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As the technology node in semiconductor manufacturing continuously shrinks, the etch-induced etch bias introduced during the etching process cannot be ignored and necessitates correction. The prevailing approach to addressing this issue is model-based etch bias correction. This method involves simulating the etching process by training an etch model that predicts the bias between the After Development Inspection (ADI) contour and the After Etch Inspection (AEI) contour. However, the reliability of the etch data for model training is compromised due to pattern shrinkage during Scanning Electron Microscope (SEM) imaging, which impairs the model’s prediction accuracy. To mitigate these issues, the contour averaging method is frequently employed, although it lacks thorough theoretical explanation and experimental verification. In this study, we validate the effectiveness of contour averaging theoretically and empirically. A relationship is derived between the prediction error of the etch model and the number of averaged contours, showing that contour averaging minimizes measurement errors of etch data. We also demonstrate the improved prediction accuracy of etch model using contour averaging, with both real and generated etch data. [ABSTRACT FROM AUTHOR]

Stroke survivors usually exhibit concurrent motor and cognitive impairment. Historically, rehabilitation strategies post-stroke occur separately in terms of motor and cognitive functions. However, recent studies show that hand motor interventions can have a positive impact on cognitive recovery. In this work, we introduce AMBER (portAble and Modular device for comprehensive Brain Evaluation and Rehabilitation), a new device developed for the evaluation and rehabilitation of both hand motor function and cognition simultaneously. AMBER is a simple, portable, ergonomic and cheap device based on Force Sensitive Resistors, in which every finger interaction is recorded to provide information about finger strength, processing speed, and memory status. This paper presents the requirements of the device and the design of the system. In addition, a pilot study was conducted with 36 healthy individuals using the evaluation module of the device to assess its psychometric properties, as test-retest reliability and measurement error. Its validity was also evaluated comparing its measurements with three different gold standards for strength, processing speed and memory. The device showed good test-retest reliability for strength (ICC =0.741-0.852), reaction time (ICC =0.715 – 0.900) and memory (ICC =0.556-0.885). These measures were correlated with their corresponding gold standards (r =0.780-890). AMBER shows great potential to impact hand rehabilitation, offering therapists a valid, reliable and versatile tool to comprehensively assess patients. With ongoing advancements and refinements, it has the opportunity to significantly impact rehabilitation practices and improve patient outcomes. [ABSTRACT FROM AUTHOR]

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states. [ABSTRACT FROM AUTHOR]

Over recent decades, electrical power concepts under nonsinusoidal and unbalanced conditions have been continually researched by several academics around the world. The results from these studies present a direct relationship between the performance of electric energy meters and the physical reality of the power balance between loads and sources. As such, this study presents a wide-ranging subject analysis that contemplates analytical, computer, and laboratory developments. This, in turn, opens the possibility of the physical representation of active power under unbalanced conditions, directly impacting the amount of active energy measured by the polyphase meters for billing purposes (from different manufacturers and models) and the composition of technical losses on distribution systems. The results showed that when using the fundamental active power of positive sequence as a reference, as suggested by IEEE Std. 1459/2010, none of the meters available worldwide correctly accounts for active energy. In this context, the study also presents the results from a power measurement campaign performed with 162 different low-voltage three-phase residential consumers located on five different electric energy utilities to understand the actual levels of current unbalance on this specific type of load. In this context, the main contributions of this work are related to (i) the quantification of measurement differences between active energy meters, highlighting the lack of equity in the measurement process of active energy under unbalanced conditions and (ii) the demonstration that the components of zero and negative sequence currents, produced by unbalanced loads, increase the technical losses of distribution systems. Furthermore, the results indicate the urgency of reviewing active energy measurement protocols for billing electricity consumers.

MRI-compatible robots provide a means of studying brain function involved in complex sensorimotor learning processes, such as adaptation. To properly interpret the neural correlates of behavior measured using MRI-compatible robots, it is critical to validate the measurements of motor performance obtained via such devices. Previously, we characterized adaptation of the wrist in response to a force field applied via an MRI-compatible robot, the MR-SoftWrist. Compared to arm reaching tasks, we observed lower end magnitude of adaptation, and reductions in trajectory errors beyond those explained by adaptation. Thus, we formed two hypotheses: that the observed differences were due to measurement errors of the MR-SoftWrist; or that impedance control plays a significant role in control of wrist movements during dynamic perturbations. To test both hypotheses, we performed a two-session counterbalanced crossover study. In both sessions, participants performed wrist pointing in three force field conditions (zero force, constant, random). Participants used either the MR-SoftWrist or the UDiffWrist, a non-MRI-compatible wrist robot, for task execution in session one, and the other device in session two. To measure anticipatory co-contraction associated with impedance control, we collected surface EMG of four forearm muscles. We found no significant effect of device on behavior, validating the measurements of adaptation obtained with the MR-SoftWrist. EMG measures of co-contraction explained a significant portion of the variance in excess error reduction not attributable to adaptation. These results support the hypothesis that for the wrist, impedance control significantly contributes to reductions in trajectory errors in excess of those explained by adaptation. [ABSTRACT FROM AUTHOR]

