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Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams 2019 Iowa State University

Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams

Derrick K Rollins, Sr.

Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represented by the dynamic modulus. The dynamic modulus test in indirect tension mode can be used to measure the modulus of each specific layer of asphalt pavements using representative samples. Dynamic modulus is a function of material properties, loading, and environmental conditions. Developing predictive models for dynamic modulus is efficient and cost effective. This article focuses on developing an accurate Finite Element (FE) model using mixture elastic modulus and asphalt binder ...


Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams 2019 Iowa State University

Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams

R. Christopher Williams

Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represented by the dynamic modulus. The dynamic modulus test in indirect tension mode can be used to measure the modulus of each specific layer of asphalt pavements using representative samples. Dynamic modulus is a function of material properties, loading, and environmental conditions. Developing predictive models for dynamic modulus is efficient and cost effective. This article focuses on developing an accurate Finite Element (FE) model using mixture elastic modulus and asphalt binder ...


Analytical And Numerical Modeling Of Cavity Expansion In Anisotropic Poroelastoplastic Soil, Kai Liu 2019 Louisiana State University and Agricultural and Mechanical College

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 ...


Using Transfer Learning In Network Markets, Kai Cai 2019 The Graduate Center, City University of New York

Using Transfer Learning In Network Markets, Kai Cai

All Dissertations, Theses, and Capstone Projects

Mechanism design is the sub-field of microeconomics and game theory, which considers agents have their own private information and are self-interested and tries to design systems that can produce desirable outcomes. In recent years, with the development of internet and electronic markets, mechanism design has become an important research field in computer science. This work has largely focused on single markets. In the real world, individual markets tend to connect to other markets and form a big “network market”, where each market occupies a node in the network and connections between markets reflect constraints on traders in the markets. So ...


Forecasting Model For Disease Propensity Using Ehr Data, soodabeh sarafrazi, Omar Sharif, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, Anil Kemisetti 2019 The University of San Francisco

Forecasting Model For Disease Propensity Using Ehr Data, Soodabeh Sarafrazi, Omar Sharif, Matthew Domingo, Jie Han, Michael Chang, Omid Khazaie, Anil Kemisetti

Creative Activity and Research Day - CARD

Many diseases such as diabetes and cardiovascular diseases are actionable, i.e. they are preventable by early intervention. One to two years of early warning would represent a huge advance in dealing with these conditions and could help prevent further complications such as heart disease, stroke, kidney failure, blindness, and amputation. In this project, we are developing an extensible condition forecasting model to assess the risk of diabetes and heart problems in patients in advance. Using TensorFlow, Elastic MapReduce (EMR), and AWS Sagemaker, we are training a Wide and Deep Neural Network on a dataset of more than 170 million ...


Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero PhD, Gilmer Valdes PhD, Timothy Solberg PhD, Yannet Interian PhD 2019 University of San Francisco

Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero Phd, Gilmer Valdes Phd, Timothy Solberg Phd, Yannet Interian Phd

Creative Activity and Research Day - CARD

Deep learning algorithms have become widely popular, with considerable success in fields where datasets have hundreds of thousands or million points. As deep learning is increasingly applied to the fields of medical physics and radiation oncology, a reasonable question follows: are these techniques the best approach, given the unique conditions in our field? In this study, we investigate the dependence of dataset size on the performance of deep learning algorithms compared with more traditional radiomics-based methods.


Using Principle Component Analysis To Analyze Tertiary And Quaternary Spectral Mixtures, David Burnett 2019 Olivet Nazarene University

Using Principle Component Analysis To Analyze Tertiary And Quaternary Spectral Mixtures, David Burnett

Scholar Week 2016 - present

CRISM images from Mars are expected to contain carbonates such as magnesite. Prior research has been successfully able to determine the approximate percent composition of phyllosilicates in binary lab mixtures using Principle Component Analysis (PCA). In order to expand this model to work on CRISM images, one of preliminary steps is allowing the algorithm to work on mixtures with more than two components, which was the primary purpose of this research.


An Application Of Artificial General Intelligence In Board Games, Nathan Skalka 2019 University of Nebraska at Omaha

An Application Of Artificial General Intelligence In Board Games, Nathan Skalka

Computer Science Graduate Research Workshop

No abstract provided.


Using Gleaned Computing Power To Forecast Emerging-Market Equity Returns With Machine Learning, Xida Ren 2019 College of William and Mary

Using Gleaned Computing Power To Forecast Emerging-Market Equity Returns With Machine Learning, Xida Ren

Undergraduate Honors Theses

This paper examines developing machine learning and statistic models to build forecast models for equity returns in an emergent market, with an emphasis on computing. Distributed systems were pared with random search and Bayesian optimization to find good hyperparameters for neural networks. No significant results were found.


A Machine Learning Approach To Network Intrusion Detection System Using K Nearest Neighbor And Random Forest, Ilemona S. Atawodi 2019 University of Southern Mississippi

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 ...


