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Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures. [ABSTRACT FROM AUTHOR]

This paper is an extension of the procedure pointed out to evaluate the distortion of the exponential signal, modeled as multiexponential signal with different amplitudes and time constants. This procedure evaluates the distortion by applying the Prony-like method to the signal reconstructed by zero-crossing time evaluated on the difference between the exponential original signal and the reference sinusoidal one. The extension proposed in this paper concerns with the use of the low-chirp signal as reference instead of the sinusoidal signal. The low-chirp signal permits to increase the number of the zero-crossing time in the section of exponential signal with the higher slope and decreasing in the section with the lower slope. This setting permits to estimate the distortion of the exponential signal with higher accuracy and precision. The parameters’ amplitude, phase, and start frequency of the reference low-chirp signal are used to reconstruct the exponential signal in the zero-crossing time in order to overcome the problem of the high-resolution sampling. Their estimation is carried out by the time-varying sine-fitting algorithm that processes the resulting signal that is the difference between the exponential original signal and the low-chirp signal. Advantages of the proposed extended procedure regard the direct estimation of the reference low-chirp signal parameters from the resulting signal over-sampled at low resolution, and the use of only one input acquisition channel that permits to overcome the synchronization problem between the reference and the resulting signal. The numerical and experimental tests validate the extended procedure. [ABSTRACT FROM AUTHOR]

Quantifying the level of nonlinear distortions and time-varying effects in frequency response function measurements is a first step toward the selection of an appropriate parametric model structure. In this paper, we tackle this problem in the presence of missing data, which is an important issue in large-scale low-cost wireless sensor networks. The proposed method is based on one experiment with a special class of periodic excitation signals. [ABSTRACT FROM AUTHOR]

A method for remote wattmeter (RW) adjustment is introduced in this paper. The estimated adjustment gain is intended for corrections of wattmeter’s indications. Currently, legal metrology regulations do not allow such type of adjustment of certified instruments in the field. However, the proposed adjustment could be applicable in the areas of private or industrial subaccounting, home automation, and so on. If the achievable uncertainty could be specified, the method might be suggested for the wattmeter’s calibration or verification. The implementation of remote adjustment becomes possible if both remote and reference wattmeters are connected to the same electrical grid and are equipped with a data communication channel. The main idea of the method is based on a temporary injection of the additional load at the location of remote device and acquisition of synchronized power readings by both reference and remote devices. The acquired readings are then processed to derive an adjustment gain of the RW. The proposed method aims to cancel an influence of the measurement unit propagating medium between the reference and remote devices. It is assumed that the characteristics of the medium are constant during the short period that is necessary to acquire readings. It is demonstrated that using the simulation of the proposed setup in the case of typical power losses in distribution lines, the adjustment gain can be estimated precisely. It is shown that in the case of remote measurement, the injected load power uncertainty can be reduced by averaging the acquired power samples. [ABSTRACT FROM AUTHOR]

Timing skews among channels degrade seriously the time-interleaved analog-to-digital converter (TIADC) performance, which can be improved by the blind timing skew estimation (TSE) technique. In this paper, we proposed the all-phase fast Fourier transform (ApFFT) based on spectrum sparsity signal phase relationship blind TSE (ApFFT-SSPR-BLTSE) algorithm. The ApFFT-SSPR-BLTSE algorithm reduces computational complexity based on the phase relationship of the total output from TIADC and the corresponding reference channel output compared with the existing spectrum sparsity blind TSE (SS-BLTSE) algorithm. We also utilized the ApFFT technique to increase the accuracy of phase spectral estimation. Simulation results show that the proposed ApFFT-SSPR-BLTSE algorithm, which as a reduced number of fast Fourier transforms (FFTs) and low hardware complexity, has higher accuracy for blind TSE compared to the existing SS-BLTSE algorithm. In addition, this paper presents an efficient hardware architecture of the ApFFT-SSPR-BLTSE algorithm on the Xilinx Virtex-6 vlx550tff1759 field-programmable gate array (FPGA) chip for the blind TSE of the four-channel 400-MHz 14-bit TIADC real system. The validation results show that the proposed algorithm uses only a few percent of the hardware resources of the FPGA chip, and the mismatch spurs were suppressed to better than −81.54 dB. [ABSTRACT FROM AUTHOR]

