Master of Science, The Ohio State University, 2024, Mechanical Engineering

In the progression towards SAE Level 4 automation, the functional safety of automated driving systems is deemed essential, especially in the event of faults. The ISO 26262 functional safety standard is utilized to evaluate the risks associated with malfunctions in electrical/electronic (E/E) systems, based on a subjective assessment by safety experts. Yet, this standard primarily relies on qualitative measures and lacks provisions for real-time risk estimation. In this thesis, a risk estimation methodology has been developed to fill this gap, offering a quantitative method suitable for real-time risk analysis. A diagnostic system has been created to supplement the existing onboard diagnostic modules provided by the OEM. This integration creates a dual-layer safety net, ensuring secure operation in autonomous mode and providing a reliable fallback to the human operator when required. The quantitative risk estimation model that calculates the probability of collision, accounts for sensor and actuator faults amid measurement uncertainties. Based on the estimated probability, fault behavior is dynamically classified into distinct risk regions. The system is designed to respond appropriately to the situation by tailoring mitigating actions from minor adjustments to fallback protocols based on the level of risk and the type of fault. The proposed framework is illustrated through scenario-based testing via multiple simulations and closed-course evaluation using the test vehicle. This research has been conducted to contribute towards OSU's team, Buckeye AutoDrive, participating in Year 3 of the SAE AutoDrive Challenge II.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering; Systems Design; Transportation

Doctor of Philosophy, The Ohio State University, 2011, Computer Science and Engineering

Computer surgical simulation has a great deal of potential in medical education and testing. However, there are numerous problems in integrating simulation software technology into a medical curriculum. Review and analysis of the data from the simulation is important to evaluate and assist students. A combination of realistic rendering for good translation of skills to the operating room and illustrative rendering to aid novices can help the simulation system target a wide range of students. In the context of an ongoing project to develop and improve a temporal bone surgical simulator, this document describes algorithms that address these issues and provides solutions to them. In collaboration with expert surgeons, we have met some of the technological challenges that limit surgical simulation. Storage and playback of the interactions that users have with the simulation system is achieved via a snapshot technique using forward differences for efficient compression. A technique for realistic rendering of fluid and wet surfaces in a virtual surgical environment using modern graphics hardware is explained. Using a modified distance field technique, we show how to display context around important anatomical structures in segmented datasets. A method of automatic scoring of the users of the simulator is detailed. This method involves partitioning the volume based on proximity to critical structures and then using the Earth Mover's Distance to compare the content of those partitions. Distance fields are also employed for shape analysis techniques to extract features that are used in a visualization system. This system allows expert surgeons to examine and compare the virtual mastoidectomies perfomed by residents during training.

Committee: Han-Wei Shen PhD (Committee Chair); Roger Crawfis PhD (Committee Member); Raghu Machiraju PhD (Committee Member) Subjects: Computer Science; Medical Imaging

Doctor of Philosophy, The Ohio State University, 2013, EDU Teaching and Learning

Although multiple-choice assessment formats are commonly utilized throughout the educational hierarchy, they are only capable of measuring a small subset of important disciplinary competencies and practices. Consequently, science educators require open-response format assessments that can validly measure more advanced skills and performances (e.g., producing written scientific explanations). However, open-response format assessments are not practical in many educational contexts because of the high cost of scoring, the delayed feedback to test-takers, and the lack of scoring consistency among human graders. In this study, the efficacy of automated computer scoring (ACS) of written explanations is examined relative to human scoring. This study aims to build ACS models using machine-learning methods in order to detect a suite of scientific and naive ideas in written scientific explanations, and to explore approaches for optimizing these ACS models. This study develops and evaluates nine machine-learning models to detect six scientific concepts and three naive ideas of natural selection. In addition, it examines the effects of three machine-learning parameters (i.e., n-gram selection, stop words, and misclassified data) on the performance of the ACS models. In order to test the efficacy of the ACS models, a corpus of 10,270 written evolutionary explanations--in response to a variety of items differing in surface features was gathered. The corpus was scored by expert human raters and by the ACS models, and four correspondence measures were calculated: kappa, raw agreement, precision, and recall. Methodologically, the ACS models were built using the SMO (Sequential Minimal Optimization) algorithm in the LightSIDE software. Repeated-measures ANOVAs, Pearson correlations, and logarithmic regressions were used to examine the effects of the three machine learning parameters on human-computer correspondence measures, and to examine the effects of sample size on model performa (open full item for complete abstract)

Committee: Ross H. Nehm PhD (Advisor); David L. Haury PhD (Committee Member); Lin Ding PhD (Committee Member) Subjects: Educational Evaluation; Educational Technology; Science Education