This article presents an improved equivalent-input-disturbance (EID) approach to actively rejecting exogenous disturbances for a plant. A control system based on the approach includes a new EID estimator constructed by embedding integrals to the conventional EID estimator. These integrals enable the estimator to improve the disturbance-estimation precision without amplifying the effect of measurement noise. Stability conditions of the control system are obtained using separation theorem. The system design ensures that the control system satisfies the stability conditions. Experiments of a rotational control system demonstrate the validity of the approach. [ABSTRACT FROM AUTHOR]

Although the GPS-based positioning is ubiquitous for its high precision, the high power consumption brought by the high sampling frequency and the poor GPS signal penetration limits its availability in locating low-power mobile devices (especially mobile phones). As a promising complement, the cellular-based positioning has attracted great attention since it consumes much less power as well as its higher availability. However, the sparsity of cellular-based data (due to lower sampling rate) and large localization errors make the measurement accuracy becomes the main challenge of the cellular-based positioning. hidden Markov model can well solve the problem of positioning error of GPS data, but it is less accurate when applied to map matching of cellular-base data. Therefore, to improve accuracy, in this article, we propose a novel algorithm called the tailored hidden Markov model (THMM) that is optimized for the cellular-based data. Specifically, the geometric, the topological, and the probabilistic characteristics have been considered and fully exploited in the THMM design. Our proposed schemes are evaluated using real-world motor vehicle movement trajectories collected in Tianjin and the experimental results are encouraging compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Wide-area fault location (WAFL) refers to the estimation of fault distance on the faulted line using PMU data. A system of linear equations is formulated for WAFL, taking advantage of both voltage and current synchrophasors. This results in a generalized closed-form solution for the fault distance using the weighted least-squares method. The main contribution of the letter is the rigorous derivation of the equation weights based on the statistical distributions of the superimposed errors, i.e., the differences between the errors of the corresponding pre- and post-fault synchrophasors. The method's effectiveness, robustness against different factors, and superiority over existing methods are demonstrated by extensive simulations and comparison studies conducted on the IEEE 39-bus test system. [ABSTRACT FROM AUTHOR]

To eliminate the measurement errors of insulation parameters caused by the internal impedance of VT (voltage transformer) and the damping resistance in resonant grounding distribution networks, a new online measurement technology based on an extended zero-sequence circuit is proposed. A constant current signal with variable frequency is injected into distribution network through a VT, and the return voltage is measured from another unloaded VT. An extended zero-sequence circuit is established to ensure that the injected current and the return voltage directly act on the insulation branch to ground; thus, the influence of the VT internal impedance can be eliminated. The resonance frequency is obtained according to the phase difference between the injected current and the return voltage, and the measurement error caused by damping resistance can be eliminated. Then, the capacitance to ground and leakage conductance can be accurately calculated. A measuring device based on the proposed technology was developed, and tests were carried out on a 10-kV distribution network laboratory and an actual 10-kV substation. The test results show that the proposed method has high measurement accuracy. The measurement process does not require a power interruption, and it does not affect the normal operation of the power system. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Approximate computing is an emerging strategy to improve the energy efficiency of many error-tolerant applications. To design an approximate circuit automatically, many approximate logic synthesis (ALS) methods have been proposed, among which many are greedy. To improve the synthesis quality of these greedy methods, one key is to calculate the errors of all candidate approximate transformations accurately. However, the traditional simulation-based method is time consuming. Instead, many existing methods just perform quick but inaccurate error estimation. In this work, to improve both the accuracy and runtime of error estimation, we propose VECBEE, a versatile efficiency–accuracy configurable batch error estimation method for greedy ALS. It is based on Monte Carlo simulation and an efficient technique to capture whether a signal change due to an introduced approximation will be propagated to each primary output. VECBEE is generally applicable to any statistical error measurement, such as error rate and average error magnitude, and any graph-based circuit representation. It allows a flexible tradeoff between the error estimation accuracy and the runtime, while even the fully accurate version is much faster than the traditional simulation-based method. We apply VECBEE to two representative greedy ALS methods and demonstrate its effectiveness in generating better approximate circuits. The code of VECBEE is made open source. [ABSTRACT FROM AUTHOR]

Aiming at the misdiagnosis problem of the interturn short-circuit (ITSC) fault detection method caused by the inverter nonlinearity and the current measurement errors, a d-axis residual voltage-based fault diagnostic strategy is proposed for sensorless control of the surface permanent magnet synchronous motor (SPMSM). The model of the three-phase wye-connected SPMSM with ITSC fault is established, and the expression of the d-axis residual voltage caused by the ITSC fault, the inverter nonlinearity, the current measurement errors and the motor parameter errors is derived. The proposed fault diagnostic strategy can eliminate the current relative gain coefficient error and locate the ITSC fault by using the eighth and second harmonics of the estimated d-axis residual voltage, respectively. The experimental results verify the effectiveness of the proposed fault diagnostic strategy. [ABSTRACT FROM AUTHOR]

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection-based schemes, an adaptive iterative learning control-based online suppression method is proposed in this article. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform. [ABSTRACT FROM AUTHOR]

