Data assimilation combines (imperfect) knowledge of a flow's physical laws with (noisy, time- lagged, and otherwise imperfect) observations to produce a more accurate prediction of flow statistics. Assimilation by...
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Data assimilation combines (imperfect) knowledge of a flow's physical laws with (noisy, time- lagged, and otherwise imperfect) observations to produce a more accurate prediction of flow statistics. Assimilation by nudging (from 1964), while non-optimal, is easy to implement and its analysis is clear and well-established. Nudging's uniform in time accuracy has even been established under conditions on the nudging parameter X and the density of observational locations, H , Larios et al. (2019). One remaining issue is that nudging requires the user to select a key parameter. The conditions required for this parameter, derived through & aacute;priori (worst case) analysis are severe (Section 2.1 herein) and far beyond those found to be effective in computational experience. One resolution, developed herein, is self-adaptive parameter selection. This report develops, analyzes, tests, and compares two methods of self-adaptation of nudging parameters. One combines analysis and response to local flow behavior. The other is based only on response to flow behavior. The comparison finds both are easily implemented and yields effective values of the nudging parameter much smaller than those of & aacute;priori analysis.
Nanosecond pulsed lasers with flexible temporal designs have presented encouragingly excellent characteristics in particular applications. Unfortunately, even the currently optimal temporal shaping schemes based on it...
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Nanosecond pulsed lasers with flexible temporal designs have presented encouragingly excellent characteristics in particular applications. Unfortunately, even the currently optimal temporal shaping schemes based on iterative correction of optimization algorithms still suffer from extremely low shaping efficiency in nanosecond pulsed fiber master oscillator power amplifier (MOPA) systems. Users are forced to make trade-offs between iteration cost and shaping precision. Through theoretical analysis and experimental verification, we reveal that pulse misjudgment caused by backward pulse energy transfer and poor performance of optimization algorithms are key factors limiting shaping efficiency, and we propose an ultra-efficient temporal shaping scheme based on innovative pulse fitting method and adaptive ratio algorithm. In a nanosecond pulsed fiber MOPA system with over 40-dB gain, we have achieved high-precision arbitrary temporal designs (close to systematic measurement error (similar to 0.5%)) with very few iterations (similar to 10), which is a remarkable progress in terms of shaping efficiency and shaping precision.
In this study, nonsingular modeling and cross-domain trajectory tracking control problems for a special class of coaxial hybrid aerial-underwater vehicles (HAUVs) are investigated. Coaxial HAUVs need to effectively ov...
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In this study, nonsingular modeling and cross-domain trajectory tracking control problems for a special class of coaxial hybrid aerial-underwater vehicles (HAUVs) are investigated. Coaxial HAUVs need to effectively overcome the influence of hydrodynamic factors when moving underwater, so the attitude angle required by coaxial HAUVs is much larger than that in the air. The attitude representation based on quaternion modeling is adopted to avoid the inherent singularity of Euler angle modeling. A cascade sliding mode control and proportion differentiation (SMC-PD) controller is proposed, which is used to position trajectory and attitude quaternion tracking control, respectively. An adaptive sliding mode controller based on disturbance observer (DO) enhancement is adopted in the outer loop to carry trajectory tracking control. At the same time, the expected attitude angle is calculated by the outer loop (position) and is converted into the expected quaternion. With reference to the idea of enhanced robustness in active disturbance rejection control (ADRC), a feedforward proportion derivation (PD) controller based on DO enhancement is used to track the desired quaternion. A variable parameter adaptive algorithm based on the learning rate is introduced in the cascaded SMC-PD controller. The error convergence speed of the system is further improved by adaptively changing the controller parameters. The stability of the proposed control scheme is proved by using the Lyapunov theory. The numerical simulation results show that the controller has good robustness and effectiveness.
We describe the development and application of a robot vision based adaptive algorithm for the quality control of the braided sleeving of high pressure hydraulic pipes. With our approach, we can successfully overcome ...
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We describe the development and application of a robot vision based adaptive algorithm for the quality control of the braided sleeving of high pressure hydraulic pipes. With our approach, we can successfully overcome the limitations, such as low reliability and repeatability of braided quality, which result from the visual observation of the braided pipe surface. The braids to be analyzed come in different dimensions, colors, and braiding densities with different types of errors to be detected, as presented in this paper. Therefore, our machine vision system, consisting of a mathematical algorithm for the automatic adaptation to different types of braids and dimensions of pipes, enables the accurate quality control of braided pipe sleevings and offers the potential to be used in the production of braiding lines of pipes. The principles of the measuring method and the required equipment are given in the paper, also containing the mathematical adaptive algorithm formulation. The paper describes the experiments conducted to verify the accuracy of the algorithm. The developed machine vision adaptive control system was successfully tested and is ready for the implementation in industrial applications, thus eliminating human subjectivity.
In this paper, we apply the stochastic perturbation technique to solve the optimal control problem governed by elliptic partial differential equation with small uncertainty in the random input. We first use finite-dim...
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In this paper, we apply the stochastic perturbation technique to solve the optimal control problem governed by elliptic partial differential equation with small uncertainty in the random input. We first use finite-dimensional noise assumption and perturbation technique to establish the first-order and second-order deterministic optimality systems, and then discretize the two systems by standard finite-element method. Furthermore, we derive a posteriori error estimators for the finite-element approximation of the state, co-state and control in two different norms, respectively. These error estimators are then used to build our adaptive algorithm. Finally, some numerical examples are presented to verify the effectiveness of the derived estimators.
In this article, we focus on computing the quantiles of a random variable f (x) , where X is a [0, 1] (d)-valued random variable, d is an element of N*, and f : [0, 1](d)-> R is a deterministic Lipschitz function. ...
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In this article, we focus on computing the quantiles of a random variable f (x) , where X is a [0, 1] (d)-valued random variable, d is an element of N*, and f : [0, 1](d)-> R is a deterministic Lipschitz function. We are particularly interested in scenarios where the cost of a single function evaluation is high, while the law of X is assumed to be known. In this context, we propose a deterministic algorithm to compute deterministic lower and upper bounds for the quantile of f(X) at a given level alpha is an element of (0, 1). With a fixed budget of N function calls, we demonstrate that our algorithm achieves an exponential deterministic convergence rate for d = 1 ( O (rho (N)) with alpha is an element of (0,1)) and a polynomial deterministic convergence rate for d >1(O(N- 1 /d-1 ) ) and show the optimality of those rates. Furthermore, we design two algorithms, depending on whether the Lipschitz constant of f is known or unknown.
This paper presents a SerDes receiver for medium-reach interconnection in a 28-nm CMOS process. It employs a CTLE and an adaptive quarter-rate loop-unrolling 5-tap DFE utilizing an SS-LMS algorithm to enable adaptive ...
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This paper presents a SerDes receiver for medium-reach interconnection in a 28-nm CMOS process. It employs a CTLE and an adaptive quarter-rate loop-unrolling 5-tap DFE utilizing an SS-LMS algorithm to enable adaptive adjustment of tap coefficients under different channels. The proposed DFE contains CML-based summer with CMFB technology and two-stage dynamic comparator with an offset calibration loop. Simulation results show that this receiver can operate at 25 Gb/s data rate with a power efficiency of 5.99 pJ/bit, Its BER is less than 1E-12 and eye-wide-opening is 0.67 UI under 20.6-dB channel loss at 12.5 GHz Nyquist frequency.
Several classical adaptive optimization algorithms, such as line search and trust-region methods, have been recently extended to stochastic settings where function values, gradients, and Hessians in some cases, are es...
