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Full-Text Articles in Physical Sciences and Mathematics
Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman
Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman
All Undergraduate Theses and Capstone Projects
Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor …
Implementation And Usage Of Low-Cost Turbines For Power Generation In Water Networks, Luis Javier Ortiz Osornio
Implementation And Usage Of Low-Cost Turbines For Power Generation In Water Networks, Luis Javier Ortiz Osornio
All Graduate Theses, Dissertations, and Other Capstone Projects
The following APP is part of an investigation and development, carried out to design, and implement a hydroelectric turbine of horizontal axis, in order to generate electrical energy in rural areas, utilizing existing infrastructure or natural waterways such as irrigation canals, piping, rivers and streams. Every industrialized country, as well as, most of the developing nations, have a stake in agriculture and thus access to the infrastructure required for irrigation purposes. Artificial irrigation canals offer advantages such as a clean continuous flow, with the possibility of flow regulation: this together with their vast availability as agricultural infrastructure constitute the main …
Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan
Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan
All Graduate Theses, Dissertations, and Other Capstone Projects
The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over …