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Work11

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Dissertation11

Creator

Cambo, Scott Allen1
Chen Liang1
Elfar, Amr1
Ger, Stephanie Kwang-Yu1
Hujsak, Karl1
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Keyword

Machine Learning[remove]11
Reinforcement Learning2
Activation Functions1
Ad Avoidance1
Autonomous Driving1
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Subject

Computer science4
Computer Science2
Materials Science2
Applied mathematics1
Artificial intelligence1
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en8

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Design of Tungsten-free, L12-strengthened Cobalt-based Superalloys

Description:

Recent developments have enabled L12-strengthened Co-based superalloys, which have thepotential to surpass Ni-based superalloys as the material of choice for the hottest sections of turbine blades due to cobalt’s 40 ºC higher melting point. The most-studied branch of Co-based superalloys are based on the L12 phase Co3(Al,W); however, there is...

Keyword:

Superalloys, Cobalt, Microstructure, Machine Learning, Creep, and High Entropy Alloys

Subject:

Materials Science

Creator:

Ohl, Brandon

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_971014 and http://dissertations.umi.com/northwestern:16450
Learning from Limited and Imperfect Data in Cyber-Physical System

Description:

Machine learning is seeping into every fabric in various practical domains such as autonomous driving, wearable computing, and smart buildings. However, in the actual development and integration, especially when the learning-based components are frequently included as components of large complex systems where the physical instances can be included as interactable...

Keyword:

Transfer Learning, Reinforcement Learning, Computer Vision, Building HVAC Control, Image Classification, and Machine Learning

Subject:

Computer engineering and Computer science

Creator:

Xu, Shichao

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_983783 and http://dissertations.umi.com/northwestern:16519
Digitization for Innovative Platform Design and Advertising

Description:

Technology that processes text, audio and video, as well as location data, has revolutionized many industries by enabling innovative operations for customer retention. To retain transactions for a platform and viewers for advertisers, this dissertation leverages novel digital tools to analyze consumer behavior, proposes original economic frameworks to guide platform...

Keyword:

Ad Avoidance, Multimedia Data, Platform, Geolocation, Machine Learning, and Disintermediation

Subject:

Marketing, Economics, and Information technology

Creator:

Xie, Yingkang

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

05/31/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16589 and etdadmin_upload_986414
Integrating Heterogeneous Traffic Data Sources with High Definition Maps in Autonomous Driving

Description:

Automated driving has become a very popular topic in the recent years and is becoming more and more of a reality. In this new trend, High Definition (HD) maps play an important role in many ways that will provide a safer and more efficient driving experience, especially in terms of...

Keyword:

Data Consumption, Prediction, Autonomous Driving, High Definition Maps, and Machine Learning

Subject:

Computer science

Creator:

Zang, Andi

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

06/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_879487 and http://dissertations.umi.com/northwestern:15935
Model Positionality: A Novel Framework for Data Science with Subjective Target Concepts

Description:

Data Science and related fields like Artificial Intelligence, Machine Learning, and Statistics provide indispensable research methods for understanding a wide variety of phenomena from large datasets. However, as methodical and empirical as these methods aim to be, there are many subjective and discretionary choices that the data scientist must make...

Keyword:

human computation, inter-annotator agreement, algorithmic bias, content moderation, and Machine Learning

Subject:

Information science, Artificial intelligence, and Computer science

Creator:

Cambo, Scott Allen

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

06/28/2021

Date Created:

2021-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_810456 and http://dissertations.umi.com/northwestern:15510
Recurrent Neural Network Architectures for Sequence Classification and Explanation

Description:

This thesis focuses on applications of recurrent neural networks (RNNs) for three aspects of sequential classification. In the first chapter, a novel method to generate synthetic minority data generation to improve imbalanced classification is discussed. Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic...

Keyword:

Explainability, Recurrent Neural Networks, and Machine Learning

Subject:

Applied mathematics

Creator:

Ger, Stephanie Kwang-Yu

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/01/2021

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:15394 and etdadmin_upload_780385
Computational Electron Microscopy for the Characterization of Soft, Hard, and Hybrid Materials

Description:

Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....

Keyword:

Imaging, Materials Imaging, Soft Materials, Crystallography, Machine Learning, and Electron Microscopy

Subject:

Nanoscience, Materials Science, and Computer science

Creator:

Hujsak, Karl

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/12/2020

Date Created:

2018-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:14515 and etdadmin_upload_638887
Predictive Traffic Operations and Control of Connected and Automated Vehicle Systems

Description:

Connected and automated vehicle (CAV) technology is a disruptive transportation development with potentially transformative impacts on society and the economy. CAV systems promise to significantly reduce human-caused road crashes, improve traffic flow performance, and lower pollutant emissions. However, realizing those benefits requires strategic planning for the deployment of CAV systems...

Keyword:

Speed Harmonization, Connected and Automated Vehicles, Shockwave Detection, and Machine Learning

Subject:

Transportation

Creator:

Elfar, Amr

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/12/2020

Date Created:

2019-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:14667 and etdadmin_upload_662894
Integrating Machine Learning and Symbolic Reasoning: Learning to Generate Symbolic Representations from Weak Supervision

Description:

Machine learning and symbolic reasoning have been two main approaches to build intelligent systems. Symbolic reasoning has been used in many applications by making use of expressive symbolic representations to encode prior knowledge, conduct complex reasoning and provide explanations. Recently, machine learning has enabled various successful applications by learning from...

Keyword:

Structural Alignment, Structured Prediction, Semantic Parsing, Machine Learning, Reinforcement Learning, and Symbolic Reasoning

Subject:

Computer Science

Creator:

Chen Liang

Owner:

Scholarly Digital Publishing

Date Uploaded:

10/28/2019

Date Modified:

10/28/2019

Date Created:

2018-01-01

Resource Type:

Dissertation
Methods for Images, Time Series, and Activation Functions with Deep Neural Networks

Description:

In this work, we explore the utility of the three main types of neural networks: feed forward, convolutional, and recurrent. While using these networks, we develop a new way to model multiagent trajectory data, explore the use of multiple activation functions for neurons at each layer of a neural network,...

Keyword:

Stock Prediction, Deep Learning, Basketball Trajectory, Activation Functions, and Machine Learning

Subject:

Engineering Sciences and Applied Mathematics

Creator:

Mark Harmon

Owner:

Scholarly Digital Publishing

Date Uploaded:

10/14/2019

Date Modified:

10/14/2019

Date Created:

2018-01-01

Resource Type:

Dissertation

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