Engineering Commons^™

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

Short Term Energy Forecasting For A Microgird Load Using Lstm Rnn, Akhil Soman

Masters Theses

Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting.

In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as …

Implementation Of A Neuromorphic Development Platform With Danna, Jason Yen-Shen Chan

Masters Theses

Neuromorphic computing is the use of artificial neural networks to solve complex problems. The specialized computing field has been growing in interest during the past few years. Specialized hardware that function as neural networks can be utilized to solve specific problems unsuited for traditional computing architectures such as pattern classification and image recognition. However, these hardware platforms have neural network structures that are static, being limited to only perform a specific application, and cannot be used for other tasks. In this paper, the feasibility of a development platform utilizing a dynamic artificial neural network for researchers is discussed.

Digital-To-Analog Converter Interface For Computer Assisted Biologically Inspired Systems, Nicholas Conley Poore

Masters Theses

In today's integrated circuit technology, system interfaces play an important role of enabling fast, reliable data communications. A key feature of this work is the exploration and development of ultra-low power data converters. Data converters are present in some form in almost all mixed-signal systems; in particular, digital-to-analog converters present the opportunity for digitally controlled analog signal sources. Such signal sources are used in a variety of applications such as neuromorphic systems and analog signal processing. Multi-dimensional systems, such as biologically inspired neuromorphic systems, require vectors of analog signals. To use a microprocessor to control these analog systems, we must …