Engineering Commons^™

Sensitivity Analysis Of An Agent-Based Simulation Model Using Reconstructability Analysis, Andey M. Nunes, Martin Zwick, Wayne Wakeland

Systems Science Faculty Publications and Presentations

Reconstructability analysis, a methodology based on information theory and graph theory, was used to perform a sensitivity analysis of an agent-based model. The NetLogo BehaviorSpace tool was employed to do a full 2^k factorial parameter sweep on Uri Wilensky’s Wealth Distribution NetLogo model, to which a Gini-coefficient convergence condition was added. The analysis identified the most influential predictors (parameters and their interactions) of the Gini coefficient wealth inequality outcome. Implications of this type of analysis for building and testing agent-based simulation models are discussed.

Machine-Learning-Based Hybrid Method For The Multilevel Fast Multipole Algorithm, Jia Jing Sun, Sheng Sun, Yongpin P. Chen, Lijun Jiang, Jun Hu

Electrical and Computer Engineering Faculty Research & Creative Works

In this letter, a hybrid translation computation method for the multilevel fast multipole algorithm (MLFMA) is proposed based on machine learning. The hybrid method combines both generalized regression neural network (GRNN) and artificial neural network (ANN) to replace the traditional translation procedure during the solving process of the MLFMA. Based on the data corresponding to every one of the interaction list boxes at each level, the hybrid neural network is trained. Comparing with the previous machine learning method in this field, the proposed model is more general, and with lower complexity since it inherits the accuracy of the GRNN as …

Geographic Data Mining And Knowledge Discovery, Liangdong Deng

FIU Electronic Theses and Dissertations

Geographic data are information associated with a location on the surface of the Earth. They comprise spatial attributes (latitude, longitude, and altitude) and non-spatial attributes (facts related to a location). Traditionally, Physical Geography datasets were considered to be more valuable, thus attracted most research interest. But with the advancements in remote sensing technologies and widespread use of GPS enabled cellphones and IoT (Internet of Things) devices, recent years witnessed explosive growth in the amount of available Human Geography datasets. However, methods and tools that are capable of analyzing and modeling these datasets are very limited. This is because Human Geography …

Sports Analytics: Putting The Fun Back Into Analytics, Walt Degrange

Operations Management Presentations

With the recent success of sports teams heavily using analytics (Dodgers, Patriots, Capitals, Warriors, Leicester City F.C.), does this mean that analytics has gained a foothold in the sports world? I use a k-means clustering model to determine if performance since 2015 in the four major US sports can support this question. And is there a career path that a high school student can use to become a sports analytics professional? This presentation attempts to answer that question by exploring all the areas of the application of analytics in sports. The final point the brief makes is that by using …

Development Of A Low Power, Low Cost Rural Railway Intersection Smart Detection And Warning System, Sara Ahmed, Samer Dessouky, Raymond Downing

Data

This project explores a different approach to provide preemptive warning for train detection at grade-crossings to increase safety and reduce motor vehicle congestion. The development of a novel, low cost, low power, and off rail right-of-way (ROW) detection and warning system will be presented. A background of track circuits, which is the rail industries standard for train detection, will also be provided to highlight the benefits and challenges of the rail industry installing a system at every grade-crossings that lack any type of active warning. The benefits of using thermal imaging instead of traditional video for computer vision will also …

Development Of A Low Power, Low Cost Rural Railway Intersection Smart Detection And Warning System, Sara Ahmed, Samer Dessouky, Raymond Downing

Publications

This project explores a different approach to provide preemptive warning for train detection at grade-crossings to increase safety and reduce motor vehicle congestion. The development of a novel, low cost, low power, and off rail right-of-way (ROW) detection and warning system will be presented. A background of track circuits, which is the rail industries standard for train detection, will also be provided to highlight the benefits and challenges of the rail industry installing a system at every grade-crossings that lack any type of active warning. The benefits of using thermal imaging instead of traditional video for computer vision will also …

Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis

Undergraduate Research & Mentoring Program

Recurrent neural networks (RNNs) are a form of machine learning used to predict future values. This project uses RNNs tor predict future values for a flight simulator. Coded in Python using the Keras library, the model demonstrates training loss and validation loss, referring to the error when training the model.

A Bibliometric Survey On The Reliable Software Delivery Using Predictive Analysis, Jalaj Pachouly, Swati Ahirrao, Ketan Kotecha

Library Philosophy and Practice (e-journal)

Delivering a reliable software product is a fairly complex process, which involves proper coordination from the various teams in planning, execution, and testing for delivering software. Most of the development time and the software budget's cost is getting spent finding and fixing bugs. Rework and side effect costs are mostly not visible in the planned estimates, caused by inherent bugs in the modified code, which impact the software delivery timeline and increase the cost. Artificial intelligence advancements can predict the probable defects with classification based on the software code changes, helping the software development team make rational decisions. Optimizing the …

Classifying Reflectance Targets Under Ambient Light Conditions Using Passive Spectral Measurements, Ali Hamidisepehr, Michael P. Sama, Joseph S. Dvorak, Ole O. Wendroth, Michael D. Montross