This paper considers a problem of in situ estimation of the weight values of individual items that are moving as a bulk on a conveyor in a nonoriented and nonsingulated way. In logistics and material handling processes, it is a challenge to measure the weight of each package moving on a conveyor without halting the conveyor operation. This is not only because measuring weight requires physical contact between the target item and the measuring instrument, but also because each item must be separated (or singulated) from others for it to be weighed. Such a singulation task causes a significant delay in the transport process, especially when the conveyor is heavily loaded with items. Because of this issue, despite the importance of weight information for correct pricing and safe loading (for both air and ground vehicles), the weight has largely been ignored in many logistical sites such as sorting facilities or distribution centers. To address these issues, this paper proposes a new instrumentation device called the in-motion weight sensor (IMWS) array that consists of a grid of mini-checkweighers operating separately from each other as independent weight measurement cells. Each IMWS unit is equipped with a mini-conveyor mounted on a load cell, and provides the composite load data for a group of items being carried within the IMWS system. A depth camera mounted above the IMWS surface identifies both the location and the orientation of each package and provides the real-time measurement of its partial areas that intersect with the surface of load cells in contact with each package. The load cell measurements and the vision data are then fused to construct the measurement equation that is linear in the unknown weight values of all the packages being transported within the IMWS system. To extract individual weight values from these composite load data, we implement the recursive least square (RLS) estimation to the IMWS array. The feasibility of the proposed idea has been verified with experimental tests using the full-scale prototype IMWS array system. The results show that IMWS array is capable of estimating the unknown weight with the mean error around ±3% error and the standard deviation of 9 ~ 13% error for cases where 3 ~ 5 randomly oriented packages are moving in a nonsingulated way. [ABSTRACT FROM AUTHOR]

The significance of measurement data transfer over unreliable channel has emerged in the last decade, due to the spread of sensor networks and the idea of Internet of things. This paper investigates the behavior of the fast Fourier transform (FFT)-based power spectral density (PSD) estimation in the case of data loss. There are different methods available to estimate the PSD, but the hegemony of the FFT is beyond dispute, especially in real-time applications. This paper investigates the behavior of the PSD estimator in the case of different data loss models, and then offers some simple solutions on how the data loss can be handled in PSD estimation, when only moderate computing resources are available. The efficiency of the proposed method is demonstrated by the simulation and measurement results. [ABSTRACT FROM AUTHOR]

Distribution system state estimation (DSSE) tools are required to estimate the real operating conditions of power distribution networks. The accuracy of the quantities estimated through DSSE plays a key role in the effectiveness of the management and control functions. This paper presents a comprehensive mathematical analysis aimed at highlighting the most important factors affecting the accuracy of the voltage profile obtained by means of weighted least squares estimators. The proposed analysis shows the way in which the uncertainty components of the voltage estimations depend on the measurement system available on the field. In particular, this paper considers synchrophasor measurements provided by phasor measurement units and hybrid measurement systems also presenting conventional devices. Tests performed on a 95-bus network prove the validity of the theoretical analysis and show the importance of the results also from a meter placement perspective, emphasizing the requirements needed to achieve specific accuracy targets. [ABSTRACT FROM AUTHOR]

Reliability of any electrical system heavily depends on the accuracy of the model used for representing the integrated device components. The design of renewable power sources and energy management systems using ultracapacitor demands adoption of precise mathematical representation of ultracapacitor dynamics. An appropriate model of ultracapacitor not only allows an accurate assessment of its charge/discharge characteristics but also provides a precise estimation of energy storage and power delivery capabilities. However, due to the lack in the usability of data/information provided by the manufacturer, conventional calculus-based ultracapacitor modeling and energy estimation approach often leads to its inaccurate and inefficient utility. In this regard, this paper has focused on fractional calculus-based analytical formulation for power, energy, and efficiency measurement of ultracapacitor and has deployed an upgraded model of the ultracapacitor, which in turn provides a better estimation of its energy efficiency. The work presented in this paper has been supported by extensive experimentation with the ultracapacitor under different input excitations, considering the dependence of energy storage (state of energy) and power delivery capacity of ultracapacitor on the charge/discharge profile. Furthermore, the hybrid optimization-based analysis recommends the requirement and challenges for the design and development of optimal charging/discharging of ultracapacitors for achieving higher energy efficiency. [ABSTRACT FROM AUTHOR]

This paper considers estimation of a quantized constant in noise when using uniform and nonuniform quantizers. Estimators based on simple arithmetic averages, on sample statistical moments and on the maximum-likelihood procedure are considered. It provides expressions for the statistical efficiency of the arithmetic mean by comparing its variance to the proper Cramér–Rao lower bound. It is conjectured that the arithmetic mean is optimal among all estimators with an exactly known bias. Conditions under which its statistical performance are improved by the other estimation procedures when the exact bias is not known are found and analyzed. Using simulations and analysis of experimental data, it is shown that both moment-based and maximum-likelihood-based estimators provide better results, when the noise standard deviation is comparable with the quantization step and the noise model of quantization can not be applied. [ABSTRACT FROM PUBLISHER]

Missing data are an important issue in large-scale low-cost wireless sensor networks. In these measurement applications, sensor failure, data transmission errors, and data removal for saving transmission power are the main causes for the missing data. This paper studies the impact of missing data on the measurement of the time-variant frequency response func- tion (TV-FRF) of linear time-variant systems. A nonparametric procedure for estimating the missing data and the TV-FRF is proposed. The theory is illustrated by simulations and experiments. [ABSTRACT FROM PUBLISHER]

