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Full-Text Articles in Physical Sciences and Mathematics
Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin
Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin
The Summer Undergraduate Research Fellowship (SURF) Symposium
Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced …
Topological Methods For The Quantification And Analysis Of Complex Phenotypes, Patrick S. Medina, Rebecca W. Doerge
Topological Methods For The Quantification And Analysis Of Complex Phenotypes, Patrick S. Medina, Rebecca W. Doerge
Conference on Applied Statistics in Agriculture
Quantitative Trait Locus (QTL) mapping of complex traits, such as leaf venation or root structures, require the phenotyping and genotyping of large populations. Sufficient genotyping is accomplished with cost effective high-throughput assays, however labor costs often makes sufficient phenotyping prohibitively limited. In order to develop efficient high-throughput phenotyping platforms for complex traits algorithms and methods for quantifying these traits are needed. It is often desirable to study the spatial organization of these phenotypes from the images generated by high-throughput platforms. With the goal of quantifying the traits, many approaches try to identify several core traits useful in describing the phenotypic …
Statistical Methods In Topological Data Analysis For Complex, High-Dimensional Data, Patrick S. Medina, R W. Doerge
Statistical Methods In Topological Data Analysis For Complex, High-Dimensional Data, Patrick S. Medina, R W. Doerge
Conference on Applied Statistics in Agriculture
The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This paper provides an introductory overview of the mathematical underpinnings of Topological Data Analysis, the workflow to convert samples of data to topological summary statistics, and some of the statistical methods developed for performing inference on these topological summary statistics. The intention of this non-technical overview is to motivate statisticians who are interested in learning more about the subject.
A Survey On Intrusion Detection Approaches, A Murali M. Rao
A Survey On Intrusion Detection Approaches, A Murali M. Rao
International Conference on Information and Communication Technologies
Intrusion detection plays one of the key roles in computer security techniques and is one of the prime areas of research. Usages of computer network services are tremendously increasing day by day and at the same time intruders are also playing a major role to deny network services, compromising the crucial services for Email, FTP and Web. Realizing the importance of the problem due to intrusions, many researchers have taken up research in this area and have proposed several solutions. It has come to a stage to take a stock of the research results and project a comprehensive view so …
New Tools For New Times, Terry C. Nelsen, Debra E. Palmquist
New Tools For New Times, Terry C. Nelsen, Debra E. Palmquist
Conference on Applied Statistics in Agriculture
The purpose of this presentation is to challenge statisticians to develop new tools needed by modern scientists. We are in the midst of a Scientific Revolution being driven by computers and the internet. Scientists are gathering huge amounts of data on the usual measurements while continually developing new instruments for new measurements. Data sets full of measurements which may pertain to the scientist's research are easily available on the internet. Scientists are being overwhelmed with data. Agricultural producers and consumers are asking for more information. Scientists need new tools to evaluate variation. They need help with sampling - numbers of …