Search

We study the list decodability of different ensembles of codes over the real alphabet under the assumption of an omniscient adversary. It is a well-known result that when the source and the adversary have power constraints $P $ and $N $ respectively, the list decoding capacity is equal to $\frac {1}{2}\log \frac {P}{N}$. Random spherical codes achieve constant list sizes, and the goal of the present paper is to obtain a better understanding of the smallest achievable list size as a function of the gap to capacity. We show a reduction from arbitrary codes to spherical codes, and derive a lower bound on the list size of typical random spherical codes. We also give an upper bound on the list size achievable using nested Construction-A lattices and infinite Construction-A lattices. We then define and study a class of infinite constellations that generalize Construction-A lattices and prove upper and lower bounds for the same. Other goodness properties such as packing goodness and AWGN goodness of infinite constellations are proved along the way. Finally, we consider random lattices sampled from the Haar distribution and show that if a certain conjecture that originates in analytic number theory is true, then the list size grows as a polynomial function of the gap-to-capacity. [ABSTRACT FROM AUTHOR]

This article addresses the opportunistic use of satellite downlink attenuation measurements for the site-specific estimation of rainfall rate. In particular, this article proposes a regression model characterized by the introduction of correction elements extracted from the frequency-domain analysis of the attenuation time series, and the use of a simple linear regression function with a small modification, which allows to using a negative intercept with the goal of minimizing the maximum error (large underestimation/overestimation). The performance, assessed using attenuation measurements of a satellite downlink in Q/V band shows that the proposed model allows to estimate the rainfall rate with low average error and low maximum error with respect to a more standard ITU model, so as to offer more reliable estimations. [ABSTRACT FROM AUTHOR]

Objective: Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. Methods: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. Results: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. Conclusion: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. Significance: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map. [ABSTRACT FROM AUTHOR]

This article proposes a proportional-integral (PI) event-triggered control strategy for networked nonlinear systems subject to unmatched uncertainties. The influences of the unmatched uncertainties and external disturbances on the output channel are attenuated by an extended-state-observer-based output feedback controller through a state transformation. The outputs of the controller are transmitted in an event-triggered fashion by comparing the difference between the current sampling error and its integration with respect to time. We prove the stability of the closed-loop event-triggered control system by regulating the trajectories of the observation and tracking errors into a compact set. Further, the relationships between the control performance and designed parameters are investigated quantitatively. In addition, Zeno-freeness is guaranteed by proving the existence of a nonzero minimum inter-event time. Finally, the effectiveness of the proposed PI-type event-triggered control strategy in reducing signal transmission rate is validated through numerical examples and various experiments. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator. We study the mean squared error as a function of the sample size (and hence the number of bits). We consider three settings: first, a centralized setting, where an encoder may release $n$ bits given a sample of size $n$ , and for which there is no asymptotic penalty for quantization; second, an adaptive setting in which each bit is a function of the current observation and previously recorded bits, where we show that the optimal relative efficiency compared to the sample mean is precisely the efficiency of the median; lastly, we show that in a distributed setting where each bit is only a function of a local sample, no estimator can achieve optimal efficiency uniformly over the parameter space. We additionally complement our results in the adaptive setting by showing that one round of adaptivity is sufficient to achieve optimal mean-square error. [ABSTRACT FROM AUTHOR]

Point cloud is a discrete and unordered expression of 3D data. A lot of methods have been proposed to solve the problem in 3D object classification and scene recognition. To handle the huge amount of unordered point cloud, down-sampling before processing is needed. The shortage of existing sampling methods is the lack of geometry information consideration, which is essential for point cloud classification and segmentation tasks. Our method is mainly motivated by the observation that points with a high curvature variation can depict the outlines of objects. Thus, we propose a curvature variation based sampling method for point cloud classification and segmentation tasks. We aim to sample points with high curvature variations, which are considered to be more suitable for classification and segmentation tasks than the traditional sampling method. We combine the proposed sampling algorithm with the existing sampling method for multiple information fusion, and a higher accuracy and mean IoU can be achieved. The experimental results verify the advantage of considering curvature variation in classification and segmentation tasks. [ABSTRACT FROM AUTHOR]

