Entry resistance in smoke detectors was investigated using experimental and analytical approaches. The experimental work consisted of measuring velocity inside the sensing chamber of smoke detectors with a two-component Laser Doppler Velocimeter and exposing addressable smoke detectors to four different aerosol sources. The velocity measurements and exposure tests were performed in NIST's Fire Emulator / Detector Evaluator under steady state flow conditions in the range of 0.08 to 0.52 m/s. The addressable detectors were a photoelectric and an ionization detector. A specially constructed rectangular detector model was also used for the interior velocity measurements in order to have geometry compatible with numerical approaches, such as computational fluid dynamics modeling or a two-dimensional analytical solution. The experimental data was used to investigate the fluid mechanics and mass transport processes in the entry resistance problem. An inlet velocity boundary condition was developed for the smoke detectors evaluated in this study by relating the external velocity and detector geometry to the internal velocity by way of a resistance factor. Data from the exposure tests was then used to characterize the nature of aerosol entry lag and sensor response. The time to alarm for specific alarm points was determined in addition to performing an exponential curve fit to obtain a characteristic response time. A mass transport model for smoke detector response was developed and solved numerically. The mass transport model was used to simulate the response time data collected in the experimental portion of this study and was found, in general, to underestimate the measured response time by up to 20 seconds. However, in the context of wastebasket fire scenario the amount of underprediction in the model is 5 seconds or less which is within the typically polling interval time of 5 to 10 seconds for an addressable system. Therefore, the mass transport model results developed using this proposed engineering framework show promise and are within the expected uncertainty of practical fire protection engineering design situations.

Consumer sentiment has the ability to provide researchers with many avenues to test existing Finance and Economic theories. Chapter 1 introduces the issues that I seek to explore within the area of Behavioral Finance. Chapter 2 utilizes thirty years of consumer sentiment data to explore extant economic theories and hypotheses. In particular, I study the Prospect Theory and the Life Cycle Investment Hypothesis. In addition, I also study how changes in consumer sentiment can foretell future stock returns for firms in different industries and of different sizes. By studying how individuals of different ages display optimism and pessimism through consumer sentiment surveys, I am able to contribute to the literature by shedding additional light on just how the important age is with respect to a person's economic outlook. One particular phenomenon that I discuss in this chapter is downside risk. I will provide further support to the existing literature which shows that gains and losses are not viewed equally by individuals. To account for this discrepancy, this paper models the time series relationship between consumer sentiment and stock returns using asymmetric response models. Chapter 3 builds upon the previous chapter's findings by using consumer sentiment to explore if this index can forecast housing market variables such as changes in home sales and home prices. Given the recent financial market turmoil that stemmed from the U.S. housing market debacle, this chapter is timely. Using widely cited housing indices, I explore regional differences in the U.S. housing market and how the sentiment of local consumers can possibly affect their housing markets. I also include analyses in which the age of the consumer is accounted for to see if evidence of the Life Cycle Investment Hypothesis emerges. This theory postulates that younger individuals are more likely to demand housing as a financial asset and if this were true, I hypothesize that changes in younger individuals' sentiment would have more forecasting power with respect to future housing sales and price changes. Lastly, I conclude this dissertation with Chapter 4 which includes additional discussions of the issues studied.

