient policies. As sprinting mechanisms proliferate, system managers will need tools to set policies so that response time goals are met. I provide a method to measure the efficiency of sprinting policies and a framework to create response time models for sprinting mechanisms such as DVFS, CPU throttling, cache allocation, and core scaling. I compared sprinting policies used in competitive solutions with policies found using our models.

Committee: Christopher Stewart PHD (Advisor); Radu Teodorescu PHD (Committee Member); Xiaorui Wang PHD (Committee Member); Xiaodong Zhang PHD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, Case Western Reserve University, 2021, Management

Stakeholders in their cities and communities are increasingly concerned with how sustainable development initiatives are reconfiguring social, economic, political, ecological, built system resources towards the development of sustainable cities. Measuring city-level sustainability performance and implementing concrete sustainable development initiatives toward flourishing cities are among the biggest challenges societies face as we move into the first quarter of the twenty-first century. However, many cities, particularly in the U.S., have stagnated and are declining in their progress towards achieving sustainable cities. Reductionist approaches to managing sustainability and promoting change have not been sufficient to reverse the effects of climate change nor increase social well-being metrics within communities. Integrative, whole systems management approaches are emerging as viable options that are expected to be effective in tackling the challenges at the scale of organizational systems, cities, and communities. My empirical motivation is to extend the literature on integrative ecosystem management approaches that seek to transform cities as sustainable ecosystems filled with a flourishing vitality. I employed a mixed methods approach consisting of qualitative and quantitative methods. The three studies in this dissertation provide empirical evidence interpreted through multiple theoretical lenses. The benefit of the mixed methods approach was to examine various aspects and dimensions of sustainable development in cities ecosystems. These studies seek to explain how integrative systems management can serve as a viable and effective method to address the challenges of transitioning cities into sustainable ecosystems. Study 1 examines factors that lead to successful sustainable development implementation in cities based on interview data from sustainability managers. Study 2 covers a scale development study that observes Appreciative Inquiry (AI) platforms as the (open full item for complete abstract)

Committee: Chris Laszlo PhD (Committee Chair); Kalle Lyytinen PhD (Committee Member); Jacqueline Stavros DM (Committee Member); Matthew Cole PhD (Committee Member) Subjects: Management; Organizational Behavior; Sustainability; Systems Design; Urban Planning