Matching Items (6)
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- All Subjects: Computer Science
- All Subjects: Multi-channel Detection
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Description
Energy management system (EMS) is at the heart of the operation and control of a modern electrical grid. Because of economic, safety, and security reasons, access to industrial grade EMS and real-world power system data is extremely limited. Therefore, the ability to simulate an EMS is invaluable in researching the EMS in normal and anomalous operating conditions.
I first lay the groundwork for a basic EMS loop simulation in modern power grids and review a class of cybersecurity threats called false data injection (FDI) attacks. Then I propose a software architecture as the basis of software simulation of the EMS loop and explain an actual software platform built using the proposed architecture. I also explain in detail the power analysis libraries used for building the platform with examples and illustrations from the implemented application. Finally, I will use the platform to simulate FDI attacks on two synthetic power system test cases and analyze and visualize the consequences using the capabilities built into the platform.
I first lay the groundwork for a basic EMS loop simulation in modern power grids and review a class of cybersecurity threats called false data injection (FDI) attacks. Then I propose a software architecture as the basis of software simulation of the EMS loop and explain an actual software platform built using the proposed architecture. I also explain in detail the power analysis libraries used for building the platform with examples and illustrations from the implemented application. Finally, I will use the platform to simulate FDI attacks on two synthetic power system test cases and analyze and visualize the consequences using the capabilities built into the platform.
ContributorsKhodadadeh, Roozbeh (Author) / Sankar, Lalitha (Thesis advisor) / Xue, Guoliang (Thesis advisor) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2019
Description
Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.
ContributorsCrider, Lauren Nicole (Author) / Cochran, Douglas (Thesis director) / Renaut, Rosemary (Committee member) / Kosut, Oliver (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
Description
Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.
ContributorsHirte, Amanda (Author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Generative Adversarial Networks (GANs) have emerged as a powerful framework for generating realistic and high-quality data. In the original ``vanilla'' GAN formulation, two models -- the generator and discriminator -- are engaged in a min-max game and optimize the same value function. Despite offering an intuitive approach, vanilla GANs often face stability challenges such as vanishing gradients and mode collapse. Addressing these common failures, recent work has proposed the use of tunable classification losses in place of traditional value functions. Although parameterized robust loss families, e.g. $\alpha$-loss, have shown promising characteristics as value functions, this thesis argues that the generator and discriminator require separate objective functions to achieve their different goals. As a result, this thesis introduces the $(\alpha_{D}, \alpha_{G})$-GAN, a parameterized class of dual-objective GANs, as an alternative approach to the standard vanilla GAN. The $(\alpha_{D}, \alpha_{G})$-GAN formulation, inspired by $\alpha$-loss, allows practitioners to tune the parameters $(\alpha_{D}, \alpha_{G}) \in [0,\infty)^{2}$ to provide a more stable training process. The objectives for the generator and discriminator in $(\alpha_{D}, \alpha_{G})$-GAN are derived, and the advantages of using these objectives are investigated. In particular, the optimization trajectory of the generator is found to be influenced by the choice of $\alpha_{D}$ and $\alpha_{G}$. Empirical evidence is presented through experiments conducted on various datasets, including the 2D Gaussian Mixture Ring, Celeb-A image dataset, and LSUN Classroom image dataset. Performance metrics such as mode coverage and Fréchet Inception Distance (FID) are used to evaluate the effectiveness of the $(\alpha_{D}, \alpha_{G})$-GAN compared to the vanilla GAN and state-of-the-art Least Squares GAN (LSGAN). The experimental results demonstrate that tuning $\alpha_{D} < 1$ leads to improved stability, robustness to hyperparameter choice, and competitive performance compared to LSGAN.
ContributorsOtstot, Kyle (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2023
Description
The presence of strategic agents can pose unique challenges to data collection and distributed learning. This dissertation first explores the social network dimension of data collection markets, and then focuses on how the strategic agents can be efficiently and effectively incentivized to cooperate in distributed machine learning frameworks.
The first problem explores the impact of social learning in collecting and trading unverifiable information where a data collector purchases data from users through a payment mechanism. Each user starts with a personal signal which represents the knowledge about the underlying state the data collector desires to learn. Through social interactions, each user also acquires additional information from his neighbors in the social network. It is revealed that both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation for a given total payment budget. In the second half, a federated learning scheme to train a global learning model with strategic agents, who are not bound to contribute their resources unconditionally, is considered. Since the agents are not obliged to provide their true stochastic gradient updates and the server is not capable of directly validating the authenticity of reported updates, the learning process may reach a noncooperative equilibrium. First, the actions of the agents are assumed to be binary: cooperative or defective. If the cooperative action is taken, the agent sends a privacy-preserved version of stochastic gradient signal. If the defective action is taken, the agent sends an arbitrary uninformative noise signal. Furthermore, this setup is extended into the scenarios with more general actions spaces where the quality of the stochastic gradient updates have a range of discrete levels. The proposed methodology evaluates each agent's stochastic gradient according to a reference gradient estimate which is constructed from the gradients provided by other agents, and rewards the agent based on that evaluation.
ContributorsAkbay, Abdullah Basar (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Kosut, Oliver (Committee member) / Ewaisha, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
Eigenvalues of the Gram matrix formed from received data frequently appear in sufficient detection statistics for multi-channel detection with Generalized Likelihood Ratio (GLRT) and Bayesian tests. In a frequently presented model for passive radar, in which the null hypothesis is that the channels are independent and contain only complex white Gaussian noise and the alternative hypothesis is that the channels contain a common rank-one signal in the mean, the GLRT statistic is the largest eigenvalue $\lambda_1$ of the Gram matrix formed from data. This Gram matrix has a Wishart distribution. Although exact expressions for the distribution of $\lambda_1$ are known under both hypotheses, numerically calculating values of these distribution functions presents difficulties in cases where the dimension of the data vectors is large. This dissertation presents tractable methods for computing the distribution of $\lambda_1$ under both the null and alternative hypotheses through a technique of expanding known expressions for the distribution of $\lambda_1$ as inner products of orthogonal polynomials. These newly presented expressions for the distribution allow for computation of detection thresholds and receiver operating characteristic curves to arbitrary precision in floating point arithmetic. This represents a significant advancement over the state of the art in a problem that could previously only be addressed by Monte Carlo methods.
ContributorsJones, Scott, Ph.D (Author) / Cochran, Douglas (Thesis advisor) / Berisha, Visar (Committee member) / Bliss, Daniel (Committee member) / Kosut, Oliver (Committee member) / Richmond, Christ (Committee member) / Arizona State University (Publisher)
Created2019