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Articles 1 - 3 of 3
Full-Text Articles in Engineering
Developing Leading And Lagging Indicators To Enhance Equipment Reliability In A Lean System, Dhanush Agara Mallesh
Developing Leading And Lagging Indicators To Enhance Equipment Reliability In A Lean System, Dhanush Agara Mallesh
Masters Theses
With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput.
The goal of this thesis is to predict …
An Analog Cmos Particle Filter, Trevor Watson
An Analog Cmos Particle Filter, Trevor Watson
Masters Theses
Particle filters are used in a variety of image processing and machine learning applications. Their main use in these applications is to gather information about a system of objects, by using partial or noisy observations collected from sensors. These observations are used to associate points of interest in the observations with objects and maintain this association through a series of observations.
In this paper I will investigate the performance of a particle filter implemented in 130nm analog CMOS hardware. The design goal of the particle filter is low-microwatt power consumption. Using analog hardware, rather than digital ASICs or CPUs I …
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan
Masters Theses
Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.
State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to …