No description available.

Determining the amount of resources needed, specifically food and water, following a hurricane is not a straightforward task. Through this research effort, an estimating tool was developed that takes into account key demographic and evacuation behavioral effects, as well as hurricane storm specifics to estimate the number of meals required for the first fourteen days following a hurricane making landfall in the State of Florida. The Excel based estimating tool was created using data collected from four hurricanes making landfall in Florida during 2004-2005. The underlying model used in the tool is a Regression Decision Tree with predictor variables including direct impact, poverty level, and hurricane impact score. The hurricane impact score is a hurricane classification system resulting from this research that includes hurricane category, intensity, wind field size, and landfall location. The direct path of a hurricane, a higher than average proportion of residents below the poverty level, and the hurricane impact score were all found to have an effect on the number of meals required during the first fourteen days following a hurricane making landfall in the State of Florida

No description available.

No description available.

No description available.

No / Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.

The phasor measurement unit (PMU) is considered to be one of the most important measuring devices in the future of power systems. The distinction comes from its unique ability to provide synchronized phasor measurements of voltages and currents from widely dispersed locations in an electric power grid. The commercialization of the global positioning satellite (GPS) with accuracy of timing pulses in the order of 1 microsecond made possible the commercial production of phasor measurement units. Simulations and field experiences suggest that PMUs can revolutionize the way power systems are monitored and controlled. However, it is perceived that costs and communication links will affect the number of PMUs to be installed in any power system. Furthermore, defining the appropriate PMU system application is a utility problem that must be resolved. This thesis will address two key issues in any PMU initiative: placement and system applications. A novel method of PMU placement based on incomplete observability using graph theoretic approach is proposed. The objective is to reduce the required number of PMUs by intentionally creating widely dispersed pockets of unobserved buses in the network. Observable buses enveloped such pockets of unobserved regions thus enabling the interpolation of the unknown voltages. The concept of depth of unobservability is introduced. It is a general measure of the physical distance of unobserved buses from those known. The effects of depth of unobservability on the number of PMU placements and the errors in the estimation of unobserved buses will be shown. The extent and location of communication facilities affects the required number and optimal placement of PMUs. The pragmatic problem of restricting PMU placement only on buses with communication facilities is solved using the simulated annealing (SA) algorithm. SA energy functions are developed so as to minimize the deviation of communication-constrained placement from the ideal strategy as determined by the graph theoretic algorithm. A technique for true real time monitoring of voltage security using synchronized phasor measurements and decision trees is presented as a promising system application. The relationship of widening bus voltage angle separation with network stress is exploited and its connection to voltage security and margin to voltage collapse established. Decision trees utilizing angle difference attributes are utilized to classify the network voltage security status. It will be shown that with judicious PMU placement, the PMU angle measurement is equally a reliable indicator of voltage security class as generator var production. A method of enhancing the weighted least square state estimator (WLS-SE) with PMU measurements using a non-invasive approach is presented. Here, PMU data is not directly inputted to the WLS estimator measurement set. A separate linear state estimator model utilizing the state estimate from WLS, as well as PMU voltage and current measurement is shown to enhance the state estimate. Finally, the mathematical model for a streaming state estimation will be presented. The model is especially designed for systems that are not completely observable by PMUs. Basically, it is proposed to estimate the voltages of unobservable buses from the voltages of those observable using interpolation. The interpolation coefficients (or the linear state estimators, LSE) will be calculated from a base case operating point. Then, these coefficients will be periodically updated using their sensitivities to the unobserved bus injections. It is proposed to utilize the state from the traditional WLS estimator to calculate the injections needed to update the coefficients. The resulting hybrid estimator is capable of producing a streaming state of the power system. Test results show that with the hybrid estimator, a significant improvement in the estimation of unobserved bus voltages as well as power flows on unobserved lines is achieved. / Ph. D.