In complex indoor environments, there is often no Line of Sight (LOS) path between the transmitter and the receiver due to walls, moving people, and many other obstacles. Traditional localization method based on LOS measurement is no longer available which provides lower localization accuracy in Non-Line of Sight (NLOS) environments. In response to this problem, this paper proposes a joint localization algorithm for scatterers and target user based on indoor prior information in NLOS scenarios. The algorithm makes use of the prior localization information of the access point (AP) and the wall to determine the position of the scatterers and target user simultaneously. Since the established constrained optimization problem is non-convex which is a difference of convex (DC) problem, it is transformed into a convex one based on a Taylor series expansion (TSE) method before it is solved. At the same time, as only a single AP signal can be received normally, the proposed method is then extended to the scenario with only one AP. Simulation and real-world testing results both show that not only the proposed method based on multiple APs, but also the method based on a single AP achieve high positioning accuracy, which are better than the existing positioning methods mentioned in this article as well. [ABSTRACT FROM AUTHOR]

Millimeter-wave vehicle-to-vehicle communication systems with multiple-input multiple-output antenna array have drawn significant attention because it is expected to satisfy the growing bandwidth requirements for the cooperative intelligent transportation systems. However, maintaining high-quality communication links between communicating vehicles without high overhead is a challenging problem due to the high mobility of vehicles. In this paper, we propose a beam management algorithm with spatially distributed antenna subarrays instead of a single co-located antenna array. Unlike existing methods, we use distributed antenna subarrays to exploit the geometric relationship of the beam directions and the relative distance between communicating vehicles. We reduce the beam alignment errors by minimizing the sum of squared errors between the estimated beam direction after the beam training process and the refined beam direction derived from the measured position and velocity data. Additionally, a position data request protocol is devised to reduce the beam refining errors. Numerical results demonstrate that the proposed beam management algorithm successfully reduces the beam alignment errors and it is robust to position and velocity measurement errors. [ABSTRACT FROM AUTHOR]

DC bias in a transformer is a threat to the safe and efficient operation of dual active bridge (DAB) converters. Detecting a dc component in milliamperes mixed with a large ac current is difficult. This article proposes a current sensor based on ac magnetic flux (MF) cancellation. The proposed sensor utilizes a current transformer (CT) to extract the ac component in the measured current. Then, a power op-amp copies the CT signal to an additional winding to generate an ac MF opposite to that of the main ac current. Hence, the sensor range required to measure the dc is greatly reduced for a much smaller absolute measurement error. Using only a CT and a simple op-amp circuit, the proposed solution features low complexity and power consumption. The relative error in a range of 15-A ac current is 0.038%, showing a five-fold improvement compared to off-the-shelf sensors. The proposed sensor is verified on a 1-kW DAB prototype, where the dc bias is suppressed to nearly null owing to the proposed sensor. [ABSTRACT FROM AUTHOR]

We prove a tight and close-to-optimal lower bound on the effectiveness of local quantum measurements (without classical communication) at discriminating any two bipartite quantum states. Our result implies, for example, that any two orthogonal quantum states of a $n_{A}\times n_{B}$ bipartite quantum system can be discriminated via local measurements with an error probability no larger than $\frac {1}2 \left ({1 - \frac {1}{c \min \{n_{A}, n_{B}\}} }\right)$ , where $1\leq c\leq 2\sqrt {2}$ is a universal constant, and our bound scales provably optimally with the local dimensions $n_{A},n_{B}$. Mathematically, this is achieved by showing that the distinguishability norm $\|\cdot \|_{ \mathrm {LO}}$ associated with local measurements satisfies that $\|\cdot \|_{1}\leq 2\sqrt {2} \min \{n_{A},n_{B}\} \|\cdot \|_{ \mathrm {LO}}$ , where $\|\cdot \|_{1}$ is the trace norm. [ABSTRACT FROM AUTHOR]

In this brief, the adaptive event-triggered control problem is investigated for a class of uncertain high-order fully actuated (HOFA) systems. Different from the existing works on HOFA systems, this brief considers the unknown control matrix function, which only requires its maximum and minimum eigenvalues to be unknown a priori. To deal with the problem, the adaptive technique is applied to estimate the unknown parameters. To save the energy in signal transmission, the controller and its event-triggered mechanism are co-designed. By means of the Lyapunov stability theory, it is rigorously proved that the proposed controller can render that all signals of the closed-loop system are bounded. Finally, the effectiveness of the theoretical result is demonstrated on the basis of the numerical simulation on the RLC circuit. [ABSTRACT FROM AUTHOR]

Inertial navigation for autonomous underwater vehicles (AUVs) is challenging because of the drift error caused by the noise and measurement errors of inertial sensors, typically packaged as an inertial measurement unit (IMU), integrated over time. To mitigate the drift error, recent AUV state estimation approaches incorporate external references or environmental information obtained from exteroceptive sensors, with increased costs and limited operational domains. For improved navigation under sensor constraints, this article proposes an active perception framework that exploits vehicle motion to estimate the flow state together with the vehicle state using IMU and depth sensors only. The proposed framework uses the estimated flow state as external information to improve vehicle state estimation. We construct a linear time-varying system for the flow state, separated from a nonlinear system for the vehicle state. This formulation allows us to analyze uniform complete observability for the flow state, which is found to depend on vehicle motion. Then, along with vehicle and flow state estimators, we design a vehicle controller to enable vehicle motion to maximize an information metric pertaining to estimation performance based on either observability or constructability Gramian for the flow state. The proposed framework is validated through simulations for a case study with a vehicle descending through the water column in a time-varying flow field. The effectiveness of the framework is demonstrated by comparing results obtained from its four implementations with those from baseline approaches without active perception. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of voltage sensors on the measurement of transient voltages for power semiconductors in a Double Pulse Test (DPT) environment. We adapt previously published models that were developed for current sensors and apply them to voltage sensors to evaluate their suitability for DPT applications. Similarities and differences between transient current and voltage sensors are investigated and the resulting methodology is applied to commercially available and experimental voltage sensors. Finally, a selection aid for given measurement tasks is derived that focuses on the measurement of fast-switching power semiconductors.

