Dynamic Resampling Research Articles

Aileron fault detection with dynamic resampling based on fuzzy entropy and multi-branch neural network

The complex movements of unmanned aerial vehicles (UAVs) and feature loss at low resampling rates will result in the misidentification of aileron damage. In this paper, a fault detection method based on fuzzy entropy and a multi-branch neural network (MBNN) is proposed to shorten the fault detection time by reducing the resampling rate. The optimal size of the sliding window sampler is determined by analysing the UAV’s movement using fuzzy entropy, ensuring that the captured flight data segments reflect the UAV’s flight status with a smaller size. The multi-branch structure and feature-sharing mechanism in the network are used to adapt to feature loss at low sampling rates. The resampling rate is dynamically adjusted in accordance with the change in the UAV’s fault status, which is calculated from MBNN’s output using the Kullback Leibler (KL) divergence. Experimental results show that the proposed method can improve the real-time performance of UAV fault detection and maintain high accuracy.

Label noise correction for crowdsourcing using dynamic resampling

Crowdsourcing provides a cost-effective labeling solution for the acquisition of labeled training samples for machine learning by employing workers on the Internet. A common approach to improving the label quality is to employ a truth inference method to infer integrated labels for samples from their multiple noisy labels obtained from different crowd workers. Although the quality of integrated labels is significantly improved compared with that of the original noisy ones, it still cannot completely eliminate the noises inevitably existing in the integrated labels. To further improve the label quality, this paper proposes a novel label noise correction method for crowdsourcing based on dynamic resampling (DRNC). DRNC first divides the dataset with inferred labels into a clean set and a noisy set through a filter. According to a certain proportion, the clean set and the noisy set are resampled to train multiple heterogeneous classifiers, which form an ensemble classifier. Then, the dataset is divided by the ensemble classifier into a new sub-noisy set and a sub-clean set. The whole process repeats multiple rounds, generating multiple sub-clean sets. Finally, these sub-clean sets are used to train classifiers, which jointly correct the wrong labels in the dataset by voting. Experimental results on 25 simulated and 4 real-world datasets consistently show that the proposed DRNC averagely improves the quality of labels as well as the quality of learned models in the range of 1 to 10 percentage points, compared with four state-of-the-art crowdsourcing noise correction methods.

An Application of Surrogate and Resampling for the Optimization of Success Probability from Binary-Response Type Simulation

Since traditional derivative-based optimization for noisy simulation shows bad performance, evolutionary algorithms are considered as substitutes. Especially in case when outputs are binary, more simulation trials are needed to get near-optimal solution since the outputs are discrete and have high and heterogeneous variance. In this paper, we propose a genetic algorithm called SARAGA which adopts dynamic resampling and fitness approximation using surrogate. SARAGA reduces unnecessary numbers of expensive simulations to estimate success probabilities estimated from binary simulation outputs. SARAGA allocates number of samples to each solution dynamically and sometimes approximates the fitness without additional expensive experiments. Experimental results show that this novel approach is effective and proper hyper parameter choice of surrogate and resampling can improve the performance of algorithm.

A new beam synchronous processing architecture with a fixed frequency processing clock. Application to transient beam loading compensation in the CERN SPS machine

New projects being implemented or planned at CERN, such as the LHC Injectors Upgrade (LIU), the High-Luminosity LHC (HL-LHC) or the Future Circular Collider (FCC), motivate the change of several architectural paradigms in the Low Level RF systems (LLRF). In the upgraded Super Proton Synchrotron (SPS) LLRF, the distribution of RF, the revolution frequency, settings and synchronization timings will rely on a deterministic link, the White Rabbit (WR), exploiting a distributed topology. The digital electronics (FPGA) will now use a fixed frequency clock extracted locally in each node from this data stream. The paper presents a new solution for algorithms that treat beam-induced perturbations as they must solve the challenge to tune their processing to the instantaneous revolution frequency, Beam Synchronous Processing (BSP). We use dynamic resampling of uniform sampled data as tuning element between a sweeping spectrum and the static processing implemented with fixed frequency clocks. The paper applies the novel Beam Synchronous Processing method to transient beam loading compensation. It presents a One Turn delay FeedBack (OTFB) for the SPS based on a cascade of two variable ratio resamplers.

