Estimation Error Research Articles

An Approach to Estimate the Temperature of an Induction Motor under Nonlinear Parameter Perturbations Using a Data-Driven Digital Twin Technique

To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging.

Theoretical analysis for estimating laser integrated linewidth via frequency-noise power spectral density

The measurement of a laser linewidth is significant in metrology, coherent optical communications, high-resolution sensing, and LIDAR. Firstly, in this study, we theoretically explain why estimating an integrated linewidth via a frequency-noise power spectral density (PSD) is valid. We find that the previous methods estimating the integrated linewidth via the frequency-noise PSD result from Gaussian approximation and obtain a more general consequence. Secondly, according to the theory, we propose the Voigt approximation method to improve the estimation performance. The simulation results show the Voigt approximation estimation error is lower than 5%. Finally, based on the Voigt approximation, the relationship between the interference visibility and laser linewidth is found, providing a possible convenient approach to measuring the linewidth.

Implant Cost Awareness Among Orthopaedic Surgeons at a Single Academic Institution

Introduction Despite the increasing costs of implants, surgeons’ awareness of these costs varies widely. The purpose of this study is to determine baseline awareness of sports-medicine-related implant cost among orthopaedic surgeons. Methods A survey including images of 10 different orthopaedic implants related to sports medicine was sent to 18 surgeons. All included surgeons practice in a single ambulatory surgery center and routinely perform sports-medicine-related procedures. Surgeons were asked to estimate the cost of each implant and answer questions about surgeon characteristics. Cost estimates were compared with implant cost found in the institutional charge master database. Results Cost estimates varied widely among providers regardless of fellowship training. The average percent error among all providers was 54.3%. Surgeons that rated knowledge of implant cost to be important most or all of the time had an average cost estimate error of 60.7% versus 48.0% error in their counterparts that stated that knowledge of cost was important to them only some of the time, though this did not reach statistical significance. Low-cost implants were more likely to be overestimated while high-cost implants were more often underestimated. Years of experience, sports medicine fellowship training, and involvement in contract negotiations had no effect on implant cost accuracy. Conclusion Implant cost awareness is dishearteningly low sports medicine orthopaedists despite the large economic impact of these devices. There is considerable room for improving cost awareness in sports medicine.

Nonlinear Flux Linkage Observer with Model Reference Adaptive System for Improved Permanent Magnet Synchronous Motor Control

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change.

An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance.

Whole-brain T2 mapping with radial sampling and retrospective motion correction at 3T.

Several barriers prevent the use of whole-brain T2 mapping in routine use despite increasing interest in this parameter. One of the main barriers is the long scan time resulting in patient discomfort and motion corrupted data. To address this challenge, a method for accurate whole-brain T2 mapping with a limited acquisition time and motion correction capabilities is investigated. A 3D radial multi-echo spin-echo sequence was implemented with optimized sampling trajectory enabling the estimation of intra-scan motion, subsequently used to correct the raw data. Motion corrected echo images are then reconstructed with linear subspace constrained reconstruction. Experiments were carried out on phantom and volunteers at 3T to evaluate the accuracy of the T2 estimation, the sensitivity to lesions and the efficiency of the correction on motion corrupted data. Whole-brain T2 mapping acquired in less than 7 min enabled the depiction of lesions in the white matter with longer T2. Data retrospectively corrupted with typical motion traces of pediatric patients highly benefited from the motion correction by reducing the error in T2 estimates within the lesions. All datasets acquired on seven volunteers, with deliberate motion, also showed that motion corrupted T2 maps could be improved with the retrospective motion correction both at the voxel level and the structure level. A whole-brain T2 mapping sequence with retrospective intra-scan motion correction and reasonable acquisition time is proposed. The method necessitates advanced iterative reconstruction strategies but no additional navigator, external device, or increased scan time is required.

Evaluating the accuracy of population-specific versus generic stature estimation regression equations in a South African sample.

Accurate estimates of stature play an important role in the personal identification of unknown decedents, however a drawback in the application of many stature estimation equations is the need for known sex and population, the assignment of which can be challenging. Researchers have formulated equations for stature estimation that are neither population- nor sex-specific and thereof the aim of this study was to assess the applicability of these stature estimation equations proposed by Albanese et al.. (2016) (Albanese J, Tuck A, Gomes J, Cardoso HFV (2016) An alternative approach for estimating stature for long bones that is not population- or group-specific. Forensic Sci Int 259:59-68). The physiological length of the femur, condylar malleolar length of the tibia and a combination of these measurements, collected from Magnetic Resonance Imaging Scanograms of adult (20-60 years) White South African males (n = 30) and females (n = 44) were used to assess the accuracy of the Albanese et al. (Albanese J, Tuck A, Gomes J, Cardoso HFV (2016) An alternative approach for estimating stature for long bones that is not population- or group-specific. Forensic Sci Int 259:59-68). sex-specific and generic stature estimation equations. The stature estimates were compared with measured living stature (LSM), using paired t-tests. Results indicated that the Albanese et al. (Albanese J, Tuck A, Gomes J, Cardoso HFV (2016) An alternative approach for estimating stature for long bones that is not population- or group-specific. Forensic Sci Int 259:59-68). equations underestimated living stature by between 1.1 and 5.0cm. These underestimations were significantly different between the LSM and the sex-specific estimates for females and the LSM and the generic estimates for males and the tibia for sex-specific equation. All stature estimates however fell in between two standard error of estimates for the sex-specific equations for males and the generic equations for the females. Although, the equations by Albanese et al. (Albanese J, Tuck A, Gomes J, Cardoso HFV (2016) An alternative approach for estimating stature for long bones that is not population- or group-specific. Forensic Sci Int 259:59-68). can be used to estimate stature in White South Africans in certain cases, the use of sex/population-specific equations remains the method of choice.

