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2020

Neural networks

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Articles 1 - 19 of 19

Full-Text Articles in Engineering

Intelligent Recognition Of Tunnel Stratum Based On Advanced Drilling Tests, Yu-Wei Fang, Zhen-Jun Wu, Qian Sheng, Hua Tang, Dong-Cai Liang Dec 2020

Intelligent Recognition Of Tunnel Stratum Based On Advanced Drilling Tests, Yu-Wei Fang, Zhen-Jun Wu, Qian Sheng, Hua Tang, Dong-Cai Liang

Rock and Soil Mechanics

The reliable recognition of strata in front of tunnel face is significant for the stability and safety of the tunnel engineering project. Traditional advanced geological forecasting methods could not ensure high identification accuracy, low cost and short construction time simultaneously, and they can’t satisfy the universality of stratum identification under different geological conditions. The advanced forecasting efficiency could be significantly enhanced if the drilling data of surrounding rocks in front of the tunnel face can be obtained while performing the conventional advanced borehole to attain the rock conditions at different drilling depths in real time, which would be convenient and …

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Developments Of Machine Learning Potentials For Atomistic Simulations, Howard Yanxon Dec 2020

Developments Of Machine Learning Potentials For Atomistic Simulations, Howard Yanxon

UNLV Theses, Dissertations, Professional Papers, and Capstones

Atomistic modeling methods such as molecular dynamics play important roles in investigating time-dependent physical and chemical processes at the microscopic level. In the simulations, energy and forces, sometimes including stress tensor, need to be recalculated iteratively as the atomic configuration evolves. Consequently, atomistic simulations crucially depend on the accuracy of the underlying potential energy surface. Modern quantum mechanical modeling based on density functional theory can consistently generate an accurate description of the potential energy surface. In most cases, molecular dynamics simulations based on density functional theory suffer from highly demanding computational costs. On the other hand, atomistic simulations based on …

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Formal Verification Of The Adversarial Robustness Property Of Deep Neural Networks Through Dimension Reduction Heuristics, Refutation-Based Abstraction, And Partitioning, Joshua Smith Dec 2020

Formal Verification Of The Adversarial Robustness Property Of Deep Neural Networks Through Dimension Reduction Heuristics, Refutation-Based Abstraction, And Partitioning, Joshua Smith

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Neural networks are tools that are often used to perform functions such as object recognition in images, speech-to-text, and general data classification. Because neural networks have been successful at approximating these functions that are difficult to explicitly write, they are seeing increased usage in fields such as autonomous driving, airplane collision avoidance systems, and other safety-critical applications. Due to the risks involved with safety-critical systems, it is important to provide guarantees about the networks performance under certain conditions. As an example, it is critically important that self driving cars with neural network based vision systems correctly identify pedestrians 100% of …

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Approaches To Solving Problems Of Optimization Of Solving Monitoring Problems Based On Natural Computing Algorithms, D.T. Muhamediyeva Nov 2020

Approaches To Solving Problems Of Optimization Of Solving Monitoring Problems Based On Natural Computing Algorithms, D.T. Muhamediyeva

Chemical Technology, Control and Management

To find approximate optimization solutions, many algorithms are used, seven of which are considered in this work: fuzzy sets, artificial neural networks, genetic algorithm, ant algorithm, particle swarm algorithm, DNA computation, and a new approach based on artificial immune systems (IIS). All these methods belong to the direction of "natural computing", ie. model certain biological processes, the algorithms of which nature has created for millions of years. It should be noted that the efficiency of one or another algorithm depends on the characteristics of the initial data of the problem, so it is impossible to unambiguously determine which of the …

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Ramp Metering For A Distant Downstream Bottleneck Using Reinforcement Learning With Value Function Approximation, Yue Zhou, Kaan Ozbay, Pushkin Kachroo, Fan Zuo Oct 2020

Ramp Metering For A Distant Downstream Bottleneck Using Reinforcement Learning With Value Function Approximation, Yue Zhou, Kaan Ozbay, Pushkin Kachroo, Fan Zuo

Electrical & Computer Engineering Faculty Research

Ramp metering for a bottleneck located far downstream of the ramp is more challenging than for a bottleneck that is near the ramp. This is because under the control of a conventional linear feedback-type ramp metering strategy, when metered traffic from the ramp arrive at the distant downstream bottleneck, the state of the bottleneck may have significantly changed from when it is sampled for computing the metering rate; due to the considerable time, these traffic will have to take to traverse the long distance between the ramp and the bottleneck. As a result of such time-delay effects, significant stability issue …

