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Articles 1 - 16 of 16
Full-Text Articles in Engineering
Optimization Of Data Processing Based On Accounting For Factors Of External Expenses, Regulation And Correction Of Variables ., I.I Jumanov, S.M Xolmonov
Optimization Of Data Processing Based On Accounting For Factors Of External Expenses, Regulation And Correction Of Variables ., I.I Jumanov, S.M Xolmonov
Chemical Technology, Control and Management
Methods and simplified computational schemes for optimizing data processing for systems operating in conditions of limited a priori information, changes in the characteristics of external influences, uncertainty of parameters have been developed. To describe a non-stationary object, non-linear identification models are considered, constraints, input conditions for obtaining possible values of output variables are defined. An approach aimed at using identification technologies based on generalization of capabilities of dynamic models, neural networks (NN), mechanisms for regulating variable computing schemes of structural network components, as well as learning algorithms of the NN is proposed. A generalized algorithm for learning NN based on …
Efficient Machine Learning: Models And Accelerations, Zhe Li
Efficient Machine Learning: Models And Accelerations, Zhe Li
Dissertations - ALL
One of the key enablers of the recent unprecedented success of machine learning is the adoption of very large models. Modern machine learning models typically consist of multiple cascaded layers such as deep neural networks, and at least millions to hundreds of millions of parameters (i.e., weights) for the entire model. The larger-scale model tend to enable the extraction of more complex high-level features, and therefore, lead to a significant improvement of the overall accuracy. On the other side, the layered deep structure and large model sizes also demand to increase computational capability and memory requirements. In order to achieve …
Amplifying The Prediction Of Team Performance Through Swarm Intelligence And Machine Learning, Erick Michael Harris
Amplifying The Prediction Of Team Performance Through Swarm Intelligence And Machine Learning, Erick Michael Harris
Master's Theses
Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to …
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Doctoral Dissertations
In the past decades, the computing capability has shown an exponential growth trend, which is observed as Moore’s law. However, this growth speed is slowing down in recent years mostly because the down-scaled size of transistors is approaching their physical limit. On the other hand, recent advances in software, especially in big data analysis and artificial intelligence, call for a break-through in computing hardware. The memristor, or the resistive switching device, is believed to be a potential building block of the future generation of integrated circuits. The underlying mechanism of this device is different from that of complementary metal-oxide-semiconductor (CMOS) …
Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns
Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify …
Leveraging Eye Structure And Motion To Build A Low-Power Wearable Gaze Tracking System, Addison Mayberry
Leveraging Eye Structure And Motion To Build A Low-Power Wearable Gaze Tracking System, Addison Mayberry
Doctoral Dissertations
Clinical studies have shown that features of a person's eyes can function as an effective proxy for cognitive state and neurological function. Technological advances in recent decades have allowed us to deepen this understanding and discover that the actions of the eyes are in fact very tightly coupled to the operation of the brain. Researchers have used camera-based eye monitoring technology to exploit this connection and analyze mental state across across many different metrics of interest. These range from simple things like attention and scene processing, to impairments such as a fatigue or substance use, and even significant mental disorders …
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Masters Theses & Specialist Projects
In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks) …
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire
Civil and Environmental Engineering Faculty Publications
This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they …
Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle, Nadeem Khan
Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle, Nadeem Khan
Mechanical & Aerospace Engineering Theses & Dissertations
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system …
Development Of The Multicoupling Dynamic Model Of Process Of Extraction Of Phosphoric Acid On The Basis Of An Artificial Neural Network, D.P Mukhitdinov, F.A Ergashev, A.V Schultz
Development Of The Multicoupling Dynamic Model Of Process Of Extraction Of Phosphoric Acid On The Basis Of An Artificial Neural Network, D.P Mukhitdinov, F.A Ergashev, A.V Schultz
Chemical Technology, Control and Management
The deals with the problems of mathematical modeling and optimization of complex technological processes for obtaining extraction phosphoric acid based on the use of an artificial neural network. The use of the function of changing the concentrations from the residence time of the components in the reactor to find the mass consumption of phosphoric flour is substantiated and proposed. The work of a trained neural network for stabilizing perturbations is shown when input of input data with distortion is fed to its input.
