To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry redundant sensors due to size, weight, and power (SWaP) constraints. Faults in these sensors can cause undesired behavior, including system instability. Thus, detection of faults in these non-redundant sensors is of paramount importance.The problem of detecting sensor faults in non-redundant sensors on board autonomous aircraft is non-trivial. Factors that make development of a solution difficult include both an inability to perfectly characterize systems and sensors as well as the SWaP constraints inherent with small UAV. An additional challenge is the ability of a fault-detection method to strike a balance between false-alarm rate and detection rate.This thesis explores two model-based methods of fault-detection for non-redundant sensors, a Kalman filter based method and a particle filter based method. The Kalman filter based method employs tests of mean and covariance on the normalized innovation sequence to detect faults, while the particle filter based method uses a function of the average particle weights.The Kalman filter based approach was implemented in real time on board an autonomous rotorcraft using an extended Kalman Filter (EKF). Faults tested included varied levels of bias, drift, and increased noise. Metrics included false-alarm rate, detection rate, and delay to detection. The particle filter based approach was implemented on a simulated system. This was then compared with an implementation of the EKF based approach for the same system. The same fault types and metrics were also used for these tests.The EKF based method of fault-detection performed well onboard the autonomous rotorcraft and should be generalizable to other systems for which an EKF or Kalman filter can be implemented. The theory indicates that the particle filter based algorithm should have performed better, though the simulations showed poor detection characteristics in comparison to the Kalman filter based method. Future work should be performed to explore improvements to the particle filter based method.

High-cycle thermal fatigue arising from turbulent mixing of non-isothermal flows is a key issue associated with the life management and extension of nuclear power plants. The induced thermal loads and damage are not fully understood yet. With the aim of acquiring extensive data sets for the validation of codes modeling thermal mixing at reactor conditions, thermocouples recorded temperature time series at the inner surface of a vertical annular volume where turbulent mixing occurred. There, a stream at either 333 K or 423 K flowed upwards and mixed with two streams at 549 K. Pressure was set at 72E5 Pa. The annular volume was formed between two coaxial stainless-steel tubes. Since the thermocouples could only cover limited areas of the mixing region, the inner tube to which they were soldered was lifted, lowered, and rotated around its axis, to extend the measurement region both axially and azimuthally. Trends, which stemmed from the variation of the experimental boundary conditions over time, were subtracted from the inner-surface temperature time series collected. An estimator assessing intensity and inhomogeneity of the mixing process in the annulus was also computed. In addition, a frequency analysis of the detrended inner-surface temperature time series was performed. In the cases examined, frequencies between 0.03 Hz and 0.10 Hz were detected in the subregion where mixing inhomogeneity peaked. The uncertainty affecting such measurements was then estimated. Furthermore, a preliminary assessment of the radial heat flux at the inner surface was conducted. / <p>QC 20161116</p>

In this study, I examine the employment effects on average earnings and duration to work during a 45 month period, after receiving social assistance (SA) in October 2019, compared to receiving unemployment insurance (UI) the same month. A distinction is made between two treatment groups; receiving SA in addition to UI (treatment I) and receiving SA (treatment II). Using propensity score matching (PSM), I estimate the average treatment effects on the treated on earnings, as well as duration to work by using the Kaplan-Meier survival estimator with the matched observations. I use newly released Swedish administrative micro level data of individuals’ monthly labour market status (BAS) between 2020-2023, from Statistics Sweden. During this thesis process, where Statistics Sweden allowed me data access, I was allowed an additional year of data, for 2019. Results showed that the inflow of SA recipients in October 2019, on average had 25.5 percent lower earnings between November 2019-July 2023, compared to the inflow of UI recipients the same month. In addition, the inflow of SA recipients in October 2019, on average spent 4 months longer in unemployment, compared to those receiving UI the same month. However, results were insignificant when comparing effects between the inflow of those receiving SA in addition to UI in October 2019 with the inflow of UI recipients the same month. Results for this group were insignificant for both employment outcomes; average earnings and duration to work.

In this study, I examine the employment effects on average earnings and duration to work during a 45 month period, after receiving social assistance (SA) in October 2019, compared to receiving unemployment insurance (UI) the same month. A distinction is made between two treatment groups; receiving SA in addition to UI (treatment I) and receiving SA (treatment II). Using propensity score matching (PSM), I estimate the average treatment effects on the treated on earnings, as well as duration to work by using the Kaplan-Meier survival estimator with the matched observations. I use newly released Swedish administrative micro level data of individuals’ monthly labour market status (BAS) between 2020-2023, from Statistics Sweden. During this thesis process, where Statistics Sweden allowed me data access, I was allowed an additional year of data, for 2019. Results showed that the inflow of SA recipients in October 2019, on average had 25.5 percent lower earnings between November 2019-July 2023, compared to the inflow of UI recipients the same month. In addition, the inflow of SA recipients in October 2019, on average spent 4 months longer in unemployment, compared to those receiving UI the same month. However, results were insignificant when comparing effects between the inflow of those receiving SA in addition to UI in October 2019 with the inflow of UI recipients the same month. Results for this group were insignificant for both employment outcomes; average earnings and duration to work.

