Traditional insurance models build on the assumption of independence of risks. One of the main causes of the recent financial crisis, this assumption has facilitated the quantification of risks for decades, but it has often lead to risks' under-estimation and as a result under-pricing. Importantly, one of the prime pillars of the novel concept of Enterprise Risk Management is the requirement that insurance companies have a clear understanding of risks' interconnections within the risk portfolios. However, modeling dependence is not an easy call.
Machine learning is the concept where a computer can be trained to recognize data and predict future outcomes based on the trends that exist in the data. This method of analysis has not been used on engine data, specifically in-line oil. Oil is an engines lifeblood and a lot of data can be collected and engine health can be predicted based on these measurements. This project aims to deploy machine learning concepts in the area of engine failure prediction. A special sensor equipped with the machine learning algorithm will be able to report all vital signs of an engine in a matter of minutes.
Due to the current economic downturn, especially the lower crude oil price, the drilling success rate become the most important goal for any oil/gas company. For a start-up company, any failure in drilling will be a disaster. To this end, the Deep Treasure Corp wishes that through the combination of mature hydrocarbon prediction techniques and new research results in seismic inversion, the success rate of hydrocarbon prediction, the theoretical basis for well placement can be provided in Roncott field, which will improve the success rate in drilling.
InteliRain Inc. has developed an effective sprinkler system producing excellent uniformity water distribution for regular or irregular areas but using 30% less water compared to most existing standard industry system. However, the performance of the InteliRain system deteriorates rapidly when it is tested in an open field with wind effect. In this project, the intern will develop a mathematical model and computational algorithm to simulate the InteliRain system for cases with wind effect.
A critical issue in the oil and gas industry is to quantify the composition of fluids flowing back from the hydraulic fracturing process. This quantification is usually carried out by a manual process (frequently via a visual test) to estimate the water and oil produced from a well flow back process. A sample of these onsite tests are sent to laboratories for chemical analysis. This process has been the status quo for decades. This approach is manual, prone to error, and does not lend itself to sophisticated real time analysis.
Machine learning is the concept where a computer can be trained to recognize data and predict future outcomes based on the trends that exist in the data. This method of analysis has not been used on engine data, specifically in-line oil. Oil is an engine’s lifeblood and a lot of data can be collected and engine health can be predicted based on these measurements. This project aims to deploy machine learning concepts in the area of engine failure prediction.
Diseases, there are a number of different serotypes that can cause infection. The vaccine is often targeted towards one or some of the serotypes. There is accumulating evidence that when serotype-specific vaccines are used, other non-vaccine serotypes may gain a competitive advantage and spread in larger magnitudes. This has raised the concern of serotype replacement when vaccination is used against a single or several serotypes of a disease. In other words, serotypes that not targeted by the vaccine are able to able to fill the ecological niche left open by the vaccine-targeted serotypes.
Harvesting energy from renewable resources, such as wind and ocean waves, is an important issue facing our world today. With the increase in carbon dioxide levels in the atmosphere, there is a need to move away from nonrenewable resources and to find new methods for capturing energy. Wind turbines operate most efficiently within a narrow band of wind speeds, outside of which the amount of electricity they produce plummets.
Acidification of fetal blood presents one of the greatest risks to the fetus during childbirth. Current monitoring technologies focusing on recording fetal heart rate are poor indicators of fetal stress levels, and provide minimal assistance in clinical decision-making. This is due to a lack of understanding about which features of fetal heart rate best represent blood acid levels.