Neural Network On Virtualization System, As A Way To Manage Failure Events Occurrence On Cloud Computing, Khoi Minh Pham
Neural Network On Virtualization System, As A Way To Manage Failure Events Occurrence On Cloud Computing, Khoi Minh Pham
Electronic Theses, Projects, and Dissertations
Cloud computing is one important direction of current advanced technology trends, which is dominating the industry in many aspects. These days Cloud computing has become an intense battlefield of many big technology companies, whoever can win this war can have a very high potential to rule the next generation of technologies. From a technical point of view, Cloud computing is classified into three different categories, each can provide different crucial services to users: Infrastructure (Hardware) as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS). Normally, the standard measurements for cloud computing reliability level is …
An Investigation Of The Cortical Learning Algorithm, Anthony C. Samaritano
An Investigation Of The Cortical Learning Algorithm, Anthony C. Samaritano
Theses and Dissertations
Pattern recognition and machine learning fields have revolutionized countless industries and applications from biometric security to modern industrial assembly lines. The fields continue to accelerate as faster, more efficient processing hardware becomes commercially available. Despite the accelerated growth of the pattern recognition and machine learning fields, computers still are unable to learn, reason, and perform rudimentary tasks that humans and animals find routine. Animals are able to move fluidly, understand their environment, and maximize their chances of survival through adaptation - animals demonstrate intelligence. A primary argument in this thesis that we have not yet achieved a level of intelligence …
Comparison Of Google Image Search And Resnet Image Classification Using Image Similarity Metrics, David Smith
Comparison Of Google Image Search And Resnet Image Classification Using Image Similarity Metrics, David Smith
Computer Science and Computer Engineering Undergraduate Honors Theses
In this paper, we compare the results of ResNet image classification with the results of Google Image search. We created a collection of 1,000 images by performing ten Google Image searches with a variety of search terms. We classified each of these images using ResNet and inspected the results. The ResNet classifier predicted the category that matched the search term of the image 77.5% of the time. In our best case, with the search term “forklift”, the classifier categorized 92 of the 100 images as forklifts. In the worst case, for the category “hammer”, the classifier matched the search term …
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …
Fdm Machine Learning: An Investigation Into The Utility Of Neural Networks As A Predictive Analytic Tool For Go Around Decision Making, John Bro
Journal of Applied Sciences and Arts
Loss-of-control events during the approach-to-landing phase of flight account for a large share of fatalities in general aviation. During this critical transition towards the runway it is essential that an aircraft is stabilized. Pilot discretion and judgment is used to determine if an aircraft is suited to either land or go-around, based on an assessment of approach conditions. Many landing incidents and accidents could be prevented with improved go-around decisions. The purpose of this research is to investigate the utility of neural networks in modeling those decisions using historic aircraft flight data. Data collected from nearly 2,000 hours of training …
Developing A Recurrent Neural Network With High Accuracy For Binary Sentiment Analysis, Kevin Cunanan
Developing A Recurrent Neural Network With High Accuracy For Binary Sentiment Analysis, Kevin Cunanan
CMC Senior Theses
Sentiment analysis has taken on various machine learning approaches in order to optimize accuracy, precision, and recall. However, Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) account for the context of a sentence by using previous predictions as additional input for future sentence predictions. Our approach focused on developing an LSTM RNN that could perform binary sentiment analysis for positively and negatively labeled sentences. In collaboration with Mariam Salloum, I developed a collection of programs to classify individual sentences as either positive or negative. This paper additionally looks into machine learning, neural networks, data preprocessing, implementation, and resulting comparisons.