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Accurately determining if the sample parameters from a natural gas pipeline's sampling system reflect the fluid characteristics of the main pipe has been a significant industry concern for many years. In this paper, samples of natural gas in a horizontal pipeline are investigated. CFD is used in this work and the turbulence is considered in the simulation. Firstly, the critical diameter for particles affected by gravity within such pipeline is determined. And then, the effects of the operation pressure and velocity of sampling branches on sample parameters, and the influence of particle density on these sample parameters, are analyzed. Finally, four different structures of sample branches for natural gas in a horizontal pipeline are compared. It is found that 100 μm is the critical diameter at which particles are affected by gravity; the operating pressure of the sampling branch has a significant impact on the particle mass concentration. The particle density has little impact on the sampling system. Overall, the design of the sampling branches does not cause significant sampling errors. This study provides guidance for optimal sampling in existing natural gas pipelines and enables effective monitoring of particle impurity content and properties in natural gas. [ABSTRACT FROM AUTHOR]

Statistical tests like t test are used to test the hypothesis for comparison of mean between two groups. The result from a t test is used to determine if there a significant difference between the two samples mean, which cannot be attributed to sampling error or to random occurrence. A t test is considered a parametric test, meaning test samples should meet assumptions of normality, equal variances and independence. The article aims to calculate the power of unpaired t test using simulation in Microsoft Excel. [ABSTRACT FROM AUTHOR]

The sampling consensus of 2nd-order multi-agent systems with time-varying topology was investigated based on the constant position difference and the consistent speed. Firstly, the virtual leader was introduced and the sampling consensus problem of multi-agent systems was transformed into the stability problem of the corresponding error system. Secondly, with estimation of the sampling errors, the influence of sampling errors on system consistency was studied. Finally, by virtue of the Lyapunov stability theory, the stability of the constructed error system was analyzed, and a sufficient condition for the stability of the error system was given. The numerical simulation results verify the effectiveness and correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

We consider partially observed binary matched-pair data. We assume that the incomplete subjects are missing at random. Within this missing framework, we propose an EM-algorithm based approach to construct an interval estimator of the proportion difference incorporating all the subjects. In conjunction with our proposed method, we also present two improvements to the interval estimator through some correction factors. The performances of the three competing methods are then evaluated through extensive simulation. Recommendation for the method is given based on the ability to preserve type-I error for various sample sizes. Finally, the methods are illustrated in two real-world data sets. An R-function is developed to implement the three proposed methods. [ABSTRACT FROM AUTHOR]

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment. [ABSTRACT FROM AUTHOR]

The angular method (AS) cannot be used in long-distance propagation because it produces severe numerical errors due to the sampling problem in the transfer function. Two ways can solve this problem in AS for long-distance propagation. One is zero-padding to make sure that the calculation window is wide enough, but it leads to a huge calculation burden. The other is a method called band-limited angular spectrum (BLAS), in which the transfer function is truncated and results in that the calculation accuracy decreases as the propagation distance increases. In this paper, a new method called modified scaling angular spectrum (MSAS) to solve the problem for long-distance propagation is proposed. A scaling factor is introduced in MSAS so that the sampling interval of the input plane can be adjusted arbitrarily unlike AS whose sampling interval is restricted by the detector's pixel size. The sampling interval of the input plane is larger than the detector's pixel size so the size of calculation window suitable for long-distance field propagation in the input plane is smaller than the size of the calculation window required by the zero-padding. Therefore, the method reduces the calculation redundancy and improves the calculation speed. The results from simulations and experiments show that MSAS has a good signal-to-noise ratio (SNR), and the calculation accuracy of MSAS is better than BLAS. [ABSTRACT FROM AUTHOR]

This paper proposes observer-based piecewise multi-linear controllers for nonlinear systems using feedback and observer linearizations. The piecewise model is a nonlinear approximation and fully parametric. Feedback linearizations are applied to stabilize the piecewise multi-linear control system. Furthermore, observer linearizations are more conservative in modeling errors compared with feedback linearizations. In this paper, we propose robust observer designs for piecewise multi-linear systems. Moreover, we design piecewise multi-linear controllers that combine the robust observer with various performance such as a regulator and tracking controller. These design methods realize a separation principle that allows an observer and a regulator to be designed separately. Examples are demonstrated through computer simulation to confirm the feasibility of our proposals. [ABSTRACT FROM AUTHOR]

