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A model-based estimation process is implemented in simulation of a water transmission line for the purpose of leak detection. The objective of this thesis is aimed at determining, through simulation results, the effectiveness of the Extended Kalman Filter for leak detection. Water distribution systems often contain large amounts of unknown losses. The range in magnitude of losses varies from 10 to over 50 percent of the total volume of water pumped. The result is a loss of product, including water and the chemicals used to treat it, environmental damage, demand shortfalls, increased energy usage and unneeded pump capacity expansions. It is clear that more control efforts need to be implemented on these systems to reduce losses and increase energy efficiencies. The problems of demand shortfalls, resulting from lost product, are worsened by the limited availability of water resources and a growing population and economy. The repair of leakage zones as they occur is not a simple problem since the vast majority of leaks, not considered to be major faults, go undetected. The leak detection process described in the work of this thesis is model based. A transient model of a transmission line is developed using the Method of Characteristics. This method provides the most accurate results of all finite-difference solutions to the two partial differential equations of continuity and momentum that describe pipe flow. Simulations are run with leakage within the system and small transients are added as random perturbations in the upstream reservoir head. The head measurements at the two pipe extremes are used as inputs into the filter estimation process. The Extended Kalman Filter is used for state estimation of leakage within the transmission line. The filter model places two artificial leakage states within the system. The estimates of these fictitious leakage states are then used to locate the actual position and magnitude of leakage within the transmission line. This method is capable of locating one leak within the line. The results of the Extended Kalman Filter (EKF) process show that it is capable of locating the position and magnitude of small leaks within the line. It was concluded that the EKF could be used for leak detection, but field tests need to be done to better quantify the ability of these methods. It is recommended that a multiple filtering method be implemented that may be able to locate the occurrence of multiple leakage.

A model based algorithm of pipeline flow is developed and tested to determine if the model is capable of detecting a leak in a pipeline. The overall objective of this research is to determine the feasibility of applying the Extended Kalman Filter and a new technique defined as the Extended Boundary Approach to the detection of leakages in a physical water distribution system. <p>The demands on the water supply system increase as the human population grows and expands throughout the world. Water conservation is required to ensure an adequate supply of water remains for future generations. One way to conserve this water is by reducing the leakages in underground water distribution systems. Currently between 10 to 50 percent of the pumped water is lost due to unrecognized leakages. This results in a huge revenue loss of water, chemicals and energy that is required for transporting the water. The detection of underground leakages is a very complex problem because many leakages are small and go unnoticed by todays leak detection technology. <p>A model based leak detection technique is developed and tested in this thesis. The Method of Characteristics is used to develop a model of a single pipeline. This method is extensively used and provides the most accurate results of the two partial differential equations of continuity and momentum that describe pipe flow. The Extended Kalman Filter is used to estimate two fictitious leakages at known locations along the pipeline. In order to ensure the model is observable four pressure measurements are needed at equally spaced nodes along the pipeline. With the development of the Extended Boundary Approach only the upstream and downstream pressure measurements are required, however; the upstream and downstream flow measurements are also required. Using the information from the two fictitious leaks the actual leak location and magnitude can be determined. This method is only capable of detecting one leak in a single pipeline. <p>The results of the developed model show that the approach is capable of theoretically determining the leak location and magnitude in a pipeline. However, at this time, the feasibility of implementing the proposed leak detection method is limited by the required level of accuracy of the sensors which is beyond that found in todays technology. It was also found that the EKF used primarily steady state information to predict the leakage. It is recommended that further research explore alternate models which might better enhance the EKF approach using transient information from the pipeline. This may allow implementation on a real pipeline.

A model-based estimation process is implemented in simulation of a water transmission line for the purpose of leak detection. The objective of this thesis is aimed at determining, through simulation results, the effectiveness of the Extended Kalman Filter for leak detection. Water distribution systems often contain large amounts of unknown losses. The range in magnitude of losses varies from 10 to over 50 percent of the total volume of water pumped. The result is a loss of product, including water and the chemicals used to treat it, environmental damage, demand shortfalls, increased energy usage and unneeded pump capacity expansions. It is clear that more control efforts need to be implemented on these systems to reduce losses and increase energy efficiencies. The problems of demand shortfalls, resulting from lost product, are worsened by the limited availability of water resources and a growing population and economy. The repair of leakage zones as they occur is not a simple problem since the vast majority of leaks, not considered to be major faults, go undetected. The leak detection process described in the work of this thesis is model based. A transient model of a transmission line is developed using the Method of Characteristics. This method provides the most accurate results of all finite-difference solutions to the two partial differential equations of continuity and momentum that describe pipe flow. Simulations are run with leakage within the system and small transients are added as random perturbations in the upstream reservoir head. The head measurements at the two pipe extremes are used as inputs into the filter estimation process. The Extended Kalman Filter is used for state estimation of leakage within the transmission line. The filter model places two artificial leakage states within the system. The estimates of these fictitious leakage states are then used to locate the actual position and magnitude of leakage within the transmission line. This method is capable of locating one leak within the line. The results of the Extended Kalman Filter (EKF) process show that it is capable of locating the position and magnitude of small leaks within the line. It was concluded that the EKF could be used for leak detection, but field tests need to be done to better quantify the ability of these methods. It is recommended that a multiple filtering method be implemented that may be able to locate the occurrence of multiple leakage.