This article proposes a compensation method for gain and offset errors in the current sensing of three-phase AC machine drives with three current sensor measurements during exact or close to standstill conditions. Errors are treated sequentially based on the elliptical trajectory method, a mathematical manipulation by demodulating faulted time-varying $d,q$ currents using the machine position. As three current sensors are used, a correction factor through measured neutral current is proposed such that the impact of machine resistance deviation and inverter non-idealities are minimized. The method does not incorporate any additional hardware, making it low-cost and straightforward in its implementation. It is proposed as a tool for initial commissioning before the machine runs. It is robust to rotor position perturbations and does not need a mechanical lock for compensation in standstill conditions. The effectiveness of the suggested approach is shown by analysis, modelling, and experimental findings acquired from a hardware prototype by mimicking the actual use of high-precision mechatronics.

Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimization is crucial to derive accurate topographic and deformation information. In this work, we analyze the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1–2 cm using a local GNSS reference station and up to 10–15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products.

Lidar sensors play an essential role in the perception system of automated vehicles. Fault Detection, Isolation, Identification, and Recovery (FDIIR) systems are essential for increasing the reliability of lidar sensors. Knowing the influence of different faults on lidar data is the first crucial step towards fault detection for lidar sensors in automated vehicles. We investigate the influences of sensor cover contaminations on the output data, i.e., on the lidar point cloud and full waveform. Different contamination types were applied (dew, dirt, artificial dirt, foam, water, and oil) and the influence on the output data of the single beam lidar RIEGL LD05-A20 and the automotive mechanically spinning lidar Ouster OS1-64 was evaluated. The LD05-A20 measurements show that dew, artificial dirt, and foam lead to unwanted reflections at the sensor cover. Dew, artificial dirt over the entire transmitter, and foam measurements lead to severe faults, i.e., complete sensor blindness. The OS1-64 measurements also show that dew can lead to almost complete sensor blindness. The results look promising for further studies on fault detection and isolation, since the different contamination types lead to different symptom combinations.

Lidar is an important component of the perception suite for automated systems. The effects of vibration on lidar point clouds are mostly unknown, despite the lidar’s wide adaption and usual application under conditions where vibration occurs frequently. In this study, we performed controlled vibration tests from 6 to 2000 Hz at 9 and 12 m/s2 in vertical direction on the automotive lidar OS1-64 by Ouster. An information loss emerged which is mostly independent from frequency and acceleration. The loss of points is randomly distributed and does not correlate with range, intensity, or ring number (the horizontal line of the rotating lidar unit). The resonance frequency of 1426 Hz proved to be unproblematic as no pronounced negative effects on the point cloud could be identified. For vibration detection, the internal Inertial Measurement Unit (IMU) of the OS1-64 is accurate and sufficient for vibrations up to 50 Hz. Above 50 Hz, external IMUs would be required for vibration detection. Counting the number of points on a target close to the edges was investigated as an exemplary way to detect vibration purely based on the point cloud, i.e., independent of the lidar’s IMU.

A primary requirement for the transmission system operator is an accurate knowledge of grid parameters. Moreover, the availability of effective and accurate monitoring tools allows the proper operation of power transmission grids. However, in spite of the now widespread possibility of having monitoring systems based on synchronized measurements, the monitoring applications can be affected not only by the inevitable uncertainty sources but also by the simplified or incomplete modeling of the network components. For this reason, the impact on power system monitoring and control applications of tap-changing transformer models is a key point. In this scenario, this article presents a method to estimate simultaneously line parameters, tap changer ratios, and systematic measurement errors associated with the instrument transformers. The method exploits a flexible model of the tap-changing transformer based on a parameter representing the ratio between the two winding impedances of the transformer. The proposal is based also on the suitable modeling of the measurement chain and on the constraints introduced by the equations of involved transmissions lines and transformers. The validation has been carried out by means of tests performed on the IEEE 14 Bus Test Case.

Strapdown inertial navigation usually relies on the extended Kalman filter (EKF) as the workhorse, which requires a proper initialization of the attitude. The optimization-based alignment (OBA) approach is widely employed to provide the coarse attitude information for the subsequent fine alignment or navigation stage by EKF. However, there still lacks a reliable quality index to assess the attitude accuracy of OBA. To tackle this problem, this article characterizes the OBA attitude error by vector measurement errors. For specific applications, such as the stationary alignment and odometer/GPS-aided in-motion alignment, the errors of vector measurements in OBA are further formulated in terms of raw sensor errors of gyroscopes, accelerometers, and odometer/GPS velocities. The covariance of attitude errors is analytically derived. Simulations and field experiments are performed to verify the covariance analysis. [ABSTRACT FROM AUTHOR]

