Lower Estimation Error Research Articles

Application of the Gaussian modeling algorithm to a Shack-Hartmann wavefront sensor for daylight adaptive optics.

Daytime use of Shack-Hartmann wavefront sensor (SHWS)-based adaptive optics (AO) at a telescope is hindered by the fluctuating bright sky background. Therefore, we propose a Gaussian modeling centroid extraction algorithm that performs real-time daylight AO closed-loop corrections. The algorithm is executed in the process of modeling and updating each pixel of the consecutive SHWS images. The simulation results show that our method can provide a lower centroid estimation error (CEE) for bright sky background conditions. We also performed a field experiment on a 2 m ground-based telescope and achieved a daylight AO correction of 2.1 times diffraction limit for a star with a visual magnitude of 4.7 (HIP113116) when the elevation angle of the sun was 7.7°. This represents the first publicly reported daylight natural guide star AO correction result on a 2 m class telescope.

An Improved Variable Kernel Density Estimator Based on L2 Regularization

The nature of the kernel density estimator (KDE) is to find the underlying probability density function (p.d.f) for a given dataset. The key to training the KDE is to determine the optimal bandwidth or Parzen window. All the data points share a fixed bandwidth (scalar for univariate KDE and vector for multivariate KDE) in the fixed KDE (FKDE). In this paper, we propose an improved variable KDE (IVKDE) which determines the optimal bandwidth for each data point in the given dataset based on the integrated squared error (ISE) criterion with the L2 regularization term. An effective optimization algorithm is developed to solve the improved objective function. We compare the estimation performance of IVKDE with FKDE and VKDE based on ISE criterion without L2 regularization on four univariate and four multivariate probability distributions. The experimental results show that IVKDE obtains lower estimation errors and thus demonstrate the effectiveness of IVKDE.

Thermal Properties and Combustion-Related Problems Prediction of Agricultural Crop Residues

The prediction and pre-evaluation of the thermal properties and combustion-related problems (e.g., emissions and ash-related problems) are critical to reducing emissions and improving combustion efficiency during the agricultural crop residues combustion process. This study integrated the higher heating value (HHV) model, specific heat model, and fuel indices as a new systematic approach to characterize the agricultural crop residues. Sixteen linear and non-linear regression models were developed from three main compositions of the ultimate analysis (e.g., C, H, and O) to predict the HHV of the agricultural crop residues. Newly developed HHV models have been validated with lower estimation errors and a higher degree of accuracy than the existing models. The specific heat of flue gas during the combustion process was estimated from the concentrations of C, H, O, S, and ash content under various excess air (EA) ratios and flue gas temperatures. The specific heat of agricultural crop residues was between 1.033 to 1.327 kJ/kg·K, while it was increased by decreasing the EA ratios and elevating the temperature of the flue gas. Combustion-related problems, namely corrosions, PM1.0 emissions, SOx, HCl, and ash-related problems were predicted using the fuel indices along with S and Cl concentrations, and ash compositions. Results showed that agricultural crop residues pose a severe corrosion risk and lower ash sintering temperature. This integrated approach can be applied to a wide range of biomass before the actual combustion process which may predict thermal-chemical properties and reduce the potential combustion-related emissions.

Sparse Bayesian Learning Kalman Filter-based Channel Estimation for Hybrid Millimeter Wave MIMO Systems: A Frequency Domain Approach

In this paper, channel estimation for Millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) communication system is presented. In the frequency domain, the Sparse Bayesian Learning-based Kalman Filter (SBL-KF) scheme is developed to estimate the sparse channel vector of hybrid mmWave MIMO architecture. The mmWave MIMO hybrid architecture provides reduced power loss and achievable data rates at mmWave frequencies. Frequency-selective channels are sparse at mmWave frequencies. The recovery of the sparse channel is estimated using different channel estimation methods. The hybrid precoding technique achieves both multiplexing gain and array gain in mmWave channels. Simulation results of the proposed SBL-KF scheme show low estimation error over the existing Orthogonal Matching Pursuit, Simultaneous Orthogonal Matching Pursuit, Simultaneous Weighted Orthogonal Matching Pursuit algorithm and SBL scheme. The complexity of the online estimation SBL-KF algorithm is low and it is ideally suited for implementation in an efficient mmWave MIMO system.

Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis

Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusion-based framework for meteorological drought modeling for the historical (1983–2016) and future (2020–2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multi-source products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations. For more accurate drought evaluation, all stations were classified into different clusters using the K-means clustering algorithm based on ground-based nSPI. Then, four Individual Artificial Intelligence (IAI) models, including Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multi-Layer Perceptron (MLP), and General Regression Neural Network (GRNN), were developed for drought modeling within each cluster. Finally, two advanced fusion-based methods, including Multi-Model Super Ensemble (MMSE) as a linear weighted model and a nonlinear model called machine learning Random Forest (RF), combined results by IAI models using different remotely sensed datasets. The proposed framework was implemented to simulate each remotely sensed precipitation data for the future based on CORDEX regional climate models (RCMs) under RCP4.5 and RCP8.5 scenarios for drought projection. The efficiency of IAI and fusion models was evaluated using statistical error metrics, including the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The proposed methodology was employed in the Gavkhooni basin of Iran, and results showed that the RF model with the lowest estimation error (RMSE of 0.391 and R2 of 0.810) had performed well compared to all other models. Finally, the resilience, vulnerability, and frequency of probability metrics indicated that the 12-month time scale of drought affected the basin more severely than other time scales.

Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals.

Freehand exercises help improve physical fitness without any requirements for devices or places. Existing fitness assistant systems are typically restricted to wearable devices or exercising at specific positions, compromising the ubiquitous availability of freehand exercises. In this paper, we develop MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver placed on the ground. MobiFit passively monitors the ubiquitous cellular signals sent by the base station, which frees users from the space constraints and deployment overheads and provides accurate repetition counting, exercise type recognition and workout quality assessment without any attachments to the human body. The design of MobiFit faces new challenges of the uncertainties not only on cellular signal payloads but also on signal propagations because the sender (base station) is beyond the control of MobiFit and located far away. To tackle these challenges, we conducted experimental studies to observe the received cellular signal sequence during freehand exercises. Based on the observations, we constructed the analytic model of the received signals. Guided by the insights derived from the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis and extracts low-frequency features from each repetition for type recognition. Extensive experiments were conducted in both indoor and outdoor environments, which collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1% and low repetition duration estimation error within 0.3 s. Besides, the experiments show that MobiFit works both indoors and outdoors and supports multiple users exercising together.

Introducing Parameter Clustering to the OED Procedure for Model Calibration of a Synthetic Inducible Promoter in S. cerevisiae

In recent years, synthetic gene circuits for adding new cell features have become one of the most powerful tools in biological and pharmaceutical research and development. However, because of the inherent non-linearity and noisy experimental data, the experiment-based model calibration of these synthetic parts is perceived as a laborious and time-consuming procedure. Although the optimal experimental design (OED) based on the Fisher information matrix (FIM) has been proved to be an effective means to improve the calibration efficiency, the required calculation increases dramatically with the model size (parameter number). To reduce the OED complexity without losing the calibration accuracy, this paper proposes two OED approaches with different parameter clustering methods and validates the accuracy of calibrated models with in-silico experiments. A model of an inducible synthetic promoter in S. cerevisiae is adopted for bench-marking. The comparison with the traditional off-line OED approach suggests that the OED approaches with both of the clustering methods significantly reduce the complexity of OED problems (for at least 49.0%), while slightly improving the calibration accuracy (11.8% and 19.6% lower estimation error in average for FIM-based and sensitivity-based approaches). This study implicates that for calibrating non-linear models of biological pathways, cluster-based OED could be a beneficial approach to improve the efficiency of optimal experimental design.

Multiple Team Membership, Performance, and Confidence in Estimation Tasks.

Multiple team membership (MTM) is a form of work organization extensively used nowadays to flexibly deploy human resources across multiple simultaneous projects. Individual members bring in their cognitive resources in these multiple teams and at the same time use the resources and competencies developed while working together. We test in an experimental study whether working in MTM as compared to a single team yields more individual performance benefits in estimation tasks. Our results fully support the group-to-individual (G-I) transfer of learning, yet the hypothesized benefits of knowledge variety and broader access to meta-knowledge relevant to the task in MTM as compared to single teams were not supported. In addition, we show that individual estimates improve only when members are part of groups with low or average collective estimation errors, while confidence in individual estimates significantly increases only when the collective confidence in the group estimates is average or high. The study opens valuable venues for using the dynamic model of G-I transfer of learning to explore individual learning in MTM.

