Engineering Commons^™

Convolution And Autoencoders Applied To Nonlinear Differential Equations, Noah Borquaye

Electronic Theses and Dissertations

Autoencoders, a type of artificial neural network, have gained recognition by researchers in various fields, especially machine learning due to their vast applications in data representations from inputs. Recently researchers have explored the possibility to extend the application of autoencoders to solve nonlinear differential equations. Algorithms and methods employed in an autoencoder framework include sparse identification of nonlinear dynamics (SINDy), dynamic mode decomposition (DMD), Koopman operator theory and singular value decomposition (SVD). These approaches use matrix multiplication to represent linear transformation. However, machine learning algorithms often use convolution to represent linear transformations. In our work, we modify these approaches to …

Dual-Site Photoplethysmography Sensing For Noninvasive Continuous-Time Blood Pressure Monitoring Using Artificial Neural Network, Anas Mohmmad Rabab’Ah

Theses

Millions of people worldwide struggle from high blood pressure, often known as hypertension, and it is a major health concern that can lead to serious cardiovascular diseases, including heart attacks and many other consequences. Blood pressure monitoring that is reliable and accurate is crucial to the detection and management of hypertension. Although invasive techniques, such as arterial catheterization, are considered to be the most accurate means of evaluating blood pressure, they can be painful, time-consuming and carry a risk of complications.
This thesis presents the development of a real time non-invasive blood pressure monitoring system based on commercially available microcontroller …

A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang

Electronic Theses, Projects, and Dissertations

Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …

Deep Learning For Power Flow Estimation And High Impedance Fault Detection, Kun Yang

Electronic Theses and Dissertations

My thesis is divided into two parts.

The first part is: “Optimal Power Flow Estimation Using One-Dimensional Convolutional Neural Network [1]“. Optimal power flow (OPF) is an important research topic in power system operation and control decisions. Traditional OPF problems are solved through dynamic optimization with nonlinear programming techniques. For a large power system with large amounts of variables and constraints, the solving process would take a long time. This paper presents a new method to quickly estimate the OPF results using a one-dimensional convolutional neural network (1D-CNN). The OPF problem is treated as a high-dimensional mapping between the load …

Comparing The Performance Of Different Machine Learning Models In The Evaluation Of Solder Joint Fatigue Life Under Thermal Cycling, Jason Scott Ross

Dissertations and Theses

Predicting the reliability of board-level solder joints is a challenging process for the designer because the fatigue life of solder is influenced by a large variety of design parameters and many nonlinear, coupled phenomena. Machine learning has shown promise as a way of predicting the fatigue life of board-level solder joints. In the present work, the performance of various machine learning models to predict the fatigue life of board-level solder joints is discussed. Experimental data from many different solder joint thermal fatigue tests are used to train the different machine learning models. A web-based database for storing, sharing, and uploading …

Prediction Of Blast-Induced Ground Vibrations: A Comparison Between Empirical And Artificial-Neural-Network Approaches, Luis F. Velasquez

Theses and Dissertations--Mining Engineering

Ground vibrations are a critical factor in the rock blasting process. The instantaneous load application exerted by the gas pressure during the detonation process acts on the blasthole walls creating dynamic stresses in the adjacent rock. This triggers different sorts of stress waves, mainly divided into two categories: body and surface waves. The first comprises the P and the S waves, while the second comprises Rayleigh waves. These waves spread concentrically starting at the blast location and move along the ground surface and its interior, being attenuated as they reach further distances.

In most cases, and accepting the hypothesis that …

Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou

MSU Graduate Theses

In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep …

Estimating Air Pollution Levels Using Machine Learning, Srujay Rao Devaraneni

Master's Projects

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air …