This dissertation concerns the development of an interactive machine learning method for refinement and analysis of segmented computed tomography (CT) images. This method uses higher-level domain-dependent knowledge to improve initial image segmentation results. A knowledge-based refinement and analysis system requires the formulation of domain knowledge. A serious problem faced by knowledge-based system designers is the knowledge acquisition bottleneck. Knowledge acquisition is very challenging and an active research topic in the field of machine learning and artificial intelligence. Commonly, a knowledge engineer needs to have a domain expert to formulate acquired knowledge for use in an expert system. That process is rather tedious and error-prone. The domain expert's verbal description can be inaccurate or incomplete, and the knowledge engineer may not correctly interpret the expert's intent. In many cases, the domain experts prefer to do actions instead of explaining their expertise. These problems motivate us to find another solution to make the knowledge acquisition process less challenging. Instead of trying to acquire expertise from a domain expert verbally, we can ask him/her to show expertise through actions that can be observed by the system. If the system can learn from those actions, this approach is called learning by demonstration. We have developed a system that can learn region refinement rules automatically. The system observes the steps taken as a human user interactively edits a processed image, and then infers rules from those actions. During the system's learn mode, the user views labeled images and makes refinements through the use of a keyboard and mouse. As the user manipulates the images, the system stores information related to those manual operations, and develops internal rules that can be used later for automatic postprocessing of other images. After one or more training sessions, the user places the system into its run mode. The system then accepts new images, and uses its rule set to apply postprocessing operations automatically in a manner that is modeled after those learned from the human user. At any time, the user can return to learn mode to introduce new training information, and this will be used by the system to updates its internal rule set. The system does not simply memorize a particular sequence of postprocessing steps during a training session, but instead generalizes from the image data and from the actions of the human user so that new CT images can be refined appropriately. Experimental results have shown that IntelliPost improves the segmentation accuracy of the overall system by applying postprocessing rules. In tests two different CT datasets of hardwood logs, the use of IntelliPost resulted in improvements of 1.92% and 9.45%, respectively. For two different medical datasets, the use of IntelliPost resulted in improvements of 4.22% and 0.33%, respectively. / Ph. D.

The goal of the proposed procedure in this dissertation is the implementation of phasor measurement unit (PMU) based instability detection for islanding prediction procedures using decision tree and neural network modeling. The islanding in the power system define as a separation of the coherent group of generators from the rest of the system due to contingencies, in the case that all generators are coherent together after introducing a fault, it is called stable or non-islanding. The main philosophy of islanding detection in the proposed methodology is to use decision trees and neural network data mining algorithms, performed off-line, to determine the PMU locations, detection parameters, and their triggering values for islanding detection. With the information obtained from accurate system models PMUs can be used online to predict system islanding with high reliability. The proposed approach is proved using a 4000 bus model of the California system. Before data mining was performed, a large number of islanding and non-islanding cases were created for the California model. PMUs data collection was simulated by collecting the voltage and current information in all 500 kV nodes in the system. More than 3000 cases were collected and classified by visual inspection as islanding and non-islanding cases. The proposed neural network and decision tree procedures captured the knowledge for the correct determination of system islanding with a small number of PMUs. / Ph. D.

In this thesis we propose a novel modification to Bayesian decision tree methods. We provide a historical survey of the statistics and computer science research in decision trees. Our approach facilitates covariate selection explicitly in the model, something not present in previous research. We define a transformation that allows us to use priors from linear models to facilitate covariate selection in decision trees. Using this transform, we modify many common approaches to variable selection in the linear model and bring these methods to bear on the problem of explicit covariate selection in decision tree models. We also provide theoretical guidelines, including a theorem, which gives necessary and sufficient conditions for consistency of decision trees in infinite dimensional spaces. Our examples and case studies use both simulated and real data cases with moderate to large numbers of covariates. The examples support the claim that our approach is to be preferred in large dimensional datasets. Moreover, our approach shown here has, as a special case, the model known as Bayesian CART. / Ph. D.