A model based algorithm of pipeline flow is developed and tested to determine if the model is capable of detecting a leak in a pipeline. The overall objective of this research is to determine the feasibility of applying the Extended Kalman Filter and a new technique defined as the Extended Boundary Approach to the detection of leakages in a physical water distribution system. <p>The demands on the water supply system increase as the human population grows and expands throughout the world. Water conservation is required to ensure an adequate supply of water remains for future generations. One way to conserve this water is by reducing the leakages in underground water distribution systems. Currently between 10 to 50 percent of the pumped water is lost due to unrecognized leakages. This results in a huge revenue loss of water, chemicals and energy that is required for transporting the water. The detection of underground leakages is a very complex problem because many leakages are small and go unnoticed by todays leak detection technology. <p>A model based leak detection technique is developed and tested in this thesis. The Method of Characteristics is used to develop a model of a single pipeline. This method is extensively used and provides the most accurate results of the two partial differential equations of continuity and momentum that describe pipe flow. The Extended Kalman Filter is used to estimate two fictitious leakages at known locations along the pipeline. In order to ensure the model is observable four pressure measurements are needed at equally spaced nodes along the pipeline. With the development of the Extended Boundary Approach only the upstream and downstream pressure measurements are required, however; the upstream and downstream flow measurements are also required. Using the information from the two fictitious leaks the actual leak location and magnitude can be determined. This method is only capable of detecting one leak in a single pipeline. <p>The results of the developed model show that the approach is capable of theoretically determining the leak location and magnitude in a pipeline. However, at this time, the feasibility of implementing the proposed leak detection method is limited by the required level of accuracy of the sensors which is beyond that found in todays technology. It was also found that the EKF used primarily steady state information to predict the leakage. It is recommended that further research explore alternate models which might better enhance the EKF approach using transient information from the pipeline. This may allow implementation on a real pipeline.

Program attacks are one of the oldest and fundamental cyber threats. They compromise the confidentiality of data, the integrity of program logic, and the availability of services. This threat becomes even severer when followed by other malicious activities such as data exfiltration. The integration of primitive attacks constructs comprehensive attack vectors and forms advanced persistent threats. Along with the rapid development of defense mechanisms, program attacks and data leak threats survive and evolve. Stealthy program attacks can hide in long execution paths to avoid being detected. Sensitive data transformations weaken existing leak detection mechanisms. New adversaries, e.g., semi-honest service provider, emerge and form threats. This thesis presents theoretical analysis and practical detection mechanisms against stealthy program attacks and data leaks. The thesis presents a unified framework for understanding different branches of program anomaly detection and sheds light on possible future program anomaly detection directions. The thesis investigates modern stealthy program attacks hidden in long program executions and develops a program anomaly detection approach with data mining techniques to reveal the attacks. The thesis advances network-based data leak detection mechanisms by relaxing strong requirements in existing methods. The thesis presents practical solutions to outsource data leak detection procedures to semi-honest third parties and identify noisy or transformed data leaks in network traffic. / Ph. D.

Many cities seek utilities monitoring with centrally managed Internet of Things (IoT) systems. This requires the development of numerous reliable low-cost wireless sensors, such as water temperature and flow meters, that can transmit information from subterranean pipes to surface-mounted receivers. Traditional radio communication systems are either unable to penetrate through multiple feet of earthen and manmade material, or have impractically large energy requirements which necessitate either frequent replacement of batteries, or a complex (and expensive) built-in energy harvesting system. Magnetic signaling systems do not suffer from this drawback: low-frequency electromagnetic waves have been shown to penetrate well through several feet of earth and water. In the past, these signals were too weak for practical use; however, this has changed with the recent proliferation of high-sensitivity magnetometers and compact rare-earth magnets. A permanent magnet can be either rotated or vibrated to create an oscillating magnetic field. Utilizing this phenomenon, two types of magnetic transmitter are investigated in this study: one which uses a propeller to directly rotate a diametrically magnetized neodymium magnet; and a second in which a permanent magnet is oscillated back-and-forth across a novel soft-magnet Y-stator, which projects a switching magnetic field. In principle, these oscillating magnetic fields can be used for communication from subterranean infrastructure sensors—such as flow meters and leak detection devices—to an aboveground long range (LoRa) radio-networked Arduino receiver equipped with a magnetometer. Simulation software models the oscillating electromagnetic fields produced by the Y-stator configuration. Laboratory performance and field tests establish the capability of two IoT-linked leak-detection sensors that use magnetic telemetry. Remote datalogging demonstrates the viability of integrating many sensors and surface receivers into a single LoRa wireless IoT network.