Power quality is described by voltage fluctuation measures. Voltage fluctuation is understood as “a series of voltage changes or a continuous variation of the rms or peak value of the voltage.” Voltage variation is most often manifested by flicker, which might become obnoxious at a high enough intensity. One of the standard measures of voltage fluctuation is \boldsymbol Pst indicator. One of its major flaws is its limited diagnostic capabilities. Voltage fluctuation indices are the measure, which provides much bigger range of diagnostic capabilities. However, it has not been acknowledged as a standard measure. The initial part of this paper includes the description of the accepted method of reproducing voltage variation. It also presents general characteristics of the \boldsymbol Pst indicator and voltage fluctuation indices. The next part of this paper describes the methods of converting voltage fluctuation indices into \boldsymbol Pst indicator and vice versa. The capability of estimating the \boldsymbol Pst indicator on the basis of the indices has been confirmed. Three examples of measurement results of both these measures in low-voltage (LV) power grids have been presented. These results have proved the connection between the estimated values (on the basis of voltage fluctuation indices) and the results of the \boldsymbol Pst indicator measurements. [ABSTRACT FROM AUTHOR]

Theoretical studies on nondata aided (blind) parameter estimation algorithms for signals have been carried out over the past decades. However, most of them have not been implemented and measured in order to validate their feasibility. In this paper, at first, the blind parameter estimation algorithms have been presented, which facilitate to the design of a blind wireless receiver (BWR) where information bits are retrieved successfully. Next, a software-defined radio testbed has been implemented and a measurement method setup is proposed. Here, an uncertainty analysis for the estimation of single carrier system parameters, such as carrier frequency, symbol rate, and symbol timing offset, has been presented in terms of normalized mean-squared error between estimated and true values of the parameter. The success rate of blind modulation classification has been presented for four classes of a modulation scheme. These preliminary studies and measurement results demonstrate that the proposed BWR prototype is capable of estimating the concerned parameters and constellation symbols over an indoor propagation environment. The symbol-error rate for quadrature phase shift keying modulation is finally gained through simulations. [ABSTRACT FROM AUTHOR]

This paper presents an instrument for finding the location of an object in 3-D space. The location is determined using estimates of the object’s distance from static anchor points with known coordinates. This process involves three main challenges: 1) a way of measuring the Euclidean distance between two points; 2) estimating system parameters such as the position of the anchor points, the speed of sound, and data acquisition delay; and 3) computing the position of the object from its distance to the anchor points. This paper addresses each of these challenges. A wideband acoustic signal is used for distance estimation. Nonlinear regression techniques are used to estimate the positions of the anchors and jointly optimize other system parameters. After calibration, a similar method is also used, this time using the optimized parameter set, to estimate the position of the target object. The design and performance of a prototype system, based on the proposed method, are discussed. The prototype, using five anchors and 13 reference locations, achieved a mean positioning accuracy of 0.86 mm over a test volume spanning 0.5 m $\times0.5$ m $\times0.25$ m. [ABSTRACT FROM AUTHOR]

In this paper, we discuss nonparametric estimation of the probability density function (PDF) of a univariate random variable. This problem has been the subject of a vast amount of scientific literature in many domains, while statisticians are mainly interested in the analysis of the properties of proposed estimators, and engineers treat the histogram as a ready-to-use tool for a data set analysis. By considering histogram data as a numerical sequence, a simple approach for PDF estimation is presented in this paper. It is based on basic notions related to the reconstruction of a continuous-time signal from a sequence of samples. When estimating continuous PDFs, it is shown that the proposed approach is as accurate as kernel-based estimators, widely adopted in the statistical literature. Conversely, it can provide better accuracy when the PDF to be estimated exhibits a discontinuous behavior. The main statistical properties of the proposed estimators are derived and then verified by simulations related to the common cases of normal and uniform density functions. The obtained results are also used to derive optimal, i.e., minimum integral of the mean square error, estimators. [ABSTRACT FROM AUTHOR]

An estimate of a measurand using a nonlinear function of uncorrelated input quantities can be done by either applying the nonlinear function to the means of the input quantities (Method 1) or calculating the mean of a set of values obtained from propagating individual measurement values through the nonlinear function (Method 2). This paper proposes an improvement over the standard Method 2 procedures when the input quantities are assumed to be statistically independent and the nonlinear function has a general sum-of-product form, which covers many common measurement models. This paper shows that the proposed new approach (called Method 2S), if applicable, always produces a mean-squared error smaller than that of the conventional Method 2 procedures. The proposed approach improves the efficiency of Type-A evaluation as well as the Monte Carlo method. It also well complements the mainstream practices in the measurement uncertainty evaluation. [ABSTRACT FROM AUTHOR]

