Browsing by Subject "Detection"
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Item Algorithms for detecting missing voltage and motor starting events(2016-08) Cho, Wanki; Santoso, Surya; Baldick, RossThe objective of this work is to develop algorithms to detect voltage variation events in power quality data and to figure out their characteristics. Voltage variation events covered in this work are missing voltage and motor starting events. The first part of this work describes the fundamental concept, characteristics and sources of voltage variation events. Next, this work describes the algorithms to detect voltage variation events. The verification of the algorithms is performed using simulated data from PSCAD/EMTDC and actual field data. The results show that the performance of the algorithms and the characteristics of voltage variation events are satisfactory. This work contributes to the development of detecting voltage variation events from a real distribution system and the analysis of voltage variation events.Item Analysis of smuggler movement on multiple transportation networks(2011-05) Goshev, Stefan Antoanov; Morton, David P.; Popova, ElmiraWe analyze an interdiction problem in which a nuclear-material smuggler can traverse multiple transportation networks, wherein each edge has an indigenous probability of evasion. Our objective is to determine the optimal locations of a limited number of radiation detectors at United States ports of entry across multiple networks (maritime, road and rail) so as to minimize the smuggler's total probability of evasion, from origin to destination. We choose geographically diverse potential origins and give the smuggler freedom to move across and between transportation networks. Further, we consider two different models of smuggler behavior in this context. Our analysis aims to provide a complete prioritization and picture of the threat at all ports of entry, leading to insight into good practical locations for detectors.Item Camouflage detection & signal discrimination : theory, methods & experiments(2022-05-05) Das, Abhranil; Geisler, Wilson S.; Reichl, L. E.; Florin, Ernst-Ludwig; Marder, MichaelCamouflage is an amazing feat of evolution, but also impressive is the ability of biological visual systems to detect them. They are the result of an evolutionary arms race that exposes many detection strategies and their limits. In this thesis, we investigate the principles of human detection of maximally-camouflaged objects, i.e. whose texture exactly mimics the background texture. Chapter 1 introduces and contextualizes the problem. In chapter 2, we develop a theory and model that extracts the relevant information in the image, and uses biologically plausible computations on them for detection. In chapter 3, we present a series of experiments which measured human camouflage detection ability along different dimensions of the task, such as across different textures and shapes. Chapter 5 is a reference on some methods and analysis used in the study. Chapter 6 describes mathematical methods and software on statistical signal discrimination that we developed to solve questions in visual detection, but with wider applications in other fields.Item Computer algorithm to detect and predict machine faults using cloud-based vibration data(2015-05) Olivares Villamediana, Ignacio Javier; Fernandez, Benito R.; Bukowitz, DavidIn this research a machine fault detection and diagnostic algorithm is presented. The algorithm uses time wave-form acceleration data stored in a server for cloud computing to calculate RMS and Peak values from it and give information to the user for maintenance schedule. Detection algorithm analyses the change in time of the acceleration signals and establish urgency and severity of the studied machines. Furthermore, the diagnosis sub-system studies also the change in time of the signals in frequency domain to give a forecast of the possible existing fault by discarding faults throughout a predetermined decision table. Simulated and real cases are performed to show the efficiency and results of using the algorithm as well.Item Development of a fluorescence model for the determination of constants associated with binding, quenching, and FRET efficiency and development of an immobilized FRET-peptide sensor for metal ion detection(2012-08) Casciato, Shelly Lynn, 1984-; Holcombe, James A.; Liljestrand, Howard M.This thesis presents a modeling program to obtain equilibrium information for a fluorescent system. Determining accurate dissociation constants for equilibrium processes involving a fluorescent mechanism can prove to be quite challenging. Typically, titration curves and non-linear least squares fitting of the data using computer programs are employed to obtain such constants. However, these approaches only consider the total fluorescence signal and often ignore other energy transfer processes within the system. The current model considers the impact on fluorescence from equilibrium binding (viz., metal-ligand, ligand-substrate, etc.), quenching and resonance energy transfer. This model should provide more accurate binding constants as well as insights into other photonic