Proposed Estimation Method Research Articles

Time-interleaved system mismatch estimation based on correlation function and particle swarm optimization algorithm.

The time-interleaved analog-to-digital converters (TIADCs) technique is an efficient solution to improve the sampling rate of the acquisition system with low-speed ADCs. However, channel mismatches such as gain mismatch, time skew mismatch, and offset mismatch may seriously degrade the performance of TIADC. Furthermore, for high-speed signal acquisition, the gain and time skew mismatches would vary with the signal frequency, and the traditional fixed model does not work any longer. In this paper, a series of sinusoidal signals are adopted to estimate the variable mismatches. First, an autocorrelation-based approach is presented to estimate the gain mismatch. The information about the gain mismatch is extracted from the autocorrelation function of sub-ADC output samples. Then, the time skew mismatch is estimated by utilizing the particle swarm optimization algorithm. The reported simulation results show that the mismatches can be accurately estimated. Finally, a commercial 12.5 GSPS four-channel TIADC system is utilized to verify the performance of the proposed method. The spurious free dynamic range of the system can be improved by about 20 dB, and the effectiveness of the proposed estimation method is demonstrated.

A Data-Driven Indirect Estimation of Machine Parameters for Smart Production Systems

Automated measurement of the machine reliability parameters for a production system enables a continuous update of the mathematical model of the system, which can be used for various analyses toward productivity improvement. However, the continuous update may be impeded by some machines of which automated parameter measurements are out of order. Such a situation has been observed, for instance, when some of the machines in the line cannot save log files or Internet of Things devices that measure these machines stop functioning. In this context, this article addresses the problem of estimating the reliability parameters of those machines while avoiding a direct manual measurement (by humans) of uptime and downtime. It turns out that those parameters can be computed using buffer-related data of the neighboring machines along with the system information. With this, a continuous update of the model is possible even though some machines stop recording their status in an automated manner. The method is indirect as opposed to direct manual measurement. The results are derived for synchronous serial production lines with Bernoulli and also exponential reliability characteristics. Our simulation studies verify the accuracy of the proposed estimation methods.

A Joint Acceleration Estimation Method Based on a High-Order Disturbance Observer

Joint acceleration feedback is widely used in the design of controllers and observers since joint accelerations reflect the joint dynamics of robots, especially in physical human-robot interaction. However, joint acceleration acquisition is a technical difficulty for robots. The dynamics-based methods can achieve joint acceleration estimation using only a nominal model. Still, the performance of these methods is limited by the fast time-varying disturbances in the system. This letter proposes a joint acceleration estimation method based on a high-order disturbance observer. This method can observe and compensate for fast time-varying lumped disturbances in the observer while maintaining joint acceleration estimation performance at low frequencies. The finite-time stability of the proposed estimation method is proved using the Lyapunov theory. Simulations and experiments with a lower limb rehabilitation robot are implemented to verify the performance of the proposed method.

Cascade Takagi–Sugeno fuzzy observer design for nonlinear uncertain systems with unknown inputs: A sliding mode approach

This article investigates the design of Takagi–Sugeno (TS) fuzzy model-based observers for nonlinear systems with parametric uncertainties and unknown inputs. To address this challenging problem, two observers are constructed in cascade. Based on the sliding mode technique, the first observer allows to examine a new system whose both state and output equations are subject to uncertainties but without unknown inputs. The second Luenberger-type observer is designed for the new system where the effects of uncertainties on the estimation error can be canceled. The TS fuzzy observer design is recast as optimization problems under linear matrix inequalities, which can be effectively solved using convex optimization technique. The new cascade observer structure enables a simultaneous estimation of the system states, the unknown inputs and the uncertainties of the original nonlinear system. The effectiveness and advantage of the proposed estimation method is demonstrated via two numerical examples including a nonlinear vehicle application.

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain.

Web surveys have replaced Face‐to‐Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID‐19 pandemic‐related restrictions. However, this mode still faces significant limitations in obtaining probability‐based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability‐based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability‐based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID‐19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web‐based survey samples with the help of machine‐learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.

LOAD ANGLE ESTIMATION OF A SYNCHRONOUS GENERATOR USING DYNAMICAL NEURAL NETWORKS

Load angle estimation process of the operating synchronous generator on the power system is defined based on dynamic neural networks. In the process of forming the estimation model the choice of input values and structures of dynamic neural network is explored and defined. Data from recorded responses to the network at standard operating conditions and at malfunctioning of a synchronous generator are used for learning the dynamic neural network. The comparison of the results obtained using the defined method of load angle estimation with the results obtained by measurement, reveals the applicability of the proposed estimation method based on dynamic neural networks.