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of power line communications networks it is helpful to implement data routing strategies, while in power distribution networks and smart microgrids it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm in a setup that is typical of power line distribution networks. [ABSTRACT FROM AUTHOR]

As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment. [ABSTRACT FROM AUTHOR]

Time-of-flight (TOF) measures are valuable in the estimation of displacements, distances, and moving fluids’ speed, e.g., the wind. Of the different types of methods that measure wind speed, the ultrasound is highlighted. As part of the calculation required to acquire the wind speed, one must find the time that an ultrasonic wave takes to travel from one particular place to another. This time is known as TOF. The biggest challenge in measuring TOF is to determine the correct moment when the ultrasonic wave arrived at the reception because it will be immersed in noise and its envelope probably will be corrupted by the actions of the wind. The methodology proposed by this paper presents a combined algorithm between an extended Kalman Filter (EKF) and a zero-crossing detector (ZCD) technique. The EKF method is responsible for the noise elimination stage and estimation of the real ultrasound’s envelope, while the ZCD detects the correct TOF of the estimated signal. This paper also presents the flaws and solutions found for each technique. It was shown that for speeds up to 75 km/h, the coefficient that was found to be $R^{2}\,\,=0.99977$. [ABSTRACT FROM AUTHOR]

This paper presents a modified version of the sine wave histogram test for analog-to-digital converter (ADC) characterization. To accurately estimate the transition levels, the input signal has to fulfill strict conditions, which are coherent sampling and uniform distribution of the phases. Noncoherent sampling of the sine wave leads to systematic errors in the transition level estimation, while the nonuniform distribution of the phases increases the variance of the results using the original sine wave histogram test method. The source of the errors is the improper modeling of the histogram of the measured signal. This paper shows that the true histogram of the measured sine wave can be reconstructed by estimating the phases of the sampled signal. It is shown that integrating this information into the estimation of the transition levels can significantly decrease the effect of noncoherent sampling, thus the ADC can be characterized accurately even if the conditions are not fulfilled. The proposed method is verified on both simulated and real measurement data. [ABSTRACT FROM AUTHOR]

Synchrophasor estimation is typically performed by means of spectral analysis based on the discrete Fourier transform (DFT). Traditional DFT approaches, though, suffer from several uncertainty contributions due to the stationarity assumption, spectral leakage effects, and the finite-grid resolution. This paper addresses these limitations, by proposing a joint application of the Hilbert transform (HT) and the interpolated DFT (IpDFT) technique. Specifically, the HT enables the suppression of the spectral leakage generated by the negative image of the tones under analysis, whereas the IpDFT limits the effects of spectrum granularity. In order to relax the constraint in terms of measurement reporting latency, the proposed estimator can adopt a window length of 40 ms and yet provides a noticeable estimation accuracy with a worst-case total vector error and frequency error equal to 0.02% and 4 mHz, respectively, in steady-state conditions. In this context, this paper discusses the most suitable setting of the algorithm parameters and their effect on spurious component rejection. Moreover, a thorough metrological characterization of the algorithm estimation accuracy and responsiveness with respect to the IEEE Std. C37.118.1 is carried out in order to detect the main uncertainty sources as well as possible room for enhancement. Finally, a comparison with two consolidated IpDFT approaches shows the actual performance enhancement provided by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Atmospheric ice accretion is considered a severe safety issue for technical systems such as communication infrastructure, power lines, blades of wind turbines, and so on in cold climate regions. Among different sensing systems to detect and provide reliable information about the state of ice, capacitive sensing has been reported as a suitable technique. In capacitive sensing, measured capacitances are influenced due to ice accretion. Signal processing for ice sensors requires to estimate parameters for the state of ice from the data. In this paper, we present a model-based estimation approach for capacitive ice sensors. We analyze different electrode topologies for ice sensing and provide a statistical analysis of the sensor behavior. We hereby consider the random nature of natural ice accretion. From simulations, we show how a statistical model for the measurement process can be derived and demonstrate the construction of estimation algorithms within the Bayesian framework. We will demonstrate the capability of our approach for automated ice detection by means of long-term field data showing the ability to estimate ice layers with a precision better than 1 mm. [ABSTRACT FROM AUTHOR]

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible. [ABSTRACT FROM AUTHOR]

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient compressive sensing harmonics detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid. [ABSTRACT FROM AUTHOR]

When estimating the input-output characteristic of a static nonlinear function, input-referred noise may induce estimation bias, if model identification based on simple averages of input and output measurement data is performed. This paper considers estimation of static nonlinearities based on polynomial functions and input-output averaged data. It first illustrates how the estimation bias originates and then it describes a procedure to compensate its effects. Both simulation and experimental results are shown. Experiments are carried out to estimate the voltage-to-voltage characteristic of a diode-based electrical circuit. Practical considerations are made regarding the minimum number of samples needed to perform compensation effectively. [ABSTRACT FROM PUBLISHER]