Differential item functioning (DIF) is a psychometric issue routinely considered in educational and psychological assessment. However, it has not been studied in the context of a recently developed componential statistical model, the model with internal restrictions on item difficulty (MIRID; Butter, De Boeck, & Verhelst, 1998). Because the MIRID requires test questions measuring either single or multiple cognitive processes, it creates a complex environment for which traditional DIF methods may be inappropriate. This dissertation sought to extend the MIRID framework to detect DIF at the item-group level and the individual-item level. Such a model-based approach can increase the interpretability of DIF statistics by focusing on item characteristics as potential sources of DIF. In particular, group-level DIF may reveal comparative group strengths in certain secondary constructs. A simulation study was conducted to examine under different conditions parameter recovery, Type I error rates, and power of the proposed approach. Factors manipulated included sample size, magnitude of DIF, distributional characteristics of the groups, and the MIRID DIF models corresponding to discrete sources of differential functioning. The impact of studying DIF using wrong models was investigated. The results from the recovery study of the MIRID DIF model indicate that the four delta (i.e., non-zero value DIF) parameters were underestimated whereas item locations of the four associated items were overestimated. Bias and RMSE were significantly greater when delta was larger; larger sample size reduced RMSE substantially while the effects from the impact factor were neither strong nor consistent. Hypothesiswise and adjusted experimentwise Type I error rates were controlled in smaller delta conditions but not in larger delta conditions as estimates of zero-value DIF parameters were significantly different from zero. Detection power of the DIF model was weak. Estimates of the delta parameters of the three group-level DIF models, the MIRID differential functioning in components (DFFc), the MIRID differential functioning in item families (DFFm), and the MIRID differential functioning in component weights (DFW), were acceptable in general. They had good hypothesiswise and adjusted experimentwise Type I error control across all conditions and overall achieved excellent detection power. When fitting the proposed models to mismatched data, the false detection rates were mostly beyond the Bradley criterion because the zero-value DIF parameters in the mismatched model were not estimated adequately, especially in larger delta conditions. Recovery of item locations and component weights was also not adequate in larger delta conditions. Estimation of these parameters was more or less affected adversely by the DIF effect simulated in the mismatched data. To study DIF in MIRID data using the model-based approach, therefore, more research is necessary to determine the appropriate procedure or model to implement, especially for item-level differential functioning.

Total pressure non-uniformities in an axial flow compressor can contribute to losses in aerodynamic operability through a reduction in stall margin, pressure rise and mass flow, and to loss of structural integrity through means of high cycle fatigue (HCF). HCF is a primary mechanism of blade failure caused by vibrations at levels exceeding material endurance limits. Previous research has shown total pressure distortions to be the dominant HCF driver in aero engines, and has demonstrated the damaging results of total pressure distortion induced HCF on first stage fan and compressor blade rows [Manwaring et al., 1997]. It is, however, also of interest to know how these distortion patterns propagate through a rotor stage and impact subsequent downstream stages and engine components. With current modeling techniques, total pressure distortion magnitudes can be directly correlated to induced blade vibratory levels and modes. The ability to predict downstream distortion patterns then allows for the inference of blade vibratory response of downstream blades to inlet distortion patterns. Given a total pressure distortion excitation entering a blade row, the nonlinear Volterra series can serve as a predictor of the downstream total pressure profile and therefore provide insight into the potential for HCF in downstream blade rows. This report presents the adaption of nonlinear Volterra theory to the prediction of the transport of non-uniform total pressure distortions through an axial flow compressor. The use of Volterra theory in nonlinear system modeling relies on the knowledge of Volterra kernels, which capture the behavior of a system's response characteristics. Here an empirical method is illustrated for identifying these kernels based on total pressure distortion patterns measured both upstream and downstream of a transonic rotor of modern design. A Volterra model based on these kernels has been applied to the prediction of distortion transfer at new operating points of the same rotor with promising results. Methods for improving Volterra predictions by training Volterra kernels along individual streamlines and normalizing total pressure data sets by physics-based parameters are also investigated. / Master of Science

If customers respond differently to a campaign, it is worthwhile to find those customers who respond most positively and direct the campaign towards them. This can be done by using so called incremental response analysis where respondents from a campaign are compared with respondents from a control group. Customers with the highest increased response from the campaign will be selected and thus may increase the company’s return. Incremental response analysis is applied to the mobile operator Tres historical data. The thesis intends to investigate which method that best explain the incremental response, namely to find those customers who give the highest incremental response of Tres customers, and what characteristics that are important.The analysis is based on various classification methods such as logistic regression, Lassoregression and decision trees. RMSE which is the root mean square error of the deviation between observed and predicted incremental response, is used to measure the incremental response prediction error. The classification methods are evaluated by Hosmer-Lemeshow test and AUC (Area Under the Curve). Bayesian logistic regression is also used to examine the uncertainty in the parameter estimates.The Lasso regression performs best compared to the decision tree, the ordinary logistic regression and the Bayesian logistic regression seen to the predicted incremental response. Variables that significantly affect the incremental response according to Lasso regression are age and how long the customer had their subscription.

