Physical Sciences and Mathematics Commons^™

Multi-Scale Simulation Revealing The Decomposition Mechanism Of Electrolyte On Lithium Metal Electrode, Yan-Yan Zhang, Yue Liu, Yi-Ming Lu, Pei-Ping Yu, Wen-Xuan Du, Bing-Yun Ma, Miao Xie, Hao Yang, Tao Cheng

Journal of Electrochemistry

Lithium metal is considered as an ideal anode material for next-generation high energy density batteries with its high specific capacity and low electrode potential. However, the high activity of lithium metal can lead to a series of safety issues. For example, lithium metal will continuously react chemically with the electrolyte, forming unstable the solid electrolyte (SEI) films. In addition, lithium dendrites can be formed during cycling, which can puncture the SEI film and cause short circuits in the battery. These drawbacks greatly hinder the commercial application of lithium metal. To solve the above problems, it is important to understand the …

Effects Of Electrode Shape On Lithiation Process Of Lithium-Ion Battery Electrodes, Shi-Wei Sun, Jian-Jun Nie, Yi-Cheng Song

Journal of Electrochemistry

This paper studies the influence of electrode shape on the lithiation process of lithium ion batteries. Both experimental observation and numerical simulation are employed to investigate the competitive interaction between the diffusion of lithium ions in both solid and liquid phases and the lithium intercalation reaction at the electrode surface. Experimental cells were prepared with the anode and cathode being placed parallel, leaving the latter embracing the former. An experimental device based on CCD camera was set up for in situ observation of electrode lithiation. The lithiation levels of the graphite anodes were estimated according to the observed color profile. …

Adjusting The Alloying Degree Of Pt3Zn To Improve Acid Oxygen Reduction Activity And Stability, Tian-En Zhang, Ya-Ni Yan, Jun-Ming Zhang, Xi-Ming Qu, Yan-Rong Li, Yan-Xia Jiang

Journal of Electrochemistry

Proton exchange membrane fuel cell (PEMFC) is a new type of energy device, a relatively excellent way to achieve carbon neutrality. However, due to the relatively slow reaction rate of oxygen reduction reaction (ORR) at the cathode, platinum (Pt) is the key material of the cathode catalyst. However, Pt is a kind of noble metal, and its high cost restricts the PEMFC commercialization process. At present, the main approach is to combine transition metals with Pt to prepare Pt-based alloys and to reduce the use of Pt. Pt-based alloys are excellent catalysts for ORR, improving both the activity and stability, …

Preparation Of Pt@Basrtio3 Nanostructure And Its Properties Towards Photoelectrochemical Ammonia Synthesis, Jing Zhang, Rui-Xia Guo, Jian-Jun Fu, Shi-Bin Yin, Pei-Kang Shen, Xin-Yi Zhang

Journal of Electrochemistry

Ammonia is an important industrial raw material and a potential green energy. Using renewable energy to convert nitrogen into ammonia under ambient condition is an attractive method. However, the development of efficient photoelectrochemical ammonia synthesis catalysts remains a challenge. Perovskite such as BaSrTiO₃ (BST) is a good photocatalytic material. However, BST is active under ultraviolet light and has a high recombination rate of photogenerated electron-hole pairs. By dispersing precious metals, it can effectively regulate the absorption of sunlight by BST. In this work, we used a two-step method to prepare BST. The H₂PtCl₆·6H₂O …

Efficient Low Dimensional Representation Of Vector Gaussian Distributions, Md Mahmudul Hasan

LSU Doctoral Dissertations

This dissertation seeks to find optimal graphical tree model for low dimensional representation of vector Gaussian distributions. For a special case we assumed that the population co-variance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology. We have found the Constrained Minimum Determinant Factor Analysis (CMDFA) and Constrained Minimum Trace Factor Analysis (CMTFA) decompositions of this special $\Sigma_x$ in connection with the operational meanings of the respective solutions. Characterizing the CMDFA solution of special $\Sigma_x$, according to the second interpretation of Wyner's common information, is equivalent to solving the source coding problem of finding the minimum …

Data-Driven Design And Analysis Of Next Generation Mobile Networks For Anomaly Detection And Signal Classification With Fast, Robust And Light Machine Learning, Muhammed Furkan Küçük

USF Tampa Graduate Theses and Dissertations

This research focuses on machine (and deep) learning applications (including clustering,anomaly detection and signal classification) for self-organizing and next generation mobile networks in wireless communications. Specifically, this dissertation document will address the three different topics.