Next-generation phasor measurement units (PMU) are expected to be more accurate than existing ones, especially to address the stricter requirements of future active distribution grids. From this perspective, the influence of acquisition wideband noise (which includes multiple contributions both in amplitude and in phase) has to be carefully evaluated. In this context, the contribution of this paper is twofold. First, it provides a general framework to evaluate the effect of wideband noise on synchrophasor amplitude, phase, frequency, and rate of change of frequency (ROCOF) estimation. The results of this analysis show that the influence of wideband noise can become critical for compliance with the requirements reported in the IEEE standards C37.118.1-2011 and C37.118.1a-2014, especially for frequency and ROCOF estimation. In addition, this paper reports general guidelines to choose the PMU effective resolution and sampling rate for which the impact of wideband noise on both P Class and M Class PMUs is negligible. [ABSTRACT FROM PUBLISHER]

In this paper, we propose new guidelines for the analysis of random telegraph noise (RTN) in electronic devices. Starting from an in-depth understanding of RTN signal characteristics, we will identify the correct measurement conditions to enable RTN analysis as a characterization tool for electronic devices. The estimate of RTN statistical parameters may indeed strongly depend on the choice of measurement conditions. We will carefully consider both the measurement limits and the extraction process constraints to devise a strategy to identify RTN signals measured in conditions allowing a meaningful estimation of their parameters. The proposed strategy will be tested on a variety of different RTN signals and operating conditions. [ABSTRACT FROM PUBLISHER]

Expanded uncertainty estimation is normally required for mission-critical applications, e.g., those involving health and safety. It helps to get a distribution range of the required confidence level for the uncertainty evaluation of a system. There are a number of available techniques to estimate the expanded uncertainty. However, there is currently no commonly accepted benchmark test distribution set adopted to compare the performances of different techniques when they are used to estimate the expanded uncertainty. Without such a common benchmarking platform, the relative reliability of a particular technique in comparison to other techniques can be untrustworthy. To address the shortcoming, this paper proposes a set of analytically derived benchmark test distributions. It goes on to show the benefits of using them by comparing the performance of existing distribution fitting techniques when applied to the moment-based expanded uncertainty evaluation. The most commonly used moment-based distribution fitting techniques, such as Pearson, Tukey’s gh, Cornish–Fisher expansion, and extended generalized lambda distributions, are employed as test cases in this paper. The test distribution set proposed in this paper provides a common benchmarking platform for metrologists intending to assess the performance of different expanded uncertainty estimation techniques. Results from the performance comparison would help practitioners to make a better choice of a distribution fitting technique that would best suit their respective systems. [ABSTRACT FROM AUTHOR]

This paper proposes several extensive methods to predict the future location of an automobile. The goals of this paper are to find a more accurate way to predict the future location of an automobile by 3 s ahead, so that the prediction error can be greatly reduced with the innovative idea of merging global-positioning-system (GPS) data with geographic-information-system (GIS) data. The improvement starts by applying existing techniques to extrapolate the current GPS location. Comprehensive Kalman filters (KFs) are implemented to deal with inaccuracy in the different identified possible states an automobile could be found in, which are identified as constant locations, constant velocity, constant acceleration, and constant jerks. Then, the KFs are set up to be part of a interacting-multiple-model (IMM) system that provides the predicted future location of the automobile. To reduce the prediction error of the IMM setup, this paper imports an iterated geometrical error-detection method based on GIS data. The assumption that the automobile will remain on the road is made; therefore, the predictions of future locations that fall outside are corrected accordingly, making a great reduction to the prediction error. The actual experimental results validate our proposed system by reducing the prediction error to around half of what it would be without the use of GIS data. [ABSTRACT FROM AUTHOR]

In this paper, the characterization of ionic polymer–metal composite (IPMC)-based sensors, for possible applications related to biological systems, with respect to the influence of environmental temperature and relative humidity is investigated. This paper is second in a row devoted to the characterization of IPMC transducers with respect to the aforementioned influencing quantities. The characterization is performed by statistically investigating sensing signals in typical working conditions, and the experiments performed show that, for the investigated ranges, the effects of relative humidity are much more evident than the corresponding effects produced by temperature changes. The results reported give information that integrated IPMC transducer models, which are introduced so far, contribute to better exploit this novel technology. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach to enhance a random bearing vibration signal (repetitive character with a random part) corrupted by an oscillatory gear vibration signal. This approach, called the Taylor- $H_{\infty }$ (TH) estimator, is proposed in a variable speed condition. The latter is designed using the mathematical modeling of the raw signal in the state space. It is used to remove the oscillatory signal from the raw signal. The obtained residual signal is the enhanced signal of the bearing contribution. Different from the Vold–Kalman (VK) estimator, which results from the minimization of the mean squared error, the proposed method is based on the minimax optimization. The minimax approach for the TH estimator leads to the minimization of the estimation error for the worst possible amplification of the random signals. No prior knowledge about the statistical properties of the random signals is required. The proposed approach is assessed using both synthetic and experimental nonstationary vibration data. In addition, the squared envelope spectrum of the residual signal is analyzed to diagnose the bearings state. This paper shows the [ABSTRACT FROM AUTHOR]