processes. The equations developed for this model are discussed and are fit to experimental data from titrimetric experiments. Since the experimental data are generally in excess of the number of parameters that are needed to define the system, fitting is operated in an overdetermined mode and employs error minimization (either absolute or relative) to define goodness of fit. Examples of how changes in certain parameters affect the shape of the titrimetric curve are also presented. The detection of metal ions is very important, causing a need for the development of a metal ion sensor that provides selectivity, sensitivity, real-time in situ monitoring, and a flexible design. In order to be able to perform in situ monitoring of trace metal ions, FRET-pair labeled peptides were attached to a Tentagel[trademark] resin surface. After soaking in nonmetal and metal solutions (pH = 7.5), the resin beads gave an enhanced response in the presence of Hg²⁺ and Zn²⁺. Using a t-test, the signals of the beads that were soaked in a solution of each of these metal ions (and that of Cd²⁺) were determined to be significantly different from beads soaked in a solution without metal. However, the standard deviation between a set the beads was too large in order to differentiate a bead that was soaked in nonmetal solution versus one soaked in a metal containing solution.Item Evaluation of open-source intrusion detection systems for IPv6 vulnerabilities in realistic test network(2017-05-03) Gin, Jeremy; Evans, Brian L. (Brian Lawrence), 1965-; Bard, William CThe Internet Protocol (IP) defines the format by which packets are relayed throughout and across networks. A majority of the Internet today uses Internet Protocol version 4 (IPv4), but due to several key industries, a growing share of the Internet is adopting IPv4’s successor, Internet Protocol version 6 (IPv6) for its promise of unique addressability, automatic configuration features, built-in security, and more. Since the invention of the Internet, network security has proven a leading and worthwhile concern. The evolution of the information security field has produced an important solution for network security monitoring: the intrusion detection system (IDS). In this report, I explore the difference in detection effectiveness and resource usage of two network monitoring philosophies, signature-based and behavior-based detection. I test these philosophies, represented by leading edge passive monitors Snort and Bro, against several categories of state-of-the-art IPv6 attacks. I model an IPv6 host-to-host intrusion across the Internet in a virtual test network by including benign background traffic and mimicking adverse network conditions. My results suggest that neither IDS philosophy is superior in all categories and a hybrid of the two, leveraging each’s strengths, would best secure a network against leading IPv6 vulnerabilities.Item Human and monkey detection performance in natural images compared with V1 population responses(2017-08-14) Bai, Yoon Ho; Geisler, Wilson S.; Seidemann, EyalDetection is a fundamental task that is critical to visual behavior. The central aim of this study was to measure and model behavioral and neurophysiological performance for detecting targets under naturalistic conditions. I first measured behavioral detection performance macaques and compared it to humans. Detection thresholds were measured on uniform backgrounds and for several contrasts of natural image backgrounds. I find that (i) threshold contrast power is a linear function of background contrast power for both humans and macaques, and (ii) the relative threshold functions for humans and macaques are in good agreement, although (iii) the macaques are less sensitive overall. Subsequently, I investigated the quantitative relationship between V1 population responses and detection performance. I used voltage-sensitive dye imaging (VSDI) to measure the neural population activity in V1 for the same stimuli, while the monkeys held fixation. The spatial scale of VSDI measurements was sufficient to resolve retinotopic responses and orientation columns over the whole region activated by the target. Separate read-out strategies were used for retinotopic and columnar responses. Across multiple contrast levels of natural image backgrounds, I compared both scales of population responses between target-present and target-absent conditions to derive the signal-to-noise ratio (d’), which specifies neurometric functions. Based on this simple approach, the results show that in comparison to behavioral performances, retinotopic performances degraded at a relatively higher rate with increasing contrast masking. On the other hand, columnar performances were relatively less susceptible to contrast masking in natural image backgrounds.Item Improving electrical power grid resiliency and optimizing post-storm recovery using LiDAR and machine learning(2020-02-03) Davis, Michael Andrew, II; Bajaj, ChandrajitWhile many external factors influence resiliency, weather remains the single greatest threat to the electric power grid, and the impacts caused by significant storms can be long-lasting and widespread. When damage occurs, it is very costly to identify due to the vast size of electrical transmission and distribution circuits, which can span hundreds of miles. Pinpointing a failure in a circuit requires the expensive process of dispatching human teams to “walk the line” and physically inspect the circuit to identify damage. It is proposed that this problem can be optimized through automation, by leveraging flight vehicles, light detection and ranging (LiDAR) technology, and machine learning. The goals for this project are: 1) Investigate the feasibility of, and problems associated with, developing a system to remotely inspect electrical power transmission and distribution infrastructure with lidar. 