Low-rank tensor recovery (LRTR) is a natural extension of low-rank matrix recovery (LRMR) to high-dimensional arrays, which aims to reconstruct an underlying tensor $\boldsymbol{\mathcal {X}}$ X from incomplete linear measurements $\mathfrak {M}(\boldsymbol{\mathcal {X}})$ M (X) . However, LRTR ignores the error caused by quantization, limiting its application when the quantization is low-level. In this work, we take into account the impact of extreme quantization and suppose the quantizer degrades into a comparator that only acquires the signs of $\mathfrak {M}(\boldsymbol{\mathcal {X}})$ M (X) . We still hope to recover $\boldsymbol{\mathcal {X}}$ X from these binary measurements. Under the tensor Singular Value Decomposition (t-SVD) framework, two recovery methods are proposed—the first is a tensor hard singular tube thresholding method; the second is a constrained tensor nuclear norm minimization method. These methods can recover a real $n_1\times n_2\times n_3$ n 1 × n 2 × n 3 tensor $\boldsymbol{\mathcal {X}}$ X with tubal rank $r$ r from $m$ m random Gaussian binary measurements with errors decaying at a polynomial speed of the oversampling factor $\lambda :=m/((n_1+n_2)n_3r)$ λ : = m / ((n 1 + n 2) n 3 r) . To improve the convergence rate, we develop a new quantization scheme under which the convergence rate can be accelerated to an exponential function of $\lambda$ λ . Numerical experiments verify our results, and the applications to real-world data demonstrate the promising performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Predictive errors restrict high performance of finite control set model predictive control (FCS-MPC) in the cascaded H-bridge multilevel inverter.In order to solve this problem, the influence mechanisms of parameter mismatch, model discretization, and sampling error on voltage and current prediction errors are analyzed in this article. A hierarchical multifactorial error correction method is proposed. The known voltage prediction errors of the previous adjacent prediction periods are first fed back to calculate the voltage predictive error factors and self-correct the next voltage prediction errors. And then the current prediction errors are calibrated by the corrected voltage and the calculated current predictive error factor. This proposed method involves a simple structure and a good inhibitory ability on multifactorial prediction errors. Finally, experimental results in a three-phase five-level cascaded H-bridge multilevel inverter support the theoretical analysis and the proposed method. [ABSTRACT FROM AUTHOR]

This letter discusses performance degradation of traditional supervisory control and data acquisition (SCADA)-based static state estimation (SSE) caused by violation of the static assumption. The sensitivity of non-synchronized sampling errors against oscillations is tested through real measurement data and grid model from a subsystem of the North American power gird. The test results show that as an important reason leading to the failure of SCADA-based SSE, the non-synchronized sampling error of SCADA is sensitive to oscillations in that the error rapidly increases when oscillations occur and vanishes when the oscillations cease. [ABSTRACT FROM AUTHOR]

For AC microgrids with three-phase unbalanced loading conditions, the four-wire voltage source inverters (FW-VSIs) have become an advisable interfaced converter between the source and loads. Predictive control has been applied to FW-VSIs in recent years, but model errors are not solved well, which is caused through parameter mismatch, sampling error and time delay. Thus, the accurate following between reference value and predicted value is difficult to achieve. To this end, a dual-predictive control based on adaptive error correction (DPCEC) for the FW-VSIs is presented. Firstly, the state-space function of FW-VSIs under $\alpha$ $\beta$ 0 reference frame is built. Then, the DPCEC strategy is proposed based on the above issue. Therein, an adaptive error correction strategy is severally embedded into both the outer and inner prediction loop. Noting that the impacts of different negative factors can be simultaneously processed and corrected through adaptive error correction strategy. Not only does the proposed control strategy achieve better performance in steady state, but also retains fast dynamic response in transient state. Finally, simulation and experimental results which verify the high performance of the proposed control techniques are provided. [ABSTRACT FROM AUTHOR]

In the existing offline inductance identification methods of permanent magnet synchronous machines (PMSMs), dq-axis voltage equations are generally adopted and the inverter nonlinearity compensation is necessary, which complicates the identification process and restricts the generality. In order to solve the problem, an offline inverter-nonlinearity-immune inductance identification method is proposed in this article based on the PMSM equivalent impedance model. The method can identify the dq-axis inductances by injecting both dc and high frequency signals, where the magnetic saturation and the cross saturation are considered. Besides, an inductance surface forming algorithm is introduced based on the bilinear interpolation. The continuous dq-axis inductance surfaces can be then obtained by only a few identified data, so that the time of data sampling and processing is greatly shorten compared with the existing methods. To enhance the accuracy of the identification method, the digital delay error and the sampling error in zero current zone are investigated and compensated. The effectiveness of the proposed method is validated on three different test machines. [ABSTRACT FROM AUTHOR]

The extensive application of vector phase shift modulation method in permanent magnet synchronous motor drive systems with single dc-link current sensor results in obvious current ripple in the low-speed region. The patterns of these ripples vary greatly in different sectors, leading to nonnegligible phase current reconstruction errors. In this article, an estimation of the local average current change rate (ELAC2R) based reconstruction error suppression method within the switching cycle is proposed to resolve this problem. By comparing the actual and the ideal sampling points, the increase of current ripple and the reconstruction error caused by the combination of the vector phase shift and the single current sensor sampling in the low-speed region are modelled and analyzed. Then according to equivalent modelling of the current ripple between the ideal and the actual sampling points, the more accurate phase current information can be quickly obtained based on the sampling value. The compensation process is significantly simplified and the computation burden of the processor is reduced. Experimental results show that the performance of current reconstruction can be improved effectively with less harmonic content with the proposed method. [ABSTRACT FROM AUTHOR]

