Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Subnetwork Ensembling And Data Augmentation: Effects On Calibration, A. Çağrı Demir, Simon Caton, Pierpaolo Dondio

Articles

Deep Learning models based on convolutional neural networks are known to be uncalibrated, that is, they are either overconfident or underconfident in their predictions. Safety-critical applications of neural networks, however, require models to be well-calibrated, and there are various methods in the literature to increase model performance and calibration. Subnetwork ensembling is based on the over-parametrization of modern neural networks by fitting several subnetworks into a single network to take advantage of ensembling them without additional computational costs. Data augmentation methods have also been shown to enhance model performance in terms of accuracy and calibration. However, ensembling and data augmentation …

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …

Development Of Holographic Recording Material For Fabrication Of Solar Concentrators For Micro Satellites, Brian Rogers

Doctoral

Holographic solar concentration has been a topic of increasing interest over recent years because of the lightweight and physically compact nature of holograms, which gives them an advantage over other, conventional concentration methods. As holographic recording materials develop further, their characteristics prove to meet the demands required of them to make more effective holographic solar concentrators. The development of photovoltaics in tandem with holographic materials has led to a host of solar concentrating device designs that take advantage of the unique properties of diffractive optics. Compared to refractive optics, holographic devices are more lightweight and compact. This makes holographic devices …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

Towards Sweetness Classification Of Orange Cultivars Using Short‑Wave Nir Spectroscopy, Ayesha Zeb, Waqar Shahid Qureshi, Abdul Ghafoor, Amanullah Malik, Muhammad Imran, Alina Mirza, Mohsin Islam Tiwana, Eisa Alanazi

Articles

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR …

Do Games Reduce Math Anxiety? A Meta-Analysis, Pierpaolo Dondio, Viacheslav Filonenko, Mariana Rocha

Articles

In this paper, we meta-analyse the empirical evidence about the effectiveness of game-based interventions to reduce students' level of math anxiety. After performing a search for randomised controlled studies relevant to game-based intervention for math anxiety, 16 experimental studies with a total of 686 participants described in 11 peer-review articles met the selection criteria.A random-effects meta-analysis indicated a small and non-significant reduction of math anxiety (mean effect size ES=-0.32, CI=[-0.64,0.01]). The results were moderated by several factors: non-digital games were more effective, while digital games had a negligible mean effect size of $ES=-0.13$, $CI=[-0.33,0.08]$. The effect size was moderated also …

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm For Solving Engineering Problems, Nour Elhouda Chalabi, Abdelouahab Attia, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Hatem Masri, Ali Wagdy Mohamed

Articles

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, …

Dynamic Modeling Of Commercial-Off-The-Shelf Smart Inverters Using Power-Hardware-In-The-Loop, Bidur Poudel

Electronic Theses and Dissertations

Inverter-based generation is replacing synchronous-based generation in the power grid. In the past, due to the dominance of synchronous-based generation, inverter-based generation dynamics were typically under-modeled as they made little contribution to voltage and frequency control. Looking at the present context, several disturbances, like the Blue Cut Fire (CA), on August 16, 2016, the South Australian Blackout on September 28, 2016, and the Canyon Two Fire Disturbance (CA) on October 9, 2017, etc., are caused by the influence of converter-based resources. So, for converter-dominated power system stability analysis and control, the dynamics of an inverter-based system cannot be under-modeled. In …

Underwater Communication Acoustic Transducers: A Technology Review, Laila Shams, Tian-Bing Xu, Zhongqing Su (Ed.), Branko Glisic (Ed.), Maria Pina Limongelli (Ed.)

