The primary objective of this thesis is to study change detection problems and their applications in longitudinal and functional data. In particularly, two types of change detection problems for longitudinal data are considered. The first type of problems is on-line change detection for longitudinal data, where we focus on detecting changes within a single longitudinal data stream that arrive into the system sequentially. The other type of change detection problems that will be studied in this thesis concerns about detecting outliers from a set of longitudinal or functional data. For the first type of change detection problems, we study two novel engineering applications. The first application is studied in Chapter 2, focusing on the on-line steady state detection. The goal is to identify the transition point between transient period and steady state period. We propose a novel on-line steady state detection algorithm based on a multiple change-point state space formulation and the sequential Monte Carlo methods. Compared to other existing methods, the main contribution of this work is its significantly improved computational efficiency by the use of Rao-Blackwellization, making it a much preferred method for many on-line applications where quick processing of the data in real time is critical. Additionally, the proposed method is shown to have more robust detection performance than existing methods when dealing with different types of signals. In Chapter 3, we study the second application of change detection problems, which focuses on statistical process control for the short-run process. We propose new methods under the Bayesian framework is to track the process mean and detect on-line if the process mean is beyond certain control limits or specification limits. Our model modifies the original model proposed by Tsiamyrtzis and Hawkins (2005) and can be more flexible in handling linear trends of the process. Compared to the method proposed by Tsiamyrtzis and Hawkins (2005), the advantages of our method are two-folds. Firstly, the performance of our method is more robust to parameter misspecification and requires less knowledge of the process to make accurate estimations. Secondly, the resulted posterior inference of the process mean has a significantly reduced number of mixtures, leading to substantially save of computational and memory cost. The other type of change detection problems studied in this thesis concentrates on analysis of a set of longitudinal or functional data, which is discussed in Chapter 4. In particularly, we focus on the outlier detection for functional data, where the outlier is defined as a curve that is generated from a different process compared to normal curves. Based on the use of data depth, we propose two new depth notions, the weighted band depth and the localized weighted band depth for detecting various outliers. Our main contribution is proposing a new idea called the shape distance, which makes our methods particularly effective in detecting outliers that have different shapes with normal curves.

Twenty-two million US citizens are exposed to hazardous noise at work each year, putting them at risk for noise induced hearing loss. Noise induced hearing loss is preventable, cumulative, and irreversible with net economic impact estimated at $123 billion. While agencies such as the Occupational Safety and Health Administration have regulations in place to reduce noise induced hearing loss, these regulations are rarely enforced for agricultural workers. These workers have a low rate of hearing protection usage, with several studies finding that almost half of farmers never use hearing protection devices. Additionally, farmers have twice the hearing loss in higher frequencies and three times in mid-range frequencies than non-farmers. Use of hearing protection can reduce noise induced hearing loss, and agricultural workers are interested in increasing their usage. This makes them a promising group to target with a hearing protection intervention. This paper describes a system that combines a smartphone with a USB based noise dosimeter that can read within +/- 2 A-weighted decibels of a Class 2 sound level meter providing daily noise exposure monitoring. This device is worn by the agricultural worker throughout a work day, collecting location, accelerometer, and audio data. The data is then transferred onto the server and presented to the agricultural worker using a locally hosted website, giving personalized data of loud noise exposures that can be understood without the need for a safety specialist. The dosimeter’s data allows the agricultural worker to explore what sound pressure levels they are exposed to and get an estimate of their total noise exposure. The GPS, paired with audio clips of loud noises, allows the agricultural worker to determine what activities put them at risk of noise induced hearing loss, which are good indications of where to place hearing protection devices. The system was tested on a farm, comparing its output with several reference instruments. A-weighted, 1-second averaged sound pressure levels, GPS, and accelerometer data were collected while performing a variety of tasks indoors and outdoors. The smartphone’s external noise dosimeter read within +/- 2 dBA of the Class 2 reference dosimeter 59% of the time. The GPS devices had an average error of sub-4 meters between and the accelerometers had a mean absolute error of less than 0.1 g.