In this paper, two techniques for accurate estimation of the moment of magnetic dipole are proposed. The achievable accuracy is investigated, as a function of measurement noise affecting estimation of magnetic field cartesian components, and with respect to the induced rms voltage. The proposed techniques are validated both via simulations and experiments. [ABSTRACT FROM AUTHOR]

This paper designed a novel multiresolution analysis method and input waveform classification scheme. The proposed techniques serve as preprocessing procedures of input waveforms in order to facilitate synchrophasor estimation. Simple, scalable and time-shifted “pseudowavelets” (PWs) are employed to repeatedly calculate the correlation coefficients between the proposed PWs and input power waveforms. By scrutinizing the correlation factors with respect to frequency and time, the proposed method is capable of revealing the temporal trajectories of frequency and amplitude features of input waveforms. Such features are then leveraged to classify input waveform types. The result of such waveform classification can be applied to perform accurate synchrophasor estimation. Contrary to traditional approaches, where a single algorithm is designed to compute synchrophasor accurately for all input signal types, in this paper, a framework is proposed which enables adaptive switching of synchrophasor algorithms, so that the most suitable algorithm can be used for the identified waveform. The efficacy and efficiency of proposed methods are validated with standardized phasor measurement unit testing waveforms and simulated power system waveforms. The results prove that the PW-based waveform classification method is capable of distinguishing between power system dynamic waveforms. [ABSTRACT FROM AUTHOR]

This paper presents an innovative in-field coarse bias estimation (CBE) method for stationary intermediate-grade inertial measurement units (IMUs), which is based on the existence of normality and orthogonality errors in the attitude matrix supplied by the traditional three-axis attitude determination stationary coarse alignment (CA) stage. Differently from the currently existing in-field bias estimation formulations, the proposed CBE method is conceived to be conducted in stationary conditions, jointly with the CA, and it allows the down accelerometer bias and the north and down angular rate sensor biases to be adequately estimated. As a main contribution of this paper, we demonstrate that the proposed CBE method is able to greatly accelerate the estimation process of the aforementioned biases, contributing, hence, to shorten the IMU setup time. A comprehensive error analysis is presented, and the conclusions are twofold: 1) the proposed CBE method is valid regardless of the IMU orientation and 2) its estimation accuracy is mainly constrained by the north accelerometer bias and the position location uncertainty, which are, in general, very reduced. Results from simulated and experimental test confirm the adequacy of the outlined verifications, also attesting the superiority of the proposed CBE method in situations where, due to time constraints, stationary fine alignment procedures are not allowed to be carried out. [ABSTRACT FROM PUBLISHER]

Recently, the application of the adaptive notch filter (ANF) concept to the analysis of damped oscillations and the estimation of frequency and damping factor of such oscillations was introduced. This paper presents the application of the enhanced phase-locked loop (EPLL) to the same problem. A modified version of the EPLL is developed that includes the estimation of damping factor in addition to the already estimated variables of amplitude, phase angle, and frequency. The structure, mathematical formulation, and theoretical stability analysis of the proposed method, including guidelines for its parameter tuning, are presented. Simulation results verify the analytical derivations and the desirable performance of the proposed method. This paper concludes that the EPLL-based approach can address the problem as efficient as the ANF approach while offering more robust and accurate results at low sampling rate applications. [ABSTRACT FROM AUTHOR]

In quite some applications, the linear dynamics of the system under test evolve over time in a smooth nonperiodic manner. The dynamic behavior of such systems is uniquely described by the time-variant frequency response function (TV-FRF). However, since most real-life systems behave nonlinearly to some extent, it is important to detect and quantify the deviation between the ideal linear time-variant (LTV) framework and the true non-LTV dynamics. Therefore, in this paper, a nonparametric estimation procedure is developed for detecting and quantifying the noise level and the level of the nonlinear distortions in TV-FRF measurements using random-phase multisine excitations. As a result, the users can decide whether or not the LTV framework is accurate enough to describe the true dynamics in their particular application. The proposed measurement procedure is illustrated on a time-variant electronic circuit. [ABSTRACT FROM PUBLISHER]

