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Item Adversarial Attacks and Defense Mechanisms to Improve Robustness of Deep Temporal Point Processes(2022-08) Khorshidi, Samira; Mohler, George; Al Hasan, Mohammad; Raje, Rajeev; Durresi, ArjanTemporal point processes (TPP) are mathematical approaches for modeling asynchronous event sequences by considering the temporal dependency of each event on past events and its instantaneous rate. Temporal point processes can model various problems, from earthquake aftershocks, trade orders, gang violence, and reported crime patterns, to network analysis, infectious disease transmissions, and virus spread forecasting. In each of these cases, the entity’s behavior with the corresponding information is noted over time as an asynchronous event sequence, and the analysis is done using temporal point processes, which provides a means to define the generative mechanism of the sequence of events and ultimately predict events and investigate causality. Among point processes, Hawkes process as a stochastic point process is able to model a wide range of contagious and self-exciting patterns. One of Hawkes process’s well-known applications is predicting the evolution of viral processes on networks, which is an important problem in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used to predict viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks. Recent attempts have been made to use assortativity to address this shortcoming. This thesis illustrates how the evolution of such a viral process is sensitive to the underlying network’s structure. In Chapter 3 , we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution combined with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach, by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95% of the variance in virality on networks with the same degree distribution but different network topologies. Our results highlight the importance of graphlets and identify a small collection of graphlets that may have the most significant influence over the viral processes on a network. Due to the flexibility and expressiveness of deep learning techniques, several neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the possible adversarial attacks and the robustness of such models regarding adversarial attacks and natural shocks to systems. Furthermore, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. In Chapter 4 , we propose several white-box and black-box adversarial attacks against deep temporal point processes. Additionally, we investigate the transferability of whitebox adversarial attacks against point processes modeled by deep neural networks, which are considered a more elevated risk. Extensive experiments confirm that neural point processes are vulnerable to adversarial attacks. Such a vulnerability is illustrated both in terms of predictive metrics and the effect of attacks on the underlying point process’s parameters. Expressly, adversarial attacks successfully transform the temporal Hawkes process regime from sub-critical to into a super-critical and manipulate the modeled parameters that is considered a risk against parametric modeling approaches. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes and Covid-19 pandemic dataset as an example. Considering the security vulnerability of deep-learning models, including deep temporal point processes, to adversarial attacks, it is essential to ensure the robustness of the deployed algorithms that is despite the success of deep learning techniques in modeling temporal point processes. In Chapter 5 , we study the robustness of deep temporal point processes against several proposed adversarial attacks from the adversarial defense viewpoint. Specifically, we investigate the effectiveness of adversarial training using universal adversarial samples in improving the robustness of the deep point processes. Additionally, we propose a general point process domain-adopted (GPDA) regularization, which is strictly applicable to temporal point processes, to reduce the effect of adversarial attacks and acquire an empirically robust model. In this approach, unlike other computationally expensive approaches, there is no need for additional back-propagation in the training step, and no further network isrequired. Ultimately, we propose an adversarial detection framework that has been trained in the Generative Adversarial Network (GAN) manner and solely on clean training data. Finally, in Chapter 6 , we discuss implications of the research and future research directions.Item Distributed Nonlinear Model Predictive Control for Heterogeneous Vehicle Platoons Under Uncertainty(IEEE Xplore, 2021-09) Shen, Dan; Yin, Jianhua; Du, Xiaoping; Li, Lingxi; Electrical and Computer Engineering, School of Engineering and TechnologyThis paper presents a novel distributed nonlinear model predictive control (DNMPC) for minimizing velocity tracking and spacing errors in heterogeneous vehicle platoon under uncertainty. The vehicle longitudinal dynamics and information flow in the platoon are established and analyzed. The algorithm of DNMPC with robustness and reliability considerations at each vehicle (or node) is developed based on the leading vehicle and reference information from nodes in its neighboring set. Together with the physical constraints on the control input, the nonlinear constraints on vehicle longitudinal dynamics, the terminal constraints on states, and the reliability constraints on both input and output, the objective function is defined to optimize the control accuracy and efficiency by penalizing the tracking errors between the predicted outputs and desirable outputs of the same node and neighboring nodes, respectively. Meanwhile, the robust design optimization model also minimizes the expected quality loss which consists of the mean and standard deviation of node inputs and outputs. The simulation results also demonstrate the accuracy