The technique of the distributed fiber optic sensor system, the principle that we use Sagnac interferometer to sense time-varying physical field, can be used to measure the position of the disturbed physical field and have the ability of detecting continuous position. Based on the configuration of the Sagnac interferometer, sensing optic fiber is loop design, which is hard to be set in real surroundings, and a half length of loop fiber have to be the isolated protection of the physical field. Therefore, this essay brings up the In-Line conception to be the design direction. And we make use of the physical field of pipeline leak acoustic to detect disturbance position. The measurable range of systematic structure signal is 3¡Ñ10-4 ~ 3¡Ñ10-2 , and the dynamic range is 40 dB. On the other hand, the structure of polarization insensitive is brought up, the measurable range is 1.5¡Ñ10-3 ~ 3¡Ñ10-2 , and the dynamic range is 26 dB.

This dissertation deals with the development of a model-based diagnostic system for air brake systems that are widely used in commercial vehicles, such as trucks, tractor-trailers, buses, etc. The performance of these brake systems is sensitive to maintenance and hence they require frequent inspections. Current inspection techniques require an inspector to go underneath a vehicle to check the brake system for possible faults, such as leaks, worn brake pads, out-of-adjustment of push rods, etc. Such inspections are time consuming, labor intensive and difficult to perform on vehicles with a low ground clearance. In this context, the development of an onboard/ handheld diagnostic tool for air brakes would be of significant value. Such a tool would automate the brake inspection process, thereby reducing the inspection time and improving the safety of operation of commercial vehicles. In this dissertation, diagnostic schemes are developed to automatically detect two important and prevalent faults that can occur in air brake systems – leaks and out-of-adjustment of push rods. These diagnostic schemes are developed based on a nonlinear model for the pneumatic subsystem of the air brake system that correlates the pressure transients in the brake chamber with the supply pressure to the treadle valve and the displacement of the treadle valve plunger. These diagnostic schemes have been corroborated with data obtained from the experimental facility at Texas A&M University and the results are presented. The response of the pneumatic subsystem of the air brake system is such that it can be classified as what is known as a “Sequential Hybrid System”. In this dissertation, the term “hybrid systems” is used to denote those systems whose mathematical representation involves a finite set of governing ordinary differential equations corresponding to a finite set of modes of operation. The problem of estimating the push rod stroke is posed as a parameter estimation problem and a transition detection problem involving the hybrid model of the pneumatic subsystem of the air brake system. Also, parameter estimation schemes for a class of sequential hybrid systems are developed. The efficacy of these schemes is illustrated with some examples.

Large graph-based datasets are common to many applications because of the additional structure provided to data by graphs. Patterns extracted from graphs must adhere to these structural properties, making them a more complex class of patterns to identify. The role of graph mining is to efficiently extract these patterns and quantify their significance. In this thesis, we focus on two application domains and demonstrate the design of graph mining algorithms in these domains. First, we investigate the use of graph grammar mining as a tool for diagnosing potential memory leaks from Java heap dumps. Memory leaks occur when memory that is no longer in use fails to be reclaimed, resulting in significant slowdowns, exhaustion of available storage, and eventually application crashes. Analyzing the heap dump of a program is a common strategy used in memory leak diagnosis, but our work is the first to employ a graph mining approach to the problem. Memory leaks accumulate in the heap as classes of subgraphs and the allocation paths from which they emanate can be explored to contextualize the leak source. We show that it suffices to mine the dominator tree of the heap dump, which is significantly smaller than the underlying graph. We demonstrate several synthetic as well as real-world examples of heap dumps for which our approach provides more insight into the problem than state-of-the-art tools such as Eclipse's MAT. Second, we study the problem of multipartite graph clustering as an approach to database summarization on an integrated biological database. Construction of such databases has become a common theme in biological research, where heterogeneous data is consolidated into a single, centralized repository that provides a structured forum for data analysis. We present an efficient approximation algorithm for identifying clusters that form multipartite cliques spanning multiple database tables. We show that our algorithm computes a lossless compression of the database by summarizing it into a reduced set of biologically meaningful clusters. Our algorithm is applied to data from C. elegans, but we note its applicability to general relational databases. / Master of Science