Liu-type shrinkage strategies in zero-inflated negative binomial models with application to Expenditure and Default Data

In modeling count data with overdispersion and extra zeros, zero-inflated negative binomial (ZINB) regression model is useful. In a regression model, the multicollinearity problem arises when there are some high correlations between predictor variables. This problem leads to the maximum likelihood method will not be an efficient estimator. The ridge and Liu-type estimators have been proposed to combat the multicollinearity problem so that the Liu-type estimator is better. In this paper, we proposed the Liu-type shrinkage estimators, namely linear shrinkage, preliminary test, shrinkage preliminary test, Stein-type, and positive Stein-type Liu estimators to estimate the count parameters in the ZINB model, when some of the predictor variables have not a significant effect to predict the response variable so that a sub-model may be sufficient. The asymptotic distributional biases and variances of the proposed estimators are nicely demonstrated. We also compared the performance of the Liu-type shrinkage estimators along with the Liu-type unrestricted estimator by using an extensive Monte Carlo simulation study. The results show that the performances of the proposed estimators are superior to those based on Liu-type unrestricted estimators. We also applied the proposed estimation methods to Expenditure and Default Data.

Publisher Erratum to “Estimation of the External Forces on a Robot Hand with Fingertip Tactile Sensors”

In human-robot collaboration, physical hand-over-hand communication of the human operator’s intention is essential for realizing direct and intuitive cooperation. When an external force acts on a robot hand holding an object, the torque exerted on the finger motor becomes a combination of the grasping torque required to maintain the grasping pose and reaction torque to the external force. To analyze intended interaction, we need to recognize multiple contacts inside and outside the fingertips. In this study, an external force estimation method was developed that uses tactile sensors to measure both grasping and external forces acting on the fingertips of a robot hand. Because a tactile sensor cannot measure all the external force components, it is impossible to directly compute the magnitude and contact angle of an external force. The shear and frictional forces lost during tactile sensing are backwardly estimated according to the difference between the estimated and measured torques. The magnitude and contact angle are computed to estimate external force, which can be used to determine the intention of the user. Simulations based on real sensor responses and experiments were performed to validate the proposed estimation method.

Shape-constrained estimation in functional regression with Bernstein polynomials

Shape restrictions on functional regression coefficients such as non-negativity, monotonicity, convexity or concavity are often available in the form of a prior knowledge or required to maintain a structural consistency in functional regression models. A new estimation method is developed in shape-constrained functional regression models using Bernstein polynomials. Specifically, estimation approaches from nonparametric regression are extended to functional data, properly accounting for shape-constraints in a large class of functional regression models such as scalar-on-function regression (SOFR), function-on-scalar regression (FOSR), and function-on-function regression (FOFR). Theoretical results establish the asymptotic consistency of the constrained estimators under standard regularity conditions. A projection based approach provides point-wise asymptotic confidence intervals for the constrained estimators. A bootstrap test is developed facilitating testing of the shape constraints. Numerical analysis using simulations illustrates improvement in efficiency of the estimators from the use of the proposed method under shape constraints. Two applications include i) modeling a drug effect in a mental health study via shape-restricted FOSR and ii) modeling subject-specific quantile functions of accelerometry-estimated physical activity in the Baltimore Longitudinal Study of Aging (BLSA) as outcomes via shape-restricted quantile-function on scalar regression (QFOSR). R software implementation and illustration of the proposed estimation method and the test is provided.

Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring

Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still unmet needs in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time–frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under pre- and post-exercise conditions and were repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements were observed in terms of precision, accuracy, and stability. The performance was reflected by achieving an averaged Pearson correlation coefficient (PCC) of 93.8% on multiple subjects. We believe that the proposed estimation method will help address the needs for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.

Edge computing for vehicle battery management: Cloud-based online state estimation

The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.

