Browsing by Subject "Neural Networks"
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Item A new methodology for analyzing and predicting U.S. liquefied natural gas imports using neural networks(Texas A&M University, 2005-11-01) Bolen, Matthew ScottLiquefied Natural Gas (LNG) is becoming an increasing factor in the U.S. natural gas market. For 30 years LNG imports into the U.S. have remained fairly flat. There are currently 18 permit applications being filed in the U.S. and another 10 permit applications being filed in Canada and Mexico for LNG import terminals. The EIA (Energy Information Agency) estimates by 2025 that LNG will make up 21% of the total U.S. Natural Gas Supply. This study developed a neural network approach to forecast LNG imports into the U.S. Various input variables were gathered, organized into groups based on similarity, and then a correlation matrix was generated to screen out redundant variables. Since a limited number of data points were available I used a restricted number of input variables. Based on this restriction, I grouped the input variables into four different scenarios and then generated a forecast for each scenario. These four different scenarios were the $/MMBTU model, natural gas energy consumption model, natural gas consumption model and the energy stack model. The standard neural network approach was also used to screen the input variables. First, a correlation matrix determined which variables had a high correlation with the output, U.S. LNG imports. The ten most correlated input variables were then put into correlation matrix to determine if there were any redundant variables. Due to the lack of data points only the five most highly correlated input variables were used in the neural network simulation. A number of interesting results were obtained from this study. The energy stack model and the consumption of natural gas forecasted a non-linear trend in U.S. LNG imports, compared to the linear trend forecasted by the EIA. The energy stack model and consumption of natural gas model predicted that in 2025 U.S. LNG imports will be about 6.5 TCF, while the other three models prediction is about three times as less. The energy stack model is the most realistic model due its non-linear trend, when the rapid increase of LNG imports is going to occur, and the quantity of U.S. LNG imports predicted in 2025.Item Acquisition and Mining of the Whole Mouse Brain Microstructure(2010-10-12) Kwon, Jae-RockCharting out the complete brain microstructure of a mammalian species is a grand challenge. Recent advances in serial sectioning microscopy such as the Knife- Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical sectioning technique, have the potential to finally address this challenge. Nevertheless, there still are several obstacles remaining to be overcome. First, many of these serial sectioning microscopy methods are still experimental and are not fully automated. Second, even when the full raw data have been obtained, morphological reconstruction, visualization/editing, statistics gathering, connectivity inference, and network analysis remain tough problems due to the unprecedented amounts of data. I designed a general data acquisition and analysis framework to overcome these challenges with a focus on data from the C57BL/6 mouse brain. Since there has been no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. To address the problems, I constructed a general software framework for automated data acquisition and computational analysis of the KESM data, and conducted two scientific case studies to discuss how the mouse brain microstructure from the KESM can be utilized. I expect the data, tools, and studies resulting from this dissertation research to greatly contribute to computational neuroanatomy and computational neuroscience.Item Analysis of Data from the Barnett Shale with Conventional Statistical and Virtual Intelligence Techniques(2011-02-22) Awoleke, Obadare O.Water production is a challenge in production operations because it is generally costly to produce, treat, and it can hamper hydrocarbon production. This is especially true for gas wells in unconventional reservoirs like shale because the relatively low gas rates increase the economic impact of water handling costs. Therefore, we have considered the following questions regarding water production from shale gas wells: (1) What is the effect of water production on gas production? (2) What are the different water producing mechanisms? and (3) What is the water production potential of a new well in a given gas shale province. The first question was answered by reviewing relevant literature, highlighting observed deficiencies in previous approaches, and making recommendations for future work. The second question was answered using a spreadsheet based Water-Gas-Ratio analysis tool while the third question was investigated by using artificial neural networks (ANN) to decipher the relationship between completion, fracturing, and water production data. We will consequently use the defined relationship to predict the average water production for a new well drilled in the Barnett Shale. This study also derived additional insight into the production trends in the Barnett shale using standard statistical methods. The following conclusions were reached at the end of the study: 1) The observation that water production does not have long term deleterious effect on gas production from fractured wells in tight gas sands cannot be directly extended to fractured wells in gas shales because the two reservoir types do not have analogous production mechanisms. 2) Based on average operating conditions of well in the Barnett Shale, liquid loading was found to be an important phenomenon; especially for vertical wells. 3) A neural network was successfully used to predict average water production potential from a well drilled in the Barnett shale. Similar methodology can be used to predict average gas production potential. Results from this work can be utilized to mitigate risk of water problems in new Barnett Shale wells and predict water issues in other shale plays. Engineers will be provided a tool to predict potential for water production in new wells.Item Development and Implementation of an Artificially Intelligent Search Algorithm for Sensor Fault Detection Using Neural Networks(Texas A&M University, 2004-09-30) Singh, HarkiratThis work is aimed towards the development of an artificially intelligent search algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. The AANN can be trained to detect when sensors go faulty but the problem of locating the faulty sensor still remains. The search algorithm aids the AANN to help locate the faulty sensors and reconstruct their actual values. The algorithm uses domain specific heuristics based on the inherent behavior of the AANN to achieve its task. Common sensor errors such as drift, shift and random errors and the algorithms response to them have been studied. The issue of noise has also been investigated. These areas cover the first part of this work. The second part focuses on the development of a web interface that implements and displays the working of the algorithm. The interface allows any client on the World Wide Web to connect to the engineering software called MATLAB. The client can then simulate a drift, shift or random error using the graphical user interface and observe the response of the algorithm.Item Development of a Novel Linear Magnetostrictive Actuator(2010-10-12) Sadighi, AliThis dissertation presents the development of a novel linear magnetostrictive actuator. The magnetostrictive material used here is Terfenol-D, an alloy of the formula Tb0.3Dy0.7Fe1.92. In response to a traveling magnetic field inside the Terfenol-D element, it moves in the opposite direction with a peristaltic motion. The proposed design offers the flexibility to operate the actuator in various configurations including local and conventional three-phase excitation. The conceptual design of the linear magnetostrictive actuator was performed during which different configurations were analyzed. Finite Element Analysis (FEA) was extensively used for magnetic circuit design and analysis in conceptual design. Eventually one of these designs was chosen based on which detailed design of linear magnetostrictive actuator was carried out. A new force transmission assembly incorporates spring washers to avoid the wear due to the sudden collision of Terfenol-D element with the force transmission assembly. All mechanical parts were then fabricated at the mechanical engineering machine shop. The power electronics to operate the motor in a local three-phase mode was designed and implemented. It was demonstrated that the power consumption can be reduced significantly by operating the magnetostrictive linear actuator in the local excitation mode. A finite-element model of the actuator was developed using ATILA and an empirical model was presented using the data gathered from numerous tests performed on the actuator. The closed-loop control system was implemented using relay control which resulted in an optimal closed-loop performance. The magnetostrictive actuator has demonstrated 410-N load capacity with a travel range of 45 mm, and the maximum speed is 9 mm/min. The maximum power consumption by the motor is 95 W. The sensorless control of the linear magnetostrictive actuator was successfully conducted using two different approaches. First, using a linear-approximation method, we achieved a position estimation capability with ?1 mm error. Then, an adaptive neuro-fuzzy inference system was employed for estimating the position which resulted in a position estimation capability with only a ?0.5 mm error.Item Neural Network-Based Noise Suppressor and Predictor for Quantifying Valve Stiction in Oscillatory Control Loops(2014-12-18) Annan, Carl AshieValve stiction-induced oscillations in chemical processing systems adversely affects control loop performance and can degrade the quality of products. Estimating the degree of stiction in a valve is a crucial step in compensating for the effect. This work proposes a neural network approach to quantify the degree of stiction in a valve once the phenomenon has been detected. Several degrees of stiction are simulated in a closed loop control system by specifying the magnitude of static (fs) and dynamic (fd) friction in a physical valve model. Each simulation generates controller output OP(t) and process variable PV(t) time series data. A feed-forward neural network (the predictor) is trained to model the relationship between a given OP and PV pattern, and the stiction parameters. To test the models predictive capability, a separate set of stiction patterns are generated with and without added process noise. An inverse neural network-based nonlinear principal component analysis (INLPCA) noise-suppressor effectively extracts the underlying stiction behaviour from the noise-corrupted OP and PV stiction patterns. In the noiseless test patterns, the predictor is shown to estimate fs and fd with a 0.65% average error. In the case of the noisy test patterns, the average error achieved was 1.85%. Since the predictor is developed offline, the use of computationally intensive real-time search/optimization routines to quantify stiction is avoided. The neural networks proved to be easily implementable, highly flexible models for extracting stiction behavior from control loops and accurately quantifying stiction, as long as an adequate first-principles description of the process dynamics can be developed.Item Structural Impairment Detection Using Arrays of Competitive Artificial Neural Networks(2012-07-16) Story, BrettAging railroad bridge infrastructure is subject to increasingly higher demands such as heavier loads, increased speed, and increased frequency of traffic. The challenges facing railroad bridge infrastructure provide an opportunity to develop improved systems of monitoring railroad bridges. This dissertation outlines the development and implementation of a Structural Impairment Detection System (SIDS) that incorporates finite element modeling and instrumentation of a testbed structure, neural algorithm development, and the integration of data acquisition and impairment detection tools. Ultimately, data