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2020

Neural networks

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Full-Text Articles in Physical Sciences and Mathematics

Reinforcement Learning Environment For Orbital Station-Keeping, Armando Herrera Iii Dec 2020

Reinforcement Learning Environment For Orbital Station-Keeping, Armando Herrera Iii

Theses and Dissertations

In this thesis, a Reinforcement Learning Environment for orbital station-keeping is created and tested against one of the most used Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). This thesis also explores the foundations of Reinforcement Learning, from the taxonomy to a description of PPO, and shows a thorough explanation of the physics required to make the RL environment. Optuna optimizes PPO's hyper-parameters for the created environment via distributed computing. This thesis then shows and analysis the results from training a PPO agent six times.

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Computational Cognition And Deep Learning, Andy Malinsky Nov 2020

Computational Cognition And Deep Learning, Andy Malinsky

The Compass

No abstract provided.

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Leveraging Profanity For Insincere Content Detection: A Neural Network Approach, Swapna Gottipati, Annabel Tan, David Jing Shan Chow, Joel Wee Kiat Lim Nov 2020

Leveraging Profanity For Insincere Content Detection: A Neural Network Approach, Swapna Gottipati, Annabel Tan, David Jing Shan Chow, Joel Wee Kiat Lim

Research Collection School Of Computing and Information Systems

Community driven social media sites are rich sources of knowledge and entertainment and at the same vulnerable to the flames or toxic content that can be dangerous to various users of these platforms as well as to the society. Therefore, it is crucial to identify and remove such content to have a better and safe online experience. Manually eliminating flames is tedious and hence many research works focus on machine learning or deep learning models for automated methods. In this paper, we primarily focus on detecting the insincere content using neural network-based learning methods. We also integrated the profanity features …

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Approximate Computing With Emerging Devices, Richard Atherton Oct 2020

Approximate Computing With Emerging Devices, Richard Atherton

Undergraduate Research & Mentoring Program

Approximate computation is a new trend that explores and harnesses trade-offs between the precision and energy/power consumption of computing systems. In this project a feed-forward neural network was designed as well as several reservoir networks using different network topologies to compare the accuracy and resilience of the network against the computational complexity required.

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Modular Neural Networks For Low-Power Image Classification On Embedded Devices, Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, George K. Thiruvathukal, Yung-Hisang Lu Oct 2020

Modular Neural Networks For Low-Power Image Classification On Embedded Devices, Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, George K. Thiruvathukal, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules) to progressively classify images …

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Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He Oct 2020

Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He

Research Collection School Of Computing and Information Systems

Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real-world photographs like an artist. To this end, we propose a two-stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high-quality smoothing results in a label-free manner. Together with two other semantic-aware losses, the abstraction stage imposes different forms of regularization for creating cartoon-like flattened images. …

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Fault Diagnosis For Automobile Coating Equipments Based On Extension Neural Network, Yongwei Ye, Shedong Ren, Lianqiang Ye, Shenhao Ge, Zhiqin Qian Aug 2020

Fault Diagnosis For Automobile Coating Equipments Based On Extension Neural Network, Yongwei Ye, Shedong Ren, Lianqiang Ye, Shenhao Ge, Zhiqin Qian

Journal of System Simulation

Abstract: Aiming at the difficulty in discovering and eliminating the system faults of automobile coating equipments in time, a new method of fault diagnosis based on extension neural network was proposed. The feature of extension theory was used in managing the structured information through qualitative and quantitative description, and it was also combined by the characteristic of parallel construct in neural network. So the extension reasoning process was completed by means of the parallel distributed processing construct of the network. Matter-element input and output models were established according to the equipment monitoring parameters and fault types for the heating system. …

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Falcon: Framework For Anomaly Detection In Industrial Control Systems, Subin Sapkota, A.K.M. Nuhil Mehdy, Stephen Reese, Hoda Mehrpouyan Aug 2020

Falcon: Framework For Anomaly Detection In Industrial Control Systems, Subin Sapkota, A.K.M. Nuhil Mehdy, Stephen Reese, Hoda Mehrpouyan

Computer Science Faculty Publications and Presentations

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. …

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Cloud Resource Prediction Using Explainable And Cooperative Artificial Neural Networks, Nathan R. Nelson Aug 2020