Mechanical & Aerospace Engineering Faculty Publications

This paper provides a comprehensive review on transducer technologies for underwater communications. The popularly used communication transducers, such as piezoelectric acoustic transducers, electromagnetic acoustic transducers, and acousto-optic devices are reviewed in detail. The reasons that common air communication technologies are invalid die to the differences between the media of air and water are addresses. Because of the abilities to overcome challenges the complexity of marine environments, piezoelectric acoustic transducers are playing the major underwater communication roles for science, surveillance, and Naval missions. The configuration and material properties of piezoelectric transducers effects on signal output power, beamwidth, amplitude, and other properties …

Ai-Driven Security Constrained Unit Commitment Using Predictive Modeling And Eigen Decomposition, Talha Iqbal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Security Constrained Unit Commitment (SC-UC) is a complex large scale mix integer constrained optimization problem solved by Independent System Operators (ISOs) in the daily planning of the electricity markets. After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and a reasonable time to solve a large-scale SC-UC problem. However, exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, the computational effort can be reduced by learning from the historical data and identifying the patterns …

Dynamic Modeling, Data Reconciliation, Parameter Estimation, And Health Monitoring Of A Supercritical Power Plant, Katherine Grace Hedrick

Graduate Theses, Dissertations, and Problem Reports (ETD)

With the introduction of a larger portion of renewable sources of power coming onto the U.S. power grid in recent decades, the operational strategy of coal-fired power plants has changed significantly to focus more on flexibility in response to the changing energy market. This has naturally led to different operational challenges. Many of these challenges are focused on the boilers within these plants, as they are producing more emissions and experiencing increased damage during load-following, which in turn leads to increased costs from penalties for not achieving emission standards or maintenance costs as boilers accumulate damage from the cycling behavior. …

Modeling, Simulation, And Hardware Testing Of A Noise-Canceller Adc Architecture, Ethan R. Rando

Browse all Theses and Dissertations

Analog-to-Digital Converters (ADCs) are essential elements of most complex electronic devices. ADCs allow for an analog signal to be converted into the digital domain, and thus interpreted by a digital circuit or model. While ADCs are extremely common, they are not immune from common tradeoffs when being designed and implemented. The most prominent tradeoff when selecting or designing an ADC is whether to pursue a high conversion rate or a high resolution on the digital output. There are some ADC designs that allow for relatively high resolution while maintaining a respectable conversion rate, however these designs often come at the …

Motion Sensors And Instrumentation Based On Electrochemical Redox Reaction, Xiaoce Feng

Wayne State University Dissertations

To detect human body vital physiological signals, an electrochemical wearable sensor and a single-mode artificial mechanoreceptor have been developed and their capabilities to detect cardiorespiratory signals, as well as to detect rapid adaptive and slow adaptive responses have been preliminarily demonstrated. The electrochemical method based on I-/I3- redox couple proves to be a sensitive transduction mechanism. Furthermore, a common gate transimpedance amplifier based on gallium nitride high electron mobility transistor for the mechano-acoustic sensor and the wearable mechanoreceptor was developed. The transistor’s performance at high temperatures has been investigated, and its failure mode at high temperature has been discussed.

Safe Kids Car, Nathan Keenan, Jackson Piper, Kyle Law, Anthony Meniru

Williams Honors College, Honors Research Projects

The main goal of this project is to retrofit toy vehicles, such as Powerwheels cars, with safety systems that can protect the driver and those around the driver. Such systems will include lights, including headlights and taillights, and an automatic braking system, which will activate in the face of an inevitable collision. The system will also work with the driver to prevent collisions, providing warnings of impending danger.

Keywords: toy vehicles, collision detection, collision avoidance, automatic braking system, DC motors, automatic headlights.