streams from the Salmon Bay Bridge are autonomously recorded and interrogated by competitive arrays of artificial neural networks for patterns indicative of specific structural impairments. Heel trunnion bascule bridges experience significant stress ranges in critical truss members. Finite element modeling of the Salmon Bay Bridge testbed provided an estimate of nominal structural behavior and indicated types and locations of possible impairments. Analytical modeling was initially performed in SAP2000 and then refined with ABAQUS. Modeling results from the Salmon Bay Bridge were used to determine measureable quantities sensitive to modeled impairments. An instrumentation scheme was designed and installed on the testbed to record these diagnostically significant data streams. Analytical results revealed that main chord members and bracing members of the counterweight truss are sensitive to modeled structural impairments. Finite element models and experimental observations indicated maximum stress ranges of approximately 22 ksi on main chord members of the counterweight truss. A competitive neural algorithm was developed to examine analytical and experimental data streams. Analytical data streams served as training vectors for training arrays of competitive neural networks. A quasi static array of neural networks was developed to provide an indication of the operating condition at specific intervals of the bridge's operation. Competitive neural algorithms correctly classified 94% of simulated data streams. Finally, a stand-alone application was integrated with the Salmon Bay Bridge data acquisition system to autonomously analyze recorded data streams and produce bridge condition reports. Based on neural algorithms trained on modeled impairments, the Salmon Bay Bridge operates in a manner most resembling one of two operating conditions: 1) unimpaired, or 2) impaired embedded member at the southeast corner of the counterweight.Item Training Algorithms for Networks of Spiking Neurons(2014-12-08) Singh, NityendraNeural networks represent a type of computing that is based on the way that the brain performs computations. Neural networks are good at fitting non-linear functions and recognizing patterns. It is believed that biological neurons work similar to spiking neurons that process temporal information. In 2002, Bohte derived a backpropagation training algorithm (dubbbed as SpikeProp) for spiking neural networks (SNNs) containing temporal information as firing time of first spike. SpikeProp algorithm and its different variations were subject of many publications in the last decade. SpikeProp algorithm works for continuous weight SNNs. Implementing continuous parameters on hardware is a difficult task. On the other hand implementing digital logic on hardware is more straightforward because of many available tools. Training SNN with discrete weights is tricky because smallest change allowed in weights is a discrete step. And this discrete step might affect the accuracy of the network by huge amount. Previous works have been done for Artificial Neural Networks (ANNs) with discrete weights but there is no research in the area of training SNNs with discrete weights. New algorithms have been proposed as part of this thesis work. These algorithms work well for training discrete weights in a spiking neural network. These new algorithms use SpikeProp algorithm for choosing weights that are to be updated. Several standard classification datasets have been used to demonstrate the efficacy of proposed algorithms. It is shown that one of the proposed algorithms (Multiple Weights Multiple Steps) takes less execution time to train and the results are comparable to continuous weight SNNs in terms of accuracy.Item Transient Mixed Synapses Regulate Emerging Connectivity in Simple Neuronal Networks(2013-07-29) Richardson, Jarret KeithThe electrical synapse was first described over 50 years ago. Since that time appreciation of its complexity and importance has grown, including the hypothesis that early transient formation of these synapses is important to adult patterns of connectivity in neural networks. Presented in this dissertation are studies utilizing identified neurons in cell culture from the snail Helisoma trivolvis to examine discrete periods of electrical synapse formation during regeneration with sustained or transient expression. Extensive knowledge of connectivity patterns of the buccal neurons of Helisoma in cell culture and the ganglia, provide a useful framework for looking at modulation and manipulation of electrical synapses and their impact and emerging connectivity in a simple neuronal network. Two types of electrical connections were observed those that were transient, between a B19 and a B110 and those that were sustained, between a B19 and another B19. Dopamine (DA) modulation of forming electrical synapses (FES) produces a synapse specific effect at those either destined to be transient (TES) or sustained (SES) and may be a direct effect on the gap junctions at the synapses, as is the case at TES, or an indirect effect on other membrane currents, as seen in SES. DA modulation produces different outcomes at SES-centered networks and TES-centered networks with respect to new chemical synapse formation, demonstrating network-dependent effects of electrical synapse modulation. Pharmacological blockade of chemical and electrical components at forming mixed synapses in some cases alters subsequent synapse formation although due to the variable nature does not appear to be a direct interaction between chemical and electrical synapses. Three-cell networks appear to display a balancing mechanism for overall electrical coupling when electrical synapses are blocked suggesting a competition for some resource in the construction or trafficking of gap junctions. In addition to electrophysiological examinations, network coupling can be assessed utilizing fluorescent calcium imaging to look at coincidence of calcium changes as an output for coupling between cells. This technique provides a useful tool for less invasive studies of neuronal networks and the impact of coupling at mixed synapses.