In an on-demand testing program, some items are repeatedly used across test administrations. This poses a risk to test security. In this study, we considered a scenario wherein a test was divided into two subsets: one consisting of secure items and the other consisting of possibly compromised items. In a simulation study of multistage adaptive testing, we used three methods to detect item preknowledge: a predictive checking method (PCM), a likelihood ratio test (LRT), and an adapted Kullback–Leibler divergence (KLD-A) test. We manipulated four factors: the proportion of compromised items, the stage of adaptive testing at which preknowledge was present, item-parameter estimation error, and the information contained in secure items. The type I error results indicated that the LRT and PCM methods are favored over the KLD-A method because the KLD-A can experience large inflated type I error in many conditions. In regard to power, the LRT and PCM methods displayed a wide range of results, generally from 0.2 to 0.8, depending on the amount of preknowledge and the stage of adaptive testing at which the preknowledge was present. [ABSTRACT FROM AUTHOR]

For passage-based tests, items that belong to a common passage often violate the local independence assumption of unidimensional item response theory (UIRT). In this case, ignoring local item dependence (LID) and estimating item parameters using a UIRT model could be problematic because doing so might result in inaccurate parameter estimates, which, in turn, could impact the results of equating. Under the random groups design, the main purpose of this article was to compare the relative performance of the three-parameter logistic (3PL), graded response (GR), bifactor, and testlet models on equating passage-based tests when various degrees of LID were present due to passage. Simulation results showed that the testlet model produced the most accurate equating results, followed by the bifactor model. The 3PL model worked as well as the bifactor and testlet models when the degree of LID was low but returned less accurate equating results than the two multidimensional models as the degree of LID increased. Among the four models, the polytomous GR model provided the least accurate equating results. [ABSTRACT FROM AUTHOR]

Polynomial interpolation is widely used in channel modeling for aerospace tracking, telemetry, and command channel simulators, to accurately regenerate the satellite motion parameters with a high sampling rate from the available low sampling rate motion data. In this paper, a novel piecewise osculating interpolation approach is proposed to improve modeling precision. It uses the available distance and its higher-order derivatives at each pair of adjacent knots to construct the piecewise interpolation polynomial, thus achieving a better precision and continuity of the resulting motion parameters. Moreover, it is efficiently implemented with an interpolation filter based on a modified Farrow structure, and the designed filter can interpolate the motion data obtained both through uniform or nonuniform sampling. Additionally, a sampling optimization algorithm is proposed. It optimizes the motion data sampling by minimizing the maximum interpolation error given the average sampling interval and interpolation polynomial order, and therefore, improves the precision further without increasing the costs. The validity of the proposed approach is verified by numerical simulation, and its advantages are demonstrated by comparison with existing methods. [ABSTRACT FROM AUTHOR]

In this paper, heading reference-assisted pose estimation (HRPE) has been proposed to compensate inherent drift of visual odometry (VO) on ground vehicles, where an estimation error is prone to grow while the vehicle is making turns or in environments with poor features. By introducing a particular orientation as “heading reference,” a pose estimation framework has been presented to incorporate measurements from heading reference sensors into VO. A graph formulation is then proposed to represent the pose estimation problem under the commonly used graph optimization model. Simulations and experiments on KITTI data set and our self-collected sequences have been conducted to verify the accuracy and robustness of the proposed scheme. KITTI sequences and manually generated heading measurement with Gaussian noises are used in simulation, where rotational drift error is observed to be bounded. Compared with a pure VO, the proposed approach greatly reduces average translational localization error from 153.85 to 24.29 m and 23.80 m in self-collected stereo visual sequences with traveling distance over 4.5 km at the processing rates of 19.7 and 11.1 Hz, for the loosely coupled and tightly coupled models, respectively. Note to Practitioners—When the Global Positioning System is not available or reliable, visual odometry (VO) on ground vehicles is an efficient tool for estimating the pose, which involves translation and rotation. However, VO inherently suffers from drifting issue due to constant iterations. By adding a low-cost heading reference sensor, this paper first introduces graph optimization formulation of pose estimation and then presents a pose estimation framework which incorporates heading measurements to VO such that long-term translation and rotation estimation errors can be greatly reduced in real-time computation. As a supplementary to VO, performance may still deteriorate in environments with poor illumination conditions and high-frequency movements. The proposed approach may be further improved by fusing heading measurements from more sensors or being used to build a heading reference-assisted simultaneous localization and mapping (SLAM) system on any off-the-shelf SLAM framework. [ABSTRACT FROM AUTHOR]