Design of an Intent Recognition System for Dynamic, Rapid Motions in Unstructured Environments

Abstract In this study, we developed an offline, hierarchical intent recognition system for inferring the timing and direction of motion intent of a human operator when operating in an unstructured environment. There has been an increasing demand for robot agents to assist in these dynamic, rapid motions that are constantly evolving and require quick, accurate estimation of a user’s direction of travel. An experiment was conducted in a motion capture space with six subjects performing threat evasion in eight directions, and their mechanical and neuromuscular signals were recorded for use in our intent recognition system (XGBoost). Investigated against current, analytical methods, our system demonstrated superior performance with quicker direction of travel estimation occurring 140 ms earlier in the movement and a 11.6 deg reduction of error. The results showed that we could also predict the start of the movement 100 ms prior to the actual, thus allowing any physical systems to start up. Our direction estimation had an optimal performance of 8.8 deg, or 2.4% of the 360 deg range of travel, using three-axis kinetic data. The performance of other sensors and their combinations indicate that there are additional possibilities to obtain low estimation error. These findings are promising as they can be used to inform the design of a wearable robot aimed at assisting users in dynamic motions, while in environments with oncoming threats.

IVIM-DKIfor differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5T vs. 3T MRI.

To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)-diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5T to the traditional IVIM-DKI model at 3T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5T, n = 16 scanned at 3T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000s/mm2 up to 3 averages, 1.5T: TR = 5.774s, TE = 81ms and 3T: TR = 4.899s, TE = 100ms), T2-weighted, and T1-weighted imaging were used on the 1.5T and 3T MRI scanner, respectively. The IVIM-DKI signal was modeled using the traditional IVIM-DKI model and a novel model in which the total variation (TV) penalty function was combined with the traditional model to optimize non-physiological variations. Paired and unpaired t-tests were used to compare intra-scanner and scanner group differences in IVIM-DKI parameters obtained using the novel and the traditional models. Analysis of variance with post hoc test and receiver operating characteristic (ROC) curve analysis were used to assess the ability of parameters obtained using the novel model (at 1.5T) and the traditional model (at 3T) to characterize prostate lesions. IVIM-DKI modeled using novel model with TV spatial penalty function at 1.5T, produced parameter maps with 50-78% lower coefficient of variation (CV) than traditional model at 3T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5T estimated lower D* and f values as compared to traditional model at 3T. At 1.5T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5T. In comparison with the IVIM-DKI model at 3T, IVIM-DKI signal modeled with the TV penalty function at 1.5T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

Tracking Multiple Diffusing Particles Using Information Optimal Control.

We study the problem of tracking multiple diffusing particles using a laser scanning fluorescence microscope. The goal is to design trajectories for the laser to maximize the information contained in the measured intensity signal about the particles' trajectories. Our approach consists of a two level scheme: in the lower level we use an extremum seeking controller to track a single particle by first seeking it then orbiting around it. In the higher level controller, we decide which particle should be observed at each instant, with the goal of efficiently estimating each particle position while not losing track of any of them. Using simulations, we show that this technique is able to collect photons efficiently and to track multiple particles with low position estimation error.

Improved control strategy of dual star permanent magnet synchronous generator based tidal turbine system using sensorless field oriented control and direct power control techniques

ABSTRACT This paper presents a grid-connected tidal stream system with a focus on the reduction of the number of sensors needed by the control scheme. The electromechanical converter is a dual star permanent magnet synchronous generator (DS-PMSG) driven by a horizontal axis marine current turbine (HA-MCT). The generator is controlled via the field-oriented control (FOC) strategy, the speed and the position of the rotor are estimated using an extended Kalman filter (EKF), and the speed is regulated using a fuzzy logic controller. The grid side converter (GSC) consist of a two level voltage source inverter controlled by a virtual flux-based direct power control strategy (VF-DPC), and the current is filtered using an LCL filter. The system is simulated using Matlab/Simulink environment, the simulation results show the efficacy of the proposed control scheme, fuzzy speed regulator, and the EKF estimator, where relative mean error (MRE) of speed tracking is only 0.004, and MRE of estimated speed is as low as 7.3e-6, but a low rotor position estimation error of 1.76 degrees can be observed, however, the high number of pole pairs of the generator amplifies the effect of position error, causing divergences between the controlled dq currents seen by the control scheme and the real dq current that flows in the machine, and a decrease of system effeciency; however, the stability is not affected.

Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture

As a crucial indicator of forest growth and quality, estimating aboveground biomass (AGB) plays a key role in monitoring the global carbon cycle and forest health assessments. Novel methods and applications in remote sensing technology can greatly reduce the investigation time and cost and therefore have the potential to efficiently estimate AGB. Random forest (RF), combined with remote sensing images, is a popular machine learning method that has been widely used for AGB estimation. However, the accuracy of the ordinary linear variable selection method in the AGB estimation of coniferous forests is challenging due to the complexity of these forest biomes. In this study, spectral variables (spectral reflectance and vegetation index), land surface temperature (LST) and soil moisture were extracted from the operational land imager (OLI) and thermal infrared sensor (TIRS) of Landsat 8, and optimized RF regressions were established to estimate the AGB of coniferous forests in the Wangyedian forest farm, Inner Mongolia, Northeast China. We applied one linear (Pearson correlation coefficient (PC)) and four nonlinear (Kendall's τ coefficient (KC), Spearman coefficient (SC), distance correlation coefficient (DC) and the importance index) indices to select variables and establish optimized RF regressions for AGB estimation. The results showed that all the nonlinear indices provided significantly lower estimation errors than the linear index, in which the minimum root mean square error (RMSE) of 40.92 Mg/ha was obtained by the importance index in the nonlinear indices. In addition, the inclusion of LST and soil moisture significantly improved AGB estimation. The RMSE of the models constructed through the five indices decreased by 12.93%, 7.31%, 8.33%, 6.28% and 10.78%, respectively, following the application of the LST variable. In particular, when LST and soil moisture were both added into the model, the RMSE decreased by 31.47%. This study demonstrates that combining the nonlinear variable selection method with optimized RF regression can improve the efficiency of AGB estimation to support regional forest resource management and monitoring.

Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach.

We are overwhelmed by a deluge of data and, although its interpretation is challenging, fortunately, information technology comes to the rescue. One of the tools is artificial intelligence, allowing the identification of relationships between variables and their arbitrary classification. We focused on the assessment of both the remission of proteinuria and the deterioration of kidney function in patients with IgA nephropathy, comparing several methods of machine learning. It is of utmost importance to respond to subtle changes in kidney function, which will lead to a deceleration of the disease. This goal has been achieved by analyzing regression techniques, predicting the difference in serum creatinine concentration. We obtained the performance of the tested models which classified patients with high accuracy (Random Forest Classifier showed an accuracy of 0.8–1.0, Multi-Layer Perceptron an Area Under Curve of 0.8842–0.9035 and an accuracy of 0.7527–1.0) and regressors with a low estimation error (Decision Tree Regressor showed MAE 0.2059, RMSE 0.2645). We have demonstrated the impact of both model selection and input features on performance. Application of machine learning methods requires careful selection of models and assessed parameters. The computing power of modern computers allows searching for the models most effective in terms of accuracy.

Estimation of the Abduction/Adduction Movement of the Metacarpophalangeal Joint of the Thumb

Thumb opposition is essential for grasping, and involves the flexion and abduction of the carpometacarpal and metacarpophalangeal joints of the thumb. The high number of degrees of freedom of the thumb in a fairly small space makes the in vivo recording of its kinematics a challenging task. For this reason, along with the very limited independence of the abduction movement of the metacarpophalangeal joint, many devices do not implement sensors to measure such movement, which may lead to important implications in terms of the accuracy of thumb models. The aims of this work are to examine the correlation between thumb joints and to obtain an equation that allows thumb metacarpophalangeal abduction/adduction movement to be estimated from the other joint motions of the thumb, during the commonest grasps used during activities of daily living and in free movement. The correlation analysis shows that metacarpophalangeal abduction/adduction movement can be expressed mainly from carpometacarpal joint movements. The model thus obtained presents a low estimation error (6.29°), with no significant differences between grasps. The results could benefit most fields that do not typically include this joint movement, such as virtual reality, teleoperation, 3D modeling, prostheses, and exoskeletons.

Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis

Solar radiation (Rs) is one of the main parameters controlling the energy balance at the Earth’s surface and plays a major role in evapotranspiration and plant growth, snow melting, and environmental studies. This work aimed at evaluating the performance of seven empirical models in estimating daily solar radiation over 1990–2004 (calibration) and 2004–2010 (validation) at 13 Peruvian meteorological stations. With the same variables used in empirical models (temperature) as well as two other parameters, namely precipitation and relative humidity, new models were developed by multiple linear regression analysis (proposed models). In calibration of empirical models with the same variables, the lowest estimation errors were 227.1 and 236.3 J·cm−2·day−1 at Tacna and Puno stations, and the highest errors were 3958.4 and 3005.7 at San Ramon and Junin stations, respectively. The poorest-performing empirical models greatly overestimated Rs at most stations. The best performance of a proposed model (in terms of percentage of error reduction) was 73% compared to the average of all empirical models and 93% relative to the poorest result of empirical models, both at San Ramon station. According to root mean square errors (RMSEs) of proposed models, the worst and the best results are achieved at San Martin station (RMSE = 508.8 J·cm−2·day−1) and Tacna station (RMSE = 223.2 J·cm−2·day−1), respectively.

Neural Network Approach to Crater Detection for Lunar Terrain Relative Navigation

Terrain relative navigation can improve the precision of a spacecraft’s state estimate by providing supplementary measurements to correct for drift in an inertial measurement unit. This paper presents a crater detector, LunaNet, that uses a convolutional neural network (CNN) and image processing methods to detect craters from camera imagery taken by a spacecraft’s onboard camera. These detections are matched with known lunar craters, and these matches are used as visual landmark measurements in an extended Kalman filter (EKF). Our results show that, on average, LunaNet detects approximately twice the number of craters in an intensity image as two prior intensity-image-based crater detectors, and detects more accurate craters than the other two detectors as well. One of the challenges of using cameras for this task is that they can generate imagery with significant variations in image quality and noise levels. LunaNet is robust to four common types of image noise due to its incorporation of a CNN that is trained on diverse data. LunaNet also produces crater detections with better image persistence over a trajectory. These qualities contribute to a LunaNet-based EKF that results in consistently lower state estimation error and that outperforms the filters based on the other detectors.

Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information

A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. First, motor units (MUs) firing information were identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Our approach provides a reliable neural decoding method for dexterous finger movements. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.

GA-based approach to optimize an equivalent electric circuit model of a Li-ion battery-pack

This article presents the optimization procedure based on genetics algorithms (GA) to obtain an equivalent electric circuit model (EECM) of a Li-ion battery pack. In the first part, a series of experimental tests in time and frequency domains were carried out. These tests were used to identify the parameters of the EECM under different State-of-Charge (SoC) for a commercial battery-pack. Each EECM consists of a voltage source connected in series with a resistor and a set of k networks composed of a resistor in parallel with a capacitor, where k = 1, 2 o 3 (1RC, 2RC, and 3RC). Subsequently, parametric identification of the EECM was performed using optimization techniques. At this stage, the topology that gives the lowest estimation error was determined, where the options analyzed were to use 1RC, 2RC, or 3RC networks. The objective function consists of minimizing the mean square error between measured and calculated impedances of the different proposed circuit models. GA was used to solve this optimization problem. The minimum error obtained was 1.07% and 1.05% for the EECM with 2RC and 3RC networks, respectively. Finally, these EECMs were implemented in Matlab®/Simulink to validate the Li-ion battery-pack model response for an electric vehicle application. A hardware-in-the-loop (HIL) simulation platform was developed to simulate the performance of an electric vehicle (EV) under different driving cycles. The results show that the GA-based approach allows obtained an EECM of low order to represent the highly dynamic behavior of a Li-ion battery with high accuracy.

Disentangling temporal dynamics in attention bias from measurement error: A state-space modeling approach.

Temporal dynamics in attention bias (AB) have gained increasing attention in recent years. It has been proposed that AB is variable over trials within a single test session of the dot-probe task, and that the variability in AB is more predictive of psychopathology than the traditional mean AB score. More important, one of the dynamics indices has shown better reliability than the traditional mean AB score. However, it has been also suggested that the dynamics indices are unable to uncouple random measurement error from true variability in AB, which questions the estimation precision of the dynamics indices. To clarify and overcome this issue, the current article introduces a state-space modeling (SSM) approach to estimate trial-level AB more accurately by filtering random measurement error. The estimation error of the extant dynamics indices versus SSM were evaluated by computer simulations with different parameter settings for the temporal variability and between-person variance in AB. Throughout the simulations, SSM showed robustly lower estimation error than the extant dynamics indices. We also applied these indices to real data sets, which revealed that the dynamics indices overestimate within-person variability relative to SSM. Here SSM indicated less temporal dynamics in AB than previously proposed. These findings suggest that SSM might be a better alternative to estimate trial level AB than the extant dynamics indices. However, it is still unclear whether AB has meaningful in-session variability that is predictive of psychopathology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).