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The Relationship Between Task-Induced Stress and Time PerceptionBrosnihan, Annamarie 01 January 2023 (has links) (PDF)
A distortion of time is often reported under the presence of stress or threatening stimuli, for instance motor vehicle accidents or near-death experiences. There is a lack of research on the complexity of time distortion under stress; thus, the present study aimed to explore the relationship between stress and time perception. Given the challenges associated with producing a stress response in a laboratory setting, difficult tasks have been previously used to produce a stress response, such as anagram tasks. However, it remains unknown whether experiencing time pressure while completing a stressful task can also influence time distortion. To investigate this, participants completed either an easy or difficult anagram task and received either an unspecified time limit or no time limit to complete the task. It was hypothesized that participants would experience the greatest distortion of time when the task was difficult, and they were provided an unspecified time limit. Contrary to the hypothesis, we failed to find differences in task performance or time perception across the various conditions, which may be explained by the inability to produce a stress state. While stress manipulation was unsuccessful, the findings suggest utilizing multiple tasks may be more effective at replicating a physiological or psychological stress state. Thus, the results of this study warrant further investigation to examine the relationship between stress, time pressure, and time distortion.
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Transfer learning in laser-based additive manufacturing: Fusion, calibration, and compensationFrancis, Jack 25 November 2020 (has links)
The objective of this dissertation is to provide key methodological advancements towards the use of transfer learning in Laser-Based Additive Manufacturing (LBAM), to assist practitioners in producing high-quality repeatable parts. Currently, in LBAM processes, there is an urgent need to improve the quality and repeatability of the manufacturing process. Fabricating parts using LBAM is often expensive, due to the high cost of materials, the skilled machine operators needed for operation, and the long build times needed to fabricate parts. Additionally, monitoring the LBAM process is expensive, due to the highly specialized infrared sensors needed to monitor the thermal evolution of the part. These factors lead to a key challenge of improving the quality of additively manufactured parts, because additional experiments and/or sensors is expensive. We propose to use transfer learning, which is a statistical technique for transferring knowledge from one domain to a similar, yet distinct, domain, to leverage previous non-identical experiments to assist practitioners in expediting part certification. By using transfer learning, previous experiments completed in similar, but non-identical, domains can be used to provide insight towards the fabrication of high-quality parts. In this dissertation, transfer learning is applied to four key domains within LBAM. First, transfer learning is used for sensor fusion, specifically to calibrate the infrared camera with true temperature measurements from the pyrometer. Second, a Bayesian transfer learning approach is developed to transfer knowledge across different material systems, by modelling material differences as a lurking variable. Third, a Bayesian transfer learning approach for predicting distortion is developed to transfer knowledge from a baseline machine system to a new machine system, by modelling machine differences as a lurking variable. Finally, compensation plans are developed from the transfer learning models to assist practitioners in improving the quality of parts using previous experiments. The work of this dissertation provides current practitioners with methods for sensor fusion, material/machine calibration, and efficient learning of compensation plans with few samples.
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THE EFFECTS OF DIVERSITY INITIATIVES ON THE DISTORTION OF APPLICANT QUALIFICATIONS AND DECISION STANDARDSMoore, Jason S. 31 October 2006 (has links)
No description available.
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Disjoint and Distortion: An Essay in Manifesting ContradictionVaz, Sarah L. 27 October 2014 (has links)
No description available.
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Seeing Scary: Predicting Variation in the Scariness of the Mental Representations of SpidersYoung, Alison Isobel January 2014 (has links)
No description available.
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Rate Distortion Optimization for Interprediction in H.264/AVC Video CodingSkeans, Jonathan P. 30 August 2013 (has links)
No description available.
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Distortion directivity and circuit modeling of a needle array plasma loudspeakerSterba, Ron January 1991 (has links)
No description available.
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Sensitivity Analysis of Casting Distortion and Residual Stress Prediction Through Simulation Modeling and Experimental VerificationRagab, Adham Ezzat 12 May 2003 (has links)
No description available.
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Prediction of Geometric Distortions and Residual Stresses on Heat Treated Hot Rolled RingsGonzalez-Mendez, Jose Luis 16 December 2011 (has links)
No description available.
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Determining the Distributed Karhunen-Loève Transform via Convex Semidefinite RelaxationZhao, Xiaoyu January 2018 (has links)
The Karhunen–Loève Transform (KLT) is prevalent nowadays in communication and
signal processing. This thesis aims at attaining the KLT in the encoders and achieving
the minimum sum rate in the case of Gaussian multiterminal source coding.
In the general multiterminal source coding case, the data collected at the terminals
will be compressed in a distributed manner, then communicated the fusion center
for reconstruction. The data source is assumed to be a Gaussian random vector in
this thesis. We introduce the rate-distortion function to formulate the optimization
problem. The rate-distortion function focuses on achieving the minimum encoding
sum rate, subject to a given distortion. The main purpose in the thesis is to propose a
distributed KLT for encoders to deal with the sampled data and produce the minimum
sum rate.
To determine the distributed Karhunen–Loève transform, we propose three kinds
of algorithms. The rst iterative algorithm is derived directly from the saddle point
analysis of the optimization problem. Then we come up with another algorithm by
combining the original rate-distortion function with Wyner's common information,
and this algorithm still has to be solved in an iterative way. Moreover, we also propose
algorithms without iterations. This kind of algorithms will generate the unknown
variables from the existing variables and calculate the result directly.All those algorithms can make the lower-bound and upper-bound of the minimum
sum rate converge, for the gap can be reduced to a relatively small range comparing
to the value of the upper-bound and lower-bound. / Thesis / Master of Applied Science (MASc)
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