P.445 Dose-response relationships of antipsychotic drugs for major depressive disorder: a systematic review and meta-analysis

New projects being implemented or planned at CERN, such as the LHC Injectors Upgrade (LIU), the High-Luminosity LHC (HL-LHC) or the Future Circular Collider (FCC), motivate the change of several architectural paradigms in the Low Level RF systems (LLRF). In the upgraded Super Proton Synchrotron (SPS) LLRF, the distribution of RF, the revolution frequency, settings and synchronization timings will rely on a deterministic link, the White Rabbit (WR), exploiting a distributed topology. The digital electronics (FPGA) will now use a fixed frequency clock extracted locally in each node from this data stream. The paper presents a new solution for algorithms that treat beam-induced perturbations as they must solve the challenge to tune their processing to the instantaneous revolution frequency, Beam Synchronous Processing (BSP). We use dynamic resampling of uniform sampled data as tuning element between a sweeping spectrum and the static processing implemented with fixed frequency clocks. The paper applies the novel Beam Synchronous Processing method to transient beam loading compensation. It presents a One Turn delay FeedBack (OTFB) for the SPS based on a cascade of two variable ratio resamplers.

Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems.

In this article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance. To avoid losing informative samples during the data preprocessing of the boosting-based ensemble, both confidence and entropy are used in ECUBoost as benchmarks to ensure the validity and structural distribution of the majority samples during the undersampling. Furthermore, different from other iterative dynamic resampling methods, ECUBoost based on confidence can be applied to algorithms without iterations such as decision trees. Meanwhile, random forests are used as base classifiers in ECUBoost. Furthermore, experimental results on both artificial data sets and KEEL data sets prove the effectiveness of the proposed method.

Reference point based evolutionary multi-objective optimization with dynamic resampling for production systems improvement

PurposeStochastic simulation is a popular tool among practitioners and researchers alike for quantitative analysis of systems. Recent advancement in research on formulating production systems improvement problems into multi-objective optimizations has provided the possibility to predict the optimal trade-offs between improvement costs and system performance, before making the final decision for implementation. However, the fact that stochastic simulations rely on running a large number of replications to cope with the randomness and obtain some accurate statistical estimates of the system outputs, has posed a serious issue for using this kind of multi-objective optimization in practice, especially with complex models. Therefore, the purpose of this study is to investigate the performance enhancements of a reference point based evolutionary multi-objective optimization algorithm in practical production systems improvement problems, when combined with various dynamic re-sampling mechanisms.Design/methodology/approachMany algorithms consider the preferences of decision makers to converge to optimal trade-off solutions faster. There also exist advanced dynamic resampling procedures to avoid wasting a multitude of simulation replications to non-optimal solutions. However, very few attempts have been made to study the advantages of combining these two approaches to further enhance the performance of computationally expensive optimizations for complex production systems. Therefore, this paper proposes some combinations of preference-based guided search with dynamic resampling mechanisms into an evolutionary multi-objective optimization algorithm to lower both the computational cost in re-sampling and the total number of simulation evaluations.FindingsThis paper shows the performance enhancements of the reference-point based algorithm, R-NSGA-II, when augmented with three different dynamic resampling mechanisms with increasing degrees of statistical sophistication, namely, time-based, distance-rank and optimal computing buffer allocation, when applied to two real-world production system improvement studies. The results have shown that the more stochasticity that the simulation models exert, the more the statistically advanced dynamic resampling mechanisms could significantly enhance the performance of the optimization process.Originality/valueContributions of this paper include combining decision makers’ preferences and dynamic resampling procedures; performance evaluations on two real-world production system improvement studies and illustrating statistically advanced dynamic resampling mechanism is needed for noisy models.

Spider monkey optimisation assisted particle filter for robust object tracking

Particle filters (PFs) are sequential Monte Carlo methods that use particle representation of state‐space model to implement the recursive Bayesian filter for non‐linear and non‐Gaussian systems. Owing to this property, PFs have been extensively used for object tracking in recent years. Although PFs provide a robust object tracking framework, they suffer from shortcomings. Particle degeneracy and particle impoverishment brought by the resampling step result in abysmal construction of posterior probability density function (PDF) of the state. To overcome these problems, this work amalgamates two characteristics of population‐based heuristic optimisation algorithms: exploration and exploitation with PF implementing dynamic resampling method. The aim of optimisation is to distribute particles in high‐likelihood area according to the cognitive effect and improve quality of particles, while the objective of dynamic resampling is to maintain diversity in the particle set. This work uses very efficient spider monkey optimisation to achieve this. Furthermore, to test the efficiency of the proposed algorithm, experiments were carried out on one‐dimensional state estimation problem, bearing only tracking problem, standard videos and synthesised videos. Metrics obtained show that the proposed algorithm outplays simple PF, particle swarm optimisation based PF, and cuckoo search based PF, and effectively handles different challenges inherent in object tracking.

Robust object tracking based on local region sparse appearance model

We propose a robust object tracking algorithm based on local region sparse appearance model in this paper. In this algorithm, the object is divided into several sub-regions, and the sparse dictionaries are obtained by clustering in each sub-region. Therefore spatial structure information of the object can be captured well, and the change of object appearance can be also resisted effectively. First, the object is divided into many small patches. Then the object is divided into several sub-regions according to patch distribution again. The establishment of object dictionary base is based on combination of the dictionaries from all the sub-regions, and then space alignment between different parts of the object can be achieved. Meanwhile, noise removal and other operations in the existing sparse reconstruction error maps are performed to retain valuable information. In the updating framework, a novel flexible template set update mechanism is introduced in this paper. In this update mechanism, valuable object samples will be put into the template set. If samples are not valuable, they should not be put into the template set, even when the template set is not full. Then we use patch sparse coefficient histogram of updated templates to extract time domain information of the object in the form of weighted sum. Therefore, it can provide a reliable template basis for obtaining good candidate object. In addition, when tracking result deviates from the actual position of the object, we use a dynamic sub-region resampling method based on cosine angle to correct the position deviation timely. Therefore this method can effectively prevent the object from being completely lost. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

Dynamic resampling for alleviating sample impoverishment of particle filter

In particle filter, the use of resampling technique solves the problem of particle degeneracy to some extent, but introduces a new problem of particle impoverishment. To reconcile this dilemma, a dynamic resampling strategy is proposed, where the resampling operation is only performed on part of the particles in a step-by-step manner and the number of resampled particles is determined dynamically by a termination criterion based on the effective sample size. The new-born particles produced by resampling operations are helpful in alleviating particle degeneracy, whereas particles free from being resampled is conducive to improving the diversity of particles. Thus, a tradeoff between these two problems can be achieved. Also, two techniques, that is, recursive computation of the effective sample size and improved bisection method, for improving the computational efficiency are proposed. Simulation results conducted on two typical examples show the improved performance of the proposed method.

Dynamic resampling particle filter adaptive to changes in system model

A dynamic resampling particle filter (DRPF), integrated with scattering and migrating operations, is proposed for the flexibility in nonlinear systems subject to changes in the system model without a considerable increase in computational cost. Under the change detection with the tracking error, the scattering and migration are alternatively adopted where the scattering can boost the diversity in the particle population when no change, or slow change occurs, while the migrating updates particles with new observations if the change comes abruptly. Simulation results validate the proposed PF as a promising alternative to the existing PFs.

A segmentation method to obtain a complete geometry model of the hearing organ

We present a method for obtaining a complete geometry model of the fluid chambers of cochlea ( scalae) from tomography images. An accurate segmentation of cochlea is problematic due to the low contrast of the inner membranes of scalae. Our method of 3D segmentation is based on dynamic resampling of an original image stack to achieve a perpendicular cross-section of the scalae on all sections. Subsequently, perpendicular cross-section is being segmented using 2D active contours. The center of mass of the contour is extracted and used to predict further course of scalae centerline by Kalman filter. Cross-section contours are subsequently assembled to the total geometry model. This method has been applied to CT images, but we expect that it could be used for segmentation of strongly curved low-contrast tubular objects recorded with other tomography techniques.

Multi-level partition of unity algebraic point set surfaces

We present a multi-level partition of unity algebraic set surfaces (MPU-APSS) for surface reconstruction which can be represented by either a projection or in an implicit form. An algebraic point set surface (APSS) defines a smooth surface from a set of unorganized points using local moving least-squares (MLS) fitting of algebraic spheres. However, due to the local nature, APSS does not work well for geometry editing and modeling. Instead, our method builds an implicit approximation function for the scattered point set based on the partition of unity approach. By using an octree subdivision strategy, we first adaptively construct local algebraic spheres for the point set, and then apply weighting functions to blend together these local shape functions. Finally, we compute an error-controlled approximation of the signed distance function from the surface. In addition, we present an efficient projection operator which makes our representation suitable for point set filtering and dynamic point resampling. We demonstrate the effectiveness of our unified approach for both surface reconstruction and geometry modeling such as surface completion.

Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

Many real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques.

Beyond Traditional Statistical Methods

Today's courses in statistical methods, for the most part, focus on the same methods that were taught 30 years ago. The actual practice of statistics has moved beyond these traditional statistical methods. Modern methods—including dynamic graphics, nonlinear estimation, resampling, and other simulation-based inference methods—are being used by many scientists and engineers. However, these methods generally are not included in courses in statistical methods, especially at the undergraduate level. This article discusses the development of a collection of instructional modules, built around actual applications from science and engineering. Each module is self-contained and includes instructional materials such as: objectives, examples, lecture materials, computer implementation of the methodology, homework, class/discussion exercises, and assignments. The modules are intended as a resource for instructors to experiment with and explore the use ofmodern statistical methodology inundergraduate statistics methods courses. Two of the modules will be presented in some detail. We also discuss the use of the modules in a new course that goes beyond our traditional methods courses.