The output of renewable generation depends on the real-time weather conditions and changes rapidly; so the economic operating point of the power system varies over time. This paper aims to find the explicit mapping from variable renewable power to optimal power flow solutions. To this end, we propose a parametric distribution optimal power flow (P-DOPF) method, which gives the optimal dispatch strategy and power flow status as analytical functions of the renewable output. With the established distribution optimal power flow problem based on the relaxed Distflow model, the first step is to perform a global polyhedral approximation on the second-order cone constraints to develop a linearized formulation. The second step is to obtain the P-DOPF model by treating renewable power output as parameters; then, the P-DOPF problem gives rise to a multi-parametric linear program (mp-LP). Third, we prove that the optimal solution and optimal value of the P-DOPF are piecewise linear functions of the parameters and we design an adaptive-sampling algorithm to construct the optimal value and optimal solution functions, as well as the partition of the parameter set, subject to a given error tolerance; this algorithm is not influenced by model degeneracy, a common difficulty of existing mp-LP algorithms. The P-DOPF framework provides an explicit real-time control policy of generators in response to the renewable output. Case studies on the IEEE 33 and 69-bus systems verify the effectiveness and performance of the proposed method; by comparison, the proposed method outperforms the established affine policy method in computational efficiency and optimality by 24.5% and 4.62%, respectively. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

Quantum error mitigation (QEM) is a class of promising techniques for reducing the computational error of variational quantum algorithms. In general, the computational error reduction comes at the cost of a sampling overhead due to the variance-boosting effect caused by the channel inversion operation, which ultimately limits the applicability of QEM. Existing sampling overhead analysis of QEM typically assumes exact channel inversion, which is unrealistic in practical scenarios. In this treatise, we consider a practical channel inversion strategy based on Monte Carlo sampling, which introduces additional computational error that in turn may be eliminated at the cost of an extra sampling overhead. In particular, we show that when the computational error is small compared to the dynamic range of the error-free results, it scales with the square root of the number of gates. By contrast, the error exhibits a linear scaling with the number of gates in the absence of QEM under the same assumptions. Hence, the error scaling of QEM remains to be preferable even without the extra sampling overhead. Our analytical results are accompanied by numerical examples. [ABSTRACT FROM AUTHOR]

This article reports on the experimental validation of a method to predict radiated emissions of equipment under test in a reverberation chamber where the multiple monopole sources stirring technique is implemented. The method is validated for three different case studies: a loop, a slot in a metal enclosure, and a realistic device comprised of the chassis of a power supply for a workstation. The reconstruction of the free space electromagnetic emissions starting from measurements in a reverberation chamber is compared with measurements in an anechoic environment. A statistical analysis of the uncertainty due to a positioning error of the field sample measurement points is also proposed. [ABSTRACT FROM AUTHOR]

The annual output value of Taiwan’s wafer foundry and IC packaging test ranks first worldwide. The electronics industry has a complete industrial ecological chain in the supply chain system of global information and communication technology. In order to increase market competitiveness and increase the operational flexibility, manufacturing process outsourcing has become a trend in the business model of the electronics industry. The quality of the outsourcing affects the quality and function of the final product, the outsourcer selection is crucial to ensure the quality of the final product. This article takes the wire bonding process as an example, and proposes a fuzzy selection model of quality-based IC packaging process outsourcers. Then, this article selects the six standard deviation indicator that can fully reflect the process yield and quality level as the evaluation tool. Since the indicator contains unknown parameter, in order to improve the accuracy of estimation and overcome the uncertainty of measurement data, this paper applies mathematical programming to derive the confidence interval of six sigma quality index, and proposes a confidence-interval-based fuzzy testing meth as a green outsourcer selection tool to reduce the risk of misjudgment due to sampling errors and improve the accuracy of the selection. [ABSTRACT FROM AUTHOR]

The current sampling errors in dual-motor group (DMG) cause more adverse effects on system performance than that in a single motor system. To solve this problem, in this article, a synergistic correction strategy for the abovementioned problems in DMG fed by double inverters is proposed. First, an orthogonal pulsewidth modulation carriers strategy is proposed to imitate the “shutdown” status of motor subsystems to obtain ascertainable current relations. Then, the current values with sampling errors in DMG at several “fixed” sampling points are derived, based on which the sampling errors in DMG are corrected. Meanwhile, the rules for the action time of active vectors and output voltage range in each inverter are analyzed for each “fixed” sampling point. Finally, the proposed strategy is verified by experimental results on a DMG system, which show that the sampling errors can be precisely estimated with the proposed strategy. [ABSTRACT FROM AUTHOR]

In theera of Internet of Things (IoT), sensor plays a more vital role and its quality has a great impact on final measurement. From the viewpoint of final measurement, there are two kinds of effects: bias-only measurement and error-growth measurement. The bias-only measurement can be directly read from a sensing device, and each measurement is only distorted by the inherited bias error of the sensor, e.g., measuring temperature by a thermometer sensor. On the other hand, the error-growth measurement cannot be read directly but be calculated by combining multiple former sampling data, therefore multiple bias errors are accumulated in the measurement. For instance, in inertial navigation, the raw data sampled from an inertial measurement unit (IMU) are converted into a trajectory by multiple integral operations, so the errors of new sampling data are continuously added into the trajectory, and the deviation of the traced trajectory will reach an unacceptable level over time. Clearly, a good IMU trajectory strategy is the one with a less error-accumulation effect. Unfortunately, the analysis of error-growth effect remains not well studied, which motivates this work. This letter first proposes a theoretical error-growth effect analysis framework. Next, we use it to analyze three typical inertial methods, namely single-IMU method, gyro-free-IMU (GF-IMU) method, and $\omega$ -free accelerometer pair (OFAP) method. Finally, the theoretical derivations of the three inertial methods are proved by simulation results. [ABSTRACT FROM AUTHOR]