Abstract Online sequential extreme learning machine (OS-ELM) learns data one-by-one or chunk-by-chunk, and the recursive least square (RLS) algorithm is commonly employed to train the topological parameters of OS-ELM. Since it is hard to guarantee the smallest estimation error of the state variable by the RLS, the regression performance of the OS-ELM easily fluctuates in practical applications. To address this gap, a new training approach of the OS-ELM using Kalman filter called KFOS-ELM is proposed, and state propagation is combined into extreme learning process to obtain the OS-ELM's topological parameters. Besides, an adaptive-weighted ensemble mechanism is developed and used to dynamically tune the weight coefficients of each KFOS-ELM in the learning network. The regression performance of the proposed methodology is evaluated using benchmark datasets. The simulation results show that proposed methods are superior to the OS-ELM and EOS-ELM in terms of the regression accuracy and stability without additional computational efforts. Furthermore, an enhanced multi-sensor prognostic model based on KFOS-ELM and logistic regression (LR) model is designed for remaining useful life (RUL) prediction of aircraft engine. The experimental results confirm our viewpoints. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the state of a linear time-invariant Gaussian system using $N$ sensors, where a subset of the sensors can potentially be compromised by an adversary. In this case, locating the compromised sensors is of crucial importance for obtaining an accurate state estimate. Inspired by the clustering algorithm in machine learning, we propose a Gaussian-mixture-model-based (GMM-based) detection mechanism. It clusters the local state estimate autonomously and provides a belief for each sensor, based on which measurements from different sensors can be fused accordingly. When a subset of the sensors are under the optimal innovation-based deception attacks, we derive the remote estimation error covariance recursions under different detection mechanisms, e.g., distributed $\chi ^2$ false-data detector, centralized $\chi ^2$ false-data detector, and GMM-based detection algorithm. The performance of the proposed GMM-based detection algorithm is further evaluated through average belief in the same attack scenario. Moreover, we discuss applications of GMM-based detection algorithm on other attack scenarios, e.g., false-data injection attack, replay attack, and $\epsilon$ -stealthy attack. Simulation examples are provided to demonstrate the developed results. [ABSTRACT FROM AUTHOR]

Abstract The quest to sample and quantify a gold deposit depends on the ability to collect a representative sample and then maintain the lot's constitution throughout all sampling steps. Differences in ore type, grade, gold size, distribution, liberation and association are some variables which implies differences in procedures from one deposit to another. In a low-grade nugget environment, the final analysis depends more on the chance occurrence of a particle in the analytical aliquot rather than the actual concentration in the ore. A commonly used 30 g fire assay could result in bad exploration decisions and create a highly skewed database. The concept of equant grains simplifies particle size and distribution, and works with a uniform particle that represents the total content divided by the number of grains necessary to attain a certain precision. In this paper, we test this hypothesis in which 20 and 10 equant grains are used to simulate the grade values of six different analytical samples sizes, representing the smoky quartz of Lamego Mine. The results confirm that a 30 g final aliquot does not represent the rock and a 500 or 1000 g analytical sample is required to be assayed. Highlights • Use of equant grain size (EGS) to validate analytical sample size. • Two simulations using different EGS and sample sizes. • Simulation can replicate data set variability and be useful for selecting optimum sample mass. [ABSTRACT FROM AUTHOR]

This letter proposes a structured approach for accurate estimation of diode reverse-recovery characteristics using datasheet specifications. Considering a conductance model of a diode, the entire reverse-recovery process is divided into four operating modes. The state equations for each mode are derived taking the circuit nonidealities, viz., device capacitance and parasitic inductances, into account. Analytical expressions for reverse-recovery charge, reverse-recovery time, and diode current are derived and numerically solved to estimate the diode conductance directly from manufacturer's data. An empirical model is proposed to characterize the effects of temperature variation on the diode conductance. Numerical simulation and experimental results are presented for antiparallel diodes of three MOSFETs having different current and voltage ratings and also for a discrete p-n diode at different junction temperatures. The maximum estimation error with the proposed approach is 5.1% for peak reverse-recovery current and less than 8% for reverse-recovery time, peak voltage, and energy loss. [ABSTRACT FROM AUTHOR]