Whiteboard Drawing Device, Stefan Ilic, Andrew Adams, Vaughn Richards, James Medved

Williams Honors College, Honors Research Projects

Whiteboards have become a staple in many classrooms, especially in the context of STEM courses. However, when it comes to teaching complex topics, it can be difficult or impractical to provide accurate drawings and visualizations by hand, and in extreme cases may lead to misinterpretation of a particular topic. To fix this issue, the Whiteboard Drawing Device allows users to draw preset shapes to a whiteboard automatically, both quickly and accurately. Using either a smartphone application or a set of onboard controls, the device allows users to automatically draw straight lines, rectangles, triangles, sinusoidal waves, and circles on the whiteboard. …

Towards The Development Of A Blockchain System For Philippine Government Processes For Enhanced Transparency And Verifiability, Karlo Angelo F. Cabugwang, Raphael Christen K. Enriquez, Bienvenido E. Villabroza, Christian E. Pulmano

Department of Information Systems & Computer Science Faculty Publications

There are cases of corruption and fraud within the Philippine government that have gone under the radar, often due to a lack of transparency and verifiability. The objective of this study is to prototype a blockchain network that can run a government process as a decentralized application such that it can enhance transparency and verifiability in the public sector. This can be accomplished by identifying a government process that would be converted into a decentralized application. One of these processes would be converted into a decentralized application. Afterwards, a blockchain framework should be identified one which can create a public …

Digital Beamforming Array Phase Calibration Techniques For Multi-Pass Interferometric Sar, Kelly Cheung

Browse all Theses and Dissertations

Calibration plays a critical role in the optimal performance of algorithms in digital beamforming arrays. Phase incoherency between elements results in poor beamforming with decreased gain and higher sidelobes, leading to a decrease in accuracy and sensitivity of measurements. A similar problem exists in performing multi-pass interferometric SAR (IFSAR) processing of SAR data stacks to generate topological maps of the scene, where phase errors translate to height errors. By treating each SAR image in the data stack like an element of a uniform linear array, this thesis explores several phase calibration techniques that can be used to calibrate digital beamforming …

Static I-V Based Pim Evaluation For Spring And Fabric-Over-Foam Contacts, Kalkidan W. Anjajo, Yang Xu, Shengxuan Xia, Yuchu He, Haicheng Zhou, Hanfeng Wang, Jonghyun Park, Chulsoon Hwang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Spring Clips and Fabric-Over-Foams (FOFs) Are Widely Used in Mobile Devices for Electrical Connection Purposes. However, the Imperfect Metallic Connections Tend to Induce Passive Intermodulation (PIM), Resulting in a Receiver Sensitivity Degradation, Known as RP Desensitization. Due to the Complexity of the PIM Characterization, there is Not Yet a Way to Evaluate PIM Performance using a Simple Setup for Environments Like Factories. in This Paper, a Current-Voltage (I-V) Behavior-Based PIM Evaluation Method is Proposed and Validated with Various Metallic Contacts and Contact Forces. the Test Results Demonstrated the Feasibility of the PIM Performance Evaluation based on the Measured Static I-V …

Community And Key Player Detection For Disrupting Illicit Drug Supply Networks In Social Media Platforms – Especially On Instagram, Akassi Rachel Niamke Aman

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis focuses on the pressing issue of illicit drug trafficking and its impact on public health and safety at a global level. With the advent of digital technologies and social media platforms, combating drug trafficking has become increasingly challenging for law enforcement and researchers alike. Among these platforms, Instagram, a popular photo and video-sharing social networking platform, has emerged as a prominent hub for drug trafficking activities.

In this study, we delve into the effectiveness of community and key player detection algorithms in identifying and disrupting illicit drug supply networks on Instagram. To conduct our research, we collected real …

Computational Mechanisms Of Face Perception, Jinge Wang

Graduate Theses, Dissertations, and Problem Reports (ETD)

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for …

Enabling Cybersecure And Resilient Smart Distribution Grid With Edge Devices, Partha Sarathi Sarker

Graduate Theses, Dissertations, and Problem Reports (ETD)

The smart distribution grid achieves enhanced efficiency and flexibility through the utilization of advanced communication infrastructure, digital devices, and robust computation and control capabilities. Grid-edge devices including distributed energy resources (DERs) like solar photovoltaics (PV), battery storage systems, and intelligent electric loads like electric vehicles and smart appliances, are contributing to the growth of the smart distribution grid. This evolution is transforming the grid into a multifaceted network of interconnected devices and systems, enabling bidirectional data communication and power flows. However, these new layers of data integration and control in the smart grid introduce vulnerabilities to cyberattacks and accidental failures, …