The monitoring of photovoltaic (PV) systems is important for the optimization of their efficiency. In this paper, a low-cost smart multisensor architecture equipped with voltage, current, irradiance, temperature, and inertial sensors, for the monitoring (at the panel level) of a PV system, is presented with the aim of detecting the causes of efficiency losses. The system is based on a Wireless Sensor Networks with sensing nodes installed on each PV panel. The acquired data are then transferred to a service center where dedicated paradigms continuously perform the assessment of electrical efficiency as well the estimation of correlated causes, at the single panel level. In this paper, the detection of critical faults (temporary and permanent shadowing, dirtying, and anomalous aging) is addressed. The methodology adopted to estimate efficiency losses and related causes is based on the comparison between the measured efficiency of each PV panel and the nominal one estimated in the real operating conditions. Moreover, the anomalous aging estimation is based on the five parameter model approach that exploits a dedicated minimization paradigm to analyze the mismatch between the nominal current–voltage model of the PV panel and the measured one. The main advantage of the proposed approach is the continuous monitoring of PV plants and the assessment of possible causes of power inefficiency at the PV panel level, allowing for the implementation of a really efficient distributed fault diagnosis system. The experimental results are presented along with the analysis of the uncertainty affecting the measurement system. [ABSTRACT FROM PUBLISHER]

This paper presents a nonparametric time-domain estimation method for linear slowly time-varying systems. The proposed method uses a 2-D impulse response function. That is modeled by a generalized B-spline smoothing technique. This means that double smoothing is applied: once over the different realizations (referring to the system behavior) and once over the global time (referring to the system memory). With respect to the system dynamics using the proposed method, it is possible to reduce the model order and to decrease the effect of the disturbing noise. [ABSTRACT FROM PUBLISHER]

In a 3-D gaze tracking system, the system calibration is used to determine the positions of the light sources and the screen in the camera coordinate system, which plays an important role in the estimation of the 3-D gaze and the point of regard (PoR). The traditional way for the system calibration is the mirror-based method, in which a mirror is used to make the light sources and the screen can be captured by the camera through specular reflection. However, the mirror-based method is imprecise and complex. In this paper, a novel system-calibration method for the 3-D gaze tracking system is proposed. On the basis of the system camera, a calibration camera is placed on the opposite of the screen to constitute a multi-camera system. The images are captured by the two cameras to estimate the light sources and the screen of the system camera coordinate frame. Simulations and experiments were carried out to illustrate the validation of the method. This method solves the problem that the light sources and the screen are not in view of the system camera. In addition, it is easy to implement while ensuring high accuracy and providing a new method for other similar calibration systems. [ABSTRACT FROM AUTHOR]

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramér–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system. [ABSTRACT FROM AUTHOR]

Fast harmonic phasor measurements are very useful in power system protection applications, e.g., high-impedance fault location and intelligent method-based islanding detection. To address this problem, a novel harmonic phasor estimator is proposed in this paper, especially for P-class phasor measurement units (PMUs). The exponential functions are used to model the decaying dc (ddc) component, and the optimal time constant (TC) set for the ddc model is searched based on an enumeration method. In addition, the dynamic harmonic phasors are modeled based on the Shannon sampling theorem. As a result, a filter bank is designed for harmonic phasor and frequency estimations. The rate of change of frequency (ROCOF) of the harmonic is estimated based on a second-order fitting method. The response time of the proposed method for harmonic phasor estimation is always no longer than 40 ms. Simulation test results indicate that the proposed method is more accurate than the Taylor–Fourier transform (TFT) under ddc offset and frequency deviation conditions. In particular, the proposed method is more robust to ddc’s TC variations. An experimental test is implemented to demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Tomographic imaging based on acoustic wave time-of-flight (TOF) measurement is considered to be a promising technique for monitoring temperature fields due to it being noninvasive, relatively low cost, having a high temporal resolution, and being easy to use. Accurate estimation of the TOF is key in improving the quality of the reconstructed images. In this paper, an improved TOF estimation method based on the Akaike information criterion (AIC) is developed. Two improvements are studied in comparison to the conventional weighted AIC TOF estimation methods against crosstalk interference and sound reflections: an adaptive window is applied for accurate AIC TOF estimation; a phase correction method is used to further improve the temporal resolution and noise robustness of the TOF estimation. The feasibility and effectiveness of the developed methods are validated in two experiments. [ABSTRACT FROM AUTHOR]

This paper focuses on the time-difference-of-arrival (TDOA)-based direction finding for a low-altitude source with an acoustic sensor array. The source bearing in space must be estimated by the joint azimuth-elevation estimation. The azimuth may be the only concern for some scenarios, where the elevation is trivial; however, an extra parameter about elevation needs to be estimated in the joint estimation. When a planar array is introduced to replace the spatial array, the azimuth-only estimation solution with the original TDOA measurements is proposed. This solution is simpler than the joint estimation by excluding the elevation calculation. The mathematical relation between the TDOAs of spatial and planar arrays is derived, and their azimuth Cramer–Rao lower bounds (CRLBs) are compared. The CRLBs together with the pseudo-linear estimation and Taylor series least squares method are evaluated for estimating the source azimuth by simulations as well as real-life data. The results demonstrate that the azimuth-only estimation would create a modest precision improvement over the joint azimuth-elevation estimation. The effect of array altitude variation is also examined, suggesting that the relative heights among sensors have little influence on the estimation of source azimuth. [ABSTRACT FROM AUTHOR]