In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and dq-axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the dq-axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis. [ABSTRACT FROM AUTHOR]

Recently, a connection between the age of information and remote estimation error was found in a sampling problem of Wiener processes: If the sampler has no knowledge of the signal being sampled, the optimal sampling strategy is to minimize the age of information; however, by exploiting causal knowledge of the signal values, it is possible to achieve a smaller estimation error. In this paper, we generalize the previous study by investigating a problem of sampling a stationary Gauss-Markov process named the Ornstein-Uhlenbeck (OU) process, where we aim to find useful insights for solving the problems of sampling more general signals. The optimal sampling problem is formulated as a constrained continuous-time Markov decision process (MDP) with an uncountable state space. We provide an exact solution to this MDP: The optimal sampling policy is a threshold policy on instantaneous estimation error and the threshold is found. Further, if the sampler has no knowledge of the OU process, the optimal sampling problem reduces to an MDP for minimizing a nonlinear age of information metric. The age-optimal sampling policy is a threshold policy on expected estimation error and the threshold is found. In both problems, the optimal sampling policies can be computed by low-complexity algorithms (e.g., bisection search and Newton’s method), and the curse of dimensionality is circumvented. These results hold for (i) general service time distributions of the queueing server and (ii) sampling problems both with and without a sampling rate constraint. Numerical results are provided to compare different sampling policies. [ABSTRACT FROM AUTHOR]

Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers. However, it is still unclear if such methods are truly competitive with respect to conventional methods in realistic scenarios and under practical constraints. In addition to enabling accurate signal reconstruction on realistic channel models, MU-MIMO receive algorithms must allow for easy adaptation to a varying number of users without the need for retraining. In contrast to existing work, we propose an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. It preserves the interpretability and scalability of the LMMSE receiver, while improving its accuracy in two ways. First, convolutional neural networks (CNNs) are used to compute an approximation of the second-order statistics of the channel estimation error which are required for accurate equalization. Second, a CNN-based demapper jointly processes a large number of orthogonal frequency-division multiplexing (OFDM) symbols and subcarriers, which allows it to compute better log likelihood ratios (LLRs) by compensating for channel aging. The resulting architecture can be used in the up- and downlink and is trained in an end-to-end manner, removing the need for hard-to-get perfect channel state information (CSI) during the training phase. Simulation results demonstrate consistent performance improvements over the baseline which are especially pronounced in high mobility scenarios. [ABSTRACT FROM AUTHOR]

Many studies take sampling to apply fuzzy C-means (FCM) to very large data sets. However, comparing with the plenty algorithms for sampling-based FCM, few research works theoretically study the generalization error. In this article, we take the concept probably approximately correct learning to study it, and define the generalization error as the maximum difference between the empirical risks and the real risks of solutions in solution space. First, we analyze this generalization error under finite solution space, and prove two theorems by Hoeffding's inequality, where one of them relies on a reasonable hypothesis. Then, we discuss the situation in which the solution space is infinite, and propose a theorem and a corollary by another hypothesis, where the results under finite solution space are used in the proofs. Finally, we bound the generalization error from the perspective of the FCM algorithm's convergence, where we take Taylor expansion to transform the risk function to the linear form and estimate the upper bound of its Vapnik-Chervonenkis (VC) dimension. The hypotheses proposed in this article are all intuitive and common phenomena in practice. Our results show the upper bound of the generalization error under the given minimum probability, which can offer insight into the stability of sampling-based FCM and can guide in its application. [ABSTRACT FROM AUTHOR]

In this work, we propose a real-time noncontact thickness measurement system for conductive materials based on an inductor–capacitor (LC) resonant eddy-current sensor. For the aluminum, copper, stainless steel 304, and carbon steel samples of various thicknesses, the in-phase and quadrature components of the sensor output taken during liftoff scans are found to be a series of quadratic-like curves on the phase versus logarithmic amplitude diagram. These curves can be used to generate the standard phase–amplitude relations for thickness conversion. The phase–amplitude relations of the thickness between the values of standard samples are calculated by cubic spline interpolation. With the standard database of phase–amplitude relations, the noncontact thickness measurement was implemented in real time for the copper foil sample with an error of ±3% when the liftoff distance is less than 5 mm. The liftoff tolerance is as large as 100% of the probe diameter, indicating that the proposed method is useful for online quality inspection of conductor films on flexible substrates. [ABSTRACT FROM AUTHOR]