and effectiveness of the proposed approach under two different traffic scenarios.Item Energy-Efficient and Robust QoS Control for Wireless Sensor Networks Using the Extended Gur Game(MDPI, 2025-01-25) Zhong, Xiaoyang; Liang, Yao; Li, Yimei; Computer Science, Luddy School of Informatics, Computing, and EngineeringOutdoor wireless sensor networks (WSNs) operate autonomously in dynamic and unattended real-world environments, where sensor nodes are typically powered by their batteries. In hash outdoor settings, such as mountainous regions or underwater locations, recharging or replacing sensor node batteries is particularly challenging. For these WSN deployments, ensuring quality of service (QoS) control while conserving energy is crucial. This paper presents a novel QoS control algorithm for WSNs, built on extensions to the Gur game framework. The proposed approach not only enhances QoS performance compared to existing Gur game-based WSN control algorithms but also addresses their fundamental energy consumption challenges, enabling sustainable communication and extended network lifetimes. We evaluate the approach through comprehensive TinyOS-based WSN simulations and comparisons with existing algorithms. The results demonstrate that our approach, referred to as the robust Gur game, significantly enhances QoS control and achieves a 27.33% improvement in energy efficiency over the original Gur game and shuffle algorithms, showcasing the significant benefits of the proposed method.Item Multi-Threshold Low Power-Delay Product Memory and Datapath Components Utilizing Advanced FinFET Technology Emphasizing the Reliability and Robustness(2020-12) Yadav, Avinash; Rizkalla, Maher E.; Ytterdal, Trond; Lee, John J.In this thesis, we investigated the 7 nm FinFET technology for its delay-power product performance. In our study, we explored the ASAP7 library from Arizona State University, developed in collaboration with ARM Holdings. The FinFET technology was chosen since it has a subthreshold slope of 60mV/decade that enables cells to function at 0.7V supply voltage at the nominal corner. An emphasis was focused on characterizing the Non-Ideal effects, delay variation, and power for the FinFET device. An exhaustive analysis of the INVx1 delay variation for different operating conditions was also included, to assess the robustness. The 7nm FinFET device was then employed into 6T SRAM cells and 16 function ALU. The SRAM cells were approached with advanced multi-corner stability evaluation. The system-level architecture of the ALU has demonstrated an ultra-low power system operating at 1 GHz clock frequency.Item Robustness Improvement of Computationally Efficient Cooperative Fuzzy Model Predictive-Integral Sliding Mode Control of Nonlinear Systems(IEEE, 2021) Farbood, Mohsen; Veysi, Mohammad; Shasadeghi, Mokhtar; Izadian, Afshin; Niknam, Taher; Aghaei, Jamshid; Engineering Technology, Purdue School of Engineering and TechnologyThis paper introduces a systematic and comprehensive method to design a constrained fuzzy model predictive control (MPC) cooperated with integral sliding mode control (ISMC) based on the Takagi-Sugeno (T-S) fuzzy model for uncertain continuous-time nonlinear systems subject to external disturbances. The proposed controller benefits from the robustness, optimality, and practical constraints considerations. The robustness against the uncertainties and matched external disturbances is achieved by the proposed ISMC without iterative calculation for obtaining the robust invariant set. The MPC schemes are designed separately based on the both quadratic and non-quadratic Lyapunov functions. By the proposed MPC, the states of the system reach the desired values in the optimal, constrained, and robust manner against the unmatched external disturbances. New linear matrix inequalities (LMIs) conditions are proposed to design both the proposed MPC schemes. Also, the practical constraints on the control signals are guaranteed in the design procedure based on the invariant ellipsoid set. To evaluate the effectiveness of the suggested strategy, some simulation and experimental tests were run.Item Second-order reliability methods: a review and comparative study(Springer Nature, 2021) Hu, Zhangli; Mansour, Rami; Olsson, Mårten; Du, Xiaoping; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologySecond-order reliability methods are commonly used for the computation of reliability, defined as the probability of satisfying an intended function in the presence of uncertainties. These methods can achieve highly accurate reliability predictions owing to a second-order approximation of the limit-state function around the Most Probable Point of failure. Although numerous formulations have been developed, the lack of full-scale comparative studies has led to a dubiety regarding the selection of a suitable method for a specific reliability analysis problem. In this study, the performance of commonly used second-order reliability methods is assessed based on the problem scale, curvatures at the Most Probable Point of failure, first-order reliability index, and limit-state contour. The assessment is based on three performance metrics: capability, accuracy, and robustness. The capability is a measure of the ability of a method to compute feasible probabilities, i.e., probabilities between 0 and 1. The accuracy and robustness are quantified based on the mean and standard deviation of relative errors with respect to exact reliabilities, respectively. This study not only provides a review of classical and novel second-order reliability methods, but also gives an insight on the selection of an appropriate reliability method for a given engineering application.Item The Bayesian Regularized Quantile Varying Coefficient Model(Elsevier, 2023) Zhou, Fei; Ren, Jie; Ma, Shuangge; Wu, Cen; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthThe quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.