Advances In Design Methodology In Swelling Shale Rock In Southern Ontario, Thomas R.A. Lardner 2019 The University of Western Ontario

Advances In Design Methodology In Swelling Shale Rock In Southern Ontario, Thomas R.A. Lardner

Electronic Thesis and Dissertation Repository

As infrastructure requirements increase in southern Ontario, excavations within swelling rock formations will become more frequent and larger. The objective of this study is to advance design capability for structures in swelling rock through three aspects: i) developing a practical swelling model for design engineers, ii) investigate two crushable/compressible materials for the mitigation of swelling rock effects, and iii) observe and analyze the behaviour of swelling rock to current excavation techniques.

A swelling rock constitutive model has been developed. The swelling parameters include the horizontal and vertical free swell potential, threshold stress, and critical stress as well as a ...


Recipe For Disaster, Zac Travis 2019 University of New Mexico

Recipe For Disaster, Zac Travis

MFA Thesis Exhibit Catalogs

Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as ...


Modern Yard Sale Application, Lauren Epling, Matthew Piasecki 2019 California Polytechnic State University, San Luis Obispo

Modern Yard Sale Application, Lauren Epling, Matthew Piasecki

Computer Science

YardSail is a modern application that provides users a place to post and view local Yard Sales. There is an astounding need for a safe space where users can comfortably post their yard sale address and items for all locals to easily see (without needing to drive down a specific street to find out). Currently, there does not exist an application for users that accomplishes what we set out to accomplish. As a team, we truly believe YardSail could be a popular application that helps users sail through the experience of hosting or visiting a yard sale.


Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell 2019 St George Police Department

Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell

Journal of Digital Forensics, Security and Law

Throughout the digital forensic community, chip-off analysis provides examiners with a technique to obtain a physical acquisition from locked or damaged digital device. Thermal based chip-analysis relies upon the application of heat to remove the flash memory chip from the circuit board. Occasionally, a flash memory chip fails to successfully read despite following similar protocols as other flash memory chips. Previous research found the application of high temperatures increased the number of bit errors present in the flash memory chip. The purpose of this study is to analyze data collected from chip-off analyses to determine if a statistical difference exists ...


A Framework For Evaluating Model-Driven Self-Adaptive Software Systems, Basel Magableh 2019 Dublin Institute of Technology

A Framework For Evaluating Model-Driven Self-Adaptive Software Systems, Basel Magableh

Articles

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately ...


A Deep Recurrent Q Network Towards Self-Adapting Distributed Microservices Architecture (In Press), Basel Magableh 2019 Dublin Institute of Technology

A Deep Recurrent Q Network Towards Self-Adapting Distributed Microservices Architecture (In Press), Basel Magableh

Articles

One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture ...


Context Oriented Software Middleware, Basel Magableh 2019 Dublin Institute of Technology

Context Oriented Software Middleware, Basel Magableh

Articles

This article proposes a new paradigm for building an adaptive middleware that supports software systems with self-adaptability and dependability. In this article, we wish to explore how far we can support the engineering of self- adaptive applications using a generic and platform-independent middleware architecture provided by non-specialised programming languages such as Context-Oriented Programming (COP), and Aspect-Oriented Programming (AOP), and not limited to a specific platform or framework. This gives the software developers the flexibility to construct a self-adaptive application using a generic and reusable middleware components that employ popular design patterns, instead of forcing the software developers to use a ...


Deep Q Learning For Self Adaptive Distributed Microservices Architecture (In Press), Basel Magableh 2019 Dublin Institute of Technology

Deep Q Learning For Self Adaptive Distributed Microservices Architecture (In Press), Basel Magableh

Articles

One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behavior, and provide a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. The behaviour of a microservices architecture is continuously changing overtime, which makes it a challenging task to use a statistical model to identify both the normal and abnormal behaviour of the services running. The performance of the microservices cluster could fluctuate around the demand to accommodate scalability, orchestration and load balancing demands ...


A Multiline Anchor Concept For Floating Offshore Wind Turbines, Casey Fontana 2019 University of Massachusetts Amherst

A Multiline Anchor Concept For Floating Offshore Wind Turbines, Casey Fontana

Doctoral Dissertations

Floating offshore wind turbines (FOWTs) hold great potential for the renewable energy industry, but capital costs remain high. In efforts to increase FOWT substructure efficiency and reduce costs, this thesis investigates a novel multiline anchor concept in which FOWTs share anchors instead of being moored separately. The goal of this thesis is to evaluate the force dynamics, design, and potential cost reduction of the system. Anchor forces are simulated using the NREL 5 MW reference turbine and OC4-DeepCwind semisubmersible platform, and multiline anchor force is computed as the vector sum of the contributing mooring line tensions.

The use of a ...


On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa 2019 University of North Florida

On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa

UNF Graduate Theses and Dissertations

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of ...


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