For the electromagnetic eddy current (EC) testing, various methods have been proposed for reducing the lift-off error on the measurement of samples. In this article, instead of eliminating the measurement error caused by the lift-off effect, an algorithm has been proposed to directly measure the lift-off distance between the sensor and nonmagnetic conductive plates. The algorithm is based on a sample-independent inductance (SII) feature. That is, under high working frequencies, the inductance is found to be sensitive to the lift-off distance and independent of the test piece under an optimal single high working frequency (43.87 kHz). Furthermore, the predicted lift-off distance is used for the thickness prediction of the nonmagnetic conductive samples using an iterative method. Considering the EC skin depth, the thickness prediction is operated under a single lower frequency (0.20 kHz). As the inductance has different sensitivities to the lift-off and thickness, the prediction error of the sample thickness is different from that of the lift-off distance. From the experiments on three different nonmagnetic samples—aluminum, copper, and brass, the maximum prediction error of the lift-off distance and sample thickness is 1.1 mm and 5.42%, respectively, at the lift-off of 12.0 mm. [ABSTRACT FROM AUTHOR]

The stochastic Richardson extrapolation-based numerical error quantification (StREEQ) technique has been applied to accomplish a code verification for the cavity systemgenerated electromagnetic pulse (SGEMP) simulation code LASER-SGEMP. Here, the grid convergence index method estimates the discretization error caused by the finite difference, while the bootstrap sampling demonstrates the sampling error arising from the inherent stochastic noise. The electric field waveforms obtained by the LASER-SGEMP code agree well with the high precision solution from a reference. According to the StREEQ method, the convergence value of the electric field at 1.8 ns is calculated to be 0.955 V/m and the numerical error characterized by the 95% confidence interval is [0.948, 0.964] V/m, while the high precision solution is 0.961 V/m. Therefore, the code verification has supported the accuracy of the LASER-SGEMP code, and the StREEQ technique is proved to be capable of estimating the numerical error of the cavity SGEMP simulation. [ABSTRACT FROM AUTHOR]

Due to the unbalanced three-phase currents and the torque ripples and speed fluctuations to the drive caused by the current sampling errors of three-phase current sensors and corresponding signal processing circuits, the estimation on current sampling errors is of great importance to eliminate these adverse effects. The observer-based methods, however, present two defects: first, they could increase the computational burden to the drives, thus limiting their practical applications; second, their high dependence on the system speed or position feedback information might degrade the estimation accuracy. To solve these problems, this article proposed a fixed-point sampling method of lower branch flows and three phase-currents, which adopts the relations between the measured currents, rather than any complex algorithm, to estimate the current sampling errors. To coordinate with the proposed strategy, a fixed-point sampling scheme was accordingly rolled out. Compared with the observer-based methods, as a fixed-point one, much applicable to actual digital controllers, it could reduce the computational burden of the drive. Meanwhile, its effectiveness was verified by experimental results on a 1.5-kW IPMSM prototype, which showed accurate estimated current sampling errors and the effectively eliminated torque ripples and speed fluctuations. [ABSTRACT FROM AUTHOR]

Because of the time-sharing sampling logic, the three-phase currents reconstructed by single dc-link shunt present significant errors in the permanent magnet synchronous motor drives. A traceback-prediction current correction method for sampled currents is proposed in this article to eliminate the reconstruction errors. According to the analysis of time-sharing sampling errors, the concept of equivalent current change rate during one pulsewidth modulation (PWM) period is introduced, and the phase current characteristic model based on state observer is established. Both the equivalent and instantaneous current change rates can be obtained by the observer-based calculation method. In order to improve the current reconstruction precision, the ultimate values of three-phase currents can be predicted by adopting the proposed traceback-prediction current correction method, which does not require major computation and is easy to implement with no restrictions on the PWM strategy. The effectiveness of the proposed method is verified by experimental results. [ABSTRACT FROM AUTHOR]

The accuracy of the adaptive cross approximation (ACA) algorithm, a popular method for the compression of low-rank matrix blocks in method of moment computations, is sometimes seriously compromised by unpredictable errors in the convergence criterion. This article proposes an alternative criterion based on global sampling of the error in the elements of the ACA compressed matrix. The sampling error depends not only on the size of the sample but also on the population distribution of the error, which makes it difficult to control the error independently of the underlying problem. However, as argued and demonstrated in this article, the distribution of the error converges to the same unique probability distribution function for all low-rank matrices. Complementing the sampling criterion with a simple mechanism to detect this convergence, we arrive at a criterion that controls the error irrespective of the underlying problem. As a practical example, the RCS of a moderate size metallic ogive is computed to illustrate the merits of the proposed criterion. The proposed algorithm may also be useful in other methods that approximate low-rank matrices by interpolation of a reduced set of its elements. [ABSTRACT FROM AUTHOR]