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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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Analysis Of Information Security Methods In Biosystems And Application Of Intelligent Tools In Information Security Systems, Sherzod Sayfullaev Jul 2020

Analysis Of Information Security Methods In Biosystems And Application Of Intelligent Tools In Information Security Systems, Sherzod Sayfullaev

Chemical Technology, Control and Management

In this paper, the methods of information protection in bio systems are studied. The paper considers the use of intelligent tools in information security systems and the use of adaptive information security systems. Several articles on the field of information protection in bio systems are analyzed. Disadvantages and advantages of neural network technologies in modern information security systems are described. The characteristics of bio systems and the specificity of DNA, the main features of the DNA code that provide information security and functional stability of bio systems data protection structure. Application of intelligent tools to create a comprehensive adaptive protection …

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Continuous Learning In A Single-Incremental-Task Scenario With Spike Features, Ruthvik Vaila, John Chiasson, Vishal Saxena Jul 2020

Continuous Learning In A Single-Incremental-Task Scenario With Spike Features, Ruthvik Vaila, John Chiasson, Vishal Saxena

Electrical and Computer Engineering Faculty Publications and Presentations

Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is trained on a first task and the same DNN is trained on the next task it forgets the first task. This phenomenon of forgetting previous tasks is also referred to as catastrophic forgetting. On the other hand a mammalian brain outperforms DNNs in terms of energy efficiency and the ability to learn sequentially without catastrophically forgetting. Here, we use bio-inspired Spike Timing Dependent Plasticity (STDP) in the feature extraction layers of the network …

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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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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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Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola Apr 2020

Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola

Faculty Publications

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed …

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Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee Mar 2020

Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee

Theses and Dissertations

The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground …

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Improving Aeromagnetic Calibration Using Artificial Neural Networks, Mitchell C. Hezel Mar 2020

Improving Aeromagnetic Calibration Using Artificial Neural Networks, Mitchell C. Hezel

Theses and Dissertations

The Global Positioning System (GPS) has proven itself to be the single most accurate positioning system available, and no navigation suite is found without a GPS receiver. Even basic GPS receivers found in most smartphones can easily provide high quality positioning information at any time. Even with its superb performance, GPS is prone to jamming and spoofing, and many platforms requiring accurate positioning information are in dire need of other navigation solutions to compensate in the event of an outage, be the cause hostile or natural. Indeed, there has been a large push to achieve an alternative navigation capability which …

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Predicting Upper Atmospheric Weather Conditions Utilizing Long-Short Term Memory Neural Networks For Aircraft Fuel Efficiency, Garrett A. Alarcon Mar 2020

Predicting Upper Atmospheric Weather Conditions Utilizing Long-Short Term Memory Neural Networks For Aircraft Fuel Efficiency, Garrett A. Alarcon

Theses and Dissertations

Aviation fuel is a major component of the Air Force (AF) budget, and vital for the core mission of the AF. This study investigated the viability of LSTMs to increase the accuracy of deterministic NWP models, while also investigating the ability to reduce model generation time. Increased forecast accuracy for wind speeds could be implemented into existing flight path models to further increase fuel efficiency, while reduced modeling times would allow flight planners to generate a flight plan in rapid response situations. The most viable model consisted of an ensemble of six LSTMs trained o six coordinates. The model's error …

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Neuro-Fuzzy Modeling For Predictive Control Systems With Complex Technological Processes And Production, Yusupbekov Nodirbek, Shukhrat Gulyamov, Malika Doshchanova Feb 2020

Neuro-Fuzzy Modeling For Predictive Control Systems With Complex Technological Processes And Production, Yusupbekov Nodirbek, Shukhrat Gulyamov, Malika Doshchanova

Chemical Technology, Control and Management

The paper implements a modification of a fuzzy neural network, which is suitable for predictive control purposes. Adaptation of a multidimensional programmable controller based on a neural algorithm for the back propagation of forecasting errors is proposed, as well as neural parametric identification of a fuzzy mathematical model of complex technological processes and production based on experimental data and expert estimates.

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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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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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Predicting Imports In Java Code With Graph Neural Networks, Aleksandr Fedchin Jan 2020

Predicting Imports In Java Code With Graph Neural Networks, Aleksandr Fedchin

Senior Projects Spring 2020

Programmers tend to split their code into multiple files or sub-modules. When a program is executed, these sub-modules interact to produce the desired effect. One can, therefore, represent programs with graphs, where each node corresponds to some file and each edge corresponds to some relationship between files, such as two files being located in the same package or one file importing the content of another. This project trains Graph Neural Networks on such graphs to learn to predict future imports in Java programs and shows that Graph Neural Networks outperform various baseline methods by a wide margin.

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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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