Biosystems and Agricultural Engineering Faculty Publications

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable …

Short-Term Load Forecasting Of Microgrid Via Hybrid Support Vector Regression And Long Short-Term Memory Algorithms, Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi

Electrical and Computer Engineering Publications

© 2020 by the authors. Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target …

Lstm Forecasts For Smart Home Electricity Usage, Rosemary E. Alden, Huangjie Gong, Cristinel Ababei, Dan M. Ionel

Power and Energy Institute of Kentucky Faculty Publications

With increasing of distributed energy resources deployment behind-the-meter and of the power system levels, more attention is being placed on electric load and generation forecasting or prediction for individual residences. While prediction with machine learning based approaches of aggregated power load, at the substation or community levels, has been relatively successful, the problem of prediction of power of individual houses remains a largely open problem. This problem is harder due to the increased variability and uncertainty in user consumption behavior, which make individual residence power traces be more erratic and less predictable. In this paper, we present an investigation of …

A Hybrid Framework Using A Qubo Solver For Permutation-Based Combinatorial Optimization, Siong Thye Goh, Sabrish Gopalakrishnan, Jianyuan Bo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optimization (QUBO) solver. To do so, transformations are required to change a constrained optimization model to an unconstrained model that involves parameter tuning. We propose techniques to overcome the challenges in using a QUBO solver that typically comes with limited numbers of bits. First, to smooth the energy landscape, we reduce the magnitudes of the input without compromising optimality. We propose a machine learning approach to tune the parameters for good performance effectively. To handle possible infeasibility, we introduce …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …

Structural Health Monitoring Of Pipelines In Radioactive Environments Through Acoustic Sensing And Machine Learning, Michael Thompson

FIU Electronic Theses and Dissertations

Structural health monitoring (SHM) comprises multiple methodologies for the detection and characterization of stress, damage, and aberrations in engineering structures and equipment. Although, standard commercial engineering operations may freely adopt new technology into everyday operations, the nuclear industry is slowed down by tight governmental regulations and extremely harsh environments. This work aims to investigate and evaluate different sensor systems for real-time structural health monitoring of piping systems and develop a novel machine learning model to detect anomalies from the sensor data. The novelty of the current work lies in the development of an LSTM-autoencoder neural network to automate anomaly detection …

Automatic Detection Of Dynamic And Static Activities Of The Older Adults Using A Wearable Sensor And Support Vector Machines, Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Physical Therapy Faculty Articles and Research

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant …

A Data-Driven Approach For Winter Precipitation Classification Using Weather Radar And Nwp Data, Bong Chul Seo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study describes a framework that provides qualitative weather information on winter precipitation types using a data-driven approach. The framework incorporates the data retrieved from weather radars and the numerical weather prediction (NWP) model to account for relevant precipitation microphysics. To enable multimodel-based ensemble classification, we selected six supervised machine learning models: k-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and multi-layer perceptron. Our model training and cross-validation results based on Monte Carlo Simulation (MCS) showed that all the models performed better than our baseline method, which applies two thresholds (surface temperature and atmospheric layer thickness) for …

Moving Targets: Addressing Concept Drift In Supervised Models For Hacker Communication Detection, Susan Mckeever, Brian Keegan, Andrei Quieroz

Conference papers

Abstract—In this paper, we are investigating the presence of concept drift in machine learning models for detection of hacker communications posted in social media and hacker forums. The supervised models in this experiment are analysed in terms of performance over time by different sources of data (Surface web and Deep web). Additionally, to simulate real-world situations, these models are evaluated using time-stamped messages from our datasets, posted over time on social media platforms. We have found that models applied to hacker forums (deep web) presents an accuracy deterioration in less than a 1-year period, whereas models applied to Twitter (surface …

Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson

Faculty Publications

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture …

A Convolutional Neural Network For Fast Fluence Estimation In Complex Tissues, Nicholas Blasey, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging is a non-invasive diagnostic imaging technique that gives images of photoabsorbers based on their absorption of optical energy. These optical absorption properties can then be linked to important tissue properties. For the method to be quantitative, however, it is necessary to have an accurate estimation of the light fluence in the tissue. The current gold standard in addressing the fluence estimation problem, a Monte Carlo Simulation, is costly in time and computation. In this work, we developed a deep neural network to quickly and accurately estimate light fluence in arbitrary tissue types and geometries. The network was …

Conference Roundup: Smart Cataloging - Beginning The Move From Batch Processing To Automated Classification, Rachel S. Evans

Articles, Chapters and Online Publications

This article reviewed the Amigos Online Conference titled “Work Smarter, Not Harder: Innovating Technical Services Workflows” keynote session delivered by Dr. Terry Reese on February 13, 2020. Excerpt:

"As the developer of MarcEdit, a popular metadata suite used widely across the library community, Reese’s current work is focused on the ways in which libraries might leverage semantic web techniques in order to transform legacy library metadata into something new. So many sessions related to using new technologies in libraries or academia, although exciting, are not practical enough to put into everyday use by most librarians. Reese’s keynote, titled Smart Cataloging: …