First, in the study titled “Performance analysis of neural network topologies and hyperparameters for deep clustering”, we explore the relationship between the clustering performance and network complexity. Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers have proposed creative learning representations to utilize …

Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri

Electronic Thesis and Dissertation Repository

Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …

Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha

Electrical and Computer Engineering Faculty Publications

Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and …

Multi-Device Data Analysis For Fault Localization In Electrical Distribution Grids, Jacob D L Hunte

Electronic Thesis and Dissertation Repository

The work presented in this dissertation represents work which addresses some of the main challenges of fault localization methods in electrical distribution grids. The methods developed largely assume access to sophisticated data sources that may not be available and that any data sets recorded by devices are synchronized. These issues have created a barrier to the adoption of many solutions by industry. The goal of the research presented in this dissertation is to address these challenges through the development of three elements. These elements are a synchronization protocol, a fault localization technique, and a sensor placement algorithm.

The synchronization protocol …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

Practical Considerations And Applications For Autonomous Robot Swarms, Rory Alan Hector

LSU Doctoral Dissertations

In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical …

Representing And Analyzing The Dynamics Of An Agent-Based Adaptive Social Network Model With Partial Integro-Differential Equations, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

We formulated and analyzed a set of partial integro-differential equations that capture the dynamics of our adaptive network model of social fragmentation involving behavioral diversity of agents. Previous results showed that, if the agents’ cultural tolerance levels were diversified, the social network could remain connected while maintaining cultural diversity. Here we converted the original agent-based model into a continuous equation-based one so we can gain more theoretical insight into the model dynamics. We restricted the node states to 1-D continuous values and assumed the network size was very large. As a result, we represented the whole system as a set …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Lyapunov-Based Economic Model Predictive Control For Detecting And Handling Actuator And Simultaneous Sensor/Actuator Cyberattacks On Process Control Systems, Henrique Oyama, Dominic Messina, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

The controllers for a cyber-physical system may be impacted by sensor measurement cyberattacks, actuator signal cyberattacks, or both types of attacks. Prior work in our group has developed a theory for handling cyberattacks on process sensors. However, sensor and actuator cyberattacks have a different character from one another. Specifically, sensor measurement attacks prevent proper inputs from being applied to the process by manipulating the measurements that the controller receives, so that the control law plays a role in the impact of a given sensor measurement cyberattack on a process. In contrast, actuator signal attacks prevent proper inputs from being applied …

Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …

Foam-Based Floatovoltaics: A Potential Solution To Disappearing Terminal Natural Lakes, Koami Soulemane Hayibo, Joshua M. Pearce

Electrical and Computer Engineering Publications

Terminal lakes are disappearing worldwide because of direct and indirect human activities. Floating photovoltaics (FPV) are a synergistic system with increased energy output because of water cooling, while the FPV reduces water evaporation. This study explores how low-cost foam-based floatovoltaic systems can mitigate the disappearance of natural lakes. A case study is performed on 10%–50% FPV coverage of terminal and disappearing Walker Lake. Water conservation is investigated with a modified Penman-Monteith evapotranspiration method and energy generation is calculated with an operating temperature model experimentally determined from foam-based FPV. Results show FPV saves 52,000,000 m3/year of water and US$6,000,000 at 50% …

Aerial Radiation Detection Identification And Measurement System Detector Material Comparison Study, Benjamin C. Troxell, Kacey D. Mcgee, Christina L. Dugan

Faculty Publications

The 20th Chemical Biological Radiological Nuclear and Explosives Command (CBRNE) currently utilizes an airborne sodium iodide gamma and beta detection system to map radiation fields over large areas of interest. The 20th CBRNE explored emergent detector technologies utilizing two detection materials; thallium-activated cesium iodide and high purity germanium (HPGe). These detectors were simulated at various altitudes and compared to background measurements. The sodium iodide detector failed to provide isotopic discrimination at distance. The thallium-activated cesium iodide CsI(Tl) detector provided sufficient absolute efficiency and energy resolution to identify isotopics at distance. The HPGe detector provided the best energy resolution. However, current …

