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Full-Text Articles in Engineering
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 …
Temporal Decomposition For Multi-Interval Optimization In Power Systems, Farnaz Safdarian
Temporal Decomposition For Multi-Interval Optimization In Power Systems, Farnaz Safdarian
LSU Doctoral Dissertations
Large optimization problems are frequently solved for power systems operation and analysis of electricity markets. Many of these problems are multi-interval optimization with intertemporal constraints. The size of optimization problems depends on the size of the system and the length of the considered scheduling horizon. Growing the length of the scheduling horizon increases the computational burden significantly and might make solving the problem in a required time span impossible. Many simplifications and approximation techniques are applied to reduce the computational complexity of multi-interval scheduling problems and make them solvable in a reasonable time span. Geographical decomposition is presented in the …
Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer
Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer
LSU Doctoral Dissertations
In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …