Browsing by Subject "Neural network"
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Item A study of instrumental method for suiting fabric hand evaluation and classification(2014-08) Wang, Keqing; Chen, Jonathan Yan; Craig, JaneIn the textile and apparel industry, fabric end-use preference and selection criteria are largely based on fabric hand because it relates to both the mechanical properties and aesthetic appearance of fabrics. This paper examines a method to grade fabric hand based on Kawabata’s measurements and neural network modeling. The proposed method is verified by comparing the hand graded by the neural network model to Kawabata’s total hand value. Ninety-five commercial fabrics from different manufacturers were tested using Kawabata evaluation system (KES-FB). Cluster analysis using SAS classified the suiting fabric samples into four groups in this study. The test results of fabric mechanical properties show similarities and dissimilarities between woven and knitted suiting fabrics. In comparison, woven suiting fabrics are less subject to shear and bending deformation. Knitted fabrics have a higher total hand value than woven fabrics with a smoother surface. Cluster analysis well divided the suiting fabric samples into four groups describing different fabric performance. The training dataset in the neural network model was selected based on information from the clustering results. The training model was proved to be accurate with a low MSE of 4 × 10-8. The model successfully graded the test samples with values ranged from 0 to 1. Additionally, the validity for grading fabric hand using the neural network technique was examined by analyzing the correlation between the hand graded by neural network model and Kawabata’s equations. The regression analysis shows a relatively strong correlation (p<0.0001, R2= 0.6363) between neural network grades and Kawabata’s grades.Item Adaptation in a deep network(2011-05) Ruiz, Vito Manuel; Pillow, Jonathan W.; Miikkulainen, Risto; Fiete, Ila; Geisler, Wilson; Seidemann, EyalThough adaptational effects are found throughout the visual system, the underlying mechanisms and benefits of this phenomenon are not yet known. In this work, the visual system is modeled as a Deep Belief Network, with a novel “post-training” paradigm (i.e. training the network further on certain stimuli) used to simulate adaptation in vivo. An optional sparse variant of the DBN is used to help bring about meaningful and biologically relevant receptive fields, and to examine the effects of sparsification on adaptation in their own right. While results are inconclusive, there is some evidence of an attractive bias effect in the adapting network, whereby the network’s representations are drawn closer to the adapting stimulus. As a similar attractive bias is documented in human perception as a result of adaptation, there is thus evidence that the statistical properties underlying the adapting DBN also have a role in the adapting visual system, including efficient coding and optimal information transfer given limited resources. These results are irrespective of sparsification. As adaptation has never been tested directly in a neural network, to the author’s knowledge, this work sets a precedent for future experiments.Item Grid cell attractor networks: development and implications(2015-12) Widloski, John Eric; Fiete, Ila; Marder, Michael P., 1960-; Gordon, Vernita; Pillow, Jonathan; Swinney, HarryAt the foundation of our ability to plan trajectories in complex terrain is a basic need to establish one’s positional bearings in the environment, i.e., to self-localize. How does the brain perform self-localization? How does a net- work of neurons conspire to solve this task? How does it self organize? Given that there might be multiple solutions to this problem, with what certainty can we say that any such model faithfully captures the neural structure and dynamics as it exists in the brain? This thesis presents a collection of three theoretical works aimed at addressing these problems, with a particular focus on biological plausibility and amenability to testing experimentally. I first introduce the context within which the work in the thesis is situ- ated. Chapter 1 provides a framework for understanding algorithmically how the brain might solve the problem of self-localization and how a neural circuit could be organized to perform self-localization based on the integration of self-motion cues, an operation known as path integration. We also introduce the neurobiology that underlies self-localization, with special emphasis on the cell types found in and around the hippocampus. We discuss the case that a particular class of cells – grid cells – subserve path integration, because of their peculiar spatial response properties and their anatomical positioning as the recipients of self-motion information. Continuous attractor models are introduced as the favored description of the grid cell circuit. Key open questions are introduced as motivation for the subsequently described work. I next focus on the question of how the grid cell circuit may have organized. In Chapter 2, it is demonstrated that an unstructured immature neural network, when subjected to biologically plausible inputs and learning rules, can learn to produce grid-like spatial responses and perform path integration. This model makes a number of predictions for experiment which are described at length. In Chapter 3, I describe a theoretically motivated experimental probe of the organization and dynamics of the grid cell circuit. The proposed experiment relies on sparse neural recordings of grid cells together with global perturbations of the circuit (and is thus experimentally feasible). It promises to yield special insight into the hidden structure of the grid cell circuit. Finally, in Chapter 4, I provide an analytical treatment of pattern formation dynamics in the grid cell circuit. This work focuses on nonlinear effects.Item Hierarchical modeling of multi-scale dynamical systems using adaptive radial basis function neural networks: application to synthetic jet actuator wing(Texas A&M University, 2004-09-30) Lee, Hee EunTo obtain a suitable mathematical model of the input-output behavior of highly nonlinear, multi-scale, nonparametric phenomena, we introduce an adaptive radial basis function approximation approach. We use this approach to estimate the discrepancy between traditional model areas and the multiscale physics of systems involving distributed sensing and technology. Radial Basis Function Networks offers the possible approach to nonparametric multi-scale modeling for dynamical systems like the adaptive wing with the Synthetic Jet Actuator (SJA). We use the Regularized Orthogonal Least Square method (Mark, 1996) and the RAN-EKF (Resource Allocating Network-Extended Kalman Filter) as a reference approach. The first part of the algorithm determines the location of centers one by one until the error goal is met and regularization is achieved. The second process includes an algorithm for the adaptation of all the parameters in the Radial Basis Function Network, centers, variances (shapes) and weights. To demonstrate the effectiveness of these algorithms, SJA wind tunnel data are modeled using this approach. Good performance is obtained compared with conventional neural networks like the multi layer neural network and least square algorithm. Following this work, we establish Model Reference Adaptive Control (MRAC) formulations using an off-line Radial Basis Function Networks (RBFN). We introduce the adaptive control law using a RBFN. A theory that combines RBFN and adaptive control is demonstrated through the simple numerical simulation of the SJA wing. It is expected that these studies will provide a basis for achieving an intelligent control structure for future active wing aircraft.Item Investigatory Brain-Computer Interface utilizing a single EEG sensor(2013-05) Shamlian, Daniel G.; Abraham, Jacob A.A Human-Machine Interface is a device that allows humans to inter- act with and use machines. One such device is a Brain-Computer Interface which allows the user to communicate to a computer system through thought patterns. A commonly used technique, electroencephalography, uses multiple sensors positioned on the subject’s cranium to extract electrical changes as a representation of thought patterns. This report investigates the use of a single EEG sensor as a user-friendly BCI implementation. The primary goal of this report is to determine if specific mental tasks can be reliably detected with such a system.Item Neural network analysis of sparse datasets ?? an application to the fracture system in folds of the Lisburne Formation, northeastern Alaska(Texas A&M University, 2005-11-01) Bui, Thang DinhNeural networks (NNs) are widely used to investigate the relationship among variables in complex multivariate problems. In cases of limited data, the network behavior strongly depends on factors such as the choice of network activation function and network initial weights. In this study, I investigated the use of neural networks for multivariate analysis in the case of limited data. The analysis shows that special attention should be paid when building and using NNs in cases of limited data. The linear activation function at the output nodes outperforms the sigmoidal and Gaussian functions. I found that combining network predictions gives less biased predictions and allows for the assessment of the prediction variability. The NN results, along with conventional statistical analysis, were used to examine the effects of folding, bed thickness, structural position, and lithology on the fracture properties distributions in the Lisburne Formation, folded and exposed in the northeastern Brooks Range of Alaska. Fracture data from five folds, representing different degrees of folding, were analyzed. In addition, I modeled the fracture system using the discrete fracture network approach and investigated the effects of fracture properties on the flow conductance of the system. For the Lisburne data, two major fracture sets striking north/south and east/west were studied. Results of the NNs analysis suggest that fracture spacing in both sets is similar and weakly affected by folding and that stratigraphic position and lithology have a strong effect on fracture spacing. Folding, however, has a significant effect on fracture length. In open folds, fracture lengths in both sets have similar averages and variances. As the folds tighten, both the east/west and north/south fracture lengths increase by a factor of 2 or 