Inference for stress–strength reliability of multi-state system with dependent stresses and strengths using improved generalized survival signature

Multi-state systems are widely used in engineering field, which usually contain complex internal structures and variable external environmental stresses. Discussing the reliability of such system has theoretical significance and application values. In this paper, a new stress–strength model for multi-state system consisting of multi-type multi-state components is introduced. Each type of components has two dependent strengths, which is suffered from two dependent stresses in working environment, while the strengths and stresses are independent. We discuss inferential procedures for stress–strength reliability of the multi-state system using the proposed improved generalized survival signature in the case of all types of components exposed to common pair of dependent stresses. Based on the assumption that strength variables follow Weibull and exponential distributions, as well as the stress variables, the expressions of stress–strength reliability of such multi-state system in different states are derived by using Gumbel and Clayton copulas. The pseudo maximum likelihood estimation is employed to estimate the dependence parameters and maximum likelihood estimation, asymptotic confidence interval, parametric bootstrap confidence interval and transformation-based confidence interval to make inference for stress–strength reliability. Monte Carlo simulations are carried out to compare the performances of the proposed estimation methods. Finally, a real data analysis is presented to illustrate the developed model and inferential procedures.

On outliers detection and prior distribution sensitivity in standard skew-probit regression models

Regression models with probit and logit link functions are the most frequently used for binary response variables. However, traditional approaches may not be adequate when data are unbalanced. This paper deals with standard skew-probit regression models. Parameters were estimated through a new Bayesian approach which consists of the use of Hamiltonian Monte Carlo (HMC) and the original likelihood function. Simulation studies assessed the efficiency of the estimation method and the sensitivity of prior distributions for parameters related to asymmetry calculating the RMSE (root mean square error). The proposed estimation method was compared when used for detecting outliers. The results show that the proposed method is more efficient than INLA and is successful in the recovery of true parameter values. The sensitivity study enabled the proposal of a new prior distribution configuration for the asymmetry parameter, and the randomized quantile residual proved to be more suitable for detecting outliers. The methodology was applied to a diabetes dataset towards illustrating the results.

Global Estimation and Compensation of Linear Effects in Coherent Optical Systems Based on Nonlinear Least Squares

This article proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on nonlinear least squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise in two steps. First, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Second, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a zero-forcing equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér–Rao lower bound, while the detection performance is compared to a modified clairvoyant equalizer.

A Revised Video Vision Transformer for Traffic Estimation With Fleet Trajectories

Real-time traffic monitoring represents a key component for transportation management. The increasing penetration rate of connected vehicles with positioning devices encourages the utilization of trajectory data for real-time traffic monitoring. The use of commercial fleet trajectory data could be seen as the first step towards mobile sensing networks. The main objective of this research is to estimate space occupancy of a single road segment with partially observed trajectories (commercial fleet trajectories in our case). We first formulate the trajectory-based traffic estimation as a video computing problem. Then, we reconstruct trajectory series into video-like data by performing spatial discretization. Following this, video input is embedded using a tubelet embedding strategy. Finally, a Revised Video Vision Transformer (RViViT) is proposed to estimate traffic state from video embeddings. The proposed RViViT is tested on a public dataset of naturalistic vehicle trajectories collected from German highways around Cologne during 2017 and 2018. The results witness the effectiveness of the proposed method in traffic estimation with partially observed trajectories.

Effective lung ventilation estimation based on 4D CT image registration and supervoxels

Most computed tomography (CT)-derived ventilation estimation methods rely on deformation fields of image registration to track the volume variations of each voxel, which are easily affected by image registration results and motion artifacts of four-dimensional (4D) CT images. To attack these problems, a lung ventilation estimation method based on 4D CT image registration and supervoxels is proposed in this paper. In order to avoid estimation errors caused by image artifacts and noises in the methods of directly estimating voxel-wise ventilation, images corresponding to maximum exhale phase are represented by multi-level supervoxels. Then, according to the relationship between registered CT values in Hounsfield units (HU) and regional volume change, a ventilation estimation method is designed to calculate the whole ventilation of each supervoxel. To accurately recover the ventilation of each voxel from the supervoxel region, a linear programming model is established to obtain the ventilation of each voxel, and the final estimated lung ventilation image is obtained by averaging the recovered results of all supervoxels layers. To evaluate our proposed ventilation estimation method, we calculate voxel-wise Spearman coefficient rs and Dice similarity coefficient (DSC for low function lung DSClow and high function lung DSChigh) of various CT-derived ventilation methods on VAMPIRE dataset. The experimental results on VAMPIRE dataset show that the average rs values between our proposed method can achieve 0.38 (-0.17–0.70). Accordingly, the average DSClow and DSChigh values of our proposed method are 0.40 (0.17–0.67) and 0.36 (0.08–0.61) respectively.