Imitation Learning For Swarm Control Using Variational Inference, Hafeez Olafisayo Jimoh

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarms are groups of robots that can coordinate, cooperate, and communicate to achieve tasks that may be impossible for a single robot. These systems exhibit complex dynamical behavior, similar to those observed in physics, neuroscience, finance, biology, social and communication networks, etc. For instance, in Biology, schools of fish, swarm of bacteria, colony of termites exhibit flocking behavior to achieve simple and complex tasks. Modeling the dynamics of flocking in animals is challenging as we usually do not have full knowledge of the dynamics of the system and how individual agent interact. The environment of swarms is also very noisy …

Machine Learning For Biosensors, Gayathri Anapanani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and …

Optimal Deployment Of Air Vehicle As Communication Relay For Multiple Ground Vehicles, Juan David Pabon Arias

Graduate Theses, Dissertations, and Problem Reports (ETD)

Heterogeneous teams of both air and ground mobile vehicles can combine the advantages of mobility, sensing capability, and operation time when performing complex tasks. However, when ground vehicles operate in cluttered environments with randomized obstacles, they may experience line of sight (LoS) obstructions and loss of communication due to those obstacles. To mitigate this issue, an airborne relay can be positioned in the vicinity of the ground vehicles to aid communication by establishing two-hop communication links between the vehicles.

This thesis develops an analytical framework to calculate the probability of spanning a two-hop communication between a pair of ground vehicles …

A Phase Change Memory And Dram Based Framework For Energy-Efficient And High-Speed In-Memory Stochastic Computing, Supreeth Mysore

Theses and Dissertations--Electrical and Computer Engineering

Convolutional Neural Networks (CNNs) have proven to be highly effective in various fields related to Artificial Intelligence (AI) and Machine Learning (ML). However, the significant computational and memory requirements of CNNs make their processing highly compute and memory-intensive. In particular, the multiply-accumulate (MAC) operation, which is a fundamental building block of CNNs, requires enormous arithmetic operations. As the input dataset size increases, the traditional processor-centric von-Neumann computing architecture becomes ill-suited for CNN-based applications. This results in exponentially higher latency and energy costs, making the processing of CNNs highly challenging.

To overcome these challenges, researchers have explored the Processing-In Memory (PIM) …

Application Of Conventional Feedforward And Deep Neural Networks To Power Distribution System State Estimation And State Forecasting, James Paul Carmichael

Theses and Dissertations--Electrical and Computer Engineering

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn …

Modeling The Early Visual System, Nicholas Lanning

Theses and Dissertations--Electrical and Computer Engineering

There are two encoding schema present in simple cells in the early visual system of vertebrates: the retinal simple cells activate highly when the receptive field contains a center surround stimulus, while the primary visual cortex’s (V1) simple cells activate highly when the receptive field contains visual edges. Work has been done in the past to enforce constraints on visual machine learning such that the retinal or V1 encoding is learned, but this work is often done to emulate retinal and V1 encoding in a vacuum. Recent work using convolutional neural networks focuses on anatomical constraints along with a supervised …

Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland

College of Graduate Studies: Theses & Dissertations

This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with …

Teaching Data Acquisition Through The Arduino-Driven Home Weather Station Project, Sheryl Dutton, Kurt Galderisi, Murat Kuzlu, Otilia Popescu, Vukica Jovanovic

Engineering Technology Faculty Publications

The main objective of this paper is to present one possible way to engage undergraduate students in designing a system that uses the Internet of Things (IoT) strategy for data acquisition and management. The MATLAB home weather station project presented here was developed by a team of students for the senior design course in the Electrical Engineering Technology undergraduate program at Old Dominion University (ODU). The main purpose of this project was for undergraduate students to learn how to create a localized, compact, and precise weather station. Utilizing various sensors, both homemade and sourced online, this weather station is capable …