Quantitative Prediction Of Fractures In Shale Using The Lithology Combination Index, Zhengchen Zhang, Pingping Li, Yujie Yuan, Kouqi Liu, Jingyu Hao, Huayao Zou

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Fractures, which are related to tectonic activity and lithology, have a significant impact on the storage and production of oil and gas in shales. To analyze the impact of lithological factors on fracture development in shales, we selected the shale formation from the Da’anzhai member of the lower Jurassic shales in a weak tectonic deformation zone in the Sichuan Basin. We defined a lithology combination index (LCI), that is, an attribute quantity value of some length artificially defined by exploring the lithology combination. LCI contains information on shale content at a …

Advancing Ecohydrology In The 21st Century: A Convergence Of Opportunities, Andrew J. Guswa, Doerthe Tetzlaff, John S. Selker, Darryl E. Carlyle-Moses, Elizabeth W. Boyer, Michael Bruen, Carles Cayuela, Irena F. Creed, Nick Van De Giesen, Domenico Grasso, David M. Hannah, Janice E. Hudson, Sean A. Hudson, Shin'ichi Iida, Robert B. Jackson, Gabriel G. Katul, Tomo'omi Kumagai, Pilar Llorens, Flavio Lopes Ribeiro, Beate Michalzik, Kazuki Nanko, Christopher Oster, Diane E. Pataki, Catherine A. Peters, Andrea Rinaldo, Daniel Sanchez Carretero, Branimir Trifunovic, Maciej Zalewski, Marja Haagsma, Delphis F. Levia

Engineering: Faculty Publications

Nature-based solutions for water-resource challenges require advances in the science of ecohydrology. Current understanding is limited by a shortage of observations and theories that can further our capability to synthesize complex processes across scales ranging from submillimetres to tens of kilometres. Recent developments in environmental sensing, data, and modelling have the potential to drive rapid improvements in ecohydrological understanding. After briefly reviewing advances in sensor technologies, this paper highlights how improved measurements and modelling can be applied to enhance understanding of the following ecohydrological examples: interception and canopy processes, root uptake and critical zone processes, and up-scaled effects of land …

An Iot Framework For Modeling And Controlling Thermal Comfort In Buildings, Fadi Alsaleem, Mehari K. Tesfay, Mostafa Rafaie, Kevin Sinkar, Dhaman Besarla, Parthiban Arunasalam

Durham School of Architectural Engineering and Construction: Faculty Publications

Humans spend more than 90% of their day in buildings, where their health and productivity are demonstrably linked to thermal comfort. Building thermal comfort systems account for the largest share of U.S energy consumption. Despite this high-energy cost, due to building design complexity and the variety of building occupant needs, addressing thermal comfort in buildings remains a difficult problem. To overcome this challenge, this paper presents an Internet of Things (IoT) approach to efficiently model and control comfort in buildings. In the model phase, a method to access and exploit wearable device data to build a personal thermal comfort model …

Alexa, Ask My Library: How Do I Build A Custom Skill To Extend Reference Services?, Christopher M. Jimenez

Works of the FIU Libraries

The Reference Technology team at Florida International University recently published an Alexa Skill that incorporates the LibAnswers API into the device’s answer bank. We have several Echo Show devices at our public service desks to meet the demands of extended hours while also enhancing public service presence beyond the reference desk.

The Green Library at FIU’s Modesto Maidique Campus now operates on a 24/5 schedule, allowing students to access library facilities at any time during the week. In addition, both the Hubert Library and the Engineering Library Service Center stay open past times when personal reference assistance is available. This …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Bibliometric Study Of Bibliometric Papers About Clustering, Preeti Mulay, Rahul Raghvendra Joshi, Archana Chaudhari

Library Philosophy and Practice (e-journal)

Bibliometric survey or bibliometric review papers generally analyses the work done previously by eminent personalities, authors, countries and various institutions which was published in giant databases like Scopus, Web of Science, Google Scholar, Research Gate and others. Bibliometric papers provide amalgamation of wide range of research papers from journals, conferences, reviews and other papers, which are working papers, papers with results, proposals and few of them are survey papers etc. Bibliometric papers are One-Stop-Solution for the readers and upcoming researchers to get acquainted entirely about the specific topic / domain. Bibliometric papers also help in smartly locating research-gaps for the …

A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin

Faculty Publications

In this work, the behavior of dilute interstitial helium in W–Mo binary alloys was explored through the application of a first principles-informed neural network (NN) in order to study the early stages of helium-induced damage and inform the design of next generation materials for fusion reactors. The neural network (NN) was trained using a database of 120 density functional theory (DFT) calculations on the alloy. The DFT database of computed solution energies showed a linear dependence on the composition of the first nearest neighbor metallic shell. This NN was then employed in a kinetic Monte Carlo simulation, which took into …

Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey J. Wagner, Clay Koschnick, Steven J. Schuldt, Jada B. Williams, Kevin Hallinan

Faculty Publications

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined …

Detection Of Human Vigilance State During Locomotion Using Wearable Fnirs, Masudur R. Siddiquee

FIU Electronic Theses and Dissertations

Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied …