Adaptive Sampling Scheme Research Articles

An adaptive sampling method for accurate measurement of aeroengine blades

The surface of aeroengine blades has geometrical complexity and its measurement accuracy directly affect the assessment correction of the different machining processes. A NURBS based parametric model was employed to represent the geometry of blade surface for evaluating deviation between ideal model and measurement model. In view of the various curvature at the different blade region, an adaptive surface sampling strategy with bending moment theory is proposed. The distribution and number of the check points can be determined adaptively and accurately in accordance with geometrical feature altering. The accuracy and effectiveness of the proposed method were validated through simulations. Case studies involving comparing with bidirectional isoparametric method, equal area method and hammersley sequence method showed that the deviation between the measured blade surface and its reconstructed model using the developed method is the least among all the methods while the numbers of the selected check points are the same.

Network Latency Estimation With Leverage Sampling for Personal Devices: An Adaptive Tensor Completion Approach

In recent years, end-to-end network latency estimation has attracted much attention because of its significance for network performance evaluation. Given the widespread use of personal devices, latency estimation from partially observed samples becomes more complicated due to unstable communication conditions, while measuring the latencies between all nodes in a large-scale network is infeasible and costly. Hence, reducing the measurement cost becomes critical for the latency estimation of personal device network. In this paper, we propose an adaptive sampling scheme based on leverage scores to reduce the measurement cost while achieving high estimation accuracy. Furthermore, we provide theoretical analysis to characterize the performance bounds of the proposed scheme in terms of sampling complexity and estimation error. Finally, we demonstrate the efficiency of the proposed scheme by conducting extensive simulations on both synthetic and real datasets. The results show that the proposed scheme is able to not only improve the estimation accuracy of network latency but also reduce the sample budget compared to the state-of-the-art approaches.

An aerodynamic variable-fidelity modelling framework for a low-observable UCAV

The extensive amount of aerodynamic data needed for the creation of a Stability & Control data set requires efficient and reliable tools. For this reason, the task of producing a large and dense data set in general relies on ground-facility testing and is conducted after the product definition phase, where, nevertheless, already plenty of aerodynamic data is generated by use of simple low-fidelity aerodynamic methods. However, these methods are not capable of predicting complex non-linear aerodynamic phenomena, which complicates a reliable assessment of new designs and demands more accurate predictions early in the design process. Variable-fidelity surrogate modelling offers an opportunity to include high-fidelity simulation tools at affordable effort by enriching a low-fidelity data set with just a few selected highly accurate results. In this paper, an automated aerodynamic variable-fidelity modelling process is proposed to create such an aerodynamic data set at product definition phase. It is demonstrated on an industrial-relevant, agile, low-observable unmanned combat aerial vehicle. An adaptive sampling strategy is applied which proves to be both efficient and accurate. This is supplemented by a model selection algorithm. Moreover, a comparison with a single-fidelity surrogate modelling process shows the variable-fidelity modelling strategy to be superior.

Adaptive sampling for active learning with genetic programming

Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths.

RCA-PCK: A new structural reliability analysis method based on PC-Kriging and radial centralized adaptive sampling strategy

To improve the efficiency and accuracy of reliability assessment for structures with small failure probability and time-consuming simulation, a new structural reliability analysis method (RCA-PCK) is proposed, which combines PC-Kriging model and radial centralized adaptive sampling strategy. Firstly, the PC-Kriging model is constructed by improving the basis function of Kriging model with sparse polynomials. Then, the sampling region which contributes a great impact on the failure probability is constructed by combining the radial concentration and important sampling technology. Subsequently, the k-means++ clustering technology and learning function LIF are adopted to select new training samples from each subdomains in each iteration. To avoid the sampling distance in one subdomain or the distance between the new training samples in two subdomains being too small, we construct a screening mechanism to ensure that the selected new training samples are evenly distributed in the limit state. In addition, a new convergence criterion is derived based on the relative error estimation of failure probability. Four benchmark examples are given to illustrate the convergence process, accuracy and stability of the proposed method. Finally, the transmission error reliability analysis of thermal-elastic coupled gears is carried out to prove the applicability of the proposed method RCA-PCK to the structures with strong nonlinearity and time-consuming simulation.

An improved matrix-based endovascular guidewire position simulation using fusiform ternary tree.

In this paper, a new matrix-based method is proposed to real-time determine, the guidewire position inside an arterial system. The guidewire path is obtained by the optimal path method, particularly, the fusiform ternary tree method according to the principle of minimum output value of root node. An adaptive sampling strategy, and an optimization strategy based on the proximal end and distal end of the guidewire are proposed to change the guidewire position for obtaining an ideal guidewire path. Compared to the existing methods, the proposed method can achieve 74%, 64%, and 70% improvements in accuracy for phantoms 1, 2, and 3, respectively, investigated in this work.

Progressive path tracing with bilateral-filtering-based denoising

Path tracing can generate realistic images based on virtual 3D scene models, but the images are prone to be noisy. To solve this problem, we developed a novel denoising algorithm framework. Firstly, according to the relative mean square error of the noisy pixels, we introduced a progressive adaptive sampling strategy to optimize the distribution of samples. Next, to enhance the quality of the final reconstructed images, we designed an improved bilateral filtering algorithm with use of the gradient feature to obtain the noise-free images. Experimental results demonstrate that our framework outperforms the state-of-the-art path tracing denoising methods in terms of the visual quality, numerical error , and time cost.

Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field

SUMMARYThis paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

Fast Characterization of Input-Output Behavior of Non-Charge-Based Logic Devices by Machine Learning

Non-charge-based logic devices are promising candidates for the replacement of conventional complementary metal-oxide semiconductors (CMOS) devices. These devices utilize magnetic properties to store or process information making them power efficient. Traditionally, to fully characterize the input-output behavior of these devices a large number of micromagnetic simulations are required, which makes the process computationally expensive. Machine learning techniques have been shown to dramatically decrease the computational requirements of many complex problems. We use state-of-the-art data-efficient machine learning techniques to expedite the characterization of their behavior. Several intelligent sampling strategies are combined with machine learning (binary and multi-class) classification models. These techniques are applied to a magnetic logic device that utilizes direct exchange interaction between two distinct regions containing a bistable canted magnetization configuration. Three classifiers were developed with various adaptive sampling techniques in order to capture the input-output behavior of this device. By adopting an adaptive sampling strategy, it is shown that prediction accuracy can approach that of full grid sampling while using only a small training set of micromagnetic simulations. Comparing model predictions to a grid-based approach on two separate cases, the best performing machine learning model accurately predicts 99.92% of the dense test grid while utilizing only 2.36% of the training data respectively.

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. However its prediction quality is highly dependent on the size and distribution of the given training points. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. A review of adaptive schemes for kriging proposed in the literature is presented in this article. The objective is to provide the reader with an overview of the main principles of adaptive techniques, and insightful details to pertinently employ available tools depending on the application at hand. In this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. In light of these experiments, it is found that the success of a scheme depends on the features of a specific problem and the goal of the analysis. In order to facilitate the entry into adaptive sampling a guide is provided. All experiments described herein are replicable using a provided open source toolbox.

An Energy-Efficient Step-Counting Algorithm for Smartphones

Abstract Step counting is not only the key component of pedometers (which is a fundamental service on smartphones), but is also closely related to a range of applications, including motion monitoring, behavior recognition, indoor positioning and navigation. Due to the limited battery capacity of current smartphones, it is of great value to reduce the energy consumption of such a popular service. Therefore, this paper focuses on the energy efficiency of step-counting algorithms. First of all, we formulate a theoretical error model based on the well-known auto-correlation coefficient step-counting (ACSC) algorithm, so as to analyze the factors affecting step-counting accuracy. And then, in light of this model and an adaptive sampling strategy, we propose a novel energy-efficient step-counting algorithm by adaptively substituting the computationally intensive auto-correlation with simple mean absolute deviation. On these grounds, an Android pedometer is implemented. Two individual experiments are carried out and verify both the theoretical error model and the proposed algorithm. It is shown that our algorithm outperforms two famous counterparts, i.e. the original ACSC algorithm and peak detection step-counting algorithm, in terms of both accuracy and energy efficiency.

Structural model updating using adaptive multi-response Gaussian process meta-modeling

Finite element model updating utilizing frequency response functions as inputs is an important procedure in structural analysis, design and control. This paper presents a highly efficient framework that is built upon Gaussian process emulation to inversely identify model parameters through sampling. In particular, a multi-response Gaussian process (MRGP) meta-modeling approach is formulated that can accurately construct the error response surface, i.e., the discrepancies between the frequency response predictions and actual measurement. In order to reduce the computational cost of repeated finite element simulations, an adaptive sampling strategy is established, where the search of unknown parameters is guided by the response surface features. Meanwhile, the information of previously sampled model parameters and the corresponding errors is utilized as additional training data to refine the MRGP meta-model. Two stochastic optimization techniques, i.e., particle swarm and simulated annealing, are employed to train the MRGP meta-model for comparison. Systematic case studies are conducted to examine the accuracy and robustness of the new framework of model updating.

Sensitivity-based adaptive sequential sampling for metamodel uncertainty reduction in multilevel systems

Decomposition-based technique is often used in the analysis and design of complex engineering systems for reducing the computational complexity by studying the subsystems decomposed from multilevel systems. Metamodels, as a replacement of original simulation models, can further alleviate the computational burden. However, discrepancy between the simulation models and metamodels, which is defined as metamodel uncertainty, may be introduced in the analysis process of multilevel systems owing to the lack of data. The metamodel uncertainties of sub-models will be further amplified because of the hierarchical uncertainty propagation and interaction between uncertainties, which will have a great impact on the system results. An adaptive sequential sampling strategy based on sensitivity is proposed in this paper so as to improve the prediction accuracy of system response. In this strategy, polynomial-chaos expansion is used to realize the forward propagation of metamodel uncertainty quantified by the Kriging model. The forward propagation is combined with optimization based on maximum variance criterion for searching the input locations that results in the largest variance of system response. Then, the indices of subsystems are obtained to make decisions about which subsystem needs extra samples by combining Karhunen-Loeve expansion and sensitivity analysis. The effectiveness of the proposed sequential sampling strategy method is verified by two mathematical examples and a multiscale composite material.

Quickest attack detection in smart grid based on sequential Monte Carlo filtering

Quick and accurate detection of cyber attack is key to the normal operation of the smart grid system. In this paper, a joint state estimation and sequential attack detection method for a given bus with grid frequency drift is proposed that utilizes the commonly monitored output voltage. In particular, based on a non-linear state-space model derived from the three-phase sinusoidal voltage equations, we employ the sequential Monte Carlo (SMC) filtering to estimate the system state. The output of the SMC filter is fed into a CUSUM test to detect the attack in a fastest way. Moreover, an adaptive sampling strategy is proposed to reduce the rate of taking measurements and communicating with the controller. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and efficient detection of various types of attacks in power systems.

Managing Uncertainties of Permanent Magnet Synchronous Machine by Adaptive Kriging Assisted Weight Index Monte Carlo Simulation Method

For electromagnetic devices, there exist many uncertainty sources during design stage, performance simulation, components manufacturing and assembling. These uncertainties may deteriorate the practical operation performance of electrical machines from the designed one or result in violation of constraint conditions to a certain extent. To improve robust performance of a permanent magnet synchronous machine (PMSM), uncertainties should be taken into account in its whole life cycle. To manage uncertainties in the design stage of electrical machines, this paper makes some explorations of reliability-based optimal design. Firstly, the basic knowledge of reliability based algorithm is reviewed. Then the efficient adaptive sampling strategy and the surrogate model construction approach are introduced to reliability analysis. Based on these strategies, the new reliability analysis approach is named adaptive Kriging assisted weight index Monte Carlo simulation method. Finally, the new approach is applied to reliability-based optimal design of permanent magnet synchronous motor for cogging torque reduction.

Finding hotspots: development of an adaptive spatial sampling approach

The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.

Efficient structural reliability analysis by using a PGD model in an adaptive importance sampling schema

One of the most important goals in civil engineering is to guarantee the safety of the construction. Standards prescribe a required failure probability in the order of 10^{-4} to 10^{-6}. Generally, it is not possible to compute the failure probability analytically. Therefore, many approximation methods have been developed to estimate the failure probability. Nevertheless, these methods still require a large number of evaluations of the investigated structure, usually finite element (FE) simulations, making full probabilistic design studies not feasible for relevant applications. The aim of this paper is to increase the efficiency of structural reliability analysis by means of reduced order models. The developed method paves the way for using full probabilistic approaches in industrial applications. In the proposed PGD reliability analysis, the solution of the structural computation is directly obtained from evaluating the PGD solution for a specific parameter set without computing a full FE simulation. Additionally, an adaptive importance sampling scheme is used to minimize the total number of required samples. The accuracy of the failure probability depends on the accuracy of the PGD model (mainly influenced on mesh discretization and mode truncation) as well as the number of samples in the sampling algorithm. Therefore, a general iterative PGD reliability procedure is developed to automatically verify the accuracy of the computed failure probability. It is based on a goal-oriented refinement of the PGD model around the adaptively approximated design point. The methodology is applied and evaluated for 1D and 2D examples. The computational savings compared to the method based on a FE model is shown and the influence of the accuracy of the PGD model on the failure probability is studied.

Estimating Effect of Additional Sample on Uncertainty Reduction in Reliability Analysis Using Gaussian Process

Abstract An approach is proposed to quantify the uncertainty in probability of failure using a Gaussian process (GP) and to estimate uncertainty change before actually adding samples to GP. The approach estimates the coefficient of variation (CV) of failure probability due to prediction variance of GP. The CV is estimated using single-loop Monte Carlo simulation (MCS), which integrates the probabilistic classification function while replacing expensive multi-loop MCS. The methodology ensures a conservative estimate of CV, in order to compensate for sampling uncertainty in MCS. Uncertainty change is estimated by adding a virtual sample from the current GP and calculating the change in CV, which is called expected uncertainty change (EUC). The proposed method can help adaptive sampling schemes to determine when to stop before adding a sample. In numerical examples, the proposed method is used in conjunction with the efficient local reliability analysis to calculate the reliability of analytical function as well as the battery drop test simulation. It is shown that the EUC converges to the true uncertainty change as the model becomes accurate.

AdaptiveBandit: A Multi-armed Bandit Framework for Adaptive Sampling in Molecular Simulations.

Sampling from the equilibrium distribution has always been a major problem in molecular simulations due to the very high dimensionality of the conformational space. Over several decades, many approaches have been used to overcome the problem. In particular, we focus on unbiased simulation methods such as parallel and adaptive sampling. Here, we recast adaptive sampling schemes on the basis of multi-armed bandits and develop a novel adaptive sampling algorithm under this framework, AdaptiveBandit. We test it on multiple simplified potentials and in a protein folding scenario. We find that this framework performs similarly to or better than previous methods in every type of test potential. Furthermore, it provides a novel framework to develop new sampling algorithms with better asymptotic characteristics.

On oracle-type local recovery guarantees in compressed sensing

Abstract We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on $\ell ^1$ minimization encompasses the case where (i) the measurements are block-structured samples in order to reflect the structured acquisition that is often encountered in applications and (ii) the signal has an arbitrary structured sparsity, by results depending on its support $S$. Within this framework and under a random sign assumption, the number of measurements needed by $\ell ^1$ minimization can be shown to be of the same order than the one required by an oracle least-squares estimator. Moreover, these bounds can be minimized by adapting the variable density sampling to a given prior on the signal support and to the coherence of the measurements. We illustrate both numerically and analytically that our results can be successfully applied to recover Haar wavelet coefficients that are sparse in levels from random Fourier measurements in dimension one and two, which can be of particular interest in imaging problems. Finally, a preliminary numerical investigation shows the potential of this theory for devising adaptive sampling strategies in sparse polynomial approximation.