Computational Engineering Commons^™

Numerical And Semi-Analytical Estimation Of Convective Heat Transfer Coefficient For Buildings In An Urban-Like Setting, Anwar Demsis Awol

Electronic Thesis and Dissertation Repository

Urban building arrangements such as packing density, orientation and size are known to influence the microclimate surrounding each building. Studies on the impact of urban microclimatic changes on convective heat transfer coefficient (CHTC) from a stock of buildings, however, have been rare in surveyed literature. The present study focuses on numerical and analytical investigation of CHTC from building-like models with homogeneous set of equal and unequal planar and frontal densities. Consequently, the study discusses the CHTC response in relation to broader changes in the urban surface form. Part of the process involves the development of a simplified one-dimensional semi-analytical CHTC …

Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo

Electronic Thesis and Dissertation Repository

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …

A Cfd Study On The Performance Of High Speed Planing Hulls, Mowgli J. Crosby

Masters Theses

Most high speed water craft are able to achieve high speeds through the use of a planing hull. Planing hulls use hydrodynamic forces to lift a portion of the vessel out of the water, reducing drag, and allowing for greater speeds. Determining the flow around such vessels is traditionally achieved using a scale model in a tow tank. The purpose of this study was to analyze the performance of a high speed planing hull determine the effects of several geometric features using computational fluid dynamics rather than traditional experimentation. The goal was to determine the best configuration to ensure the …

Stock Price Using Domain Specific Lexicons, Zane Turner

Computer Science and Computer Engineering Undergraduate Honors Theses

Sentiment analysis is a broad and expanding field that aims to extract and classifying opinions from textual data. Lexicon-based approaches are based on using a sentiment lexicon, a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons …

Molecular Dynamics Simulations Of Interaction Of Dna Nucleotides And Lignin Oligomers With Small Molecules And Interfaces, Xinjie Tong

LSU Doctoral Dissertations

Molecular dynamics (MD) simulations of interaction of DNA nucleotides with self-assembled monolayers (SAMs) provide valuable information that is critical to the development of a new DNA sequencing technique. We investigated the interactions and transport characteristics of mononucleotides moving through nanoslits with SAMs-covered surfaces. Our simulations focused on nanoslits in which the walls were composed of three different types of SAMs: methylformyl terminated, methyl terminated, and phenoxy terminated. The results demonstrated that the phenoxy terminated surfaces have the shortest required nanoslits length for nucleotides separation.

Using MD simulations, we also investigated the interaction of mono-lignin and oligo-lignols with lipid bilayers and …

Cyber Metaphors And Cyber Goals: Lessons From “Flatland”, Pierre Trepagnier

Military Cyber Affairs

Reasoning about complex and abstract ideas is greatly influenced by the choice of metaphors through which they are represented. In this paper we consider the framing effect in military doctrine of considering cyberspace as a domain of action, parallel to the traditional domains of land, sea, air, and space. By means of the well-known Victorian science-fiction novella Flatland, we offer a critique of this dominant cyber metaphor. In Flatland, the problems of lower-dimensional beings comprehending additional dimensions are explored at some length. Inspired by Flatland, our suggested alternate metaphor for cyber is an additional (fourth) dimension. We …

Data-Driven Predictive Maintenance Scheduling Policies For Railways, Pedro Cesar Lopes Gerum, Ayca Altay, Melike Baykal-Gürsoy

Supply Chain Management

Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the …

Size Matters: The Impact Of Training Size In Taxonomically-Enriched Word Embeddings, Alfredo Maldonado, Filip Klubicka, John D. Kelleher

Articles

Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with …

Generalized Relay Network Design And Collaborative Dispatching In Truckload Transportation, Amin Ziaeifar

Operations Research and Engineering Management Theses and Dissertations

The truckload industry faces a serious problem of high driver shortage and turnover rate which is typically around 100\%. Among the major causes of this problem are extended on-the-road times where drivers handle several truckload pickup and deliveries successively; non-regular schedules and get-home rates; and low utilization of drivers dedicated time. These are by-and-large consequences of the driver-to-load dispatching method, which is based on point-to-point dispatching or direct shipment from origin-to-destination, commonly employed in the industry. In this dissertation, we consider an alternative dispatching method that necessitates careful design of an underlying network. In this scheme, a truckload on its …

A Cfd Study Of Steady Fully Developed Laminar Flow Through A 90-Degree Bend Pipe With A Square Cross-Sectional Area, Subodh Sushant Toraskar

