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Full-Text Articles in Computational Engineering
Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson
Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson
Computer Science ETDs
In a time when data is being constantly generated by phones, vehicles, sensor net- works, social media, etc. detecting anomalies with in the data can be very crucial. In cases where we know little prior knowledge about the data, it becomes difficult to extract uncertainty about our results. In this thesis, we will propose a framework in which we can extract uncertainty distributions from data-driven modeling prob- lems. We will show some concrete examples of how to apply framework and provide some insight into what the uncertainty distributions are telling us using High Density Regions (HDRs).
Detection Of Pheromone Laying Event In Foraging Data Of Harvester Ants Using Change Point Analysis Method, Safeeul Bashir Safee
Detection Of Pheromone Laying Event In Foraging Data Of Harvester Ants Using Change Point Analysis Method, Safeeul Bashir Safee
Computer Science ETDs
Communication is an important factor in the foraging performance of social insects, such as ants. During foraging, ants keep track of food sources by using memory (site fidelity) or by communicating through pheromones. Previous field experiments showed that the rate of seed collection depends on the distribution of food in the environment. If food is spatially clustered, then it is beneficial for ants recruit nest mates to collect seeds from large clusters. However, we do not know when the recruitment occurs in natural ant population. To explore this question, we used a power law distribution to arrange seeds in piles …