Noise Errors Research Articles

Forecasting of Male Life Expectancy in Nigeria: Box-Jenkins Approach

Over the past few decades, Males’ life expectancy has been observed to be less than that of females. In the literature, little has been done to forecast the Life expectancy at birth for Males (LEM) in developing countries, especially in Nigeria. To understand the forecasting dynamics of LEM in Nigeria, this work therefore examines the series using the Box-Jenkins (1976) methodology. Pre-examination tests conducted on the series revealed that it is not only a difference stationary process of order one {I(1)} but it is also best fitted by ARIMA (1, 1, 1) model. Moreover, diagnostic checking carried out on the residuals of the fitted model confirmed that the residual is a white noise error term in that it fulfils the assumptions of homoscedasticity, stationarity, and autocorrelation. To obtain a good forecast, the LEM data spanning 1960 to 2014 (55 years) were used to train the model while the remaining LEM data spanning 2015 to 2020 (6 years) were used to test the data. For the test data, the forecasted LEM has been shown to give approximate values of the actual LEM. Findings showed that the LEM is expected to increase slightly by 0.64% within the forecasted periods spanning 2021 to 2030. Diagnostic checking, Forecasting, Homoscedasticity, Life expectancy, Male, Training sets, Testing sets

[formula omitted] gain-scheduled dynamic output feedback control with transient performance applied to electrical microgrid

This paper introduces an approach for designing gain-scheduled dynamic output feedback controllers for continuous-time Linear Parameter-Varying (LPV) systems. The aim is to improve transient response by incorporating both D-stability and H∞ criteria into the synthesis conditions. We achieve this by employing changes of variables and congruence transformations, which lead to new synthesis conditions expressed as linear matrix inequalities. Additional fine-tuned scalar parameters can be used to enhance further the controller performance in terms of less conservative H∞ guaranteed costs. To assess the effectiveness of our proposed synthesis procedure, we conduct computational experiments in the context of a microinverter-based distributed power generation system. These experiments take into account real-world operational characteristics, providing a comprehensive evaluation of our approach. The results demonstrate that the designed controller effectively ensures the desired closed-loop system behavior, even when dealing with state noise and discretization errors originating from digital implementation.

Comparative study of variant shifting-phase coding method based on noise error model

Phase shifting algorithm, which is well-known for its high-accuracy and high-resolution, plays a crucial role in fringe projection profilometry. A class of variant shifting-phase coding method (VSPCM) has been proposed for high-speed 3D measurement. The three kinds of VSPCM include the inner shifting-phase method (ISPM), the intensity-coded method (ICM) and the modified Carré algorithm (MCA). These methods project only four patterns avoiding the need for extra images, yet achieve accuracy comparable to the classical three-frequency heterodyne method. In VSPCM, the shifting-phase, as a key code-word for phase unwrapping, is encoded as a variable value. In practice, the noise and the encoding of shifting-phase have varying degrees of impact on the performance of these methods. But the coded ranges of the shifting-phase were previouly empirical values. Besides, there are no studies related to these factors, and also a lack of comparative researches on such methods. This paper compares three kinds of VSPCM for the first time. We derive the noise error models for shifting-phase and wrapped phase in three methods and examine the impact of variant shifting-phase on fringe orders and wrapped phase in these methods. We find that the abnormal shifting-phase errors predominantly cause fringe orders errors. Fortunately, median filtering can effectively handle shifting-phase errors to ensure successful phase unwrapping. To improve their accuracy, we also deduce that the optimal shifting-phase encoding range is [−77°, 77°] for ISPM, and is [−61°, 61°] for ICM, and is [34°, 74°] for MCA. For wrapped phase errors, the distributions of ICM and MCA exhibit a U-shaped distribution, while ISPM demonstrates a uniform distribution. Based on theoretical analysis and experiment results, this paper gives recommendations for better use of these methods to boost their accuracy and offers valuable guidance for selecting an appropriate VSPCM under the specific measurement requirements.

