18,605 research outputs found
Comparison of Compression Codec Algorithms to Image Datasets in 3D Gaussian Splatting Models
No full textApproximate Orthogonal Gradient Descent for the Continual Reinforcement Learning of Robot Control
No full textIncremental Nonlinear Stability Analysis of Stochastic Systems Perturbed by L\'{e}vy Noise
No full textWe present a theoretical framework for characterizing incremental stability
of nonlinear stochastic systems perturbed by compound Poisson shot noise and
finite-measure L\'{e}vy noise. For each noise type, we compare trajectories of
the perturbed system with distinct noise sample paths against trajectories of
the nominal, unperturbed system. We show that for a finite number of jumps
arising from the noise process, the mean-squared error between the trajectories
exponentially converge towards a bounded error ball across a finite interval of
time under practical boundedness assumptions. The convergence rate for shot
noise systems is the same as the exponentially-stable nominal system, but with
a tradeoff between the parameters of the shot noise process and the size of the
error ball. The convergence rate and the error ball for the L\'{e}vy noise
system are shown to be nearly direct sums of the respective quantities for the
shot and white noise systems separately, a result which is analogous to the
L\'{e}vy-Khintchine theorem. We demonstrate our results using several numerical
case studies.Comment: To be published. See
https://onlinelibrary.wiley.com/doi/10.1002/rnc.6216 for final versio
Distributed Kalman-IMM Cooperative Estimator Synthesis for Large-Scale Networked Switching Systems
No full textPredictive control of linear discrete-time Markovian jump systems by learning recurrent patterns
No full textIncorporating pattern-learning for prediction (PLP) in many discrete-time or discrete-event systems allows for computation-efficient controller design by memorizing patterns to schedule control policies based on their future occurrences. In this paper, we demonstrate the effect of PLP by designing a controller architecture for a class of linear Markovian jump systems (MJSs) where the aforementioned "patterns"correspond to finite-length sequences of modes. In our analysis of recurrent patterns, we use martingale theory to derive closed-form solutions to quantities pertaining to the occurrence of patterns: (1) the expected minimum occurrence time of any pattern from some predefined collection, (2) the probability of a pattern being the first to occur among the collection. To make our method applicable to real-world dynamics, we make two extensions to common assumptions in prior pattern -occurrence literature. First, the distribution of the mode process is unknown, and second, the true realization of the mode process is not observable. As demonstration, we consider fault-tolerant control of a dynamic topology-switching network, and empirically compare PLP to two controllers without PLP: a baseline based on the novel System Level Synthesis (SLS) approach and a topology-robust extension of the SLS baseline. We show that PLP is able to reject disturbances just as effectively as the topology-robust controller at reduced computation time and control effort. We discuss several important tradeoffs, such as the size of the pattern collection and the system scale versus the accuracy of the mode predictions, which show how different PLP implementations affect stabilization and runtime performance.Published by Elsevier Ltd.
Trading Throughput for Freshness: Freshness-aware Traffic Engineering and In-Network Freshness Control
No full textWith the advent of the Internet of Things (IoT), applications are becoming increasingly dependent on networks to not only transmit content at high throughput but also deliver it when it is fresh, i.e., synchronized between source and destination. Existing studies have proposed the metric age of information (AoI) to quantify freshness and have system designs that achieve low AoI. However, despite active research in this area, existing results are not applicable to general wired networks for two reasons. First, they focus on wireless settings, where AoI is mostly affected by interference and collision, while queueing issues are more prevalent in wired settings. Second, traditional high-throughput/low-latency legacy drop-adverse (LDA) flows are not taken into account in most system designs; hence, the problem of scheduling mixed flows with distinct performance objectives is not addressed. In this article, we propose a hierarchical system design to treat wired networks shared by mixed flow traffic, specifically LDA and AoI flows, and study the characteristics of achieving a good tradeoff between throughput and AoI. Our approach to the problem consists of two layers: freshness-aware traffic engineering (FATE) and in-network freshness control (IFC). The centralized FATE solution studies the characteristics of the source flow to derive the sending rate/update frequency for flows via the optimization problem LDA-AoI Coscheduling. The parameters specified by FATE are then distributed to IFC, which is implemented at each outport of the network's nodes and used for efficient scheduling between LDA and AoI flows. We present a Linux implementation of IFC and demonstrate the effectiveness of FATE/IFC through extensive emulations. Our results show that it is possible to trade a little throughput (5% lower) for much shorter AoI (49% to 71% shorter) compared to state-of-the-art traffic engineering.
Dataset to support the article "High-resolution 𝜙-OFDR using phase unwrap and nonlinearity suppression"
No full textThis dataset is used for realizing high resolution of phase-sensitive Optical Frequency Domain Reflectometer. It is associated with the research paper:
Guo Z, Yan J, Han G, Yu Y, Greenwood D and Marco J (2023) "High-Resolution φ-OFDR Using Phase Unwrap and Nonlinearity Suppression". Journal of Lightwave Technology, 41 (9), 2885-2891. (https://doi.org/10.1109/JLT.2023.3236775).
The data is presented as an excel file:
High_resolution_OFDR_using_phase_unwrap_and_nonlinearity_suppression.xlsx
This work was funded by High Value Manufacturing Catapult and the Engineer and Physical Sciences Research Council - EPSRC EP/V000624/1. The author Gaoce Han would like to acknowledge the China Scholarship Council for sponsoring.</span
Han Suyin (Chinese author) speaking at Dallas Brookes Hall.
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/276390Han Suyin (Chinese author) speaking at Dallas Brookes Hall.200056
Item: [1999.0081.00439] "Han Suyin (Chinese author) speaking at Dallas Brookes Hall.
Control and State-Estimation of Jump Stochastic Systems by Learning Recurrent Spatiotemporal Patterns
No full textThis thesis establishes control and estimation architectures that combine both model-based and model-free methods by theoretically characterizing several types of jump stochastic systems (JSSs), i.e., systems with random and repetitive jump phenomena. By expanding the capabilities of model-based stochastic control and estimation, there is potential for artificial intelligence to be implemented as a supplement to theory-influenced design instead of being used end-to-end. We begin by deriving sufficient conditions for stochastic incremental stability for nonlinear systems perturbed by two types of non-Gaussian noise: 1) shot noise processes represented as compound Poisson processes, and 2) finite-measure Lévy processes constructed as affine combinations of Gaussian white and Poisson shot noise processes. We then present a controller architecture based on a concept we call pattern-learning for prediction (PLP) for discrete-time/discrete-event systems, in which we can take advantage of the fact that the underlying jump process is a sequence of random variables that occurs as repeated patterns of interest. Finally, we demonstrate control and estimation for JSSs in three real-world applications. First, we consider the control of networks with dynamic topology (e.g., power grid with fault-tolerance to downed lines), for which PLP is integrated with variations of the novel system-level synthesis framework for disturbance-rejection. Second, we perform congestion control of vehicle traffic flow over metropolitan intersection networks, for which PLP is extended to pattern-learning with memory and prediction (PLMP) via the inclusion of episodic control, designed to reduce memory consumption by exploiting structural symmetries and temporal repetition in the network. Third, we perform estimation and forecasting (the dual problem to control) for epidemic spread throughout a population network under jump phenomena such as superspreader effects and the emergence of variant viruses. Our results indicate that learning patterns in the jump process makes controller/observer design efficient in data-consumption and computation time, which suggests that it can potentially be used for other JSSs in the real world
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