Engineering Commons^™

Robust Control Of Contact-Rich Robots Via Neural Bayesian Inference, Nardos Ayele Ashenafi

Boise State University Theses and Dissertations

We provide several data-driven control design frameworks for contact-rich robotic systems. These systems exhibit continuous state flows and discrete state transitions, which are governed by distinct equations of motion. Hence, it is difficult to design a single policy that can control the system in all modes. Typically, hybrid systems are controlled by multi-modal policies, each manually triggered based on observed states. However, as the number of potential contacts increase, the number of policies can grow exponentially and the control-switching scheme becomes too complicated to parameterize. To address this issue, we design contact-aware data-driven controllers given by deep-net mixture of experts. …

Data-Driven Passivity-Based Control Of Underactuated Robotic Systems, Wankun Sirichotiyakul

Boise State University Theses and Dissertations

Classical control strategies for robotic systems are based on the idea that feedback control can be used to override the natural dynamics of the machines. Passivity-based control (Pbc) is a branch of nonlinear control theory that follows a similar approach, where the natural dynamics is modified based on the overall energy of the system. This method involves transforming a nonlinear control system, through a suitable control input, into another fictitious system that has desirable stability characteristics. The majority of Pbc techniques require the discovery of a reasonable storage function, which acts as a Lyapunov function candidate that can be …

Process-Property Linkages Construction For Inkjet Printing With Machine Learning, Fataneh Jenabi

Boise State University Theses and Dissertations

Printed electronics are emerging technologies that can potentially revolutionize the manufacturing of electronic devices. One promising technology for printed electronics is inkjet printing. Inkjet printing offers both low-cost processing and high resolution. Being a subset of additive manufacturing, inkjet printing minimizes waste and is compatible with a wide range of inks. However, inkjet printing of electronic devices is still in its infancy. One major challenge for inkjet printing is the complexity of the process optimization and uncertain high throughput production. To achieve a high-quality print, there is a complex parameter space of materials and processing parameters that needs to be …

Deep Convolutional Spiking Neural Networks For Image Classification, Ruthvik Vaila

Boise State University Theses and Dissertations

Spiking neural networks are biologically plausible counterparts of artificial neural networks. Artificial neural networks are usually trained with stochastic gradient descent (SGD) and spiking neural networks are trained with bioinspired spike timing dependent plasticity (STDP). Spiking networks could potentially help in reducing power usage owing to their binary activations. In this work, we use unsupervised STDP in the feature extraction layers of a neural network with instantaneous neurons to extract meaningful features. The extracted binary feature vectors are then classified using classification layers containing neurons with binary activations. Gradient descent (backpropagation) is used only on the output layer to perform …

Assessing The Prevalence Of Suspicious Activities In Asphalt Pavement Construction Using Algorithmic Logics And Machine Learning, Mostofa Najmus Sakib

Boise State University Theses and Dissertations

Quality Control (QC) and Quality Assurance (QA) is a planned systematic approach to secure the satisfactory performance of Hot mix asphalt (HMA) construction projects. Millions of dollars are invested by government and state highway agencies to construct large-scale HMA construction projects. QC/QA is statistical approach for checking the desired construction properties through independent testing. The practice of QC/QA has been encouraged by the Federal Highway Administration (FHWA) since the mid 60’s. However, the standard QC/QA practice is often criticized on how effective such statistical tests and how representative the reported material tests are. Material testing data alteration in the HMA …

Understanding Self-Assembly And Charge Transport In Organic Solar Cells Through Efficient Computation, Evan Miller

Boise State University Theses and Dissertations

Organic solar cells capable of sustainably generating electricity are possible if: (1) The structures assembled by photoactive molecules can be controlled, and (2) The structures favorable for charge transport can be determined. In this dissertation we conduct computational studies to understand relationships between organic solar cell compounds, processing, structure and charge transport. We advance tools for encapsulating computational workflows so that simulations are more reproducible and transferable. We find that molecular dynamic simulations using simplified models efficiently predict experimental structures. We find that the mobilities of charges through these structures—as determined by kinetic Monte Carlo simulations—match qualitative trends expected with …

Machine Learning Methods To Map Stabilizer Effectiveness Based On Common Soil Properties, Amit Gajurel

Boise State University Theses and Dissertations

Unconfined compressive strength (UCS) has been widely used as one of the primary criteria for the selection of optimum type and amount of chemical stabilizer for subgrade/base stabilization. Guidelines established by various state and federal agencies aid in selecting these optimum values by recommending an initial type and amount based on a wide range of soil index properties. A significant number of laboratory trials have to be done to establish the optimum type and amount of stabilizer for a given target strength. This process takes a copious amount of time, money, and the workforce. In addition to that, the finite …

Analog Spiking Neuromorphic Circuits And Systems For Brain- And Nanotechnology-Inspired Cognitive Computing, Xinyu Wu

Boise State University Theses and Dissertations

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves …

A Memristor-Based Neuromorphic Computing Application, Adrian Rothenbuhler

Boise State University Theses and Dissertations

Artificial neural networks have recently received renewed interest because of the discovery of the memristor. The memristor is the fourth basic circuit element, hypothesized to exist by Leon Chua in 1971 and physically realized in 2008. The two-terminal device acts like a resistor with memory and is therefore of great interest for use as a synapse in hardware ANNs. Recent advances in memristor technology allowed these devices to migrate from the experimental stage to the application stage.

This Master's thesis presents the development of a threshold logic gate (TLG), which is a special case of an ANN, implemented with discrete …