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Hydraulic Geometry Relationships And Regional Curves For The Inner And Outer Bluegrass Regions Of Kentucky, Ruth Roseann Brockman
Hydraulic Geometry Relationships And Regional Curves For The Inner And Outer Bluegrass Regions Of Kentucky, Ruth Roseann Brockman
University of Kentucky Master's Theses
Hydraulic geometry relationships and regional curves are used in natural channel design to assist engineers, biologists, and fluvial geomorphologists in the efforts undertaken to ameliorate previous activities that have diminished, impaired or destroyed the structure and function of stream systems. Bankfull channel characteristics were assessed for 14 United States Geological Survey (USGS) gaged sites in the Inner Bluegrass and 15 USGS gaged sites in the Outer Bluegrass Regions of Kentucky. Hydraulic geometry relationships and regional curves were developed for the aforementioned regions.
Analysis of the regression relationships showed that bankfull discharge is a good explanatory variable for bankfull parameters such …
3d Reconstruction Using Multi-View Imaging System, Conglin Huang
3d Reconstruction Using Multi-View Imaging System, Conglin Huang
University of Kentucky Master's Theses
This thesis presents a new system that reconstructs the 3D representation of dental casts. To maintain the integrity of the 3D representation, a standard model is built to cover the blind spots that the camera cannot reach. The standard model is obtained by scanning a real human mouth model with a laser scanner. Then the model is simplified by an algorithm which is based on iterative contraction of vertex pairs. The simplified standard model uses a local parametrization method to obtain the curvature information. The system uses a digital camera and a square tube mirror in front of the camera …
Bidirectional Lao* Algorithm (A Faster Approach To Solve Goal-Directed Mdps), Venkata Deepti Kiran Bhuma
Bidirectional Lao* Algorithm (A Faster Approach To Solve Goal-Directed Mdps), Venkata Deepti Kiran Bhuma
University of Kentucky Master's Theses
Uncertainty is a feature of many AI applications. While there are polynomial-time algorithms for planning in stochastic systems, planning is still slow, in part because most algorithms plan for all eventualities. Algorithms such as LAO* are able to find good or optimal policies more quickly when the starting state of the system is known.
In this thesis we present an extension to LAO*, called BLAO*. BLAO* is an extension of the LAO* algorithm to a bidirectional search. We show that BLAO* finds optimal or E-optimal solutions for goal-directed MDPs without necessarily evaluating the entire state space. BLAO* …