Abstract MIMO and MIMO-OFDM systems are the enabling technologies for the next generation wireless systems. However, the radio signals transmitted are available for reception by a illegitimate user leading to eavesdropping. This raises severe concerns on the security of the transmission. Physical Layer security aims to exploit the wireless channel characteristics and in turn provide secure transmission robust against eavesdropping. In this paper, we investigate the Secret Key Capacity (SKC) in MIMO-OFDM systems. We derive expressions for SKC in MIMO systems and extend further to OFDM waveforms. Numerical simulations are performed for various transmit and receive antenna configurations without any estimation errors. [ABSTRACT FROM AUTHOR]

In Gaussian measurement models, the measurements are given by a known function of the unknown parameter vector, contaminated by additive zero-mean Gaussian noise. When the function is linear, the resulting maximum likelihood estimate (MLE) is well-known to be efficient [unbiased, with a mean square estimation error (MSE) matrix attaining the Cramér–Rao lower bound (CRLB)]. However, when the function is nonlinear, the MLE is only asymptotically efficient. The classical derivation of its asymptotic efficiency uses a first-order perturbation analysis, relying on a “small-errors” assumption, which under subasymptotic conditions turns inaccurate, rendering the MLE generally biased and inefficient. Although a more accurate (higher-order) performance analysis for such cases is of considerable interest, the associated derivations are rather involved, requiring cumbersome notations and indexing. Building on the recent assimilation of tensor computations into signal processing literature, we exploit the tensor formulation of higher-order derivatives to derive a tractable formulation of a higher (up to third-) order perturbation analysis, predicting the bias and MSE matrix of the MLE of parameter vectors in general nonlinear models under subasymptotic conditions. We provide explicit expressions depending on the first three derivatives of the nonlinear measurement function, and demonstrate the resulting ability to predict the “efficiency gap” (relative excess MSE beyond the CRLB) in simulation experiments. We also provide MATLAB code for easy computation of our resulting expressions. [ABSTRACT FROM AUTHOR]

A novel estimation scheme that combines Bayesian and lower bound estimating radio frequency identification tag population size is proposed. The developed methodology is based on the fusion between the Bayesian and lower bound estimating techniques. It turns out that the fusion rule is built up thanks to an existing linear relationship between the cited techniques. Simulation results show that the developed technique significantly improves the accuracy of the estimating tag quantity and presents less estimation error. Also, the resulting advanced dynamic framed slotted ALOHA protocol considerably improves the performance and efficiency of the radio frequency identification anti-collision compared with the most recent protocols using others estimating methods. [ABSTRACT FROM AUTHOR]

Aligning scales in vertical equating carries a number of challenges for practitioners in contexts such as large-scale testing. This paper examines the impact of high and low discrimination on the results of vertical equating when the Rasch model is applied. A simulation study is used to show that different levels of discrimination introduce systematic error into estimates. A second simulation study shows that for the purpose of vertical equating, items with high or low discrimination contain information about translation constants that contains systematic error. The impact of differential item discrimination on vertical equating is examined and subsequently illustrated in terms of a real data set from a large-scale testing program, with vertical links between grade 3 and 5 numeracy tests. Implications of the results for practitioners conducting vertical equating with the Rasch model are identified, including monitoring progress over time. Implications for other item response models are also discussed. [ABSTRACT FROM AUTHOR]

Purpose: Multishot interleaved echo‐planar imaging (iEPI) can achieve higher image resolution than single‐shot EPI for diffusion tensor imaging (DTI), but its application is limited by the prolonged acquisition time. To reduce the acquisition time, a novel model‐based reconstruction for simultaneous multislice (SMS) and parallel imaging (PI) accelerated iEPI DTI is proposed. Materials and methods: DTI datasets acquired by iEPI with SMS and PI acceleration can be regarded as 3D k‐space data, which is undersampled along both the slice and phase encoding directions. Instead of reconstruction of individual diffusion‐weighted image, diffusion tensors are directly estimated by the joint reconstruction of undersampled 3D k‐space from all diffusion‐encoding directions using a model‐based formulation to exploit the correlation across different directions. DTI simulation and in vivo acquisition were used to demonstrate the superior performance of the proposed method. Results: The proposed method reduced the estimation errors and artifacts than traditional parallel imaging reconstruction in DTI simulation. In the in vivo DTI experiment, the acquisition time of 4‐shot iEPI was reduced from 11 min 7 s to 3 min 53 s with an acceleration factor of 4, and the image quality and precision of quantitative parameters were comparable with the fully sampled acquisition. Conclusions: The proposed model‐based reconstruction for iEPI DTI with SMS and PI can achieve fourfold acceleration while maintaining high accuracy for tensor measurements. [ABSTRACT FROM AUTHOR]

In this paper we compare two different methods of estimating the error variances of two or more independent data sets. One method, called the "three-cornered hat" (3CH) method, requires three data sets. Another method, which we call the "two-cornered hat" (2CH) method, requires only two data sets. Both methods have been used in previous studies to estimate the error variances associated with a number of physical and geophysical data sets. A key assumption in both methods is that the errors of the data sets are not correlated, although some studies have considered the effect of the partial correlation of representativeness errors in two or more of the data sets. We compare the 3CH and 2CH methods using a simple model to simulate three and two data sets with various error correlations and biases. With this model, we know the exact error variances and covariances, which we use to assess the accuracy of the 3CH and 2CH estimates. We examine the sensitivity of the estimated error variances to the degree of error correlation between two of the data sets as well as the sample size. We find that the 3CH method is less sensitive to these factors than the 2CH method and hence is more accurate. We also find that biases in one of the data sets has a minimal effect on the 3CH method, but can produce large errors in the 2CH method. [ABSTRACT FROM AUTHOR]

Many recently developed data-driven fault estimation methods are restricted to minimum-phase systems so that their practical applications are limited. In this paper, the data-driven fault estimation for non-minimum phase (NMP) systems is studied, for which the main difficulty is that the unstable zeros of an NMP system will result in a growing fault-estimation error. To deal with this problem, the inverse of an NMP system is equivalently formulated as a mixed causal and anti-causal system, and the proposed fault estimator is the sum of a stable causal filter and a stable anti-causal filter. The proposed fault estimator is shown to be asymptotically unbiased and its performance is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks. [ABSTRACT FROM AUTHOR]

In this paper, a algorithm which is used for carrier frequency offset (CFO) estimation is proposed in coherent optical communications system with binary phase-shift keying (BPSK) modulation. The proposed algorithm utilizes the frequency divider to broaden the range of the estimation algorithm. In a 5-GBaud coherent BPSK system, simulation results show that when 12dB signal-to-noise ratio (SNR) and 512 samples are utilized, a 8 frequency divider can expand the range of the frequency offset estimation from [-0.5GHz, +0.5GHz] to [-4GHz, +4GHz] and the CFO estimation error can be obtained less than 5MHz. The paper presents a novel algorithm that features the large estimation range for carrier frequency offset while estimation error is not enlarged. The experimental results verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We study the square‐root LASSO method for high‐dimensional sparse linear models with weakly dependent errors. The asymptotic and non‐asymptotic bounds for the estimation errors are derived. Our results cover a wide range of weakly dependent errors, including α‐mixing, ρ‐mixing, ϕ‐mixing, and m‐dependent types. Numerical simulations are conducted to show the consistency property of square‐root LASSO. An empirical application to financial data highlights the importance of the results and method. [ABSTRACT FROM AUTHOR]

In this article, we propose an automatic bandwidth selection of the recursive kernel density estimators with missing data in the context of global and local density estimation. We showed that, using the selected bandwidth and a special stepsize, the proposed recursive estimators outperformed the non recursive ones in terms of estimation error in the case of global estimation. However, the recursive estimators are much better in terms of computational costs. We corroborated these theoretical results through simulation studies and on the simulated data of the Aquitaine cohort of-1-infected patients and on the Coriell cell lines using the chromosome number 11. [ABSTRACT FROM AUTHOR]

This paper proposes a globally and exponentially convergent predictive observer for attitude and position estimation based on landmark measurements and velocity (angular and linear) readings. It is assumed that landmark measurements are available with time-delay. The maximum value of the sensor delay under which the estimation error converges to zero is calculated. Synthesis of the observer is based on a representation of rigid-body kinematics and sensor delay, formulated via ordinary and partial differential equations (ODE-PDE). Observability condition specifies necessary and sufficient landmark configuration for convergence of attitude and position estimation error to zero. Finally, for implementation purposes, a PDE-free realization of the predictive observer is proposed. Simulation results are presented to demonstrate performance and convergence properties of the predictive observer in case of a wheeled mobile robot. [ABSTRACT FROM AUTHOR]

Real-time information about vehicle mass and road slope is important for vehicle handling and stability control. This paper proposes a two-layer based adaptive parameter estimator to estimate the vehicle mass and road slope under longitudinal moving condition. In the first layer of the estimator, the slowly changed vehicle parameter, such as vehicle mass or road slope on the smooth road, is estimated by a computationally efficient adaptive law. Then in the second layer of the estimator, an online adaptive observer is applied to obtain the real-time information of the road slope on the uneven road. The proposed estimator is first verified by the numerical simulations. A scaled remote control car is then built to conduct the experimental tests. By using the measured data from the scaled vehicle, the proposed parameter estimator is validated to be able to estimate the vehicle mass and road slope in the real experimental conditions. In order to clearly present the estimation performance and compare estimation results, the root mean square (RMS) values and the normalised root mean square (NRMS) values of the estimation errors are calculated. The experimental results indicate that the obtained average RMS values of vehicle mass estimation error and road slope estimation error are 0.44 and 4.06, respectively. Road slope estimation shows bigger estimation error due to the severer vibration caused by climbing up the steep road. [ABSTRACT FROM AUTHOR]

Multiscale computational models strive to produce accurate and efficient numerical simulations of systems involving interactions across multiple spatial and temporal scales that typically differ by several orders of magnitude. Some such models utilize a hybrid continuum-atomistic approach combining continuum approximations with first-principles-based atomistic models to capture multiscale behavior. Following the heterogeneous multiscale method framework for developing multiscale computational models, unknown continuum scale data can be computed from an atomistic model. Concurrently coupling the two models requires performing numerous atomistic simulations which can dominate the computational cost of the method. When the resulting continuum data is noisy due to sampling error, stochasticity in the model, or randomness in the initial conditions, filtering can result in significant accuracy gains in the computed multiscale data without increasing the size or duration of the atomistic simulations. In this work, we demonstrate the effectiveness of spectral filtering for increasing the accuracy of noisy multiscale data obtained from atomistic simulations. Moreover, we present a robust and automatic method for closely approximating the optimum level of filtering in the case of additive white noise. The improved accuracy of this filtered simulation data leads to a dramatic computational savings by allowing for shorter and smaller atomistic simulations to achieve the same desired multiscale simulation precision. [ABSTRACT FROM AUTHOR]

The problem of differentiating a function with bounded second derivative in the presence of bounded measurement noise is considered in both continuous-time and sampled-data settings. Fundamental performance limitations of causal differentiators, in terms of the smallest achievable worst-case differentiation error, are shown. A robust exact differentiator is then constructed via the adaptation of a single parameter of a linear differentiator. It is demonstrated that the resulting differentiator exhibits a combination of properties that outperforms existing continuous-time differentiators: it is robust with respect to noise, it instantaneously converges to the exact derivative in the absence of noise, and it attains the smallest possible—hence optimal—upper bound on its differentiation error under noisy measurements. For sample-based differentiators, the concept of quasi-exactness is introduced to classify differentiators that achieve the lowest possible worst-case error based on sampled measurements in the absence of noise. A straightforward sample-based implementation of the proposed linear adaptive continuous-time differentiator is shown to achieve quasi-exactness after a single sampling step as well as a theoretically optimal differentiation error bound that, in addition, converges to the continuous-time optimal one as the sampling period becomes arbitrarily small. A numerical simulation illustrates the presented formal results. [ABSTRACT FROM AUTHOR]

High frequency (HF) signal injection is an effective sensorless control scheme for interior permanent-magnet synchronous motor (IPMSM) drives to achieve low and zero speed operation. However, the audible noise produced by the injected HF signal is often very shrill and harsh to hear, which restricts the actual application. In order to reduce the audible noise, a novel pseudo-random HF square-wave voltage injection scheme is proposed in this paper. The HF voltages with two different frequencies are randomly injected into the estimated rotor reference frame cycle by cycle, and a corresponding signal demodulation method for extracting the rotor position information is presented. Based on the principle analysis of this random frequency injection scheme, the digital time-delay effect in HF signal is considered and a compensation method for signal demodulation is proposed, which is effective in reducing position estimation error. Then, the power spectra density (PSD) in fixed frequency and pseudo-random frequency injection schemes are compared both theoretically and experimentally. The distribution of HF voltage and current PSD is extended by using the proposed injection scheme. Finally, this sensorless control method is verified by simulation and experiment on a 2.2-kW IPMSM drive platform. [ABSTRACT FROM AUTHOR]

In this paper, the discrete-time exponential forgetting factor observer is specialised for the joint estimation of state and parameters, leading to an adaptive observer in which the decay rates of the estimation errors for the two distinct objects can be quasi-separated. The effectiveness of the observer is demonstrated through numerical simulation. [ABSTRACT FROM AUTHOR]

Power estimation has become a strongly desired feature in Electronic System Level (ESL) simulations. Most existing power estimation approaches for this abstraction level require component models with observable internals. However, most ESL models of modern processors are delivered as black box components. This work presents a tool-based ESL power estimation methodology for black box models and its extension for multiple clock frequencies. The evaluation uses hardware measurements of the ARM Cortex-A9 subsystem of the OMAP4460 chip for reference. The achieved estimation error is 5% on average for fixed-frequency power models and 7% for multifrequency power models. [ABSTRACT FROM AUTHOR]

In a recent work by the authors the concept of Fading Gaussian Deterministic filter was investigated. The algorithm is based on a set of equations derived from the minimization of a cost function where earlier data are progressively de-weighted by a fading factor. In such a way, the estimation was proved to be less prone to problem unknowns. A tuning procedure was proposed that allows the resulting globally best estimator to evaluate the covariance of an effective measurement noise and the true estimation error, without any a-priori assumption. In the present paper, a general formulation is derived where the observed system is influenced by a control input. Also, a proof is derived for the proposed tuning criterion, which is shown to provide, under certain assumptions, the fading factor that best dampens the modeling errors with respect to measurement noise. The validity of the proposed approach is investigated by means of both numerical simulations and an experimental campaign, where height estimation is performed by fusing information from MEMS accelerometers and a barometric altimeter. [ABSTRACT FROM AUTHOR]

We present upper bounds for the numerical errors introduced when using operator splitting methods to integrate transport and non-linear chemistry processes in global chemical transport models (CTM). We show that (a) operator splitting strategies that evaluate the stiff non-linear chemistry operator at the end of the time step are more accurate, and (b) the results of numerical simulations that use different operator splitting strategies differ by at most 10%, in a prototype one-dimensional non-linear chemistry–transport model. We find similar upper bounds in operator splitting numerical errors in global CTM simulations. [ABSTRACT FROM AUTHOR]

A novel extended state observer, which feeds back the output estimation error via both nonlinear and switching terms, is put forward for the first time in this paper. No longer neglecting the lumped uncertainty׳s first time derivative, the problem of disturbance observer design is transformed into the problem of state observer design in the presence of external disturbance. The switching term of the output estimation error is employed to counteract the adverse effect of external disturbance. The newly developed extended state observer provides an attractive solution to the issue of high precision motion control system. Both numerical simulation and experimentation on a speed turntable with temperature box are implemented to verify the performance of the proposed newly developed extended state observer. [ABSTRACT FROM AUTHOR]

This work studies vector representation in the attitude filtering problem. Because vector coordinates must be estimated in lieu of the abstract vector itself, the choice of representation is important. A challenge arises in attitude estimation when the coordinates of a vector are sought with respect to the attitude state coordinate frame. Although a wealth of research examines filtering with attitude state constraints or discontinuities, this work focuses on vector state estimation with respect to the attitude state frame. Specifically, an alternative vector state error is defined using common coordinates over all vector error realizations. A modified extended Kalman filter is developed with this error realization as its foundation. Numerical simulations are conducted, and results obtained using the new filter are compared with those generated by employing a standard estimate error definition. [ABSTRACT FROM AUTHOR]

Tracking a mobile object presents many challenges, especially when the tracked object is autonomous or semiautonomous and may move unpredictably. The use of autonomous mobile sensor systems allows for greater opportunity to track the mobile object but does not always yield an estimate of the tracked object’s location that minimizes the estimation error. This paper presents a methodology to optimize the sensor system locations, given a single object and a fixed number of sensor systems, to achieve a position estimate that minimizes the estimation error. The tracking stations may then be controlled to achieve and maintain this optimal position, under position constraints. The theory predicts that given n sensor systems and one object there is a sensor system configuration that will yield a position estimate that minimizes the estimation error. A mathematical basis for this theory is presented and simulation and experimental results for two and three sensor system cases are shown to illustrate the effectiveness of the theory in the laboratory. [ABSTRACT FROM AUTHOR]

Covariance-based DoA estimation (CB-DoA) algorithms represent lower computational complexity alternatives to the traditional ESPRIT approach. This paper investigates CB-DoA using Krylov-subspace techniques (including Arnoldi's and Lanczos' updates) with respect to the resulting computational cost and estimation error performance. The proposed modifications also allow an automatic estimation of the number of sources. Computational analyses performed for the resulting CB-DoA algorithm indicate cost savings above $$60\,\%$$ in comparison with the standard CB-DoA implementation, which already represents a $$20\,\%$$ improvement upon its ESPRIT counterpart, at an equivalent mean squared error level, as verified in numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the Unscented Predictive Filter (UPF) is derived based on unscented transformation for nonlinear estimation, which breaks the confine of conventional sigma-point filters by employing Kalman filter as subject investigated merely. In order to facilitate the new method, the algorithm flow of UPF is given firstly. Then, the theoretical analyses demonstrate that the estimate accuracy of the model error and system for the UPF is higher than that of the conventional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of Unscented Predictive Filter (UPF) for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system׳s initial estimation error, disturbing noise terms as well as the model error are small enough, which is the core part of the UPF theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system. [ABSTRACT FROM AUTHOR]

Rate control plays an important role in the rapid development of high-fidelity video services. As the High Efficiency Video Coding (HEVC) standard has been finalized, many rate control algorithms are being developed to promote its commercial use. The HEVC encoder adopts a new R-lambda based rate control model to reduce the bit estimation error. However, the R-lambda model fails to consider the frame-content complexity that ultimately degrades the performance of the bit rate control. In this letter, a gradient based R-lambda (GRL) model is proposed for the intra frame rate control, where the gradient can effectively measure the frame-content complexity and enhance the performance of the traditional R-lambda method. In addition, a new coding tree unit (CTU) level bit allocation method is developed. The simulation results show that the proposed GRL method can reduce the bit estimation error and improve the video quality in HEVC all intra frame coding. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of locating a target node (TN) moving along a corridor in a large industrial environment by means of ultrawide band signaling from fixed anchor nodes (ANs) uniformly positioned at the same height on both sides of the corridor. For a representative geometry of a large indoor (industrial) scenario, we formulate an analytical approach to the optimized placement (in terms of internode distance) of ANs using the criterion of minimizing the average mean square error (MSE) in the time-difference-of-arrival-based estimated positions of the TN. Under the assumption of a fixed variance of the range estimation error, we derive a simple closed-form expression for the optimal inter-AN distance in terms of the corridor width and the height of the ANs. The effectiveness of the analytical approach is confirmed by simulations. We also show that the proposed approach allows the MSE in the TN position estimates to reach the Cramer Rao lower bound. [ABSTRACT FROM AUTHOR]

Recent studies have provided that the vehicle trajectories generated by car-following models may not represent the real driving characteristics, thus leading to significant emission estimation errors. In this paper, two of the most widely used car-following models, Wiedemann and Fritzsche models, were selected and analyzed based on the massive field car-following trajectories in Beijing. A numerical simulation method was designed to generate the following car’s trajectories by using the field trajectories as the input. By comparing the simulated and the filed data, the representativeness of the simulated regime fractions and VSP distributions were evaluated. Then, the mechanism of car-following models was investigated from the aspects of regime determination and the acceleration rule in each regime. Further, the regime threshold parameters and acceleration model were optimized for emission estimations. This study found that the “Following” regime threshold of SDX and the maximum acceleration in “Free Driving” regime are critical parameters for Wiedemann model. The differences between the Wiedemann simulated VSP distribution and the field one can be reduced separately by applying the optimized SDX and maximum acceleration model individually. However, a much sharper reduction was observed by optimizing both parameters simultaneously, and the emission estimation errors were further reduced, which were less than 4% in the case studies. Fritzsche model generated more realistic VSP distributions and emissions, while the maximum accelerations could be further optimized for high speed conditions. [ABSTRACT FROM AUTHOR]

Counting or estimating the number of tags is crucial for RFID system. Researchers have proposed several fast cardinality estimation schemes to estimate the quantity of a batch of tags within a short time frame. However, existing estimation schemes scarcely consider the privacy issue. Without effective protection, the adversary can utilize the responding signals to estimate the number of tags as accurately as the valid reader. To address this issue, we propose a novel privacy-preserving estimation scheme, termed as MEAS, which provides an active RF countermeasure against the estimation from invalid readers. MEAS comprises of two components, an Estimation Interference Device (EID) and two well-designed Interference Blanking Estimators (IBE). EID is deployed with the tags to actively generate interfering signals, which introduce sufficiently large estimation errors to invalid or malicious readers. Using a secret interference factor shared with EID, a valid reader can perform accurate estimation via two IBEs. Our theoretical analysis and simulation results show the effectiveness of MEAS. Meanwhile, MEAS can also maintain a high estimation accuracy using IBEs. [ABSTRACT FROM AUTHOR]

This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation. [ABSTRACT FROM AUTHOR]