Road hazards have been threatening drivers’ safety. Drivers should perceive road hazards in order to reduce or eliminate risks (Shinar, 2007a). Hazard perception is a cognitive ability which can be improved through practice (Mark S. Horswill, Hill, & Wetton, 2015). Failures in hazard perception can cause adversities (Spainhour, Brill, John Sobanjo, Jerry Wekezer, & Primus Mtenga, 2005). Hence, understanding the cognitive process of hazard perception is important for improving drivers’ reactions and creating human-like autonomous systems. While there is no agreement among researchers about the concept of hazard perception (Baars, 1993; Mica R. Endsley, 1995b; Flach, 1994), most accepted views describe hazard perception as a process (Banbury, 2017). One of the predominant cognitive theories which describes hazard perception as a process is Neisser’s action-perception (Neisser, 1987). Neisser’s model relates hazard perception to mental map of the world and includes limitations of drivers’ memories. The action-perception model alongside other dominant hazard perception theories which describe the phenomenon can only be valid if drivers have mental models of hazards (Mica R. Endsley, 2000). Mental models which can facilitate the hazard perception process cannot be described by dominant theories that describe mental models as cognitive structural and functional models (Preece et al., 1994) In fact, the mental model which can be used for hazard perception and risk anticipation should explain how hazard can fail a traffic system and cause crash. Thus, the first experiment explores the existence of such a mental model by using Schema World Action Research Method (SWARM) cognitive probes. The result proves the existence of subjective mental models of hazards. Mental models are subjective, and drivers can have different preference of actions to a hazard accordingly. Automation of driving requires drivers to monitor the Autonomous vehicles (AVs) behaviors and takeover the control when needed (Banks & Stanton, 2017). For a successful monitoring of AVs drivers should build an appropriate level of trust in systems according to systems reliability (Lee & See, 2004). AVs should produce acceptable results to be considered reliable and drivers should develop accurate mental models of AVs actions and limitations (Walliser, 2011). Drivers will evaluate systems actions by their subjective mental models including mental models of hazards. However, AVs are designed by their designers and have limitations in replicating human-like reactions to hazards (Don Norman, 2016). The second experiment investigates how discrepancies between design models of AVs and drivers’ mental models including mental models of hazards can influence drivers’ trust in automation. In this part, naturalistic method and a Tesla S is used and participants are interviewed after five days of driving by using advanced automated systems. Results show that drivers use their mental models of hazards to predict hazardous scenarios and takeover the control before hazards materialize. Additionally, findings reveal how complexity of the system can result in function confusion, mode confusion, and misinterpreting AVs capabilities, which can result in abuse of automated systems. Results reveal that there is a need for adequate training of drivers on autonomous and advanced systems.

The increasing use of in-vehicle information systems (IVISs), such as navigation devices and MP3 players, can jeopardize safety by introducing distraction into driving. One way to address this problem is to develop distraction mitigation systems, which adapt IVIS functions according to driver state. In such a system, correctly identifying driver distraction is critical, which is the focus of this dissertation. Visual and cognitive distractions are two major types of distraction that interfere with driving most compared with other types. Visual and cognitive distraction can occur individually or in combination. The research gaps in detecting driver distraction are that the interactions of visual and cognitive distractions have not been well studied and that no accurate algorithm/strategy has been developed to detect visual, cognitive, or combined distraction. To bridge these gaps, the dissertation fulfilled three specific aims. The first aim demonstrated the layered algorithm developed based on data mining methods could improve the detection of cognitive distraction from my previous studies. The second aim developed estimation algorithms for visual distraction and demonstrated a strong relationship of the estimated distraction with the increased risk of real crashes using the naturalistic data. The third objective examined the interaction of visual and cognitive distractions and developed an effective strategy to identify combined distraction. Together these aims suggest that driver distraction can be detected from performance indicators using appropriate quantitative methods. Data mining techniques represent a promising category of methods to construct such detection algorithms. When combined in a sequential way, visual distraction dominates the effects of distraction while cognitive distraction reduces the overall impairments of distraction on driver performance. Therefore, it is not necessary to detect cognitive distraction if visual distraction is present. These approaches to detecting distraction can be also generalized to estimate other performance impairments, such as driver fatigue.