Traditional machine learning methods work well under a general assumption that the training data and the testing data should be drawn from the same distribution. However, this assumption does not hold in many real-world applications. For two structure-similar but specification-different connectors with intermittent fault, one connector has sufficient intermittent fault data with severity labeled but another connector has no labeled intermittent fault data. In this situation, the severity evaluation model trained by the samples from the former connector will have large evaluation errors for the samples from the latter connector. For realizing the transfer of the intermittent fault severity evaluation model between two similar but different connectors, this article proposes a transfer learning long short term memory (LSTM) network-based severity evaluation method for intermittent faults of an electrical connector. Conventional transfer learning methods with maximum mean discrepancy (MMD) have two shortcomings: 1) MMD only considers the mean discrepancy between the source and target domains and 2) MMD only measures the marginal distribution discrepancy of the source and target domains but ignored the conditional distribution discrepancy of different labels. Therefore, this article proposes a modified MMD metric which not only considers the higher order statistics but also the label discrepancy information to enhance the severity evaluation accuracy. Finally, this transfer learning model is applied in the intermittent fault experiment of three electrical connectors, and the effectiveness of the proposed method is verified using comparisons with four state-of-art transfer learning methods. [ABSTRACT FROM AUTHOR]

Reliability of the prepared fluidic samples is a major concern for automated sample preparation using microfluidic biochips, where induced errors in the resultant concentration values severely affect the assay outcome. However, the existing design automation techniques have not thoroughly considered the reliability model to reduce the induced concentration errors during sample preparation. This article proposes a fast reliability-aware sample preparation (RASP) method for determining the optimized sequence of mixing steps (mixing process) with the enhanced reliability. In RASP, a probabilistic concentration prediction model is proposed for analyzing the reliability of a given mixing process. Based on this probabilistic model, a lookup table construction algorithm along with the table query method is proposed to obtain the optimized mixing process. The simulation results show that for any user-specified target concentration, RASP can effectively determine the optimized mixing process, which generates the droplets with target concentration within the error tolerance of 0.1%. Compared with the state-of-the-art sample preparation algorithm, RASP improves the reliability-related accuracy by 91.4% on average via 2048 testcases. [ABSTRACT FROM AUTHOR]

A capacitor-free control scheme is presented for primary-side regulation (PSR) flyback converter to reduce cost and increase system reliability. Compared to conventional voltage compensation, the proposed converter using an error amplifier sampling scheme reduces zero and pole as load decreases, and thus, the stability of the system makes significant strides especially under light loads. The proposed controller provides high stability and fast load transient response even in capacitor-free scheme. Besides, a ramp voltage with reduced slope is allowed to pursue a wider output load range and further reduce switching frequency for lighter load. The proposed controller is implemented in 0.18-μm 100-V BCD process and occupies a die size (with pads) of 1.3 × 1.2 mm2. The experimental results show that the deviations of output voltage are within ±0.35% under different inputs and loads while the peak power efficiency of 87.3% is achieved. [ABSTRACT FROM AUTHOR]

Image set recognition has been widely applied in many practical problems like real-time video retrieval and image caption tasks. Due to its superior performance, it has grown into a significant topic in recent years. However, images with complicated variations, e.g., postures and human ages, are difficult to address, as these variations are continuous and gradual with respect to image appearance. Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations. In this work, a Discriminant Residual Analysis (DRA) method is proposed to improve the classification performance by discovering discriminant features in related and unrelated groups. Specifically, DRA attempts to obtain a powerful projection which casts the residual representations into a discriminant subspace. Such a projection subspace is expected to magnify the useful information of the input space as much as possible, then the relation between the training set and the test set described by the given metric or distance will be more precise in the discriminant subspace. We also propose a nonfeasance strategy by defining another approach to construct the unrelated groups, which help to reduce furthermore the cost of sampling errors. Two regularization approaches are used to deal with the probable small sample size problem. Extensive experiments are conducted on benchmark databases, and the results show superiority and efficiency of the new methods. [ABSTRACT FROM AUTHOR]

The blowback caused by the separation of rail and armature at muzzle is an important factor for the degradation of bore insulator. A small caliber launcher was modified by the transparent material of polycarbonate for capturing the process of blowback in inner bore with a high-speed camera. Three groups of experiments with the same peak linear current density and various muzzle currents were carried out. Nesting insulator samples were used to reduce the measurement error in cutting samples after experiments. Insulation performance of postshot samples was conducted from surface morphology, surface flashover voltage, and partial surface conductivity. Results show that the effects of blowback are related to muzzle current. With the increases of muzzle current, the average flashover voltage of samples in the region of 300–600 mm (600–900 mm) decreases from 9.98 kV/cm (5.37 kV/cm) to 3.59 kV/cm (3.01 kV/cm). Moreover, the number of samples that the flashover voltage is less than 9 kV/cm (half value of the original samples) increases from 7 to 12, which indicates that the damage area caused by blowback is gradually expanding with the muzzle current. Correspondingly, the partial surface conductivity of samples in 300–900 mm region increases with muzzle current. The increase of surface conductivity should be the main reason for the sharply decrease of surface flashover voltage. [ABSTRACT FROM AUTHOR]

Learning large-scale fuzzy cognitive maps (FCMs) with the sparse attribute automatically from time series without prior knowledge remains a challenging problem. Most existing automated learning methods were applied to learn small-scale FCMs, and the learned FCMs are much denser than the maps constructed by human experts. Learning FCMs is the procedure of judging whether there are connecting edges and determining the weights of connecting edges. Thus, we transform the problem of learning FCMs into a biobjective optimization problem with two objects of minimizing the measure error and the number of nonzero entries, respectively. To solve this optimization problem, a preference-based iterative thresholding evolutionary biobjective optimization algorithm for learning FCMs is proposed. The strategy focuses on the knee area of the Pareto front (PF) with preference on the solutions near the true sparsity. Moreover, an initialization operator based on random forest is proposed to increase the speed of convergence toward the PF. The experiments on large-scale synthetic data with varying sizes and densities and the application to the gene regulatory network reconstruction problem have been conducted to demonstrate that our proposal matches or exceeds the existing state-of-the-art FCM learning approaches in most cases in terms of four measures, namely, Data_Error, Out_of_Sample_Error, Model Error, and SS_Mean. The Data_Error obtained by the proposed method can achieve 3.07E-06 even when the number of nodes reaches 200. We also demonstrate the effectiveness of the proposed initialization operator and the preference-based strategy, which can result in a fast convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an effective sliding mode controller (SMC) for a grid-connected 7-level packed U-cell (PUC7) inverter. The aim is to design a simple controller that deals effectively with the complex control problem of the PUC7 inverter (multiobjective control problem). The selection of the control actions is achieved according to the system state error at every sampling time, regardless of the previous values, which makes the control technique model-independent. The control algorithm evaluates online two cost functions (one for each state error), which are derived on the basis of sliding mode theory, and it selects the optimal control input in order to satisfy the reaching conditions of the two cost functions. Compared with the existing solutions, the proposed SMC technique ensures lower average switching frequency by tuning the hysteresis bandwidth of the capacitor-voltage error. The fast implementation, needless of gains tuning, and simple design procedure are the main features of the proposed algorithm. Simulation and experimental results are presented to prove the effectiveness of the proposed technique in controlling the PUC7 inverter with high dynamic performance and robustness against disturbances and parameters mismatch. [ABSTRACT FROM AUTHOR]

Big sensing data is commonly encountered from various surveillance or sensing systems. Sampling and transferring errors are commonly encountered during each stage of sensing data processing. How to recover from these errors with accuracy and efficiency is quite challenging because of high sensing data volume and unrepeatable wireless communication environment. While Cloud provides a promising platform for processing big sensing data, however scalable and accurate error recovery solutions are still need. In this paper, we propose a novel approach to achieve fast error recovery in a scalable manner on cloud. This approach is based on the prediction of a recovery replacement data by making multiple data sources based approximation. The approximation process will use coverage information carried by data units to limit the algorithm in a small cluster of sensing data instead of a whole data spectrum. Specifically, in each sensing data cluster, a Euclidean distance based approximation is proposed to calculate a time series prediction. With the calculated time series, a detected error can be recovered with a predicted data value. Through the experiment with real world meteorological data sets on cloud, we demonstrate that the proposed error recovery approach can achieve high accuracy in data approximation to replace the original data error. At the same time, with MapReduce based implementation for scalability, the experimental results also show significant efficiency on time saving. [ABSTRACT FROM AUTHOR]

In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator over a channel that is modeled as a queue. The estimator reconstructs an estimate of the real-time signal value from causally received samples. We study the optimal online sampling strategy that minimizes the mean square estimation error subject to a sampling rate constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by how much the Wiener process varies during the random service time and the maximum allowed sampling rate. Further, if the sampling times are independent of the observed Wiener process, the above sampling problem for minimizing the estimation error is equivalent to a sampling problem for minimizing the age of information. This reveals an interesting connection between the age of information and remote estimation error. Our comparisons show that the estimation error achieved by the optimal sampling policy can be much smaller than those of age-optimal sampling, zero-wait sampling, and periodic sampling. [ABSTRACT FROM AUTHOR]

In the control of permanent magnet synchronous machines (PMSMs), predictive controllers have received considerable attention due to the enhanced dynamic responses. However, periodic disturbances caused by dead-time effects and sampling errors deteriorate the performances of PMSMs. In order to suppress periodic disturbances, one typical control structure is to adopt several corresponding resonant controllers connected in parallel with the main controller. Even though excellent steady-state performances can be obtained, coupling effects between reference tracking and periodic disturbance attenuation can be observed, leading to significant transient oscillations under the situation of a reference step change. In order to realize decoupling between reference tracking and disturbance attenuation, this paper proposes a dual-loop predictive control structure, in which the reference tracking is only determined by the primary predictive controller. Periodic disturbance attenuation is achieved through an embedded disturbance rejection loop. By applying the proposed controller, periodic disturbances are strongly attenuated while smooth and fast transient performances are simultaneously guaranteed. Furthermore, a wide range of low frequency harmonics can be successfully suppressed, including but not limited to the main periodic harmonics. The steady-state performance, dynamic responses, and robustness against parameter variations of the proposed controller are verified through experiments based on a 2.3-kW PMSM. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient simulation method based on importance sampling to estimate the random-coding error probability of coded modulation. The technique is valid for complex-valued modulations over Gaussian channels, channels with memory, and naturally extends to fading channels. The simulation method is built on two nested importance samplers to respectively estimate the pairwise error probability and generate the channel input and output. The effect of the respective number of samples on the overall bias and variance of the estimate of the error probability is characterized. For a memoryless channel, the estimator is shown to be consistent and with a small variance, growing with the square root of the code length, rather than the exponential growth of a standard Monte Carlo estimator. [ABSTRACT FROM AUTHOR]

Accurate forecasting-aided state estimation plays a vital role in reliable and secure operation of power systems. However, most of existing methods are unable to deal with the uncertainties that might be caused by uncertain model parameters or uncertain noise statistics. Therefore, the performance of these methods may be inevitably degraded significantly. To address these issues, based on the robust control theory, in this paper, by incorporating the modified innovation based Sage-Husa estimator of noise statistics and the proposed estimation error covariance matrix adaptive technique, a novel adaptive $H_{\infty }$ extended Kalman filter (AHEKF) is developed to realize robust forecasting-aided state estimation for power system with model uncertainties. Extensive simulations carried out on several different test systems demonstrate the efficiency and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

In this treatise, we conceive a hardware impairment aware receiver design of a large multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, taking into account the error statistics of parameter estimations. The hardware impairment includes the quantization noise, phase noise etc. There are two key contributions in this work. Firstly, consideration of error statistics of residual parameters in the receiver design improves receiver performance. In this work, we propose a linear equalizer based data detector at the receiver for a large MIMO-OFDM system and the parameters of interest are the channel impulse response (CIR) and carrier frequency offset (CFO) along with the hardware impairments awareness both at transmitter and receiver. The design criterion chosen for designing the detector equalizer is based on the linear minimum mean square error (LMMSE) one and it will consider the statistics of estimation error and hardware impairments. However, in any practical communication system, the availability of statistics for these parameter errors is inconceivable. Hence, we propose further to consider the Bayesian Crammer Rao lower bound (BCRLB) as the variance of the error with the key assumption of zero mean. Numerical simulation demonstrates that, consideration of such statistics attains an SNR gain of $2$ dB with respect to a fixed symbol error ratio (SER). It also shows that the SER performance with BCRLB matches very closely with the case, when exact error variance is used. For the second contribution, an extensive theoretical analysis has been performed to evaluate the MSE improvement of data detection with the LMMSE criterion with parameter estimation error statistics consideration in the large MIMO-OFDM system. This theoretical analysis of improved MSE has been conceived with the usage of large dimension random matrix theory and a closed-form expression has been obtained. Numerical simulation demonstrates that the theoretical analysis for MSE improvement matches closely with the simulated one. [ABSTRACT FROM AUTHOR]

Droop control is broadly used in dc power distribution systems, e.g., dc microgrids. Implementing droop control for switch mode power supplies requires the measurement of average current. Synchronous sampling is used in digital control systems to obtain the average of a triangular inductor waveform. However, the dead time, semiconductor turn-on and turn-off delays, and signal conditioning delays shift the sensed waveform and change the effective duty cycle, resulting in a misalignment and measurement error from the average value. This paper proposes a duty cycle weighted averaging method based on double sampling. By sampling twice per switching period and averaging the results using duty cycle as the weighting factor, the error due to waveform misalignment is theoretically eliminated. The result always equals the average value of the triangle. This method does not rely on the knowledge of circuit delays, making it easy to implement and robust to parameter variations. Simulations and experimental measurements validate the superior performance compared with conventional single and double sampling methods. The experiment on a droop-controlled dc–dc converter demonstrates a more accurate droop characteristic. [ABSTRACT FROM AUTHOR]

In high-speed analog-to-digital converters (ADCs), two main factors contribute to high power consumption. The first is the super linear relationship with the sampling rate; i.e., by doubling the sampling rate, the power consumption more than doubles. The second factor arises from the consumption of analog circuitry responsible to mitigate the jitter noise. By employing a multichannel sampling system, one can achieve high sampling rates by incorporating multiple low sampling-rate channels, which results in a linear scaling of power consumption with the number of channels. The main drawback of this system is the timing mismatch between the sampling channels. In this paper, we intend to jointly compensate the jitter noise and the timing mismatch between the channels using statistical methods. We first approximate the acquisition system and derive a stochastic model. Then, we propose an iterative maximum likelihood algorithm to estimate the parameters of the input signal. We further evaluate the Cramér-Rao lower bound (CRLB) for the estimation error to examine the proposed algorithm. Simulation results indicate that our algorithm is capable of closely following the CRLB curve for reasonable values of jitter noise and a wide range of timing mismatch errors. Moreover, it is shown that the mismatch-compensated multichannel sampling system performs almost equivalently to a single-channel high rate sampler without having its shortcomings. [ABSTRACT FROM AUTHOR]

We show through case studies that it is easier to estimate the fundamental limits of data processing than to construct the explicit algorithms to achieve those limits. Focusing on binary classification, data compression, and prediction under logarithmic loss, we show that in the finite space setting, when it is possible to construct an estimator of the limits with vanishing error with $n$ samples, it may require at least $n\ln n$ samples to construct an explicit algorithm to achieve the limits. [ABSTRACT FROM AUTHOR]