Understanding The Mechanism Of Deep Learning Frameworks In Lesion Detection For Pathological Images With Breast Cancer, Wei-Wen Hsu, Chung-Hao Chen, Chang Hao, Yu-Ling Hou, Xiang Gao, Yun Shao, Xueli Zhang, Jingjing Wang, Tao He, Yanhong Tai

Electrical & Computer Engineering Faculty Publications

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end …

Assessing Security Risks With The Internet Of Things, Faith Mosemann

Senior Honors Theses

For my honors thesis I have decided to study the security risks associated with the Internet of Things (IoT) and possible ways to secure them. I will focus on how corporate, and individuals use IoT devices and the security risks that come with their implementation. In my research, I found out that IoT gadgets tend to go unnoticed as a checkpoint for vulnerability. For example, often personal IoT devices tend to have the default username and password issued from the factory that a hacker could easily find through Google. IoT devices need security just as much as computers or servers …

Bistability And Switching Behavior In Moving Animal Groups, Daniel Strömbom, Stephanie Nickerson, Catherine Futterman, Alyssa Difazio, Cameron Costello, Kolbjørn Tunstrøm

Northeast Journal of Complex Systems (NEJCS)

Moving animal groups such as schools of fish and flocks of birds frequently switch between different group structures. Standard models of collective motion have been used successfully to explain how stable groups form via local interactions between individuals, but they are typically unable to produce groups that exhibit spontaneous switching. We are only aware of one model, constructed for barred flagtail fish that are known to rely on alignment and attraction to organize their collective motion, that has been shown to generate this type of behavior in 2D (or 3D). Interestingly, another species of fish, golden shiners, do exhibit switching …

Finding Signal In The Noise: High-Fidelity, Quantitative, Optical Blood Perfusion Imaging With Interference, Abdul Mohaimen Safi

USF Tampa Graduate Theses and Dissertations

For label-free, non-invasive, wide field-of-view (FOV) imaging/monitoring of blood flow, speckle-based approaches are gaining popularity. However, to obtain quantitative flow information, speckle techniques rely on the multi-exposure scheme which requires complex, bulky, and expensive instrumentation, limiting its application to preclinical studies. This dissertation directly addresses these issues. In the first part of this dissertation, we report a novel single shot synthetic multi-exposure speckle imaging (syMESI) method to synthetically produce multi-exposure images from one short single exposure speckle image using spatial binning/averaging. We demonstrate that syMESI can reimagine conventional hardware based MESI, with low-cost single exposure laser speckle imaging (LSCI) instrumentation. …

Soft Magnetic Composite Substrates For Rf/Microwave Applications, Poonam Lathiya

USF Tampa Graduate Theses and Dissertations

Novel soft magnetic ferrite materials will play a crucial role in next-generation over one trillion sensors (also known as “trillion sensor economy) related to 5G communications and internet of things, as these materials can achieve improved wireless power and signal transfer efficiency with high operation frequency. In this work, Ni-Cu-Zn and Ni-Co-Zn ferrites with high permeability, high permittivity, and low magnetic and dielectric losses were prepared for RF and microwave device applications. Frequency dispersion of RF complex permeability of Ni-Cu-Zn ferrites prepared under different applied hydraulic pressures and durations have been thoroughly investigated. The Ni0.35Cu0.19Zn0.46Fe2O4 ferrites were prepared by conventional …

Method Of Making Hinged Self-Referencing Fabry–Pérot Cavity Sensors, Jeremiah C. Williams, Hengky Chandrahalim

AFIT Patents

A method is provided for fabricating a passive optical sensor on a tip of an optical fiber. The method includes perpendicularly cleaving a tip of an optical fiber and mounting the tip of the optical fiber in a specimen holder of a photosensitive polymer three-dimensional micromachining machine. The method includes forming a three-dimensional microscopic optical structure within the photosensitive polymer that comprises a two cavity Fabry-Perot Interferometer (FPI) having a hinged optical layer that is pivotally coupled to a suspended structure. The method includes removing an uncured portion of the photosensitive polymer using a solvent. The method includes depositing a …

In Situ Characterization Of Electrode Structure And Catalytic Processes In The Electrocatalytic Oxygen Reduction Reaction, Ya-Chen Feng, Xiang Wang, Yu-Qi Wang, Hui-Juan Yan, Dong Wang