3 and become more variable. In tight folds, fracture length in the north/south direction is significantly larger than in the east/west direction. The difference in length between the two fracture sets creates a strong anisotropy in the reservoir. Given the same fracture density in both sets, the set with the greater length plays an important role for fluid flow, not only for flow along its principal direction but also in the orthogonal direction.Item Pre-injection reservoir evaluation at Dickman Field, Kansas(2011-08) Phan, Son Dang Thai; Sen, Mrinal K.; Srinivasan, Sanjay; Grand, StephenI present results from quantitative evaluation of the capability of hosting and trapping CO₂ of a carbonate brine reservoir from Dickman Field, Kansas. The analysis includes estimation of some reservoir parameters such as porosity and permeability of this formation using pre-stack seismic inversion followed by simulating flow of injected CO₂ using a simple injection technique. Liner et at (2009) carried out a feasibility study to seismically monitor CO₂ sequestration at Dickman Field. Their approach is based on examining changes of seismic amplitudes at different production time intervals to show the effects of injected gas within the host formation. They employ Gassmann's fluid substitution model to calculate the required parameters for the seismic amplitude estimation. In contrast, I employ pre-stack seismic inversion to successfully estimate some important reservoir parameters (P- impedance, S- impedance and density), which can be related to the changes in subsurface rocks due to injected gas. These are then used to estimate reservoir porosity using multi-attribute analysis. The estimated porosity falls within a reported range of 8-25%, with an average of 19%. The permeability is obtained from porosity assuming a simple mathematical relationship between porosity and permeability and classifying the rocks into different classes by using Winland R35 rock classification method. I finally perform flow simulation for a simple injection technique that involves direct injection of CO₂ gas into the target formation within a small region of Dickman Field. The simulator takes into account three trapping mechanisms: residual trapping, solubility trapping and mineral trapping. The flow simulation predicts unnoticeable changes in porosity and permeability values of the target formation. The injected gas is predicted to migrate upward quickly, while it migrates slowly in lateral directions. A large amount of gas is concentrated around the injection well bore. Thus my flow simulation results suggest low trapping capability of the original target formation unless a more advanced injection technique is employed. My results suggest further that a formation below our original target reservoir, with high and continuously distributed porosity, is perhaps a better candidate for CO₂ storage.Item Quadcopter stabilization with neural network(2016-12) Burman, Prateek; Julien, ChristineUAVs (Unmanned Aerial Vehicle), also known as drones, are becoming attractive in the consumer space due to their relatively low cost and their ability to operate autonomously with minimal human intervention. A user could program the drone with GPS coordinates, and the drone would comply with utmost precision. In order for the drone to operate a preprogrammed flight path, it requires a host of sensors for it to gather data and operate on that data in real time. For instance, a consumer drone typically has obstacle avoidance sensors, a GPS sensor for routing and navigation, and an IMU (Inertial Measurement Unit) for tracking position and orientation. These sensors play a crucial role in both stabilization and navigation of the drone. This report aims to investigate, analyze and understand the complexity involved in designing and implementing an autonomous quadcopter; specifically, the stabilization algorithms. In general, stabilization is achieved using some form of control algorithm. The report covers a popular approach for stabilization (PID Control) found with many open source libraries and contrasts it with an alternative machine learning approach (Neural Networks). Finally, a machine learning based algorithm is implemented and evaluated on a prototype quadcopter, and its results are presented.Item Standardization for intelligent detection and autonomous operation of non-structured hardware, and its application on railcar brake release operation(2015-05) Hammel, Christopher Scott; Tesar, Delbert; Ashok, PradeepkumarThis thesis introduces a standard framework for evaluating and planning for desired autonomous (or semi-autonomous) operations, then applies the framework, in detail, to the task of automating emergency brake release before rail-car decoupling. A significant hurdle to be accounted for is the lack of standardization of much of the hardware of interest in industry. Non-standardized rail car components must be formally structured as fully as possible to improve the reliability of the robotic automation. This brake release task requires either pushing or pulling a “bleed rod” that protrudes from the side of each rail car. The requirements for each step of the evaluation and planning process will be laid out in this thesis, as an example of how it should be applied to future automation tasks.