Cloud Resource Prediction Using Explainable And Cooperative Artificial Neural Networks, Nathan R. Nelson

MSU Graduate Theses

This work proposes a system for predicting cloud resource utilization by using runtime assembled cooperative artificial neural networks (RACANN). RACANN breaks up the problem into smaller contexts, each represented by a small-scale artificial neural network (ANN). The relevant ANNs are joined together at runtime when the context is present in the data for training and predictions. By analyzing the structure of a complete ANN, the influence of inputs is calculated and used to create linguistic descriptions (LD) of model behavior, so RACANN becomes explainable (eRACANN). The predictive results of eRACANN are compared against its prototype and a single deep ANN …

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Research On Asymptotic Stability For Markovian Jumping Neural Network With Unknown Transition Probabilities, Lu Yang, Shujuan Yi, Weijian Ren, Jiandong Liu Jun 2020

Research On Asymptotic Stability For Markovian Jumping Neural Network With Unknown Transition Probabilities, Lu Yang, Shujuan Yi, Weijian Ren, Jiandong Liu

Journal of System Simulation

Abstract: The analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays was addressed. The general representative dynamic stochastic neural network model was established. The considered transition probabilities were assumed to be partially unknown. The parameter uncertainties were considered to be norm-bounded. Based on Lyapunov stability theory, by constructing a suitable Lyapunov-Krasovskii function and using the stochastic analysis method, some sufficient criteria for the stability of discrete Markovian neural networks was derived. Through the Matlab LMI toolbox, solving a set of linear matrix inequalities to test criterion, the new criterion reduced …

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Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele Jun 2020

Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele

Research Collection School Of Computing and Information Systems

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantizationinduced error on the loss function involving neither gradient approximation nor …

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Using Generative Adversarial Networks To Classify Structural Damage Caused By Earthquakes, Gian P. Delacruz Jun 2020

Using Generative Adversarial Networks To Classify Structural Damage Caused By Earthquakes, Gian P. Delacruz

Master's Theses

The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most …

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Research On Image Description Method Based On Neural Network, Kong Rui, Xie Wei, Lei Tai Apr 2020

Research On Image Description Method Based On Neural Network, Kong Rui, Xie Wei, Lei Tai

Journal of System Simulation

Abstract: The automatic recognition and automatically describing image content is an important research direction to the artificial intelligence to connect the computer vision and the natural language processing. A method of describing the image content is proposed to generate the natural language by using the deep neural network model. The model consists of a convolutional neural network (CNN) and a recurrent neural network (RNN). The CNN is used to extract features of the input image to generate a fixed-length feature vector, which initializes the RNN to generate the sentences. Experimental results on the MSCOCO image description dataset show the syntactic …

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Choosing The Structure Of Convolutional Neural Networks For Face Recognition, Kabul Khudaybergenov Apr 2020

Choosing The Structure Of Convolutional Neural Networks For Face Recognition, Kabul Khudaybergenov

Bulletin of National University of Uzbekistan: Mathematics and Natural Sciences

Evaluating the number of hidden neurons and hidden layers necessary for solving of face recognition, pattern recognition and classification tasks is one of the key problems in artificial neural networks. In this note, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve face recognition tasks. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons …

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Machine Learning And Data Mining-Based Methods To Estimate Parity Status And Age Of Wild Mosquito Vectors Of Infectious Diseases From Near-Infrared Spectra, Masabho Peter Milali Apr 2020

Machine Learning And Data Mining-Based Methods To Estimate Parity Status And Age Of Wild Mosquito Vectors Of Infectious Diseases From Near-Infrared Spectra, Masabho Peter Milali

Dissertations (1934 -)

Previous studies show that a trained partial least square regresser [sic] (PLSR) from near-infrared spectra classify laboratory and semi-field raised mosquitoes into less than or ≥ to seven days old with an average accuracy of 80%. This dissertation demonstrates that training models on near-infrared spectra (NIRS) using artificial neural network (ANN) as an architecture yields models with higher accuracies than training models using partial least squares (PLS) as an architecture. In addition, irrespective of the model architecture used, direct training of a binary classifier scores higher accuracy than training a regresser and interpreting it as a binary classifier. Furthermore, for …