Journal of Electrochemistry

As an electrochemical energy conversion system, fuel cell has the advantages of high energy conversion efficiency and high cleanliness. Oxygen reduction reaction (ORR), as an important cathode reaction in fuel cells, has received extensive attention. At present, the electrocatalysts are still one of the key materials restricting the further commercialization of fuel cells. The fundamental understanding on the catalytic mechanism of ORR is conducive to the development of electrocatalysts with the enhanced activity and high selectivity. This review aims to summarize the in situ characterization techniques used to study ORR. From this perspective, we first briefly introduce the advantages of …

Synchrotron X-Rays Characterizations Of Metal-Air Batteries, Ya-Jie Song, Xue Sun, Li-Ping Ren, Lei Zhao, Fan-Peng Kong, Jia-Jun Wang

Journal of Electrochemistry

The rapid development of electric vehicles urgently requires high-energy-density batteries. Recently, metal-air batteries have attracted much attention in industry and academia for their ultra-high theoretical energy densities. However, the practical application of metal-air batteries is severely impeded by multiple drawbacks, including severe side reactions, low energy efficiency, and limited cycle life. Understanding the reaction mechanism of the cell and further developing effective strategies are beneficial for the practical application of metal-air batteries. In the past decade, advanced characterization techniques have accelerated the development of metal-air batteries. In particular, synchrotron radiation-based characterization techniques have been widely applied to the mechanistic study …

In-Situ/Operando57Fe Mössbauer Spectroscopic Technique And Its Applications In Nife-Based Electrocatalysts For Oxygen Evolution Reaction, Jafar Hussain Shah, Qi-Xian Xie, Zhi-Chong Kuang, Ri-Le Ge, Wen-Hui Zhou, Duo-Rong Liu, Alexandre I. Rykov, Xu-Ning Li, Jing-Shan Luo, Jun-Hu Wang

Journal of Electrochemistry

The development of highly efficient and cost-effective electrocatalysts for the sluggish oxygen evolution reaction (OER) remains a significant barrier to establish effective utilization of renewable energy storage systems and water splitting to produce clean fuel. The current status of the research in developing OER catalysts shows that NiFe-based oxygen evolution catalysts (OECs) have been proven as excellent and remarkable candidates for this purpose. But it is critically important to understand the factors that influence their activity and underlying mechanism for the development of state-of-the-art OER catalysts. Therefore, the development of in-situ/operando characterizations is urgently required to detect key …

An Additive Incorporated Non-Nucleophilic Electrolyte For Stable Magnesium Ion Batteries, Mao-Ling Xie, Jun Wang, Chen-Ji Hu, Lei Zheng, Hua-Bin Kong, Yan-Bin Shen, Hong-Wei Chen, Li-Wei Chen

Journal of Electrochemistry

Non-nucleophilic electrolytes are promising next-generation highly stable electrolytes for magnesium-ion batteries (MIBs). However, a passivation layer on Mg metal anode usually blocks Mg²⁺ diffusion, leading to poor reaction kinetics and low Coulombic efficiency of the Mg plating/stripping in these electrolytes. Here we explore the utilization of phenyl disulfide (PDF) as a film-forming additive for non-nucleophilic electrolytes to regulate the interfacial chemistry on Mg metal anode. Phenyl-thiolate generated from the PDF additive was found to suppress the unfavorable surface blocking layer, resulted in a high Coulombic efficiency of up to 99.5% for the Mg plating/stripping process as well as a …

Fiber-Based Electrical Energy Storage And Harvesting Devices For Wearable Electronics, Tareq Kareri

USF Tampa Graduate Theses and Dissertations

Over the past few years, smart textiles and wearable technologies have received tremendous attention due to their functionalities and characteristics, which could be used in a variety of ways in healthcare, sports/leisure, fashion, military, personal protective, and energy applications. These technologies depend on self-power systems, which require energy sources (e.g., batteries, supercapacitors, and solar cells) to power their projected functionalities in the future. Therefore, fabricating energy harvesting cells (i.e., solar cells) and energy storage cells (i.e., batteries and supercapacitors) in the form of fibers are promising solutions for powering wearable electronics due to their unique features such as flexibility and …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …