Engineering Commons^™

Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa

Theses and Dissertations

“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …

Design Of Plastic Contaminant Eliminator In Seed Cotton, Joshua H. Tandio

Theses and Dissertations

Plastic contamination in cotton is a problem in cotton industry and researchers have worked on this problem with different approaches. This thesis documents the design of mechanical and electronic real-time systems for detecting and removing plastic contaminants. The mechanical system was designed to expose plastic embedded inside the seed cotton to the sensor and to separate plastic contaminated cotton from the process stream. The detection system consisted of an embedded computer interfaced with a USB camera and Neural Network (NN) software running in it. Two NN models were tested, a transfer learning model and a built-from-scratch original model. The original …

Detecting Invasive Insects Using Uncewed Aerial Vehicles And Variational Autoencoders, Scott Daniel Stewart

Master's Theses (2009 -)

In this thesis, we use machine learning techniques to address limitations in our ability to monitor pest insect migrations. Invasive insect populations, such as the brown marmorated stink bug (BMSB), cause significant economic and environmental damages. In order to mitigate these damages, tracking BMSB migration is vital, but it also poses a challenge. The current state-of-the-art solution to track insect migrations is called mark-release-recapture. In mark-release-recapture, a researcher marks insects with a fluorescent powder, releases them back into the wild, and searches for the insects using ultra-violet flashlights at suspected migration destination locations. However, this involves a significant amount of …

Machine Learning Based Dynamic Power Dispatching And Smoothing Using Hybrid Energy Storage System For Renewable Energy Systems, Bhuvaneshwarr Ramalingam

Electrical and Computer Engineering ETDs

The stochastic fluctuations from Renewable Energy Resources (RER) have a great influence on power quality and off-grid communities. A combination of the different storage systems is accessible for RER generation intermittency and to bring about finest smoothing operating cycle compared to sole Energy Storage System (ESS). Additionally, energy management in Hybrid Energy Storage System (HESS) creates an uncertainty during power smoothing operation. This research materializes, an intelligent mechanism for power smoothing and dispatch with the introduction of hybridized storage that can accommodate the unpredictable behavior of RER under dynamic load. A feed-forward neural network is proposed as a power smoothing …

Distributed Neural Network Based Architecture For Ddos Detection In Vehicular Communication Systems, Nicholas Jaton

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

With the continued development of modern vehicular communication systems, there is an ever growing need for cutting edge security in these systems. A misbehavior detection systems (MDS) is a tool developed to determine if a vehicle is being attacked so that the vehicle can take steps to mitigate harm from the attacker. Some attacks such as distributed denial of service (DDoS) attacks are a concern for vehicular communication systems. During a DDoS attack, multiple nodes are used to flood the target with an overwhelming amount of communication packets. In this thesis, we investigated the current MDS literature and how it …

A Bibliometric Analysis On Recent Classification Techniques For Alzheimer’S Disease, Sumit Dhananjay Salunkhe, Mrinal Rahul Bachute Ph.D Guide And Associate Professor

Library Philosophy and Practice (e-journal)

Alzheimer's disease (AD) has been studied extensively to better understand the complexities of this disease and to address the numerous unanswered questions about prognosis and diagnosis. To be able to determine and allocate the resources appropriate to the research area, a detailed understanding of the research topic is much needed. Along with the tremendous expansion in the scope of neurodegenerative disease treatment research, the diversity of technologies to help the research continues to expand. Many studies have investigated into how AD affects different brain structures as the disease progresses, using various image processing methods to derive a variety of brain …

Expanding Image Datasets For Deep Learning By Evaluating Independence Through Coefficient Correlation And Mean-Squared Error, Ayman Yousef

Biomedical Engineering Undergraduate Honors Theses

With deep learning being leveraged more regularly in the field of image classification, particularly in medical imaging, network optimizations have become a field in and of itself. With open source, comprehensive medical image datasets few and far, computational dataset expansion has become a useful tool for researchers without the ability to further manually collect data. However, with the rich amount of data that imaging modalities like multi-photon microscopy collect at a time, there is potential to expand datasets through proper utilization of this data that often time goes unused. Previous deep learning studies have shown that improper expansion can conflate …

Path Planning With Deep Neural Networks, Paul Simmerling, Brendan Sayers, Paulo Alcantara Silva

Honors Scholar Theses

This report will cover the work and plans of the ECE 2107 Senior design team. The goal of the project is to design and build a fully autonomous self-driving car. This car will have a complete sensor suite including LIDAR, an IMU, a camera, and encoders. It will be based on a multi-level system where the highest level uses a neural network for advanced signal processing and analysis. The current state of the project is discussed as well as the final results. Project management and other constraints will be briefly investigated. This team is building a self driving car testbed …

Non-Linear Dimensionality Reduction Using Auto-Encoder For Optimized Malaria Infected Blood Cell Classifier, Aayush Dhakal

Honors Theses

Neural Networks have been widely used in the problem of Medical Image Analysis. However, when dealing with large images, deep networks easily exhaust computer resources, which in turn hinders training. This paper shows the efficacy of using Auto-Encoders as a dimensionality reduction tool to increase the efficiency of a Malaria Infected Blood Cell Image classifier. We show that using an autoencoder, we can reduce the dimensionality of large blood cell images effectively such that the features in the new space retain all the essential information from the original input. Then we show that the new features obtained from the autoencoder …

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and uses …

Utilizing Rotational Energy In Wind Turbine Blades With The Flywheel Mechanism And Predicting The Power Output By Neural Networking, Anamika Mishra

Browse all Theses and Dissertations

As we expand and innovate for better and safer living, there will always be a need for new energy sources. By replacing fossil fuels, renewable energy is becoming a viable option for primary power generation. That is why researchers are turning their attention to renewable energy sources and ways of making the most of them. WIND ENERGY is a promising renewable and clean energy source harvested from the wind which is plentiful on the planet. We already have the technology to harvest it, but the efficiency and power output are not optimal. In this thesis, to enhance the energy harvesting …

Utilizing Rotational Energy In Wind Turbine Blades With The Flywheel Mechanism And Predicting The Power Output By Neural Networking, Anamika Mishra

Browse all Theses and Dissertations

As we expand and innovate for better and safer living, there will always be a need for new energy sources. By replacing fossil fuels, renewable energy is becoming a viable option for primary power generation. That is why researchers are turning their attention to renewable energy sources and ways of making the most of them. WIND ENERGY is a promising renewable and clean energy source harvested from the wind which is plentiful on the planet. We already have the technology to harvest it, but the efficiency and power output are not optimal. In this thesis, to enhance the energy harvesting …

Comparative Analysis Of Different Classes Of On-Line State Estimators For Aerodynamics Angles And True Airspeed Sensors For Applications To The Sensor Failure Problem, Alexandra Anne Augsberger

Graduate Theses, Dissertations, and Problem Reports

Throughout aviation history, there have been numerous incidents due to sensor failure that have caused a range of issues from loss of control of the aircraft to crashes resulting in loss of human life. Although there are many hardware-based solutions to this problem, the threat of control hardware failure still exists. This work investigates the efficacy of implementing neural networks (NN) and Kalman filters (KF) to solve the accommodation portion of the sensor failure detection, identification, and accommodation (SFDIA) problem through on-line real-time estimation of specific aircraft dynamic parameters. The implementation of on-line estimation architectures into the aircraft flight control …

Computational Intelligent Impact Force Modeling And Monitoring In Hislo Conditions For Maximizing Surface Mining Efficiency, Safety, And Health, Danish Ali

Doctoral Dissertations

"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem.

This research study developed a rigorous mathematical model and a 3D virtual simulation model to …