Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 6 of 6
Full-Text Articles in Entire DC Network
Tactile Sensing And Position Estimation Methods For Increased Proprioception Of Soft-Robotic Platforms, Nathan Mcclain Day
Tactile Sensing And Position Estimation Methods For Increased Proprioception Of Soft-Robotic Platforms, Nathan Mcclain Day
Theses and Dissertations
Soft robots have the potential to transform the way robots interact with their environment. This is due to their low inertia and inherent ability to more safely interact with the world without damaging themselves or the people around them. However, existing sensing for soft robots has at least partially limited their ability to control interactions with their environment. Tactile sensors could enable soft robots to sense interaction, but most tactile sensors are made from rigid substrates and are not well suited to applications for soft robots that can deform. In addition, the benefit of being able to cheaply manufacture soft …
Machine Learning For Inspired, Structured, Lyrical Music Composition, Paul Mark Bodily
Machine Learning For Inspired, Structured, Lyrical Music Composition, Paul Mark Bodily
Theses and Dissertations
Computational creativity has been called the "final frontier" of artificial intelligence due to the difficulty inherent in defining and implementing creativity in computational systems. Despite this difficulty computer creativity is becoming a more significant part of our everyday lives, in particular music. This is observed in the prevalence of music recommendation systems, co-creational music software packages, smart playlists, and procedurally-generated video games. Significant progress can be seen in the advances in industrial applications such as Spotify, Pandora, Apple Music, etc., but several problems persist. Of more general interest, however, is the question of whether or not computers can exhibit autonomous …
Labor Skills In The Maintenance Department For Industry 4.0, Tomas Marzullo
Labor Skills In The Maintenance Department For Industry 4.0, Tomas Marzullo
Theses and Dissertations
Industry 4.0 is changing the manufacturing environment with its cyber-physical infrastructure to support and help increase production performance. The cyber-physical infrastructure brings new technologies such as Internet of Things, big data, cloud computing, and machine learning using advanced algorithms. To deal with this new order to preserve asset performance, industrial maintenance needs to be prepared. This study aims to understand the impact of Industry 4.0 on the skills required within industrial maintenance departments. A survey of industrial maintenance professionals finds that the majority of training comes from internal sources and that much of the information systems used for training are …
Mass Classification Of Digital Mammograms Using Convolutional Neural Networks, Elijah Franklin
Mass Classification Of Digital Mammograms Using Convolutional Neural Networks, Elijah Franklin
Theses and Dissertations
This thesis explores the current deep learning (DL) approaches to computer aided diagnosis (CAD) of digital mammographic images and presents two novel designs for overcoming current obstacles endemic to the field, using convolutional neural networks (CNNs). The first method employed utilizes Bayesian statistics to perform decision level fusion from multiple images of an individual. The second method utilizes a new data pre-processing scheme to artificially expand the limited available training data and reduce model overitting.
Machine Learning Based Disease Gene Identification And Mhc Immune Protein-Peptide Binding Prediction, Zhonghao Liu
Machine Learning Based Disease Gene Identification And Mhc Immune Protein-Peptide Binding Prediction, Zhonghao Liu
Theses and Dissertations
Machine learning and deep learning methods have been increasingly applied to solve challenging and important bioinformatics problems such as protein structure prediction, disease gene identification, and drug discovery. However, the performances of existing machine learning based predictive models are still not satisfactory. The question of how to exploit the specific properties of bioinformatics data and couple them with the unique capabilities of the learning algorithms remains elusive. In this dissertation, we propose advanced machine learning and deep learning algorithms to address two important problems: mislocation-related cancer gene identification and major histocompatibility complex-peptide binding affinity prediction. Our first contribution proposes a …
Fault Classification And Location Identification On Electrical Transmission Network Based On Machine Learning Methods, Vidya Venkatesh
Fault Classification And Location Identification On Electrical Transmission Network Based On Machine Learning Methods, Vidya Venkatesh
Theses and Dissertations
Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type …