Mechanical & Aerospace Engineering Theses & Dissertations

Fluid flow through a closed curved conduit has always been a topic of extensive research, as it has many practical and industrial applications. The flow is generally characterized by a presence of secondary flow, vortical motions and pressure losses for different flow regimes. These observed irregularities may positively or negatively impact the flow. They are beneficial for cases where mixing of fluids is required, usually observed for multiphase flow regimes or detrimental for cases involving particles in the fluid. There are also instances where a particle-laden fluid transported through the curved pipe was directly related to corrosion- erosion related problems. …

Gesture Based Control Of Semi-Autonomous Vehicles, Brian Sanders

Computational Modeling & Simulation Engineering Theses & Dissertations

The objective of this investigation is to explore the use of hand gestures to control semi-autonomous vehicles, such as quadcopters, using realistic, physics based simulations. This involves identifying natural gestures to control basic functions of a vehicle, such as maneuvering and onboard equipment operation, and building simulations using the Unity game engine to investigate preferred use of those gestures. In addition to creating a realistic operating experience, human factors associated with limitations on physical hand motion and information management are also considered in the simulation development process. Testing with external participants using a recreational quadcopter simulation built in Unity was …

Modelling The Addition Of Limestone In Cement Using Hydcem, Niall Holmes, Denis Kelliher, Mark Tyrer

Conference papers

Hydration models can aid in the prediction, understanding and description of hydration behaviour over time as the move towards more sustainable cements continues.

HYDCEM is a new model to predict the phase assemblage, degree of hydration and heat release over time for cements undergoing hydration for any w/c ratio and curing temperatures up to 450C. HYDCEM, written in MATLAB, complements more sophisticated thermodynamic models by predicting these properties over time using user-friendly inputs within one code. A number of functions and methods based on up to date cement hydration behaviour from the literature are hard-wired into the code along with …

Improve Image Classification Using Data Augmentation And Neural Networks, Shanqing Gu, Manisha Pednekar, Robert Slater

SMU Data Science Review

In this paper, we present how to improve image classification by using data augmentation and convolutional neural networks. Model overfitting and poor performance are common problems in applying neural network techniques. Approaches to bring intra-class differences down and retain sensitivity to the inter-class variations are important to maximize model accuracy and minimize the loss function. With CIFAR-10 public image dataset, the effects of model overfitting were monitored within different model architectures in combination of data augmentation and hyper-parameter tuning. The model performance was evaluated with train and test accuracy and loss, characteristics derived from the confusion matrices, and visualizations of …

Minos: Unsupervised Netflow-Based Detection Of Infected And Attacked Hosts, And Attack Time In Large Networks, Mousume Bhowmick

Boise State University Theses and Dissertations

Monitoring large-scale networks for malicious activities is increasingly challenging: the amount and heterogeneity of traffic hinder the manual definition of IDS signatures and deep packet inspection. In this thesis, we propose MINOS, a novel fully unsupervised approach that generates an anomaly score for each host allowing us to classify with high accuracy each host as either infected (generating malicious activities), attacked (under attack), or clean (without any infection). The generated score of each hour is able to detect the time frame of being attacked for an infected or attacked host without any prior knowledge. MINOS automatically creates a personalized traffic …

Development Of A Statistical Shape-Function Model Of The Implanted Knee For Real-Time Prediction Of Joint Mechanics, Kalin Gibbons

Boise State University Theses and Dissertations

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implant knee to instantaneously predict output mechanics in an efficient manner. Finite element methods were combined with Latin …

Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez

Electronic Thesis and Dissertation Repository

Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …

An Efficient Framework For The Stochastic Verification Of Computation And Communication Systems Using Emerging Technologies, Zhen Zhang

Funded Research Records

No abstract provided.

Toward A Fast And Accurate Modeling Strategy For Thermal Management In Air-Cooled Data Centers, Long Tran Bao Phan

FIU Electronic Theses and Dissertations

Computational fluid dynamics (CFD) has become a popular tool compared to experimental measurement for thermal management in data centers. However, it is very time-consuming and resource-intensive when used to model large-scale data centers, and may not be ready for real-time thermal monitoring. In this thesis, the two main goals are first to develop rapid flow simulation to reduce the computing time while maintaining good accuracy, and second, to develop a whole building energy simulation (BES) strategy for data center modeling. To achieve this end, hybrid modeling and model training methodologies are investigated for rapid flow simulation, and a multi-zone model …

The Perils And Promises Of Artificial General Intelligence, Brian S. Haney

Journal of Legislation

No abstract provided.

Machine Learning With Multi-Class Regression And Neural Networks: Analysis And Visualization Of Crime Data In Seattle, Erkin David George

Honors Projects

This article examines the implications of machine learning algorithms and models, and the significance of their construction when investigating criminal data. It uses machine learning models and tools to store, clean and analyze data that is fed into a machine learning model. This model is then compared to another model to test for accuracy, biases and patterns that are detected in between the experiments. The data was collected from data.seattle.gov and was published by the City of Seattle Data Portal and was accessed on September 17, 2018. This research will be looking into how machine learning models can be used …

Transient Flow Analysis Of A Closing Blowout Preventer Using Computational Fluid Dynamics (Cfd), Daniel Barreca

LSU Master's Theses

Reliability of blowout preventers (BOPs) is crucial for drilling and production operations. Erosion of BOP components and hydrodynamic forces on rams may cause failure of BOP elements to seal the well. Transient computational fluid dynamics (CFD) simulations of fluids within the wellbore and BOP offer quantitative and qualitative data related to this reliability during the closure of various BOP components. Since limited research has been published in transient CFD simulations of closing BOPs, this thesis discusses challenges and solutions to simulating closing blowout preventers. Single component fluids are simulated through several BOP geometries such as annular preventers, pipe rams, and …

Fifth Aeon – A.I Competition And Balancer, William M. Ritson

Master's Theses

Collectible Card Games (CCG) are one of the most popular types of games in both digital and physical space. Despite their popularity, there is a great deal of room for exploration into the application of artificial intelligence in order to enhance CCG gameplay and development. This paper presents Fifth Aeon a novel and open source CCG built to run in browsers and two A.I applications built upon Fifth Aeon. The first application is an artificial intelligence competition run on the Fifth Aeon game. The second is an automatic balancing system capable of helping a designer create new cards that do …

Cloneless: Code Clone Detection Via Program Dependence Graphs With Relaxed Constraints, Thomas J. Simko

Master's Theses

Code clones are pieces of code that have the same functionality. While some clones may structurally match one another, others may look drastically different. The inclusion of code clones clutters a code base, leading to increased costs through maintenance. Duplicate code is introduced through a variety of means, such as copy-pasting, code generated by tools, or developers unintentionally writing similar pieces of code. While manual clone identification may be more accurate than automated detection, it is infeasible due to the extensive size of many code bases. Software code clone detection methods have differing degree of success based on the analysis …

A Ulysses Pact With Artificial Systems. How To Deliberately Change The Objective Spirit With Cultured Ai, Bruno Gransche

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The article introduces a concept of cultured technology, i.e. intelligent systems capable of interacting with humans and showing (or simulating) manners, of following customs and of socio-sensitive considerations. Such technologies might, when deployed on a large scale, influence and change the realm of human customs, traditions, standards of acceptable behavior, etc. This realm is known as the "objective spirit" (Hegel), which usually is thought of as being historically changing but not subject to deliberate human design. The article investigates the question of whether the purposeful design of interactive technologies (as cultured technologies) could enable us to shape modes of …

A Machine Learning Approach To Predicting Alcohol Consumption In Adolescents From Historical Text Messaging Data, Adrienne Bergh

Computational and Data Sciences (MS) Theses

Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train …

Analytical And Numerical Modeling Of Cavity Expansion In Anisotropic Poroelastoplastic Soil, Kai Liu

LSU Doctoral Dissertations

Cavity expansion/contraction problems have attracted intensive attentions over the past several decades due to its versatile applications, such as the interpretation of pressuremeter/piezocone penetration testing results and the modelling of pile installation/tunnel excavation in civil engineering, and the prediction of critical mud pressure required to maintain the wellbore stability in petroleum engineering. Despite the fact that various types of constitutive models have been covered in the literature on this subject, the soils and/or rocks were usually treated as isotropic geomaterials.

In recognition of the above fact, this research makes a substantial extension of the fundamental cavity expansion theory by considering …

Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde

Electronic Theses and Dissertations

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and …

Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. Several studies have been conducted in the past years to develop a better understanding of the disease and therefore a better diagnosis and a better treatment by analyzing diverse data sets consisting of behavioral surveys and tests, phenotype description, and brain imagery. However, data analysis is challenged by the diversity, complexity and heterogeneity of patient cases and by the need for integrating diverse data sets to reach a better understanding of ASD. The aim of our study is to mine homogeneous groups of patients from a heterogeneous …

Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi

Electronic Theses and Dissertations

Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown …

A Machine Learning Approach To Network Intrusion Detection System Using K Nearest Neighbor And Random Forest, Ilemona S. Atawodi

Master's Theses

The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date.

A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented …