Fast Estimation Research Articles

An improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry

Fast pose estimation (PE) plays a vital role for agile autonomous robots in successfully carrying out their tasks. While Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) have been traditionally used along with Inertial Navigation Systems (INS) for PE, their viability is compromised in indoor and urban environments due to their low update rates and inadequate signal coverage. Visual-Inertial Odometry (VIO) is gaining popularity as a practical alternative to GNSS/INS systems in GNSS-denied environments. Among various VIO-based methods, the Multi-State Constraint Kalman Filter (MSCKF) has garnered significant attention due to its robustness, speed and accuracy. Nevertheless, high computational cost of image processing is still challenging for real-time implementation on resource-constrained vehicles. In this paper, an enhanced version of the MSCKF is proposed. The proposed approach differs from the original MSCKF in the feature marginalization and state pruning steps of the algorithm. This new design results in a faster algorithm with comparable accuracy. We validate the proposed algorithm using both an open-source dataset and real-world experiments. It is demonstrated that the proposed Fast-MSCKF (referred to as FMSCKF) is approximately six times faster and at least 20% more accurate in final position estimation compared to the standard MSCKF.

Fixed-time anti-disturbance control for nonlinear turbofan engine with impulsive prescribed performance

To analyze the robust control problem of turbofan engine within a large flight envelope, a fixed-time prescribed performance control scheme is proposed for the nonlinear turbofan engine in presence of unknown disturbances and possibly discontinuous reference signals. Firstly, the equilibrium manifold expansion (EME) modeling method is adopted for the turbofan engine to construct an affine nonlinear model. Then, considering the possibly discontinuous reference signals, a novel fixed-time prescribed performance function (FxTPPF) with impulsive characteristic is presented to successfully deal with the singularity problem caused by its discontinuity situation. In what follows, a fixed-time nonlinear disturbance observer (FxTNDO) is developed to realize fast estimation of disturbances within fixed time. According to the transformed error system and disturbance estimations, a fixed-time anti-disturbance composite controller is further developed. By borrowing Lyapunov stability theory, the boundedness of all error signals in the impulsive closed-loop system is proved. Finally, simulation results indicate that this control scheme can ensure rotor speeds effectively to track the desired commands within a predefined time after the appearance of a discontinuity and the tracking errors are limited to the performance bound all along.

Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA.

We here present CLUES2, a full-likelihood method to infer natural selection from sequence data that is an extension of the method CLUES. We make several substantial improvements to the CLUES method that greatly increases both its applicability and its speed. We add the ability to use ancestral recombination graphs on ancient data as emissions to the underlying hidden Markov model, which enables CLUES2 to use both temporal and linkage information to make estimates of selection coefficients. We also fully implement the ability to estimate distinct selection coefficients in different epochs, which allows for the analysis of changes in selective pressures through time, as well as selection with dominance. In addition, we greatly increase the computational efficiency of CLUES2 over CLUES using several approximations to the forward-backward algorithms and develop a new way to reconstruct historic allele frequencies by integrating over the uncertainty in the estimation of the selection coefficients. We illustrate the accuracy of CLUES2 through extensive simulations and validate the importance sampling framework for integrating over the uncertainty in the inference of gene trees. We also show that CLUES2 is well-calibrated by showing that under the null hypothesis, the distribution of log-likelihood ratios follows a χ2 distribution with the appropriate degrees of freedom. We run CLUES2 on a set of recently published ancient human data from Western Eurasia and test for evidence of changing selection coefficients through time. We find significant evidence of changing selective pressures in several genes correlated with the introduction of agriculture to Europe and the ensuing dietary and demographic shifts of that time. In particular, our analysis supports previous hypotheses of strong selection on lactase persistence during periods of ancient famines and attenuated selection in more modern periods.

Atomistic descriptor optimization using complementary Euclidean and geodesic distance information

Descriptors are physically-inspired, symmetry-preserving schemes for representing atomistic systems that play a central role in the construction of models of potential energy surfaces. Although physical intuition can be flexibly encoded into descriptor schemes, they are generally ultimately guided only by the spatial or topological arrangement of atoms in the system. However, since interatomic potential models aim to capture the variation of the potential energy with respect to atomic configurations, it is conceivable that they would benefit from descriptor schemes that implicitly encode both structural and energetic information rather than structural information alone. Therefore, we propose a novel approach for the optimisation of descriptors based on encoding information about geodesic distances along potential energy manifolds into the hyperparameters of commonly used descriptor schemes. To accomplish this, we combine two ideas: (1) a differential-geometric approach for the fast estimation of approximate geodesic distances [Zhu et al., J. Chem. Phys. 150, 164103 (2019)]; and (2) an information-theoretic evaluation metric – information imbalance – for measuring the shared information between two distance measures [Glielmo et al. PNAS Nexus, 1, 1 (2022)]. Using three example molecules – ethanol, malonaldehyde, and aspirin – from the MD22 dataset, we first show that Euclidean (in Cartesian coordinates) and geodesic distances are inequivalent distance measures, indicating the need for updated ground-truth distance measures that go beyond the Euclidean (or, more broadly, spatial) distance. We then utilize a Bayesian optimisation framework to show that descriptors (in this case, atom-centred symmetry functions) can be optimized to maximally express a certain type of distance information, such as Euclidean or geodesic information. We also show that modifying the Bayesian optimisation algorithm to minimise a combined objective function – the sum of the descriptor ↔ Euclidean and descriptor ↔ geodesic information imbalances – can yield descriptors that not only optimally express both Euclidean and geodesic distance information simultaneously, but in fact resolve substantial disagreements between descriptors optimized to encode only one type of distance measure. We discuss the relevance of our approach to the design of more physically rich and informative descriptors that can encode useful, alternative information about molecular systems.

The Quick Determination of a Fibrous Composite’s Axial Young’s Modulus via the FEM

Knowing the mechanical properties of fiber-reinforced composite materials, which are currently widely used in various industrial branches, represents a major objective for designers. This happens when new materials are used that are not yet in production or for which the manufacturer cannot give values. Given the practical importance of this problem, several methods of determining these properties have been proposed, but most of them are laborious and require a long calculation time. And, some of the proposed calculation methods are very approximate, providing only upper and lower limits for these values. Experimental measurements are obviously the optimal solution for solving this problem, but it must be taken into account that this type of method consumes time and material resources. This paper proposes a sufficiently accurate and fast estimation method for determining Young’s modulus for a homogenized fibrous material. Thus, the FEM is used to determine the natural frequencies of a standard bar, for which there are sufficiently precise classical methods to express the engineering constants according to the mechanical properties of the component phases of the homogenized material. In this paper, Young’s modulus is determined for such a material using the relationships that provide the eigenfrequencies for the longitudinal vibrations. With the adopted model, transverse and torsional vibrations are eliminated by blocking the nodes on the surfaces of the bars. In this way, more longitudinal eigenfrequencies can be obtained, so the precision in calculating Young’s modulus is increased.

Dynamic formulation and inertia fast estimation of a 5-DOF hybrid robot

As the main driving mechanism of a hybrid robot, the parallel mechanism is a nonlinear time-varying system. The load inertia of its actuated joints changes with the configuration of the robot. Analyzing and fitting the inertia variation is of great significance to the design and control of hybrid robots. By taking a hybrid robot named TriMule as an example, the variation of load inertia of each actuated joint in the whole workspace is first revealed based on the dynamic analyses of the robot. Then two methods based on the circular and elliptical membership are proposed to calculate fitted inertia over the whole workspace using inertia information at a few configurations. Finally, the fitting methods of the two membership functions are compared and discussed. The results show that the maximum value and global mean value of the fitting error of the elliptical membership method are 39.18% (51.23%) and 65.79% (81.25%) for actuated joint-1 (joint-2 and joint-3) lower than those of circular membership method, which promise a better global fitting accuracy. The proposed method can be used to estimate the joint load inertia or other control variables affected by inertia in a quick manner, allowing the algorithm to be easily integrated into the robot control system.

The Fast and Reliable Detection of Multiple Narrowband FH Signals: A Practical Framework.

Frequency hopping (FH) is a well-known technique that is commonly used in communication systems owing to its many advantages, including its strong anti-jamming capability. In this technique, basically, radio signals are transmitted by switching the carrier between different frequency channels. As a result, the FH signal is not stationary; hence, its spectrum is expected to change over time. Therefore, the task of detection and parameter estimation of FH signals is very challenging in practice. To address this challenge, the study presented in this article proposes a method that detects and estimates the parameters of multiple narrowband FH signals. In the proposed method, first, short-time Fourier transform (STFT) is utilized to analyze FH signals, and a practical binarization process based on thresholding is used to detect FH signals. Then, a new algorithm is proposed to ensure that the center frequencies of the detected signals are successfully separated. Next, another algorithm is proposed to estimate the parameters of the detected signals. After estimating the parameters for the entire spectrum, an approach is used to detect FH signals. Lastly, the hop-clustering process is applied to separate the hops into groups without time overlap. The simulation results show that the proposed method can be an efficient way for the fast and accurate parameter estimation and detection of multiple narrowband FH signals.

Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods

In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to real-world data because the likelihood function might be slow to evaluate or wholly intractable. In this work, we propose using convolutional neural networks to learn the likelihood function of a spatial process. Through a specifically designed classification task, our neural network implicitly learns the likelihood function, even in situations where the exact likelihood is not explicitly available. Once trained on the classification task, our neural network is calibrated using Platt scaling which improves the accuracy of the neural likelihood surfaces. To demonstrate our approach, we compare neural likelihood surfaces and the resulting maximum likelihood estimates and approximate confidence regions with the equivalent for exact or approximate likelihood for two different spatial processes—a Gaussian process and a Brown–Resnick process which have computationally intensive and intractable likelihoods, respectively. We conclude that our method provides fast and accurate parameter estimation with a reliable method of uncertainty quantification in situations where standard methods are either undesirably slow or inaccurate. The method is applicable to any spatial process on a grid from which fast simulations are available.

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Accelerated Simulation of Passive Analog Circuits Over GPU Using Explicit Integration Methods

Analog circuits composed by large number of nodes in a tightly coupled structure pose significant challenges due to their prohibitive CPU simulation time. This work describes a method to speed up the simulation of such circuits by means of the combination of space state formulation of circuit equations with explicit integration methods parallelized over a many-core processor such as a GPU. Although stability of explicit techniques require smaller integration steps compared to implicit methods, the proposed method employs a fast estimate of the maximum allowed step size to guarantee numerical stability, which yields a shorter simulation time for increasing complexity circuit architectures. Moreover, the proposed technique can be straightforward parallelized on a many core architecture. The proposed method is demonstrated with two examples using constant and variable coefficients respectively: an RLC interconnect and a MOS-C network to perform Gaussian filtering of medium resolution images. The results obtained have been compared to a parallel version of SPICE and show improvements up to two orders of magnitude for transient simulations depending of the circuit size.

Fast joint estimation of direction of arrival and towed array shape based on marginal likelihood maximization

This paper addresses the joint estimation problem of directions of arrival of sources and towed array shape. Ocean currents and the maneuvering of the towing platform often cause the towed array to bend rather than stay in a straight line, so the array shape must be estimated to avoid performance degradation due to array mismatch. Existing joint estimation methods suffer from high computational complexity or the need for external sensors installed on the array. Therefore, this paper proposes a fast joint estimation algorithm that solely utilizes received acoustic data. The joint estimation is transformed into a sparse reconstruction optimization problem within the Bayesian estimation framework. Firstly, by decomposing the marginal likelihood function, a closed-form solution for the signal variance in the spatial domain is obtained. Then, the towed array shape is introduced as a hyperparameter into the marginal likelihood function and optimized using the quasi-Newton method. In this way, we accomplish joint estimation and reduce computational complexity. Multi-source simulation results show that the proposed method achieves high resolution and fast computational speed. The robustness and efficiency of the proposed joint estimation method are demonstrated by experimental results in the South China Sea.

NIR-based models for the evaluation of basic density, moisture and higher heating value of industrial wood chips

ABSTRACT The quick and efficient classification of wood is crucial for optimizing processes in forestry industries, which highlights the use of near infrared (NIR) spectroscopy. This study investigated the application of NIR spectroscopy for estimating the basic density, moisture content and calorific value of wood chips of Eucalyptus urophylla × E. grandis hybrids directly in the company yard. Using two lots of industrial chips collected in the company’s yard (Lots A and B), the NIR signatures were correlated with the results obtained by standard methods via Partial Least Squares Regression (PLS-R). The predictive models presented coefficients of determination (R²) of 0.83 for basic density (Lot A), 0.81 to 0.90 for moisture content (Lots A and B) and 0.74 for calorific value (Lot A). Variations in the moisture content of wood chips impacted the information quality of spectral signatures and thus the model accuracy, highlighting the difficulty of applying this technology in field conditions. However, the NIR models can be used as a solution for fast estimation of wood chips quality in the company yard, optimizing industrial processes.

Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows.

Pulsar timing arrays perform Bayesian posterior inference with expensive Markov chain MonteCarlo (MCMC) methods. Given a dataset of ∼10-100 pulsars and O(10^{3}) timing residuals each, producing a posterior distribution for the stochastic gravitational wave background (SGWB) can take days to a week. The computational bottleneck arises because the likelihood evaluation required for MCMC is extremely costly when considering the dimensionality of the search space. Fortunately, generating simulated data is fast, so modern simulation-based inference techniques can be brought to bear on the problem. In this Letter, we demonstrate how conditional normalizing flows trained on simulated data can be used for extremely fast and accurate estimation of the SGWB posteriors, reducing the sampling time from weeks to a matter of seconds.

Monitoring and Reconstruction of Actuator and Sensor Attacks for Lipschitz Nonlinear Dynamic Systems Using Two Types of Augmented Descriptor Observers

Fault data injection attacks may lead to a decrease in system performance and even a malfunction in system operation for an automatic feedback control system, which has motive to develop an effective method for rapidly detecting such attacks so that appropriate measures can be taken correspondingly. In this study, a secure descriptor estimation technique is proposed for continuous-time Lipschitz nonlinear cyber physical systems affected by actuator attacks, sensor attacks, and unknown process uncertainties. Specifically, by forming a new state vector composed of original system states and sensor faults, an equivalent descriptor dynamic system is built. A proportional and derivate sliding-mode observer is presented so that the system states, sensor attack, and actuator attack can be reconstructed successfully. The observer gains are obtained by using linear matrix inequality to secure robustly stable estimation error dynamics. Moreover, a robust descriptor fast adaptive observer estimator is presented as a complement. Finally, the efficacy levels of the proposed design approaches are validated using a vertical take-off and landing aircraft system. Comparison studies are also carried out to assess the tracking performances of the proposed algorithms.

Leveraging independence in high-dimensional mixed linear regression.

We address the challenge of estimating regression coefficients and selecting relevant predictors in the context of mixed linear regression in high dimensions, where the number of predictors greatly exceeds the sample size. Recent advancements in this field have centered on incorporating sparsity-inducing penalties into the expectation-maximization (EM) algorithm, which seeks to maximize the conditional likelihood of the response given the predictors. However, existing procedures often treat predictors as fixed or overlook their inherent variability. In this paper, we leverage the independence between the predictor and the latent indicator variable of mixtures to facilitate efficient computation and also achieve synergistic variable selection across all mixture components. We establish the non-asymptotic convergence rate of the proposed fast group-penalized EM estimator to the true regression parameters. The effectiveness of our method is demonstrated through extensive simulations and an application to the Cancer Cell Line Encyclopedia dataset for the prediction of anticancer drug sensitivity.

Time estimation is associated with the levels of distress in patients prior to starting radiotherapy

Purpose or ObjectiveThe aim of this study was to explore the potential relationship between the time estimation and psychological distress in patients with solid tumors prior to starting radiotherapy. Materials and MethodsIn this multicenter study were included a total of 344 patients with solid tumors (197 with and 147 without metastatic disease). The time estimation was assessed by evaluating each subjects prospective estimation of how fast 1 min passed compared to the actual time. The median value (35sec) of subjective perception of time was used to group cases into two categories for experience of time. We used the National Comprehensive Cancer Network Distress Thermometer at the beginning of treatment to determine the levels of distress, where it measures distress on a scale from 0 to 10. Patients scoring 4 or above (73.5 %) were regarded as having high levels of distress. ResultsThe time estimation distributions significantly changed according to the level of distress. ROC analysis revealed that at the optimal cut off value of time estimation, patients with low and high distress levels can be discriminated with an AUC = 0.80 (95 % CI: 0.75– 0.85, p < 0.001) and with a sensitivity of 77.8 % and specificity of 73.3 %. In a multivariate logistic regression model, fast time estimation was an independent predictor of high levels of distress (OR 0.136; 95 % CI, 0.072–––0.256, p < 0.001). ConclusionTime estimation is a novel potent indicator of high levels of distress in cancer patients prior starting of radiotherapy.

Large eddy simulation and linear stability analysis of active sway control for wind turbine array wake

The convective instability of wind turbine wakes allows specific upstream forcing to amplify downstream, leading to increased wake meandering and replenishment, thereby providing a theoretical basis for active wake control. In this study, the active sway control—a strategy previously proven to enhance wake recovery at the single wind turbine level—is analyzed at the turbine array level. The similarity and differences between individual turbine wakes and the wake array are analyzed using large eddy simulations and linear stability analysis, considering both uniform and turbulent inflow conditions. For cases with uniform inflow, large eddy simulations reveal significant meandering motion in the wake array induced by active sway control at a motion amplitude of 1% rotor diameter, consistent with previous studies of standalone wind turbine wakes. Nevertheless, the sensitive frequency for the wake array extends down to St = 0.125 below the limit of St &amp;gt; 0.2 for a single wake, and the optimal control frequency for the standalone turbine wake becomes suboptimal for the array. Linear stability analysis reveals the underlying mechanism of this frequency shift as changes in the shear-layer instability due to the overlap of upstream and downstream wakes and is capable to provide fast estimation of optimal control frequencies. When inflow turbulence intensity increases, the gain of active sway control is reduced, underscoring the importance of low-turbulence environment for successfully implementing the active sway control. The reduction in wake response is captured by the linear stability analysis if the base flow accounts for the faster wake expansion caused by inflow turbulence.

Using machine learning to count Antarctic shag (Leucocarbo bransfieldensis) nests on images captured by remotely piloted aircraft systems

Using 51 orthomosaics of 11 breeding locations of the Antarctic shag (Leucocarbo bransfieldensis), we propose a method for automating counting of shag nests. This is achieved by training an object detection model based on the “You Only Look Once” (YOLO) architecture and identifying nests on sections of the orthomosaic, which are later combined with predictions for the entire orthomosaic. Our results show that the current use of Remotely Piloted Aircraft Systems (RPAS) to collect images of areas with shag colonies, combined with machine learning algorithms, can provide reliable and fast estimates of shag nest counts (F1 score > 0.95). By using data from only two shag colonies for training, we show that models can be obtained that generalise well to images of both spatially and temporally distinct colonies. The proposed practical application opens the possibility of using aerial imagery to perform large-scale surveys of Antarctic islands in search of undiscovered shag colonies. We discuss the conditions for optimal performance of the model as well as its limitations. The code, data and trained model allowing for full reproducibility of the results are available at https://github.com/Appsilon/Antarctic-nests.

Fast Noise Level Estimation via the Similarity within and between Patches

Patch level-based noise level estimation (NLE) is often inaccurate and inefficient because of the harsh criteria required to select a small number of homogeneous patches. In this paper, a fast image NLE method based on a global search for similar pixels is proposed to solve the above problem. Specifically, the mean square distance (MSD) is first expressed in the form of the standard deviation (std) and mean value of image patches. Afterward, the two values, std and mean, are calculated and stored in advance. Then, a 2D statistical histogram and summed area table are adopted to speed up the search for similar patches. Further, the most similar pixels are selected from similar patches to obtain an initial estimation. Finally, we correct the deviation of the initial estimation by re-injecting noise for secondary estimation. Experimental results show that the proposed method outperforms the state-of-the-art techniques in fast NLE and guided denoising.

Fast Cable-Force Measurement for Large-Span Cable-Stayed Bridges Based on the Alignment Recognition Method and Smartphone-Captured Video

The accurate measurement of cable force plays an important role in the structural form, structural behavior, and safety evaluation of large-span cable-stayed bridges. A fast cable-force estimation method is proposed based on the alignment recognition method and smartphone-captured video. This method can realize real-time, non-contact, and non-destructive force measurements. Videos of bridge cables were collected using a portable smartphone, and an alignment recognition method was employed to obtain the lateral displacement along the cable, followed by numerical differentiation of the lateral displacement of the cable to acquire the acceleration time history. Based on the fundamental dynamic equation of beam elements, a unified explicit formula for cable-force calculation considering service characteristics was established in the frequency method. Finally, the method was applied to the cable-force measurement during the operation and maintenance stage of the Gengcun Dou Bridge. The measured cable forces were compared with the values obtained from the on-site monitoring system. The results show that the proposed method can accurately identify the acceleration time history at different positions of the cable, and the corresponding frequency components are essentially consistent. The average relative deviation of the measured cable forces from the reference method is within 3%. The proposed cable-force measurement method is simple as regards equipment, convenient for operation, and high in efficiency, providing a new method for the measurement of cable forces in large-span cable-stayed bridges, which can offer reliable measured cable-force data for structural analysis during bridge operation and the maintenance stage.