As a common incipient fault for a compensation capacitor, the internal capacitor element breakdown fault (CEBF) causes slight changes in the steady-state voltage and current, which are difficult to accurately capture under the influence of measurement error. Therefore, to meet the demand for intelligent equipment maintenance by a smart grid, transient disturbances induced by a CEBF are used in this study to acquire element breakdown information for online monitoring of capacitor health. A transient response analysis of the CEBF in the time and frequency domains is used to establish the typical transient characteristics. Then, a multiple-kernel support vector machine algorithm is adopted to identify the transient disturbances. A method for determining the faulty capacitor unit is proposed based on the position of the waveform monitor. Under the assumption and statistics of the number of failed elements in a series group, a scheme for assessing the health condition is investigated. The proposed method can effectively obtain breakdown information to assess capacitor health with little influence of measurement error and background noise. The results of a theoretical analysis, computer simulation, lab experiment and field monitoring verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an improved dynamic state estimation (DSE) based protection (DSEBP) scheme is proposed for transmission lines in MMC-HVDC grids. Firstly, a dynamic model of the protection zone (DC line of interest) is constructed using differential and algebraic equations. The impact of frequency dependent line parameters is mitigated through the utilization of the “line mode” transmission line model. Then, a novel Runge-Kutta (RK) discretization scheme is proposed to consider the dynamics of the control variables during electromagnetic transients, yielding reduced discretization error as compared to the conventional RK scheme. Finally, the batch mode regression framework based DSE is developed to formulate the protection logic considering errors of both measurements and historical estimates. Numerical experiments have shown that the proposed DSEBP scheme can dependably detect and trip internal faults, and securely ignore the external faults. The proposed scheme achieves improved reliability and operational speed as compared with the existing DSEBP schemes. The proposed scheme only requires a typical sampling rate of 10k samples per second, has the robustness to measurement noises, and the calculation burden is low enough for practical implementations. [ABSTRACT FROM AUTHOR]

The full-cycle discrete Fourier transform (FCDFT) is commonly used to estimate phasors in numerical relays because of its immunity to harmonics and accurate identification of a decaying dc component (DDC) in a fault current. For fast protection of power systems, it is necessary to speed up the phasor estimation by using a data window below one cycle. The conventional least squares (LS) methods below one cycle approximate the DDC as the first several terms of the Taylor series expansion, which degrades the phasor-estimation accuracy. This paper proposes an LS method that uses Fourier analysis for approximation of the DDC to speed up phasor estimation without accuracy degradation. The phasor-estimation accuracy of the proposed method is investigated for the data-window length in simulations using synthetic data of fault currents containing DDC, harmonics, and measurement noise. The phasor-estimation speed is also investigated with respect to the delay of the antialiasing filter in the numerical relay. The proposed method is compared with the conventional LS and FCDFT methods in simulations using synthetic fault-current data. The feasibility of the proposed method is demonstrated through the phasor estimation of simulated transmission-line faults. [ABSTRACT FROM AUTHOR]

The Largest Normalized Residual Test (LNRT) has been widely utilized in commercial Power System State Estimation (PSSE) software for bad data identification. The LNRT has proved effective in dealing with single bad data as well as multiple non-interacting and multiple interacting but non-conforming bad data. However, it is known for a long time that when two bad data are both interacting and conforming, i.e. their errors are in agreement, the LNRT may fail to identify either one. Moreover, it has been shown recently that even two interacting and non-conforming bad data can cause the failure of the LNRT. Drawing on sensitivity analysis in linear regression, we develop normalized deleted residuals for suspected measurements so that the agreement in measurement errors are broken. Therefore, the LNRT for normalized deleted residuals will be able to identify the actual bad data point. Furthermore, in the case of AC PSSE, the method does not require calculation of a new hat matrix when a measurement is deleted from the data set. This makes the method computationally cost-effective. Simulation results for identifying different conforming and non-conforming interacting bad data proves that the proposed method can enhance the effectiveness of the LNRT. [ABSTRACT FROM AUTHOR]

A photonics-based leakage cancellation and echo signal de-chirping approach for frequency-modulated continuous-wave radar systems is proposed based on a dual-drive Mach–Zehnder modulator (DD-MZM). The de-chirp reference signal and the leakage cancellation reference signal are combined and applied to the upper arm of the DD-MZM, while the received signal, including the leakage signal and echo signals, is applied to the lower arm of the DD-MZM. When the amplitudes and delays of the leakage cancellation reference signal and the leakage signal are precisely matched, the leakage signal is canceled in the optical domain. The de-chirped signal is obtained after the leakage-free optical signal is detected in a photodetector. An experiment is performed. The cancellation depth of the leakage signal after de-chirping is around 23 dB when the center frequency and bandwidth of the linearly frequency-modulated signal are 11.5 and 2 GHz, respectively. When the leakage cancellation is not employed, the leakage will seriously affect the imaging results and distance measurement accuracy. When the leakage cancellation is enabled, the imaging results of multiple targets can be clearly distinguished, the distance measurement error of a moving target is significantly reduced to less than 10 cm, and a dynamic range increase of 21 dB is achieved. [ABSTRACT FROM AUTHOR]

Interturn field windings faults are quite common in synchronous machines, particularly in turbogenerators. The synchronous machine can operate with a certain interturn fault severity level. This paper presents a new field winding interturn fault severity estimation method based on machine learning regression algorithms. The theoretical excitation current is estimated by artificial intelligent. For this purpose, it is necessary the use of numerous healthy operational data to train the algorithm. Afterward, the algorithm estimates the field current, which is compared to the real current measured. The fault severity level is calculated from this comparison. The use of machine learning implies an improvement on the sensitivity to previous method based on the estimation of the theoretical excitation current by traditional synchronous machines models as Potier or ASA. The measurement errors increase the minimum fault severity level that can be detected. The proposed algorithm has been verified with more than 1800 experimental results in a special laboratory synchronous machine, obtaining a better estimation on the fault severity level than traditional methods. [ABSTRACT FROM AUTHOR]