2) Investigate the feasibility of developing an automated system to classify and detect damage to terrestrial transmission and distribution assets with lidar and artificial intelligence. 3) Develop a proof of concept of such a system, including a simulation of real-time lidar data collection and damage assessmentItem Interdicting smuggler movement with transparent and non-transparent assets(2012-05) Hawley, Megan Lynn; Morton, David P.; Popova, ElmiraWe analyze an interdiction problem in which a nuclear-material smuggler can traverse the rail and road ports of entry (POEs) along the Mexican and Canadian borders of the United States. Our objective is to determine the optimal locations of a limited number of transparent and non-transparent assets so as to minimize the smuggler’s total probability of evasion, from origin to destination. We choose origins in Mexico and Canada and give the smuggler a diverse set of destinations to choose from. Our analysis aims to provide a complete prioritization and picture of the threat at Mexican and Canadian POEs, leading to insight into practical locations for transparent and non-transparent assets.Item Real-time detection of illegally parked vehicles using 1-D transformation(2007-08) Lee, Jong Taek, 1983-; Aggarwal, J.K. (Jagdishkumar Keshoran), 1936-With decreasing costs of high quality surveillance systems, human activity detection and tracking has become increasingly practical. Accordingly, automated systems have been designed for numerous detection tasks, but the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. This thesis provides a methodology for detecting this event in real-time by applying a novel image projection that reduces the dimensionality of the image data and thus reduces the computational complexity of the segmentation and tracking processes. After event detection, we invert the transformation to recover the original appearance of the vehicle and to allow for further processing that may require the two dimensional data. The proposed algorithm is able to successfully recognize illegally parked vehicles in realtime in the i-LIDS bag and vehicle detection challenge datasets.Item Retina-V1 model of detectability across the visual field(2014-08) Bradley, Chris Kent; Geisler, Wilson S.A practical model is proposed for predicting the detectability of targets at arbitrary locations in the visual field, in arbitrary gray-scale backgrounds, and under photopic viewing conditions. The major factors incorporated into the model include: (i) the optical point spread function of the eye, (ii) local luminance gain control (Weber's law), (iii) the sampling array of retinal ganglion cells, (iv) orientation and spatial-frequency dependent contrast masking, (iv) broadband contrast masking, (vi) and efficient response pooling. The model is tested against previously reported threshold measurements on uniform backgrounds (the ModelFest data set and data from Foley et al. 2007), and against new measurements reported here for several ModelFest targets presented on uniform, 1/f noise, and natural backgrounds, at retinal eccentricities ranging from 0 to 10 deg. Although the model has few free parameters, it is able to account quite well for all the threshold measurements.Item Short lived radionuclide modeling from nuclear weapons test sites and nuclear power plant accidents(2014-08) Helfand, Jonathan David; Biegalski, Steven R.Nuclear accidents and weapons tests are monitored by a worldwide network of air sensors, seismic detectors and several other techniques. At the site of the incident, contaminants are distributed and can provide insight into the time of the incident and type of incident. That information can then be used to affect policy decisions or better understand health risks. In order to evaluate a post nuclear test scenario, we must better understand the background readings at potential test sites where false positive or false negative allegations are more likely (e.g. the Nevada Test Site, the Chernobyl Nuclear Power Plate, etc.) Data from these sites have been compiled and compared to high purity germanium detector background readings and activities from a hypothetical nuclear weapon test. The results indicate that the following nuclides would be the best indicator of a recent nuclear test: ⁸⁹Sr, ⁹¹Y, ⁹⁵Zr, ¹⁰³Ru, ¹²⁶Sb, ¹²⁹[superscript m]Te, ¹⁴⁷Nd, ¹⁵⁶Eu. Nuclides such as ⁹¹Sr or ⁹⁷Zr have a steady state concentration due to plutonium spontaneous fission thus would not be a good indication of a recent nuclear test.