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm (L∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${L}_{{\\infty }}$$\\end{document}) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

Abstract. A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵm≃7×10-4 g2 kg−2 s−1 for mixing ratio and ϵθ≃1.6×10-3 K2 s−1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃10-2 m2 s−3. We also demonstrate that the vertical wind ACF coefficient kw∝w′2‾ and the TKE dissipation ϵ∝w′2‾3/2. For the molecular destruction rates, we show that ϵm∝m′2‾w′2‾1/2 and ϵθ∝θ′2‾w′2‾1/2. These equations can be used for parameterizations of ϵ, ϵm, and ϵθ. All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

Sub-Bin Delayed High-Range Accuracy Photon-Counting 3D Imaging

The range accuracy of single-photon-array three-dimensional (3D) imaging systems is limited by the time resolution of the array detectors. We introduce a method for achieving super-resolution in 3D imaging through sub-bin delayed scanning acquisition and fusion. Its central concept involves the generation of multiple sub-bin difference histograms through sub-bin shifting. Then, these coarse time-resolution histograms are fused with multiplied averages to produce finely time-resolved detailed histograms. Finally, the arrival times of the reflected photons with sub-bin resolution are extracted from the resulting fused high-time-resolution count distribution. Compared with the sub-delayed with the fusion method added, the proposed method performs better in reducing the broadening error caused by coarsened discrete sampling and background noise error. The effectiveness of the proposed method is examined at different target distances, pulse widths, and sub-bin scales. The simulation analytical results indicate that small-scale sub-bin delays contribute to superior reconstruction outcomes for the proposed method. Specifically, implementing a sub-bin temporal resolution delay of a factor of 0.1 for a 100 ps echo pulse width substantially reduces the system ranging error by three orders of magnitude. Furthermore, Monte Carlo simulations allow to describe a low signal-to-background noise ratio (0.05) characterised by sparsely reflected photons. The proposed method demonstrates a commendable capability to simultaneously achieve wide-ranging super-resolution and denoising. This is evidenced by the detailed depth distribution information and substantial reduction of 95.60% in the mean absolute error of the reconstruction results, confirming the effectiveness of the proposed method in noisy scenarios.

Energy efficient noise error pattern generator for guessing decoding in bursty channels

For the hard guessing random additive noise decoding Markov order (GRAND-MO) algorithm, it is crucial to develop an efficient noise error patterns (NEPs) generator to facilitate its application in bursty channels. This paper proposes a practical hardware realization by generating the NEPs in a sequential manner. Based on classification of the four types of NEPs, we propose to iteratively calculate the “1" and the “0" permutations in the same time. Then, the novel “0" permutation regularization and bit flipping techniques are employed, through which the generation of the four types of NEPs is uniformed at the same way. Moreover, the proposed NEPs generator can generate all NEPs by using the “1" burst parameters, and is suitable for the guessing decoding of any linear block codes. Built on field programmable gate array (FPGA) implementation and comparison with existing benchmark, we show the proposed NEPs generator is a power-efficient architecture for realization. This work presents a new solution for the hardware implementation of the NEPs generator in GRAND-MO.

Actuators for Improving Robotic Arm Safety While Maintaining Performance: A Comparison Study

Since robotic arms operating close to people are becoming increasingly common, there is a need to better understand how they can be made safe when unintended contact occurs, while still providing the required performance. Several actuators and methods for improving robot safety are studied and compared in this paper. A robotic arm moving its end effector horizontally and colliding with a person’s head is simulated. The use of a conventional electric actuator (CEA), series elastic actuator (SEA), pneumatic actuator (PA) and hybrid pneumatic electric actuator (HPEA) with model-based controllers are studied. The addition of a compliant covering to the arm and the use of collision detection and reaction strategies are also studied. The simulations include sensor noise and modeling error to improve their realism. A systematic method for tuning the controllers fairly is proposed. The motion control performance and safety of the robot are quantified using root mean square error (RMSE) between the desired and actual joint angle trajectories and maximum impact force (MIF), respectively. The results show that the RMSE values are similar when the CEA, SEA, and HPEA drive the robot’s first joint. Regarding safety, using the PA or HPEA with a compliant covering can reduce the MIF below the safety limit established by the International Organization for Standardization (ISO). To satisfy this ISO safety limit with the CEA and SEA, a collision detection and reaction strategy must be used in addition to the compliant covering. The influences of the compliant covering’s stiffness and the detection delay are also studied.

Fourier-transform-only method for random phase shifting interferometry

An accurate and computationally simple phase shifting interferometry (PSI) method is developed to reconstruct phase maps without a priori knowledge of the phase shift. Previous methods developed for random PSI either do not address general sources of error or require complex iterative processes and increased computational time. Here we demonstrate a novel method that is able to extract the phase using only Fourier transform (FT). With spatial FT analysis, randomly phase-shifted data is reordered to allow performing temporal FT on the intensity, which is a function of the phase shift. Since the entire process, including order analysis and phase calculation, is based only on Fourier analysis, it is rapid, easy to implement, and addresses general sources of error. The method exhibits high performance in experiments containing random phase shifts. Moreover, simulations incorporating common experimental error sources such as random intensity noise, intensity harmonics, and phase shift errors demonstrate that the proposed method performs as good as or better than the state-of-the-art phase reconstruction techniques in terms of accuracy and time.

Correction of phase offset errors and quantification of background noise, signal-to-noise ratio, and encoded-displacement uncertainty on DENSE MRI for kinematics of the descending thoracic and abdominal aorta

Displacement encoding with stimulated echoes (DENSE) MRI is a phase contrast technique that allows the encoding of tissue displacement into the phase of the magnetic resonance signal. Recent developments in this technique allow the imaging of relatively thin structures such as the aortic wall. Quantifying background noise associated to DENSE MRI is required to assess the uncertainty of derived displacement measurements and for the design and implementation of adequate noise-reduction techniques. Although noise and error management of cardiac DENSE MRI has been previously studied, developments for aortic applications are scarce. Herein, we evaluate the noise and uncertainty of DENSE MRI scans at three different locations along the descending aorta: the distal aortic arch (DAA), the descending thoracic aorta (DTA), and infrarenal abdominal aorta (IAA). Additionally, we analyze three datasets from in vitro validation experiments with polyvinyl alcohol phantoms. We implement and evaluate the effectiveness of an offset-error correction algorithm and noise filtering techniques on DENSE MRI for aortic motion applications. Our results show that the phase signal of pixels composing the static background was normally distributed, centered on average at 0.003 ± 0.02 rad and − 0.02 ± 0.024 rad for each phase directions, suggesting that background noise is random, isotropic, and DENSE MRI has little offset errors. However, background signal noise significantly increased with elapsed time of the cardiac cycle; and was spatially heterogeneous consistently increased towards the anterior space. Background noise showed no significant differences between the 3 aortic locations and the in vitro experiments. However, SNR depended on the displacement of the region of interest, in consequence it was found significantly larger at DAA (16.7 ± 8.5, p = 0.003) and DTA (15.4 ± 7.6, p = 0.008) than at the IAA (8.0 ± 4.1), but not significantly different than the SNR of in vitro experiments (8.0 ± 3.7), and had an overall average of 13 ± 7. The applied methods significantly reduced the offset error and effect of noise on the estimation of encoded displacements. Finally, this analysis suggests that the implemented DENSE MRI protocol is adequate to assess the motion of healthy human aortas. However, the relative effect of noise increased considerably on the analysis of an ageing or diseased aortas with impaired mobility, calling for further analyses on pathologically stiffened aortas.

Controlling chaotic maps using next-generation reservoir computing.

In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.

The Bi–O edge wavefront sensor

Context. Direct detection of exoplanets around nearby stars requires advanced adaptive optics (AO) systems. High-order systems are needed to reach a high Strehl ratio (SR) in near-infrared and optical wavelengths on future giant segmented-mirror telescopes (GSMTs). Direct detection of faint exoplanets with the European Southern Observatory (ESO) Extremely Large Telescope (ELT) will require some tens of thousands of correction modes. The resolution and sensitivity of the wavefront sensor (WFS) are key requirements for this science case. We present a new class of WFSs, the bi-orthogonal Foucault knife-edge sensors (or Bi–O edge), that is directly inspired by the Foucault knife-edge test. The idea consists of using a beam-splitter producing two foci, each of which is sensed by an edge with a direction orthogonal to the other focus. Aims. We describe two implementation concepts: The Bi–O edge sensor can be realised with a sharp edge and a tip-tilt modulation device (sharp Bi–O edge) or with a smooth gradual transmission over a grey edge (grey Bi–O edge). A comparison of the Bi–O edge concepts and the four-sided classical pyramid wavefront sensor (PWS) gives some important insights into the nature of the measurements. Methods. We analytically computed the photon noise error propagation, and we compared the results to end-to-end simulations of a closed-loop AO system. Results. Our analysis shows that the sensitivity gain of the Bi–O edge with respect to the PWS depends on the system configuration. The gain is a function of the number of control modes and the modulation angle. We found that for the sharp Bi–O edge, the gain in reduction of propagated photon noise variance approaches a theoretical factor of 2 for a large number of control modes and small modulation angle, meaning that the sharp Bi–O edge only needs half of the photons of the PWS to reach similar measurement accuracy. In contrast, the PWS is twice more sensitive than the Bi–O edge in the case of very low order correction and/or large modulation angles. Preliminary end-to-end simulations illustrate some of the results. The grey version of the Bi–O edge opens the door to advanced amplitude filtering, which replaces the need for a tip-tilt modulator while keeping the same dynamic range. We show that an additional factor of 2 in reduction of propagated photon noise variance can be obtained for high orders, such that the theoretical maximum gain of a factor of 4 in photon efficiency can be obtained. A diffractive Fourier model that accurately includes the effect of modulation and control modes shows that for the extreme AO (XAO) system configuration of the ELT, the overall gain will well exceed one magnitude in guide-star brightness when compared to the modulated PWS. Conclusions. We conclude that the Bi–O edge is an excellent candidate sensor for future very high order Adaptive Optics systems, in particular on GSMTs.

Superexchange coupling of donor qubits in silicon

Atomic engineering in a solid-state material has the potential to functionalize the host with novel phenomena. STM-based lithographic techniques have enabled the placement of individual phosphorus atoms at selective lattice sites of silicon with atomic precision. Here we show that by placing four phosphorus donors spaced 10–15 nm apart from their neighbors in a linear chain, one can realize coherent spin coupling between the end dopants of the chain, analogous to the superexchange interaction in magnetic materials. Since phosphorus atoms are a promising building block of a silicon quantum computer, this enables spin coupling between their bound electrons beyond nearest neighbors, allowing the qubits to be separated by 30–45 nm. The added flexibility in architecture brought about by this long-range coupling not only reduces gate densities but can also reduce correlated noise between qubits from local noise sources that are detrimental to error-correction codes. We base our calculations on a full-configuration-interaction technique in the atomistic tight-binding basis, solving the four-electron problem exactly, over a domain of a million silicon atoms. Our calculations show that superexchange can be tuned electrically through gate voltages where it is less sensitive to charge noise and donor-placement errors. Published by the American Physical Society 2024

Space Robot Sensor Noise Amelioration Using Trajectory Shaping.

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller's bandwidth. A variety of input trajectories can be used to drive a controller's attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system's flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whiplash compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.

Text clustering based on pre-trained models and autoencoders.

Text clustering is the task of grouping text data based on similarity, and it holds particular importance in the medical field. sIn healthcare, medical data clustering is a highly active and effective research area. It not only provides strong support for making correct medical decisions from medical datasets but also aids in patient record management and medical information retrieval. With the development of the healthcare industry, a large amount of medical data is being generated, and traditional medical data clustering faces significant challenges. Many existing text clustering algorithms are primarily based on the bag-of-words model, which has issues such as high dimensionality, sparsity, and the neglect of word positions and context. Pre-trained models are a deep learning-based approach that treats text as a sequence to accurately capture word positions and context information. Moreover, compared to traditional K-means and fuzzy C-means clustering models, deep learning-based clustering algorithms are better at handling high-dimensional, complex, and nonlinear data. In particular, clustering algorithms based on autoencoders can learn data representations and clustering information, effectively reducing noise interference and errors during the clustering process. This paper combines pre-trained language models with deep embedding clustering models. Experimental results demonstrate that our model performs exceptionally well on four public datasets, outperforming most existing text clustering algorithms, and can be applied to medical data clustering.

Evaluation of measurement errors in the Patient-Oriented Eczema Measure (POEM) outcome.

The Patient-Oriented Eczema Measure (POEM) is the recommended core outcome instrument for atopic dermatitis (AD) symptoms. POEM is reported by recalling the presence/absence of seven symptoms in the last 7 days. To evaluate measurement errors in POEM recordings due to imperfect recall. Using data from a clinical trial of 247 AD patients aged 12-65 years, we analysed the reported POEM score (r-POEM) and the POEM derived from the corresponding daily scores for the same seven symptoms without weekly recall (d-POEM). We quantified recall error by comparing the r-POEM and d-POEM for 777 patient-weeks collected from 207 patients, and estimated two components of recall error: (1) recall bias due to systematic errors in measurements and (2) recall noise due to random errors in measurements, using a bespoke statistical model. POEM scores have a relatively low recall bias, but a high recall noise. Recall bias was estimated at 1.2 points lower for the r-POEM on average than the d-POEM, with a recall noise of 5.7 points. For example, a patient with a recall-free POEM of 11 (moderate) could report their POEM score anywhere from 5 to 14 (with 95% probability) because of recall error. Model estimates suggested that patients tend to recall itch and dryness more often than experienced (positive bias of less than 1 day), but less often for the other symptoms (bleeding, cracking, flaking, oozing/weeping and sleep disturbance; negative bias ranging 1-4 days). In this clinical trial data set, we found that patients tended to slightly underestimate their symptoms when reporting POEM, with significant variation in how well they were able to recall the frequency of their symptoms every time they reported POEM. A large recall noise should be taken into consideration when interpreting POEM scores.

An improved error cancellation method for high speed FMCW radar based on FPGA platform

AbstractTarget detection of frequency‐modulated continuous wave radar has the problems of high noise and low detection accuracy. The reason is due to the influence of internal hardware and external environment. In response to this issue, this article uses the field programmable gate array platform to perform frequency modulation on the radar module, time‐frequency conversion, autoregression moving average model, inverse proportion function compensation, and frequency domain selection processing on the intermediate frequency in‐phase signals collected by the oscilloscope. The algorithm proposed in this article could effectively reduce noise and system errors. Compared with traditional methods before filtering and variational mode decomposition Hilbert–Huang Transform algorithm, the effective target detection rate of this method is improved by 50%.

Traffic Target Location Estimation Based on Tensor Decomposition in Intelligent Transportation System

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

Estimation of the density for censored and contaminated data

AbstractConsider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), among others. We assume a classical additive measurement error model with Gaussian noise and unknown error variance and a random right censoring scheme. Under this setup, we develop minimal conditions under which the assumed model is identifiable when no auxiliary variables or validation data are available, and we offer a flexible estimation strategy using Laguerre polynomials for the estimation of the error variance and the density of the survival time. The asymptotic normality of the proposed estimators is established, and the numerical performance of the methodology is investigated on both simulated and real data on gestational age.

Structural Dynamic Response Reconstruction Based on Recurrent Neural Network–Aided Kalman Filter

In structural health monitoring (SHM), an important issue is the limited availability of measurement data due to the spatial sparsity of sensors installed on the structure. These measurements are insufficient to accurately depict the actual dynamic behavior and response of the structure. Therefore, full‐field (i.e., every degree of freedom) structural response reconstruction based on sparse measured data has drawn a lot of attention in recent years. Kalman filter (KF) is an effective technology for response reconstruction (also known as state estimation), providing an optimal solution for systems that can be well‐represented by a fully known Gaussian linear state‐space model. This implies that both the process noise and measurement noise follow known zero‐mean Gaussian distribution, which is impractical in many civil engineering applications considering the unavoidable modeling errors and variations of environmental conditions. To address this challenge, a data‐physics hybrid‐driven method, i.e., KalmanNet, is proposed in this study for response reconstruction of partially known systems. By integrating a recurrent neural network (RNN) module into the KF framework, KalmanNet can efficiently learn and compute the Kalman gain using available monitoring data, without any Gaussian assumptions or explicit noise covariance specifications (e.g., covariance matrices of process and measurement noise). Both numerical and experimental investigations are conducted to validate this method. The results demonstrate that under the influence of non‐Gaussian noise and modeling errors, KalmanNet can effectively and accurately reconstruct the structural response from sparse measurements in real‐time and has higher accuracy and robustness compared to traditional KF even with optimal parameter settings.