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Several classical adaptive optimization algorithms, such as line search and trust-region methods, have been recently extended to stochastic settings where function values, gradients, and Hessians in some cases, are estimated via stochastic oracles. Unlike the majority of stochastic methods, these methods do not use a pre-specified sequence of step size parameters, but adapt the step size parameter according to the estimated progress of the algorithm and use it to dictate the accuracy required from the stochastic oracles. The requirements on the stochastic oracles are, thus, also adaptive and the oracle costs can vary from iteration to iteration. The step size parameters in these methods can increase and decrease based on the perceived progress, but unlike the deterministic case they are not bounded away from zero due to possible oracle failures, and bounds on the step size parameter have not been previously derived. This creates obstacles in the total complexity analysis of such methods, because the oracle costs are typically decreasing in the step size parameter, and could be arbitrarily large as the step size parameter goes to 0. Thus, until now only the total iteration complexity of these methods has been analyzed. In this paper, we derive a lower bound on the step size parameter that holds with high probability for a large class of adaptive stochastic methods. We then use this lower bound to derive a framework for analyzing the expected and high probability total oracle complexity of any method in this class. Finally, we apply this framework to analyze the total sample complexity of two particular algorithms, STORM (Blanchet et al. in INFORMS J Optim 1(2):92-119, 2019) and SASS (Jin et al. in High probability complexity bounds for adaptive step search based on stochastic oracles, 2021. https://***/10.48550/ARXIV.2106.06454), in the expected risk minimization problem.
This paper presents a novel numerical method for simulating the transport and dispersion of pollutants in the Mediterranean sea. The governing mathematical equations consist of a barotropic ocean model with friction t...
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This paper presents a novel numerical method for simulating the transport and dispersion of pollutants in the Mediterranean sea. The governing mathematical equations consist of a barotropic ocean model with friction terms, bathymetric forces, Coriolis and wind stresses coupled to an advection-diffusion equation with anisotropic dispersion tensor and source terms. The proposed numerical solver uses a multilevel adaptive semi-Lagrangian finite element method that combines various techniques, including the modified method of characteristics, finite element discretization, coupled projection scheme based on a rotational pressure correction algorithm, and an adaptive L 2-projection. The approach employs the gradient of the concentration as an error indicator for enrichment adaptations and increasing the number of quadrature points where needed without refining the mesh. The method is shown to provide accurate and efficient simulations for pollution transport in the Mediterranean sea. The proposed approach distinguishes itself from the well-established adaptive finite element methods for incompressible viscous flows by retaining the same structure and dimension of linear systems during the adaptation process.
High-fidelity physically based groundwater flow and solute transport models have been limited for seawater intrusion remediation design because of computationally intensive evolutionary algorithms. Data-driven machine...
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High-fidelity physically based groundwater flow and solute transport models have been limited for seawater intrusion remediation design because of computationally intensive evolutionary algorithms. Data-driven machine learning approaches are promising to substitute expensive-cost groundwater numerical models within optimization due to computing efficiency. However, machine learning surrogates may accumulate error of forecasting and thereby result in infeasible optimal solutions. To achieve desired fidelity level, this study proposes a novel adaptive machine learning surrogate based multiobjective optimization method for coastal aquifer desalination. An adaptive modeling algorithm is newly introduced to iteratively retrain poorly-performed machine learning models and enhance predicting accuracy. The method is demonstrated to seek optimal extraction and injection strategies for scavenging residual saltwater trapped in an upstream aquifer behind a subsurface dam. Two conflicting objectives of minimizing the total extraction-injection rate and maximizing saltwater removal effectiveness are considered. Three machine learning models including artificial neural network (ANN), Gaussian process (GP) and response surface regression model (RSR) are developed to replace a high-fidelity seawater intrusion model for predicting chloride concentration and salinity mass. Non-dominated Sorting Genetic algorithm II (NSGA-II) is employed to derive Pareto fronts. Pareto optimal solutions obtained from machine learning models are compared against those from the seawater intrusion model. Results indicate that the developed machine learning models do not only have strong predicting capability, but also maintain good quality of Pareto optimal solutions, while achieving substantial computational saving up to 95%. Especially, the adaptively retrained RSR model inhibits error accumulation of forecasting and accelerates correct convergence to the true Pareto front. The proposed method is found
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