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots.

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Methods for predicting weld properties based on welding parameters are needed in friction stir welding (FSW). FSW is a joining process in which the resulting properties depend on the thermal cycle of the weld. Buckingham's Pi theorem and heat transfer analysis was used to identify dimensionless parameters relevant to the FSW process. Experimental data from Al 7075 and HSLA-65 on five different backing plate materials and a wide range of travel speeds and weld powers was used to create a dimensionless, empirical model relating critical weld parameters to the peak temperature rise and cooling rate of the weld. The models created have R-squared values greater than 0.99 for both dimensionless peak temperature rise and cooling rate correlations. The model can be used to identify weld parameters needed to produce a desired peak temperature rise or cooling rate. The model can also be used to explore the relative effects of welding parameters on the weld thermal response.

Ceramic-matrix composites of alumina and silicon carbide whiskers have recently found novel commercial application as electromagnetic absorbers. However, a detailed understanding of how materials issues influence the composite electrical response, which underpins this application, has been absent until now. In this project, such composites were electrically measured over a wide range of conditions and modeled in terms of various aspects of the microstructure in order to understand how they work. For this purpose, three types of composites were made by different methods from the same set of ceramic powder blends loaded with different volume fractions of whiskers. In doing so, the interfaces between whiskers, the preferred orientations of whiskers, and the structure of electrically-connected whisker clusters were varied; the whisker aspect-ratio distributions were the same for all methods. At the electrode interfaces, Schottky barriers at the junctions of the electrically-percolating wide-bandgap semiconductor whiskers on the surface were responsible for a significant portion of the total measured impedance. The associated electrical response was studied on the microscopic and macroscopic level, and the gap between these different scales was bridged. Also, a modeling approach was developed for the non-linear behavior of the composite which results from these barriers. In regards to the whiskers within the composite bulk, the effects of various factors on the wide-band frequency dependence of the dielectric response and dc conductivity were explained and contextualized for the electromagnetic absorber application. Such factors include whisker preferred orientation, electrical percolation and cluster structure, the interfaces between electrically-connected SiC whiskers, and porosity. A quantitative correlation between the anisotropy of the microstructure and that of the conductivity was found, and was understood in terms of the interfacial SiC-Al2O3-SiC conduction mechanism. This behavior was shown to differ from the behavior commonly observed for other disordered mixtures of relatively conductive particles dispersed inside insulating polymer hosts. A description of this new mechanism was developed based on an observed correlation between the temperature dependencies of the static and radio-frequency electrical responses. Also, the aforementioned non-linear response model was expanded upon to describe conduction through and across electrically-percolated clusters. The model demonstrates how loading and interface behavior influence the topology and the strength of the non-linear response of the clusters.

Methods used for correction and modeling of frequency response of sound devices are discussed in this paper. Besides classic methods of digital filter design, more advanced and complex numerial methods are reviewed, Prony and Steiglitz-McBride in particular. This paper focuses on structure utilizing parallel sections of second-order IIR filters. Methods for calculating coefficients of this structure are presented and later implemented. For selected method, utilizing dual frequency warping, an interative algorithm for automatic calculation of parameters necessary to filter design is implemented - so called Particle Swarm Optimization. Six ways of evaluation filter design precision are presented and the results are compared. Functions realizing filter design are implemented in C++, MATLAB and Python. A VST module simulating the filter in real time is also provided.