In this thesis, there are generally three contributions to the Ranking and Selection problem in discrete-event simulation area. Ranking and selection is an important problem when people want to select single or multiple best designs from alternative pool. There are two different types in discrete-event simulation: terminating simulation and steady-state simulation. For steady-state simulation, there is an initial trend before the data output enters into the steady-state, if we cannot start the simulation from steady state. We need to remove the initial trend before we use the data to estimate the steady-state mean. Our first contribution regards the application to eliminate the initial trend/initialization bias. In this thesis, we present a novel solution to remove the initial trend motivated by offline change detection method. The method is designed to monitor the cumulative absolute bias from the estimated steady-state mean. Experiments are conducted to compare our procedure with other existing methods. Our method is shown to be at least no worse than those methods and in some cases much better. After removing the initialization bias, we can apply a ranking and selection procedure for the data outputs from steady-state simulation. There are two main approaches to ranking and selection problem. One is subset selection and the other one is indifference zone selection. Also by employing directed graph, some single-best ranking and selection methods can be extended to solve multi-best selection problem. Our method is designed to solve multi-best ranking and selection. And in Chapter 3, one procedure for ranking and selection in terminating simulation is extended based full sequential idea. It means we compare the sample means among all systems in contention at each stage. Also, we add a technique to do pre-selection of the superior systems at the same time of eliminating inferior systems. This can accelerate the speed of obtaining the number of best systems we want. Experiments are conducted to demonstrate the pre-selection technique can save observation significantly compared with the procedure without it. Also compared with existing methods, our procedure can save significant number of observations. We also explore the effect of common random number. By using it in the simulation process, more observations can be saved. The third contribution of this thesis is to extend the procedure in Chapter 3 for steady-state simulation. Asymptotic variance is employed in this case. We justify our procedure in asymptotic point of view. And by doing extensive experiments, we demonstrate that our procedure can work in most cases when sample size is finite

Perceptually challenging driving environments pose a particular threat of motor vehicle crashes to elderly drivers. Augmented reality (AR) cueing is a promising technology to mitigate risk by directing a driver's attention to roadway hazards. The objective of this study was to evaluate the effectiveness of AR cues in improving driver safety in older drivers who are at increased risk for a crash due to age-related cognitive impairment. Twenty elderly (Mean= 73 years, SD= 5), licensed drivers with a range of cognitive abilities measured by a speed of processing (SOP) composite participated in a 36-mile (1 hour) drive in an interactive, fixed-base driving simulator. Each participant received AR cues to potential roadside hazards in three of six, straight, 6-mile-long-rural roadway segments. AR cueing was evaluated using response time and response rate for detecting potentially hazardous events (e.g. pedestrian alongside road), detection accuracy for non-target objects (e.g. recreational sign), and ability to maintain a consistent distance behind a lead vehicle. AR cueing aided the detection of pedestrians and warning signs, but not vehicles. Response times decreased for AR-cued warning signs. AR cues did not impair perception of non-target objects or the ability to maintain consistent distance behind a lead vehicle, including for drivers with lower SOP capacity. AR cues show promise for improving older driver safety by increasing hazard detection likelihood without interfering with secondary task performance.

The success and effectiveness of modern lean manufacturing concepts requires robust and highly reliable machinery. In this thesis, we develop several simulation studies to compare the performance of a several manufacturing systems under different maintenance polices. The main focus of this work is to compare traditional time-based maintenance policies with degradation-based predictive maintenance policies that utilize real-time sensory information to assist in decisions regarding maintenance management and component replacement. The simulation studies developed in this thesis demonstrate the benefits of using sensor-based degradation models to predict failure.

