This project deals with the development of new mathematical models and solution methods to optimize naval surface ship refit operations. The Naval Surface Ship Work Period Problem (NSWPP) is a highly complex resource-constrained project scheduling problem (RCPSP) with many work orders that are equivalent to small projects.
Machine Learning is advancing at an astounding rate. It is powered by complex models such as deep neural networks (DNNs). These models have a wide range of real-world applications, in fields like Computer Vision, Natural Language Processing, Information Retrieval and others. But Machine Learning is not without some serious limitations and drawbacks. The most serious one is the lack of transparency in their inferences, which works against relying completely in these models and leaves users with little understanding of how particular decisions are made.
Cameras are a fundamental component of modern robotic systems. As robots have become relied upon for safety-critical tasks, the need for robust sensing is apparent. Cameras have a major limitation, compared to other sensors such as LIDAR, in high-dynamic-range environments where lighting conditions rapidly change. These changes can cause visual navigation algorithms to struggle and, in some cases, fail in instances where images become severely under- or overexposed.
Similar to current efforts in the automotive industry, there is a substantial interest in developing fully autonomous trains. One of the key steps towards enabling autonomous operation is being able to accurately estimate train position and speed. In addition to this, better estimates will also increase safety and reduce distance between trains, allowing more frequent trains in peak hours. The current project deals with a method of estimating the velocity and position without using GPS measurements, which is the standard method.
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