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Are These Comments Triggering? Predicting Triggers Of Toxicity In Online Discussions, Hind Almerekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen Apr 2020

Are These Comments Triggering? Predicting Triggers Of Toxicity In Online Discussions, Hind Almerekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Understanding the causes or triggers of toxicity adds a new dimension to the prevention of toxic behavior in online discussions. In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger. The prediction model incorporates text-based features and derived features from previous studies that pertain to shifts in sentiment, topic flow, and discussion context. Our findings show that triggers of toxicity contain identifiable features and that incorporating shift features with the discussion context can be detected with a ROC-AUC score of …

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Event-Based Visual-Inertial Odometry Using Smart Features, Zachary P. Friedel Mar 2020

Event-Based Visual-Inertial Odometry Using Smart Features, Zachary P. Friedel

Theses and Dissertations

Event-based cameras are a novel type of visual sensor that operate under a unique paradigm, providing asynchronous data on the log-level changes in light intensity for individual pixels. This hardware-level approach to change detection allows these cameras to achieve ultra-wide dynamic range and high temporal resolution. Furthermore, the advent of convolutional neural networks (CNNs) has led to state-of-the-art navigation solutions that now rival or even surpass human engineered algorithms. The advantages offered by event cameras and CNNs make them excellent tools for visual odometry (VO). This document presents the implementation of a CNN trained to detect and describe features within …

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Driving Maneuver Detection Using Knowledge Distillation Networks, Kyle Windsor Mar 2020

Driving Maneuver Detection Using Knowledge Distillation Networks, Kyle Windsor

Electronic Thesis and Dissertation Repository

In this thesis, we examine the current state of Advanced Driving Assistance Systems (ADAS) and their relation to maneuver prediction in the literature. We then attempt to solve the problem of variable inter-driver behavior by applying a novel distillation learning system using RoadLab data on tracked driver cephalo-ocular gaze behavior in tandem with high-resolution CANbus data. Current training-based methods in maneuver prediction are potentially subject to underfitting as drivers may exhibit different behavior when preparing to maneuver, but it has been shown that drivers can be grouped into at least two distinct behavior models. We use this information to personalize …

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A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik Feb 2020

A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik

Augsburg Honors Review

The intent of this work is to explore the interactions of artificial neural networks and digital games. It details the development of an artificial neural network trained upon a brick breaking game like the Atari game Breakout. This network was designed with the goals of not dropping the ball and maximizing the game score. Full game and network integration was not completed. However, two versions of the network were developed to move the paddle to the right or left based on the ball's point of impact on the paddle. In preliminary testing using manual inputs, these networks eventually learned to …

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Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack Jan 2020

Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

UNL Libraries: Faculty Publications

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our …

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Final Presentation To The Library Of Congress On Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack Jan 2020

Final Presentation To The Library Of Congress On Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Conference Presentations and Speeches

This presentation to Library of Congress staff, delivered onsite on January 10, 2020, presents a tour through the demonstration project pursued by the Aida digital libraries research team with the Library of Congress in 2019-2020. In addition to providing an overview and analysis of the specific machine learning projects scoped and explored, this presentation includes a number of high-level take-aways and recommendations designed to influence and inform the Library of Congress's machine learning efforts going forward.

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Machine Learning? In My Election? It's More Likely Than You Think: Voting Rules Via Neural Networks, Daniel Firebanks-Quevedo Jan 2020

Machine Learning? In My Election? It's More Likely Than You Think: Voting Rules Via Neural Networks, Daniel Firebanks-Quevedo

Honors Papers

Impossibility theorems in social choice have represented a barrier in the creation of universal, non-dictatorial, and non-manipulable voting rules, highlighting a key trade-off between social welfare and strategy-proofness. However, a social planner may be concerned with only a particular preference distribution and wonder whether it is possible to better optimize this trade-off. To address this problem, we propose an end-to-end, machine learning-based framework that creates voting rules according to a social planner's constraints, for any type of preference distribution. After experimenting with rank-based social choice rules, we find that automatically-designed rules are less susceptible to manipulation than most existing rules, …

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Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang Jan 2020

Entity-Sensitive Attention And Fusion Network For Entity-Level Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users' posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention …

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Shipbuilding Supply Chain Framework And Digital Transformation: A Project Portfolios Risk Evaluation, Rafael Diaz, Katherine Smith, Rafael Landaeta, Antonio Padovano Jan 2020

Shipbuilding Supply Chain Framework And Digital Transformation: A Project Portfolios Risk Evaluation, Rafael Diaz, Katherine Smith, Rafael Landaeta, Antonio Padovano

VMASC Publications

Program portfolio managers in digital transformation programs have a need for knowledge that can guide decisions related to the alignment of program investments with the sustainability and strategic objectives of the organization. The purpose of this research is to illustrate the utility of a framework capable of clarifying the cost-benefit tradeoffs stemming from assessing digitalization program investment risks in the military shipbuilding sector. Our approach uses Artificial Neural Network to quantify benefits and risks per project while employing scenario analysis to quantify the effects of operational constraints. A Monte Carlo model is used to generate data samples that support the …

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Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman Jan 2020

Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman

Browse all Theses and Dissertations

As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one cause of …

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Image Features For Tuberculosis Classification In Digital Chest Radiographs, Brian Hooper Jan 2020

Image Features For Tuberculosis Classification In Digital Chest Radiographs, Brian Hooper

All Master's Theses

Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very …

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A Machine Learning System For Glaucoma Detection Using Inexpensive Machine Learning, Jon Kilgannon Jan 2020

A Machine Learning System For Glaucoma Detection Using Inexpensive Machine Learning, Jon Kilgannon

West Chester University Master’s Theses

This thesis presents a neural network system which segments images of the retina to calculate the cup-to-disc ratio, one of the diagnostic indicators of the presence or continuing development of glaucoma, a disease of the eye which causes blindness. The neural network is designed to run on commodity hardware and to be run with minimal skill required from the user by packaging the software required to run the network into a Singularity image. The RIGA dataset used to train the network provides images of the retina which have been annotated with the location of the optic cup and disc by …

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Crash Course Learning: An Automated Approach To Simulation-Driven Lidar-Basedtraining Of Neural Networks For Obstacle Avoidance In Mobile Robotics, Stanko Kruzic, Josip Music, Mirjana Bonkovic, Frantisek Duchon Jan 2020

Crash Course Learning: An Automated Approach To Simulation-Driven Lidar-Basedtraining Of Neural Networks For Obstacle Avoidance In Mobile Robotics, Stanko Kruzic, Josip Music, Mirjana Bonkovic, Frantisek Duchon

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes and implements a self-supervised simulation-driven approach to data collection used for training of perception-based shallow neural networks for mobile robot obstacle avoidance. In the approach, a 2D LiDAR sensor was used as an information source for training neural networks. The paper analyzes neural network performance in terms of numbers of layers and neurons, as well as the amount of data needed for reliable robot operation. Once the best architecture is identified, it is trained using only data obtained in simulation and then implemented and tested on a real robot (Turtlebot 2) in several simulations and real-world scenarios. …

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Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan Jan 2020

Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan

Turkish Journal of Electrical Engineering and Computer Sciences

Diabetic patients are quite hesitant in engaging in normal physiological activities due to difficulties associated with diabetes management. Over the last few decades, there have been advancements in the computational power of embedded systems and glucose sensing technologies. These advancements have attracted the attention of researchers around the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetes based on neural networks is proposed. These neural networks are used for making predictions based on the clinical data of a patient. A neural network feedback controller is also designed to provide a glycemic response by …

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A Deep Learning Approach To Mapping Irrigation: U-Net Irrmapper, Thomas Henry Colligan Iv Jan 2020

A Deep Learning Approach To Mapping Irrigation: U-Net Irrmapper, Thomas Henry Colligan Iv

Graduate Student Theses, Dissertations, & Professional Papers

Accurate maps of irrigation are essential for understanding and managing water resources in light of a warming climate. We present a new method for mapping irrigation and apply it to the state of Montana over the years 2000-2019. The method is based on an ensemble of convolutional neural networks that only rely on raw Landsat surface reflectance data. The ensemble of networks method learns to mask clouds and ignore Landsat 7 scan-line failures without supervision, reducing the need for preprocessing data or feature engineering. Unlike other approaches to mapping irrigation, the method doesn't use other mapping products like the Cropland …

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