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (Tg) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the Tg values. The SVM-based model showed the best performance with determination coefficients (R2) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles.

Mechanism and Data-Driven Fusion SOC Estimation

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy.

Mathematical Nonlinear Graph Theory Topology Layer Model for Photoelectric Tracking System

The performance of the photoelectric tracking system mainly depends on the tracking accuracy. In order to achieve the purpose of high precision tracking, controlling the power dragging device, the main component of the photoelectric tracking system, is the main means to achieve this purpose. In order to improve the tracking speed and accuracy of photoelectric tracking systems, the author proposed a mathematical nonlinear graph theory topology layer model for photoelectric tracking systems. The topology layer model and the motion node servo mechanism model of the photoelectric tracking system are studied, and the two-stage disturbance sources that affect the tracking stability are analyzed. The results show that the interference estimation error range designed by the author reaches 0.003 through comparison, it can be seen that the estimation error designed by the author is significantly smaller than the ESO estimation error, and the buffeting is small. Through comparison, it can be concluded that the estimation error of the algorithm and the disturbance observer designed by the author are significantly smaller than the estimation error of ESO, and the buffeting is small, and they have strong compensation ability for the disturbance of different frequencies. The interference observer can quickly and accurately estimate the interference and prove its stability. The effectiveness of the proposed observer and disturbance observer is fully demonstrated. The finite time integrated site selection mode disturbance observer (F-ISMDOB) is designed to quickly estimate and compensate for equivalent interference, and effectively improves the anti-interference performance of the system structure layer.

Novel calibration method for improved UWB sensor distance measurement in the context of application for 3D analysis of human movement

Integrated UWB and MIMU sensor systems have become popular for pedestrian tracking and indoor localization, since this facilitates data fusion that improves position estimation accuracy by exploiting the complementary nature of their error sources. Integrated UWB/MIMU sensors also have great potential in only on-body use for 3D analysis of human movement, as with MIMU sensors alone accurate direct estimation of (relative) body segment position is not possible. For this, a position estimation accuracy with errors smaller than 1 cm is deemed required. The lowest position estimating error with integrated UWB/MIMU systems, reported so far, is around 5 cm. The main accuracy limiting factors were found to be the systematic errors in the distance estimates from the UWB sensor. Multiple reported attempts to calibrate for these systematic errors failed to achieve the desired accuracy. This article presents a novel distance-bias calibration method that minimizes the residual systematic distance estimate errors using multiple sensors in a swarm configuration. Validation was performed against synthetic reference data and against reference data measured with an optical motion tracking system. Significantly reduced systematic distance estimate errors (≤0.5 cm) were found. These results promise to facilitate significantly better position estimates in future UWB/MIMU data fusion.

Emotional parameter estimation from emo-soundscapes dataset using Deep Convolutional Autoencoders

AbstractPredicting the emotional responses of humans to acoustic features in the surrounding environment has a highly potential of applications in different fields, ranging from videogames, therapeutic use of virtual reality to the emotional design of spaces according to their expected use. In this paper we model the estimation process of the classical emotion characterization parameters (arousal and valence) from sounds. By means of convolutional neural networks and convolutional autoencoders, the model is adjusted for the prediction of these parameters from a standard dataset [1], improving the results obtained in previous literature. The relevance of the work, apart from improving the results due to the use of autoencoders, is that it eliminates the need to compute handcrafted features, thus demonstrating the ability of convolutional neural networks to treat raw information. Other main contributions of the paper is a new way to visualize the errors in the joint estimation of arousal and valence that facilitates the evaluation of the results obtained by the models. Finally, the use of bootstrap to estimate the confidence intervals of the MSE and r2 of Deep Learning models shows that in comparison to non-overlapping samples, overlapping samples introduces performance bias.

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

Design and Modelling of Continuum Robot for Endoscopic Submucosal Dissection Surgery With Lifting Force Estimation.

Endoscopic submucosal dissection (ESD) is an effective treatment for early-stage gastrointestinal cancers. However, traditional surgical instruments lack accuracy and force-sensing. A new type of continuum robot for ESD is designed. An accurate static model of the proposed continuum robot is established, considering cases where the robot bends into C-shapes and S-shapes. A force estimation method based on an accurate static model is proposed. Then, the accuracy of the static model and force estimation is verified through experiments. Finally, an ex-organ experiment is carried out. The average position error of the proposed static model is 0.72mm, accounting for 2.57% of the total robot length. The average error of force estimation is 19.53mN. By gripping and cutting ex-porcine gastric mucosa, the robot's functionality is validated. This paper contributes to precise control and safe interaction of continuum robots.

Secure virtual coupling control of connected train platoons under cyber attacks

Virtual coupling of automated trains has emerged as a promising technology to enhance the operational efficiency and line capacity of railway transportation systems. However, the vulnerability of such systems to malicious cyber attacks poses a significant threat to their stability, reliability and applicability. This article addresses the challenge of ensuring secure virtual coupling control of connected train platoons subject to various cyber attacks, including false data injection attacks, denial-of-service attacks, and replay attacks. First, based on the low-dimensional and noisy sensor measurements, novel set-valued observers are designed for each follower train to estimate their inaccessible full longitudinal motion states despite uncertainties and unknown attacks and noises. Then, distributed secure attack-compensation-based virtual coupling control laws are developed to ensure the platoon's stability and resilience against cyber attacks. Additionally, formal stability analysis of the resultant estimation error system and platoon tracking error system is provided. Furthermore, two algorithms are presented to elaborate the offline observer and controller design process as well as their online implementation. Finally, based on the data of a realistic urban rail transit line, simulation experiments are conducted to validate the efficacy of the proposed theoretical findings.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

The Impact of Model Misspecification on Phylogenetic Network Inference

The development of statistical methods to infer species phylogenies with reticulations (species networks) has led to many discoveries of gene flow between distinct species. These methods typically assume only incomplete lineage sorting and introgression. Given that phylogenetic networks can be arbitrarily complex, these methods might compensate for model misspecification by increasing the number of dimensions beyond the true value. Herein, we explore the effect of potential model misspecification, including the negligence of gene tree estimation error (GTEE) and assumption of a single substitution rate for all genomic loci, on the accuracy of phylogenetic network inference using both simulated and biological data. In particular, we assess the accuracy of estimated phylogenetic networks as well as test statistics for determining whether a network is the correct evolutionary history, as opposed to the simpler model that is a tree. We found that while GTEE negatively impacts the performance of test statistics to determine the “treeness” of the evolutionary history of a data set, running those tests on triplets of taxa and correcting for multiple-testing significantly ameliorates the problem. We also found that accounting for substitution rate heterogeneity improves the reliability of full Bayesian inference methods of phylogenetic networks, whereas summary statistic methods are robust to GTEE and rate heterogeneity, though currently require manual inspection to determine the network complexity.

A Multi-Sensor Fusion Underwater Localization Method Based on Unscented Kalman Filter on Manifolds

In recent years, the simplified computation of position and velocity changes in nonlinear systems using Lie groups and Lie algebra has been widely used in the study of robot localization systems. The unscented Kalman filter (UKF) can effectively deal with nonlinear systems through the unscented transformation, and in order to more accurately describe the robot localization system, the UKF method based on Lie groups has been studied successively. The computational complexity of the UKF on Lie groups is high, and in order to simplify its computation, the Lie groups are applied to the manifold, which efficiently handles the state and uncertainty and ensures that the system maintains the geometric constraints and computational simplicity during the updating process. In this paper, a multi-sensor fusion localization method based on an unscented Kalman filter on manifolds (UKF-M) is investigated. Firstly, a system model and a multi-sensor model are established based on an Autonomous Underwater Vehicle (AUV), and a corresponding UKF-M is designed for the system. Secondly, the multi-sensor fusion method is designed, and the fusion method is applied to the UKF-M. Finally, the proposed method is validated using an underwater cave dataset. The experiments demonstrate that the proposed method is suitable for underwater environments and can significantly correct the cumulative error in the trajectory estimation to achieve accurate underwater localization.

MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond.

Rotational Motion Compensation for ISAR Imaging Based on Minimizing the Residual Norm

In inverse synthetic aperture radar (ISAR) systems, image quality often suffers from the non-uniform rotation of non-cooperative targets. Rotational motion compensation (RMC) is necessary to perform refocused ISAR imaging via estimated rotational motion parameters. However, estimation errors tend to accumulate with the estimated processes, deteriorating the image quality. A novel RMC algorithm is proposed in this study to mitigate the impact of cumulative errors. The proposed method uses an iterative approach based on a novel criterion, i.e., the minimum residual norm of the signal phases, to estimate different rotational parameters independently to avoid the issue caused by cumulative errors. First, a refined inverse function combined with interpolation is proposed to perform the RMC procedure. Then, the rotation parameters are estimated using an iterative procedure designed to minimize the residual norm of the compensated signal phases. Finally, with the estimated parameters, RMC is performed on signals in all range bins, and focused images are obtained using the Fourier transform. Furthermore, this study utilizes simulated and real data to validate and evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm shows dominance in the aspects of estimation accuracy, entropy values, and focusing characteristics.