The measurement error evaluation of power metering equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially under extreme environmental stresses. However, actual measurement error assessment is often disturbed by the environmental noise and insufficient input information. To address this problem, an improved local outlier factor (ILOF) method is first presented to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on box plot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely kernel support vector regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions. [ABSTRACT FROM AUTHOR]

The measurement precision of the optical encoder is influenced by coupling interferences from the system and the environment. To further improve the measurement precision of the optical encoder, in this article, a nonlinear error compensation method is proposed to extract the optimal underlying trend of the measurement error, which is based on the empirical mode decomposition (EMD) with a local-sinusoidal-assisted (LSA) scheme and an adaptive intrinsic mode function (IMF) selection scheme. To solve the inherent problem of mode-mixing phenomena existing in the decomposed IMFs from the measurement error, an LSA scheme is proposed to adaptively change the extreme distribution of the measurement error, which is based on an abnormal segment discrimination rule. An adaptive IMF selection scheme is proposed to construct the optimal underlying trend of the measurement error, which is first formulated as an optimization problem. Next, a significant factor is defined to select the eligible IMFs with more contributions to the underlying trend. Comparison experiments indicate that the eligible IMFs can be adaptively selected, and the proposed compensation method achieves an excellent compensation performance with the root-mean-square error of 0.380 $\mu$ m in the 95% confidence interval of $-$ 0.017–0.016 $\mu$ m, which is superior to the previous EMD-based compensation methods. [ABSTRACT FROM AUTHOR]

This work addresses the topic of magnetic linear position detection, a common method used in modern industries to determine linear displacement by magnetic means. One major shortcoming of this method is the inherent airgap instability, which puts strong constraints on mechanical construction tolerances and limits resolution and sensitivity. We propose a method to improve the airgap stability by adding a second magnet, which makes the field and by extension the system output (locally) independent of the distance from the source. It is shown that the measurement error for 1-D systems can be reduced by a factor of ~14 and for 2-D systems by a factor of ~5 by application of this method for a realistic example. [ABSTRACT FROM AUTHOR]

Ambiguity resolution (AR) is a fundamental problem for carrier phase based signal processing tasks to leverage the superhigh precision of wavelength-level range and velocity measurements. With the elaborately designed waveform and coordinated running of the space-based satellite system, the antenna-array observations of global navigation satellite system (GNSS) signals feature a phase measurement model. In this article, the AR of baseline estimation with GNSS carrier phase measurements only in the AR step is examined from an array signal processing perspective. The array-geometry-aided ambiguity resolution approach, coined as AGAR, is proposed for growing the baseline estimation provided by a search-free algorithm to the accuracy of the aperture level in an effective way. First, the single-epoch and search-free $2q$-order AR method is further investigated in size and statistical independence. Second, the conventional phase beampattern is defined to characterize the similarity of carrier phase measurement vector of signals from different directions. Third, a simple and effective ambiguity-lookup-table approach after the conventional phase beampattern is proposed which fulfills the goal of baseline growing to the array aperture level. Fourth, the identifiability, success rate, Cramér–Rao bound (CRB), and computation complexity are analyzed. As a result, the phase-based and complex-based processings are distinguished, resulting in an alternative analytical prediction of outlier probability that well approximates AR's success rate. The relationship between the success rate of AR and CRB is also clarified. Numerical simulations are carried out to verify the proposed analytical prediction and AR approach. The AR success rate increased from 10% to 93% at a relatively large measurement error of 0.05 cycle. [ABSTRACT FROM AUTHOR]

The accuracy of the force–torque sensors generally decreases with time under standard operating conditions. Therefore, periodic calibration for force–torque sensors is necessary. Specialized devices or subsidiary sensors are required during the traditional calibration process which bring in external measurement errors. In this article, an in situ calibration method is proposed to calibrate the force–torque sensor with a single standard mass to generate expected wrenches of the sensor during some arbitrary robot poses. Since the tilt of the mounting base of the robot can affect the direction of the gravity expressed with respect to the sensor frame, we first derive a model to express the relationship between the raw measurements and the expected wrenches under the influence of the tilted base, and then introduce subspaces and take advantage of projection and Rayleigh quotient to obtain the calibration matrix and angles of tilting simultaneously. The proposed algorithm is validated on simulation and the industrial automation (ATI) force–torque sensor. The results show that the proposed method is more cost-effective and reliable than existing technologies. [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]

Nowadays, benefiting from its strong capability of nondestructive detection, the ground-penetrating radar (GPR) has been applied to detect and reconstruct underground targets and has drawn lots of attention both in military and civilian fields. However, in the processing of GPR imaging, due to the dispersion errors caused by random distribution of various particles in soil, conventional imaging methods have disadvantages of low signal-noise ratio (SNR), low resolution, and unbalanced amplitude. In this article, to achieve high resolution and high veracity on underground targets’ 3-D reconstruction, we improved the conventional reverse time migration (RTM) algorithm in perspective of medium constitutive relationship. Besides, we extended the improved RTM method in 3-D environments and reconstructed the 3-D structure of several underground targets. To make the RTM algorithm suitable for stepped frequency continuous wave (SFCW) GPR system, we generated three excitation signal models and analyzed the effect of different excitation signals on imaging performance. Finally, through the quantitative analysis of simulation and on-vehicle experimental results, we found that the 3-D images generated by improved RTM method had higher resolution, smaller measurement error, and higher veracity than those of conventional RTM method. [ABSTRACT FROM AUTHOR]