It is widely known that young drivers are overrepresented in the crash data for a variety of reasons such as risk perception and acceptance, age, gender, experience, exposure, and social contexts. The current mitigations implemented to address the issue consist mainly of graduated driver's licenses and parental involvement programs. However, as technology begins to find its way into transportation in the form of advanced driver assistance systems, there is a need to understand whether these technologies will be a benefit or a detriment to young novice drivers. The present study investigates the reaction of young novice drivers to a control intervention lane departure warning. The results showed less urgent reactions to the warning from novice drivers as compared to their more experienced counterparts. However, no differences in perceptions of the system were found between the novice and experienced groups. Nonetheless, young novice males were found to have derogated performance compared to their novice female peers as well as older more experienced male drivers. This study is a small stepping stone in the necessary investigations of effects of advanced driver assistance systems on young novice drivers and the associated young driver safety epidemic.

In quality control applications, the most basic tasks are monitoring and fault diagnosis. Monitoring results determines if diagnosis is required, and conversely, diagnostic results aids better monitoring design. Quality monitoring and fault diagnosis are closely related but also have significant difference. Essentially. monitoring focus on online changepoint detection, whilst the primary objective of diagnosis is to identify fault root causes as an offline method. Several critical problems arise in the research of quality control: firstly, whether process monitoring is able to distinguish systematic or assignable faults and occasional deviation; secondly, how to diagnose faults with coupled root causes in complex manufacturing systems; thirdly, if the changepoint and root causes of faults can be diagnosed simultaneously. In Chapter 2, we propose a novel Bayesian statistical process control method for count data in the presence of outliers. That is, we discuss how to discern out of control status and temporary abnormal process behaviors in practice, which is incapable for current SPC methodologies. In this work, process states are modeled as latent variables and inferred by the sequential Monte Carlo method. The idea of Rao-Blackwellization is employed in the approach to control detection error and computational cost. Another contribution of this work is that our method possesses self-starting characteristics, which makes the method a more robust SPC tool for discrete data. Sensitivity analysis on monitoring parameter settings is also implemented to provide practical guidelines. In Chapter 3, we study the diagnosis of dimensional faults in manufacturing. A novel Bayesian variable selection oriented diagnostic framework is proposed. Dimensional fault sources are not explicitly measurable; instead, they are connected with dimensional measurements by a generalized linear mixed effect model, based on which we further construct a hierarchical quality-fault model to conduct Bayesian inference. A reversible jump Markov Chain Monte Carlo algorithm is developed to estimate the approximate posterior probability of fault patterns. Such diagnostic procedure is superior over previous studies since no numeric regularization is required for decision making. The proposed Bayesian diagnosis can further lean towards sparse fault patterns by choosing suitable priors, in order to handle the challenge from the diagnosability of faults. Our work considers the diagnosability in building dimensional diagnostic methodologies. We explain that the diagnostic result is trustworthy for most manufacturing systems in practice. The convergence analysis is also implemented, considering the trans-dimensional nature of the diagnostic method. In Chapter 4 of the thesis, we consider the diagnosis of multivariate linear profile models. We assume liner profiles as piece-wise constant. We propose an integrated Bayesian diagnostic method to answer two problems: firstly, whether and when the process is shifted, and secondly, in which pattern the shift occurs. The method can be applied for both Phase I and Phase II needs. For Phase I diagnosis, the method is implemented with no knowledge of in control profiles, whereas in Phase II diagnosis, the method only requires partial observations. To identify exactly which profile components deviate from nominal value, the variability of the value of profile components is marginalized out through a fully Bayesian approach. To address computational difficulty, we implement Monte Carlo Method to alternatively inspect between spaces of changepoint positions and fault patterns. The diagnostic method is capable to be applied under multiple scenarios.

In an attempt to improve the process of supplying coal by way of water to Progress Energyâs Crystal River power plant, a simulation-based technique was developed to find the best schedule of coal barges. The technique uses discrete event simulation principles to find the best solution based on two criteria: minimal demurrage cost and maximal coal tons moved. Many factors are taken into account including channel capacity, tide dependencies, weather delays, periods of scheduled down time, and percentage of trips to each coal terminal. The same technique is also used for long range planning in the decisions of capital allocation of equipment, barge contracts, and coal supplier contracts. A Graphical User Interface coupled with Visual Basic .Net (VB .Net) code is used to implement the approach in a user-friendly and maintainable environment.