Performing health surveys in developing countries and humanitarian emergencies can be challenging work because the resources in these settings are often quite limited and information needs to be gathered quickly. The Expanded Program on Immunization (EPI) sampling method provides one way of selecting subjects for a survey. It involves having field workers proceed on a random walk guided by a path of nearest household neighbours until they have met their quota for interviews. Due to its simplicity, the EPI sampling method has been utilized by many surveys. However, some concerns have been raised over the quality of estimates resulting from such samples because of possible selection bias inherent to the sampling procedure. We present an algorithm for obtaining the probability of selecting a household from a cluster under several variations of the EPI sampling plan. These probabilities are used to assess the sampling plans and compute estimator properties. In addition to the typical estimator for a proportion, we also investigate the Horvitz-Thompson (HT) estimator, an estimator that assigns weights to individual responses. We conduct our study on computer-generated populations having different settlement types, different prevalence rates for the characteristic of interest and different spatial distributions of the characteristic of interest. Our results indicate that within a cluster, selection probabilities can vary largely from household to household. The largest probability was over 10 times greater than the smallest probability in 78% of the scenarios that were tested. Despite this, the properties of the estimator with equally weighted observations (EQW) were similar to what would be expected from simple random sampling (SRS) given that cases of the characteristic of interest were evenly distributed throughout the cluster area. When this was not true, we found absolute biases as large as 0.20. While the HT estimator was always unbiased, the trade off was a substantial increase in the variability of the estimator where the design effect relative to SRS reached a high of 92. Overall, the HT estimator did not perform better than the EQW estimator under EPI sampling, and it involves calculations that may be difficult to do for actual surveys. Although we recommend continuing to use the EQW estimator, caution should be taken when cases of the characteristic of interest are potentially concentrated in certain regions of the cluster. In these situations, alternative sampling methods should be sought. / Thesis / Master of Science (MSc)

This dissertation concerns with the formulation of an improved multi-target multi-Bernoulli (MeMBer) filter and the use of the joint multi-target (JoM) estimator in an effective and efficient manner for a specific implementation of MeMBer filters. After reviewing random finite set (RFS) formalism for multi-target tracking problems and the related Bayes estimators the major contributions of this dissertation are explained in detail. The second chapter of this dissertation is dedicated to the analysis of the relationship between the multi-Bernoulli RFS distribution and the MeMBer corrector. This analysis leads to the formulation of an unbiased MeMBer filter without making any limiting assumption. Hence, as opposed to the popular cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter, the proposed MeMBer filter can be employed under the cases when sensor detection probability is moderate to low. Furthermore, a statistical refinement process is introduced to improve the stability of the estimated cardinality of targets obtained from the proposed MeMBer filter. The results from simulations demonstrate the effectiveness of the improved MeMBer filter. In Chapters III and IV, the Bayesian optimal estimators proposed for the RFS based multi-target tracking filters are examined in detail. First, an optimal solution to the unknown constant in the definition of the JoM estimator is determined by solving a multi-objective optimization problem. Thus, the JoM estimator can be implemented for tracking of a Bernoulli target using the optimal joint target detection and tracking (JoTT) filter. The results from simulations confirm assertions about its performance obtained by theoretical analysis in the literature. Finally, in the third chapter of this dissertation, the proposed JoM estimator is reformulated for RFS multi-Bernoulli distributions. Hence, an effective and efficient implementation of the JoM estimator is proposed for the Gaussian mixture implementations of the MeMBer filters. Simulation results demonstrate the robustness of the proposed JoM estimator under low-observable conditions. / Thesis / Doctor of Philosophy (PhD)

No description available.

Aircraft flight test data are processed by optimal estimation programs to estimate the aircraft state trajectory (3 DOF) and to identify the unknown parameters, including constant biases and scale factor of the measurement instrumentation system. The methods applied in processing aircraft flight test data are the iterative extended Kalman filter/smoother and fixed-point smoother (IEKFSFPS) method and the two-step estimator (TSE) method. The models of an aircraft flight dynamic system and measurement instrumentation system are established. The principles of IEKFSFPS and TSE methods are derived and summarized, and their algorithms are programmed with MATLAB codes. Several numerical experiments of flight data processing and parameter identification are carried out by using IEKFSFPS and TSE algorithm programs. Comparison and discussion of the simulation results with the two methods are made. The TSE+IEKFSFPS combination method is presented and proven to be effective and practical. Figures and tables of the results are presented.

No description available.

No description available.