Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 437 for “"Artificial Neural Network"”.
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Artificial Neural Network-Based Robotic Control
<p>Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, …
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Medical Image Registration Using Artificial Neural Network
… region of interest over time. In this thesis, artificial neural networks with curvelet keypoints are used to estimate the parameters of registration. Simulations show that the curvelet keypoints provide more accurate results than using the Discrete Cosine Transform (DCT) coefficients and Scale …
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Environmental site characterization via artificial neural network approach
… soil in any contaminated zones. Back-propagation networks were also used to characterize the MMR Demo 1 site. The purpose of the developed ANN models was to predict the concentrations of perchlorate at the MMR from appropriate input parameters. To determine the most-appropriate input parameters …
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Classification of water quality using artificial neural network
… Therefore, this research aims to use the artificial neural network (ANN) algorithm to classify water quality at Pontian Kechil, Batu Pahat and Muar river. Concentrations of pH, suspended solids (SS), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), and …
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Artificial neural network decoding of multi-h CPM
… set out the results of an investigation into the artificial neural network (ANN) decoding of multi-h continuous phase modulation (CPM) schemes. Multi-h CPM schemes offer forward error correction (FEC) capabilities for continuous transmission, digital communication systems. Multi-h CPM is reported …
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Gamma spectroscopy by artificial neural network coupled with MCNP
… becomes important when analyzing bio-samples. Artificial neural network is an attractive technique for complex systems. Although there are neural network applications on spectral analysis, training by simulated data to analyze experimental data has not been made. This study offers an …
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An artificial neural network approach to transformer fault diagnosis
This thesis presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. The goal of the research is to investigate the available transformer incipient fault diagnosis methods and then develop an ANN …
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Artificial neural network techniques to investigate potential interactions between biomarkers
… related to interactions. Here we present an Artificial Neural Network-based methodology for the study of interactions in gene transcriptomic data. This will be applied and validated in a breast cancer context.
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The classification of acoustic emission signals via artificial neural network
… acoustic emission signals. In this thesis, a new Artificial Neural Network (ANN) approach is proposed to perform this task. One of the purposes of the thesis is to explore the connection between traditional methods of statistics and modern approaches of neural networks regarding pattern …
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Synesthetic Sensor Fusion via a Cross-Wired Artificial Neural Network.
… study was to examine the behavior of two artificial neural networks cross-wired based on the synesthesia cross-wiring hypothesis. Motivation for the study was derived from the study of psychology, robotics, and artificial neural networks, with perceivable application in the domain of …
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Autonomous Artificial Neural Network Star Tracker for Spacecraft Attitude Determination
"The time required to solve the ""lost-in-space"" problem for this star tracker prototype is on average 9.5 seconds. This is an improvement over the 60 seconds needed by the current off-the-shelf autonomous star tracker by Ball Aerospace, the CT-633. Initial acquisition after launch as well as …
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Modeling red pine tree mortality: An artificial neural network approach
… and yield simulation. Multi-layer feed-forward artificial neural networks (ANN) are adopted to achieve the goal. The premise of ANN modeling approach is the ability of such networks to approximate any measurable or continuous function to any desired degree of accuracy, given enough complexity …
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Skill embeddings: artificial neural network representations for pedagogical policy development.
… items, and in particular the use of novel neural network techniques to infer the latent content of instructional and assessment events, based on observed human interaction data. Of particular interest are complex tasks requiring a mixed skill set. First, the work looks at inferring …
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Geometrically Programmed Nano-Resistors for Ultra-Robust Artificial Neural Network Accelerator
Despite the transformative advance in artificial intelligence (AI), the AI processing hardware have not matched the speed and power-efficiency requirement, restricting the realization of the full potential of AI and requiring innovation in AI hardware. Data transmission bottleneck between memory …
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Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners
Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and …
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Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)
… on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum …
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Artificial Neural Network for Predicting Heat Transfer Rates in Supercritical Carbon Dioxide
… (G = [200, 400, 600, 800, 1000]) conditions. An artificial neural network base model was trained, validated, and tested using the CFD data. The test case was strategically selected such that the artificial neural network model trained on the high heat flux and mass flux (extreme) cases. Using the …
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