Company Filing History:
Years Active: 2019
Title: Innovations by Travis Desell in Flight Parameter Prediction
Introduction
Travis Desell is an accomplished inventor based in Grand Forks, ND (US). He has made significant contributions to the field of flight data analysis and prediction through his innovative patents. With a total of 2 patents, Desell's work focuses on utilizing advanced neural networks and predictive models to enhance flight safety and efficiency.
Latest Patents
One of Desell's latest patents is titled "Flight parameter prediction using neural networks." This invention involves a neural network that processes time-series data recorded during flights. The network is trained to predict future measurements of flight parameters by comparing predictive values to actual measured values. This innovative approach allows for real-time adjustments to improve prediction accuracy.
Another notable patent is "Analyzing flight data using predictive models." This invention applies a quadratic least squares model to time-series flight parameter data, deriving mathematical signatures for each flight. By measuring similarities between flights and applying machine-learning algorithms, Desell's method identifies clusters of outliers, enhancing the analysis of flight data.
Career Highlights
Travis Desell is affiliated with the University of North Dakota, where he contributes to research and development in aviation technology. His work has garnered attention for its potential to improve flight safety and operational efficiency. Desell's innovative approach to flight data analysis positions him as a leading figure in the field.
Collaborations
Desell collaborates with talented individuals such as Sophine Clachar and James Higgins. Their combined expertise fosters a dynamic research environment that drives innovation in flight parameter analysis.
Conclusion
Travis Desell's contributions to flight parameter prediction and analysis demonstrate his commitment to advancing aviation technology. His innovative patents reflect a deep understanding of neural networks and predictive modeling, paving the way for safer and more efficient flights.