This paper considers parameter estimation of linear systems under sensor-to-estimator communication constraint. Due to the limited battery power and the traffic congestion over a large sensor ...network, each sensor is required to reduce the rate of communication between the estimator and itself. We propose an observation-driven sensor scheduling policy such that the sensor transmits only the important measurements to the estimator. Unlike the existing deterministic scheduler, our stochastic scheduling is smartly designed to well compensate for the loss of the Gaussianity of the system. This results in a nice feature that the maximum-likelihood estimator (MLE) is still able to be recursively computed in a closed form, and the resulting estimation performance can be explicitly evaluated. Moreover, an optimization problem is formulated and solved to obtain the best parameters of the scheduling policy under which the estimation performance becomes comparable to the standard MLE with full measurements under a moderate transmission rate. Finally, simulations are included to validate the theoretical results.
Objective
The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD.
Methods
Gene expression profiles for the GSE68004 ...dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING).
Results
Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs (ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC).
Conclusion
The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.
To reduce the communication cost of a sensor node, this paper is concerned with an estimation framework with scheduled measurements for a linear system. A scheduler is designed to control the ...transmission of measurements from sensor to estimator, which results in that only a subset of measurements is transmitted to the estimator. We propose an innovation based scheduler and derive an analytical expression for the Cramér-Rao lower bound (CRLB) for the given scheduling strategy. Under a communication constraint, an adaptive scheduler and a corresponding recursive estimator are jointly designed to asymptotically attain the CRLB. The structure of the estimator bears close resemblance to the standard least square estimator (LSE) with the full set of sensor measurements. Moreover, we prove that the estimation performance in terms of mean-square estimation error is comparable to the standard LSE even under a moderate communication cost. The theoretical results are verified by simulations.
We consider a 3-D problem of steering a nonholonomic vehicle to seek an unknown source of a spatially distributed signal field without any position measurement. In the literature, there exists an ...extremum seeking-based strategy under a constant forward velocity and tunable pitch and yaw velocities. Obviously, the vehicle with a constant forward velocity may exhibit certain overshoots and cannot decelerate even it approaches the source. To resolve it, we propose a regulation strategy for the forward velocity along with the pitch and yaw velocities. Under such a strategy, the vehicle decelerates near the source and stays within a small area as if it comes to a full stop, and controllers for angular velocities become succinct. We prove the local exponential convergence via the averaging technique. Finally, the theoretical results are illustrated with simulations.
In this paper, a robust support vector regression (RSVR) method with uncertain input and output data is studied. First, the data uncertainties are investigated under a stochastic framework and two ...linear robust formulations are derived. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective. Second, kernelized RSVR formulations are established for nonlinear regression problems. Both linear and nonlinear formulations are converted to second-order cone programming problems, which can be solved efficiently by the interior point method. Simulation demonstrates that the proposed method outperforms existing RSVRs in the presence of both input and output data uncertainties.
This paper proposes a novel exact asynchronous subgradient-push algorithm (AsySPA) to solve an additive cost optimization problem over digraphs where each node only has access to a local convex ...function and updates asynchronously with an arbitrary rate. Specifically, each node of a strongly connected digraph does not wait for updates from other nodes but simply starts a new update within any bounded time interval by using local information available from its in-neighbors. "Exact" means that every node of the AsySPA can asymptotically converge to the same optimal solution, even under different update rates among nodes and bounded communication delays. To address uneven update rates, we design a simple mechanism to adaptively adjust stepsizes per update in each node, which is substantially different from the existing works. Then, we construct a delay-free augmented system to address asynchrony and delays, and study its convergence by proposing a generalized subgradient algorithm, which clearly has its own significance and helps us to explicitly evaluate the convergence rate of the AsySPA. Finally, we demonstrate advantages of the AsySPA in both theory and simulation.
This paper studies a parameter estimation problem of networked linear systems with fixed-rate quantization. Under the minimum mean square error criterion, we propose a recursive estimator of ...stochastic approximation type, and derive a necessary and sufficient condition for its asymptotic unbiasedness. This motivates to design an adaptive quantizer for the estimator whose strong consistency, asymptotic unbiasedness, and asymptotic normality are rigorously proved. Using the Newton-based and averaging techniques, we obtain two accelerated recursive estimators with the fastest convergence speed of O(1/k), and exactly evaluate the quantization effect on the estimation accuracy. If the observation noise is Gaussian, an optimal quantizer and the accelerated estimators are co-designed to asymptotically approach the minimum Cramer–Rao lower bound. All the estimators share almost the same computational complexity as the gradient algorithms with un-quantized observations, and can be easily implemented. Finally, the theoretical results are validated by simulations.
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This paper investigates the attainability of the minimum average data rate for stabilization of linear systems via logarithmic quantization. It is shown that a finite-level logarithmic quantizer ...suffices to approach the well-known minimum average data rate for stabilizing an unstable linear discrete-time system under two basic network configurations. In particular, we derive explicit finite-level logarithmic quantizers and the corresponding controllers to approach the minimum average data rate.
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Though quasi-Newton methods have been extensively studied in the literature, they either suffer from local convergence or use a series of line searches for global convergence which is not easy to ...implement in the distributed setting. In this work, we first propose a line search free greedy quasi-Newton (GQN) method with adaptive steps and establish explicit non-asymptotic bounds for both the global convergence rate and local superlinear rate. Our novel idea lies in the design of multiple GQN updates, which only involves computing Hessian-vector products, to control the Hessian approximation error, and a simple mechanism to adjust stepsizes to ensure the objective function improvement per iterate. Then, we extend it to the master–worker framework and propose a distributed adaptive GQN method whose communication cost is comparable with that of first-order methods, yet it retains the superb convergence property of its centralized counterpart. Finally, we demonstrate the advantages of our methods via numerical experiments.
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In a direct data-driven approach, this paper studies the property identification (ID) problem to analyze whether an unknown linear system has a property of interest, e.g., stabilizability and ...structural properties. In sharp contrast to the model-based analysis, we approach it by directly using the input and state feedback data of the unknown system. Via a new concept of sufficient richness of input sectional data, we first establish the necessary and sufficient condition for the minimum input design to excite the system for property ID. Specifically, the input sectional data is sufficiently rich for property ID if and only if it spans a linear subspace that contains a property dependent minimum linear subspace, any basis of which can also be easily used to form the minimum excitation input. Interestingly, we show that many structural properties can be identified with the minimum input that is however unable to identify the explicit system model. Overall, our results rigorously quantify the advantages of the direct data-driven analysis over the model-based analysis for linear systems in terms of data efficiency.
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