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Full-Text Articles in Engineering
Characterizing Soil Stiffness Using Thermal Remote Sensing And Machine Learning, Jordan Ewing, T. Oommen, Paramsothy Jayakumar, Russell Alger
Characterizing Soil Stiffness Using Thermal Remote Sensing And Machine Learning, Jordan Ewing, T. Oommen, Paramsothy Jayakumar, Russell Alger
Michigan Tech Publications
Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict …
Rapid Quantification Of Biofouling With An Inexpensive, Underwater Camera And Image Analysis, Matthew R. First, Scott C. Riley, Kazi Aminul Islam, Victoria Hill, Jiang Li, Richard C. Zimmerman, Lisa A. Drake
Rapid Quantification Of Biofouling With An Inexpensive, Underwater Camera And Image Analysis, Matthew R. First, Scott C. Riley, Kazi Aminul Islam, Victoria Hill, Jiang Li, Richard C. Zimmerman, Lisa A. Drake
Electrical & Computer Engineering Faculty Publications
To reduce the transport of potentially invasive species on ships' submerged surfaces, rapid-and accurate-estimates of biofouling are needed so shipowners and regulators can effectively assess and manage biofouling. This pilot study developed a model approach for that task. First, photographic images were collected in situ with a submersible, inexpensive pocket camera. These images were used to develop image processing algorithms and train machine learning models to classify images containing natural assemblages of fouling organisms. All of the algorithms and models were implemented in a widely available software package (MATLAB©). Initially, an unsupervised clustering model was used, and three …