An improvement of equivalent circuit model for state of health estimation of lithium-ion batteries based on mid-frequency and low-frequency electrochemical impedance spectroscopy

Electrochemical impedance spectroscopy (EIS) is a non-invasive, information-rich measurement method. The biggest advantage is that it is possible to identify and analyze the battery state using a suitable equivalent circuit model (ECM) without the need for complete knowledge of the battery's past operation. Conventional equivalent circuit models (CECMs) achieve a high degree of accuracy by identifying model parameters with relatively fixed circuit components. However, CECM method fitting process may suffer from fitting failure and fitting error, resulting in poor estimation accuracy. To solve this problem, it is crucial to establish a suitable ECM with good fitting effect and high accuracy. Accordingly, this study proposes a method of mid-frequency and low-frequency domain ECM (MLECM) based on fusion SEI film resistance and charge transfer resistance. Firstly, two model building methods are presented. Then, by fitting and analyzing the model parameters of two different types of batteries, we conclude that MLECM has the advantages of fewer parameters and better parameter fitting. Finally, a method of power battery state of health (SOH) estimation based on the improved model is proposed by MLECM and the mathematical model of SOH. Validated by two datasets of experiments with different types of batteries, the results show that the maximum RMSE of the proposed estimation method is only 1.38% in the two datasets. And the average root mean squared error (RMSE) of MLECM is reduced by 0.708% compared to CECM, while the computational load of the former is reduced by 69.57% compared to the latter. Compared with the CECM method, the MLECM has high estimation accuracy, high applicability and low computational load.

Effect of Coir Fiber Surface Treatment on Interfacial Properties of Reinforced Epoxy Resin Composites.

Coir-fiber-reinforced epoxy resin composites are an environmentally friendly material, and the use of coir fibers improves the mechanical properties of epoxy resin. In order to improve the interfacial adhesion between coir fibers and the epoxy resin matrix, microwave treatment, alkali treatment, acetic anhydride modification, 3-aminopropyltriethoxysilane modification and their reasonable combination method treatments were carried out on coir fibers, respectively. Scanning electron microscopy (SEM), Fourier transform-infrared (FTIR) and X-ray diffraction (XRD) were used to analyze the effects of the different treatments on the characteristics of the coir fibers, and single-fiber pullout tests were performed on the pullout specimens made from the above coir fibers. The results calculated by the proposed estimation method show that the combination method of alkali treatment and 3-aminopropyltriethoxysilane surface modification could better enhance the interfacial bonding ability between coir fibers and epoxy resin with an interfacial shear strength and pullout energy of 6.728 MPa and 40.237 N·mm, respectively. The principal analysis shows that the method can form both mechanical interlocking and chemical bonds at the interface to enhance the interfacial bonding ability. This study provides a more suitable method for improving the interfacial properties of coir-fiber-reinforced epoxy resin composites and has implications for the study of natural fiber composites.

A Joint estimation approach to sparse additive ordinary differential equations.

Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likelihood, ODE fidelity and sparse regularization simultaneously. We design a block coordinate descent algorithm for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.

Reconstructing real object appearance with virtual materials using mobile augmented reality

In augmented reality (AR) applications, detecting and tracking real-world objects remains a challenge. Another challenge is making the loaded virtual objects truly reflect the lighting and shadow conditions of the actual environment in which they are located. In this paper, we proposed an improved method for real-time light estimation of environmental lighting in AR applications, tracking the movement of physical objects, and making the corresponding virtual objects reflect the lighting and shadows of the actual environment. First, we proposed an effective method for estimating light in an AR scene by training a deep neural network once on different scenes from the input of RGB-D images to determine the direction of the brightest light. Second, we experimented with advanced methods of detecting and tracking different types of real objects (flat surfaces, simple geometries, and complex geometries) for visualizing virtual materials on the top of these surfaces, where virtual materials will acquire the conditions of the real place in terms of lighting, reflection, and shadows depending on our proposed method for light estimation. Our method of light estimation was tested via experiments on the three types of real objects mentioned above. Results indicate that our method achieves higher resolution and lower error rate compared with other work. Our methods have achieved very good results in terms of detection accuracy, tracking accuracy, realistic visualization of materials, matching real shade, and lighting conditions.