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Full-Text Articles in Physical Sciences and Mathematics
Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu
Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu
LSU Doctoral Dissertations
In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …
Distributed Load Testing By Modeling And Simulating User Behavior, Chester Ira Parrott
Distributed Load Testing By Modeling And Simulating User Behavior, Chester Ira Parrott
LSU Doctoral Dissertations
Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system …
Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani
Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani
LSU Doctoral Dissertations
The safety of highway-railroad grade crossings (HRGC) is still an issue in the United States of America (USA). The grade crossing is where a railroad crosses a road at the same level without any over or underpass. To improve the safety of crossings, the crossings’ condition should be explored from several aspects such as engineering design (speed limit, warning signs, etc.), road condition (number of lanes, surface markings, etc.), rail design (the type of track, ballast, etc.), temporal variables (weather, visibility, time of day, lightning, etc.), social variables (population, race, etc.), and last but not least, spatial variables (the type …