This article proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with incremental encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro-actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below $\pm \text{1}\ \mu$ m when subject to a sensor quantization level of 5 $\mu$ m. [ABSTRACT FROM AUTHOR]

This paper presents an online method to monitor the conditions of the substation grounding grid. The proposed method utilizes phase-to-ground faults occurring in downstream feeders as test current sources and the neutral of a specially selected feeder as the electrode for GPR (ground potential rise) measurement. The measured fault currents and GPRs are then used to estimate substation grounding grid impedance. The method, once established, can monitor the grounding grid impedance continuously and automatically, and the trend of the impedance change can be observed. As a result, the proposed method can yield much more reliable information on the substation grounding conditions. The method also overcomes many disadvantages of the offline methods. The proposed idea, algorithm, and hardware setup are described in the paper. Factors contributing to measurement errors are investigated in detail. [ABSTRACT FROM AUTHOR]

This article presents integral-type edge event- and self-triggered policies for Lipschitz nonlinear multiagent systems, in which only edge states are employed by all controllers. An integral-type triggering function is designed to determine event instants, and the considered system can achieve Zeno-free triggering. An integral-type edge self-triggered policy is then designed to avoid sensors’ continuous measurements. Compared to traditional event-triggered schemes, the proposed strategies have relaxed triggering conditions and lowered the event frequencies. Also, the proposed edge self-triggered algorithm can avoid the requirement for continuous measurement error monitoring. Numerical simulations are given to demonstrate the effectiveness of the theoretical conclusions. [ABSTRACT FROM AUTHOR]

In this article, an adaptive output feedback compensation scheme is proposed for a class of uncertain nonlinear systems with a set of redundant actuators and a sensor. The sensor output is the only information available for output feedback. Both the actuators and the sensor may suffer from failures during operation and, therefore, all the system states are not available. To estimate the unmeasured states, a modified high-gain observer is designed which can not only ensure the closed-loop system stability but also improve the tracking performance. In the adaptive controller design, to overcome the complexity due to the unknown actuator and sensor failures coupled with the nonlinear functions and unknown parameters, an upper bound estimation method is adopted, which greatly simplifies the design procedure. It is proved that with the proposed scheme, the actuator and sensor failures can be compensated simultaneously, the reconfiguration can be adaptively updated without fault detection and isolation, and the real-tracking error can converge to a residual set, that is, mainly proportional to the sensor gain variation and measurement error. [ABSTRACT FROM AUTHOR]

The online error compensation of the three-axis magnetic sensor is the key to a high accuracy geomagnetic assisted navigation. Traditionally, the least squares or its improved methods are used for error compensation, which are offline and need to assume that measurement errors are Gaussian. In this paper, the geomagnetic measurement error is modeled based on the error source analysis, and then the compensation parameters are converted through the ellipsoid hypothesis. To eliminate the negative effect of abnormal values, iterative least squares is designed to calculate static compensation parameters and corresponding residuals, then different weights are assigned to each geomagnetic measurement data through robust estimation. Also, to avoid data saturation phenomenon and finally complete online error compensation, a forgetting factor is introduced into the iterative least squares, and the dynamic coefficients are constructed by weights of measurement data to derive the online updated compensation parameters. Simulation and experiment results both show that the online combined compensation method possesses higher adaptability, which can eliminate the influence of abnormal data effectively. And its accuracy and stability of error compensation are better than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The rapid development of sensing technologies has enabled sensors embedded into systems and generated continuous online data for anomaly monitoring and protection of the system. However, due to transmission errors and environmental noises, the online measurements collected by sensors are usually inaccurate and cannot be applied directly. A small amount of precise in situ measurements, which are accurate but costly to acquire, are usually fused with online measurements for accuracy improvement. Over the past years, there are many studies concerning such data fusion approaches for obtaining reliable fused data. However, most existing works assume the availability of in situ measurements, without providing a strategy for sampling the expensive in situ measurements. As a result, they may undersample or oversample the in situ measurements that result in large fusion errors or unnecessary costs. To address this problem, this article proposes a dynamic policy for sampling the in situ measurements adaptively using the information that the online measurements provide. Specifically, the proposed policy models the dependence between in situ sampling decisions and influencing factors using a policy network, predicts the probability of recommending in situ samplings with the policy network, and guides the sampling of in situ measurements dynamically. Theoretical and experimental results both verify the effectiveness of the proposed approach. Note to Practitioners—In modern industrial systems, a large number of online measurements of equipment parameters usually can be acquired through sensors at a small cost. However, such measurements are normally corrupted with noise and not appropriate for high-accuracy monitoring of equipment. In practice, a small number of in situ measurements, which are accurate but costly to acquire, are usually collected and fused with online measurements for accuracy improvement. Many studies have been conducted on such data fusion approaches, but most of them focus on the fusion models and overlook the role of the sampling policy of in situ measurement on the fusion results. This article addresses the problem of sampling a few expensive in situ measurements to be fused with online measurements for obtaining high-accuracy and low-cost fused data. To support this objective, a dynamic sampling policy is proposed to guide the sampling of in situ measurements and balance the benefit and cost brought by in situ samplings. Experimental results on the real-world data set show the benefits of the proposed policy compared to relevant methods. [ABSTRACT FROM AUTHOR]

