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Articles 1 - 12 of 12
Full-Text Articles in Engineering
Tomato Flower Detection And Three-Dimensional Mapping For Precision Pollination, Kaitlyn Mckensie Nelms
Tomato Flower Detection And Three-Dimensional Mapping For Precision Pollination, Kaitlyn Mckensie Nelms
Masters Theses
It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less …
Integrating Remote Sensing And Model-Based Datasets In A Machine Learning Model To Map Global Subsidence Associated With Groundwater Withdrawal, Md Fahim Hasan
Integrating Remote Sensing And Model-Based Datasets In A Machine Learning Model To Map Global Subsidence Associated With Groundwater Withdrawal, Md Fahim Hasan
Masters Theses
"Quantifying groundwater storage loss is becoming increasingly essential globally due limited availability of this major hydrologic component and its long recharge time. Groundwater overdraft gives rises to multiple adverse impacts including land subsidence and permanent groundwater storage loss. In absence of spatially dense monitoring network, publicly available in-situ data, and uniform monitoring strategies, it is challenging to assess the sustained losses from overexploitation of this resource. Remote sensing based techniques have the capacity to fill this gap to increase our groundwater monitoring capacities. Exploring the interrelation between groundwater pumping and land subsidence using remote sensing datasets can be a very …
A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman
A Convolutional Neural Network (Cnn) For Defect Detection Of Additively Manufactured Parts, Musarrat Farzana Rahman
Masters Theses
“Additive manufacturing (AM) is a layer-by-layer deposition process to fabricate parts with complex geometries. The formation of defects within AM components is a major concern for critical structural and cyclic loading applications. Understanding the mechanisms of defect formation and identifying the defects play an important role in improving the product lifecycle. The convolutional neural network (CNN) has been demonstrated to be an effective deep learning tool for automated detection of defects for both conventional and AM processes. A network with optimized parameters including proper data processing and sampling can improve the performance of the architecture. In this study, for the …
Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi
Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi
Masters Theses
Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.
Development Of A Highly Sensitive Pressure Sensing System With Custom-Built Software For Continuous Physiological Measurements, Masoud Panahi
Development Of A Highly Sensitive Pressure Sensing System With Custom-Built Software For Continuous Physiological Measurements, Masoud Panahi
Masters Theses
In this work, a pressure sensing system was designed and fabricated by developing a highly sensitive cone-structured pressure sensor with a custom-built software for physiological monitoring applications. A novel highly sensitive cone structured porous polydimethylsiloxane (PDMS) based pressure sensor capable of detecting very low-pressure ranges was developed for respiration monitoring. The pressure sensor was fabricated using a master mold, a hybrid-structured dielectric layer, and fabric-based electrodes. The master mold with inverted cone structures was created using a rapid and precise three-dimensional (3D) printing technique. The dielectric layer, with pores and cone structures, was prepared by annealing a mixture of PDMS, …
Power System Stability Assessment With Supervised Machine Learning, Mirka Mandich
Power System Stability Assessment With Supervised Machine Learning, Mirka Mandich
Masters Theses
Power system stability assessment has become an important area of research due to the increased penetration of photovoltaics (PV) in modern power systems. This work explores how supervised machine learning can be used to assess power system stability for the Western Electricity Coordinating Council (WECC) service region as part of the Data-driven Security Assessment for the Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS) project. Data-driven methods offer to improve power flow scheduling through machine learning prediction, enabling better energy resource management and reducing demand on real-time time-domain simulations. Frequency, transient, and small signal stability datasets were created using the …
Sundown: Model-Driven Per-Panel Solar Anomaly Detection For Residential Arrays, Menghong Feng
Sundown: Model-Driven Per-Panel Solar Anomaly Detection For Residential Arrays, Menghong Feng
Masters Theses
There has been significant growth in both utility-scale and residential-scale solar installa- tions in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential- scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this thesis, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and …
Training Set Density Estimation For Trajectory Predictions Using Artificial Neural Networks, Zachary Reinke
Training Set Density Estimation For Trajectory Predictions Using Artificial Neural Networks, Zachary Reinke
Masters Theses
Demand on earth orbiting surveillance systems in increasing as more equipment is put into orbit. These systems rely on predictive techniques to periodically track objects. The demand on these systems may be reduced if object trajectory data to develop scalable training sets used for training artificial neural networks (ANNs) to predict trajectories of a dynamic system. These methods use multi-variable statistics to analyze data energy content to provide the ANN with low density, feature-rich, training data. The developed techniques have been shown to increase ANN prediction accuracy while reducing the size of the training set when applied to a linear …
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Less Is More: Beating The Market With Recurrent Reinforcement Learning, Louis Kurt Bernhard Steinmeister
Masters Theses
"Multiple recurrent reinforcement learners were implemented to make trading decisions based on real and freely available macro-economic data. The learning algorithm and different reinforcement functions (the Differential Sharpe Ratio, Differential Downside Deviation Ratio and Returns) were revised and the performances were compared while transaction costs were taken into account. (This is important for practical implementations even though many publications ignore this consideration.) It was assumed that the traders make long-short decisions in the S&P500 with complementary 3-month treasury bill investments. Leveraged positions in the S&P500 were disallowed. Notably, the Differential Sharpe Ratio and the Differential Downside Deviation Ratio are risk …
Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai
Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai
Masters Theses
"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is …
Autoplug: An Automated Metadata Service For Smart Outlets, Lurdh Pradeep Reddy Ambati
Autoplug: An Automated Metadata Service For Smart Outlets, Lurdh Pradeep Reddy Ambati
Masters Theses
Low-cost network-connected smart outlets are now available for monitoring, controlling, and scheduling the energy usage of electrical devices. As a result, such smart outlets are being integrated into automated home management systems, which remotely control them by analyzing and interpreting their data. However, to effectively interpret data and control devices, the system must know the type of device that is plugged into each smart outlet. Existing systems require users to manually input and maintain the outlet metadata that associates a device type with a smart outlet. Such manual operation is time-consuming and error-prone: users must initially inventory all outlet-to-device mappings, …
Primary User Emulation Attacks In Cognitive Radio - An Experimental Demonstration And Analysis, Benjamin James Ealey
Primary User Emulation Attacks In Cognitive Radio - An Experimental Demonstration And Analysis, Benjamin James Ealey
Masters Theses
Cognitive radio networks rely on the ability to avoid primary users, owners of the frequency, and prevent collisions for effective communication to take place. Additional malicious secondary users, jammers, may use a primary user emulation attacks to take advantage of the secondary user's ability to avoid primary users and cause excessive and unexpected disruptions to communications. Two jamming/anti-jamming methods are investigated on Ettus Labs USRP 2 radios. First, pseudo-random channel hopping schemes are implemented for jammers to seek-and-disrupt secondary users while secondary users apply similar schemes to avoid all primary user signatures. In the second method the jammer uses adversarial …