A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption. To improve quality, previous works denoise a point cloud a posteriori after projecting the imperfect depth data onto 3D space. Instead, we enhance depth measurements directly on the sensed images a priori, before synthesizing a 3D point cloud. By enhancing near the physical sensing process, we tailor our optimization to our depth formation model before subsequent processing steps that obscure measurement errors. Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization. The designed model is validated (with parameters fitted) using collected empirical data from a representative depth sensor. To enhance each pixel row in a depth image, we first encode intra-view similarities between available row pixels as edge weights via feature graph learning. We next establish inter-view similarities with another rectified depth image via viewpoint mapping and sparse linear interpolation. This leads to a maximum a posteriori (MAP) graph filtering objective that is convex and differentiable. We minimize the objective efficiently using accelerated gradient descent (AGD), where the optimal step size is approximated via Gershgorin circle theorem (GCT). Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes in two established point cloud quality metrics. [ABSTRACT FROM AUTHOR]

Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently, these methods have been used in various image processing and communications applications, including denoising, spectrum sensing, and compression. Notice that estimating the covariance matrix from compressive samples leads to ill-posed minimizations with severe performance loss at high compression rates. In this regard, a regularization term is typically aggregated to the cost function to consider prior information about a particular property of the covariance matrix. Hence, this paper proposes an algorithm based on the projected gradient method to recover low-rank or Toeplitz approximations of the covariance matrix from compressive measurements. The proposed algorithm divides the compressive measurements into data subsets projected onto different subspaces and accurately estimates the covariance matrix by solving a single optimization problem assuming that each data subset contains an approximation of the signal statistics. Furthermore, gradient filtering is included at every iteration of the proposed algorithm to minimize the estimation error. The error induced by the proposed splitting approach is analytically derived along with the convergence guarantees of the proposed method. The proposed algorithm estimates the covariance matrix of hyperspectral images from synthetic and real compressive samples. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images from compressive measurements with high compression ratios ($8-15\%$ approx) in noisy scenarios. Moreover, simulations and theoretical results show that the filtering step reduces the recovery error up to twice the number of eigenvectors. Finally, an optical implementation is proposed, and real measurements are used to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

The wide bandwidth of phase-locked loop (PLL) will increase the negative real part of the output impedance of the grid-connected inverter (GCI), thus destroying the stability of the weak grids. This article proposes a novel method to improve the system stability through decoupling the PLL and grid impedance. Initially, the coupling relationship between PLL and grid impedance is revealed. It can explain why the choice of PLL bandwidth and the grid impedance magnitude is limited. Then, the decoupling method is proposed where the primary control structure of GCI does not need to change and uses the structured voltage, the product of PCC voltage and grid admittance, as the improved input of PLL. The proposed method can achieve the full-frequency passive of the output impedance of GCI even if the PLL has high bandwidth and the impedance measurement error is up to 60%, which means the adverse stability effect of PLL on the system is eliminated. The theoretical analysis is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

The H2 finite impulse response (FIR) filtering approach allows for optimal object tracking under harsh industrial conditions. In this brief, we propose a bias-constrained H2 optimal unbiased FIR (H2-OUFIR) filter for linear discrete time-invariant systems under bounded disturbances, data errors, and initial errors. The H2-OUFIR filter is derived using the backward Euler method by minimizing the squared Frobenius norm of the weighted transfer function. A bias-constrained suboptimal H2 FIR filtering algorithm using a linear matrix inequality (LMI) is also designed. Based on experimental examples of global positioning system (GPS)-based vehicle tracking and video human tracking, it is shown that the batch H2-OUFIR filter operating on short horizons with full error matrices is able to outperform the Kalman, optimal finite impulse response (OFIR), and unbiased finite impulse response (UFIR) filters. [ABSTRACT FROM AUTHOR]

A spherical Luneburg lens made of parallel planar layers of lightweight foam with embedded conducting cylindrical inserts on a uniform hexagonal grid centered in each layer is presented. This work draws on the authors’ previous paper (Ansari et al., 2020) describing a Luneburg lens that uses cubic conducting inserts on a uniform cubic grid. This previous lens, while being of lightweight and economical construction, suffered from anisotropy resulting in a focal length that varied with the inclination of the beam relative to the orientation of the cubic grid. The lens described here largely overcomes this problem and allows for simpler and more economical construction. A prototype lens designed for the 3.3–3.8 GHz band with a diameter of 400 mm and a beamwidth of 14° was tested. Radiation patterns at wide scanning angles were nearly identical, and cross-polarization for slant incident polarization was below −25 dB on boresight and below −18 dB for all angles. A characteristic of this lens construction is its extremely high efficiency. The measured gain at the mid-band was 21.6 dBi, agreeing with simulated gain based on lossless materials to within measurement error. It is shown that wider bandwidths are obtainable if the thickness of the layers is reduced. [ABSTRACT FROM AUTHOR]