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy. [ABSTRACT FROM AUTHOR]

Nonselective gas sensor array has different sensitivities to different chemicals in which each gas sensor will also produce different voltage signals when exposed to an analyte with different concentrations. Therefore, the characteristics of cross sensitivities and broad spectrum of nonselective chemical sensors promote the fast development of portable and low-cost electronic nose (E-nose). Simultaneous concentration estimation of multiple kinds of chemicals is always a challengeable task in E-nose. Multilayer perceptron (MLP) neural network, as one of the most popular pattern recognition algorithms in E-nose, has been studied further in this paper. Two structures of single multiple inputs multiple outputs (SMIMO) and multiple multiple inputs single output (MMISO)-based MLP with parameters optimization in neural network learning processing using eight computational intelligence optimization algorithms are presented in this paper for detection of six kinds of indoor air contaminants. Experiments prove that the performance in accuracy and convergence of MMISO structure-based MLP are much better than SMIMO structure in concentration estimation for more general use of E-nose. [ABSTRACT FROM PUBLISHER]

In spectral analysis, achieving coherent sampling, especially when signals have large distortion, has been a challenge for many years. This paper introduces three algorithms to resolve this issue. In comparison to previous algorithms, and two widely used methods in industry: windowing and four-parameter sine wave fit, these new algorithms are capable of obtaining accurate spectral results of the signal, while achieving high accuracy as well as computational efficiency. The novel contribution of this paper is that not only does it propose two new algorithms, but also it analyzes their advantages and limitations in detail, providing their trade-offs and different fields of applications. Extensive simulations and measurements are performed to validate these algorithms. Combined with their high accuracy, computational efficiency, and robustness against signal purity, these algorithms are readily available to be implemented for bench or on-chip testing. In addition, it is suitable for data converter spectral testing when noncoherent sampling is present, and spectrally pure test signal source is not available. [ABSTRACT FROM PUBLISHER]

This paper presents a completely new approach to measure the jitter at the output of a sample-and-hold circuit. A cyclostationary stochastic process is used to model the entire noise of the measurement system. This approach relies on the calculation of a cyclostationary variance, representing a complete description of the noise of the measurement system. This provides the basis for conducting powerful analyses of how the various noise sources contribute to the cyclostationary variance, enabling the user to gain a deep insight into the noise behavior of the system. A new jitter estimator is derived that provides some new and unprecedented features. First, it is robust against the second harmonic of the input signal, and second, it allows for the elimination of cyclostationary noise contributed by the data acquisition device. A stochastic model of the method is derived and a thorough analysis of the uncertainty of the proposed estimator is presented. This results in a formula that allows for the prediction of the measurement uncertainty and another formula that allows for the calculation of the required number of samples in order to meet a given uncertainty. The method proved to be accurate in Monte Carlo simulations and in measurements as well. A jitter in the range of 22 fs out of a 10-GS/s sample-and-hold integrated circuit was successfully measured with a standard deviation of 230 as. [ABSTRACT FROM PUBLISHER]

Wireless technologies combined with advanced computing are changing industrial communications. Industrial wireless networks can improve the monitoring and the control of the entire system by jointly exploiting massively interacting communication and distributed computing paradigms. In this paper, we develop a wireless cloud platform for supporting critical data publishing and distributed sensing of the surrounding environment. The cloud system is designed as a self-contained network that interacts with devices exploiting the time synchronized channel hopping protocol (TSCH), supported by WirelessHART (IEC 62591). The cloud platform augments industry-standard networking functions as it handles the delivery (or publishing) of latency and throughput-critical data by implementing a cooperative-multihop forwarding scheme. In addition, it supports distributed sensing functions through consensus-based algorithms. Experimental activities are presented to show the feasibility of the approach in two real industrial plant sites representative of typical indoor and outdoor environments. Validation of cooperative forwarding schemes shows substantial improvements compared with standard industrial solutions. Distributed sensing functions are developed to enable the autonomous identification of recurring cochannel interference patterns. [ABSTRACT FROM AUTHOR]

This paper presents a new time–frequency (TF) analysis method called matching synchrosqueezing wavelet transform (MSWT) to signals with fast varying instantaneous frequency (IF). The original synchrosqueezing wavelet transform (SWT) can effectively improve the readability of TF representation (TFR) of signals with slowly varying IF. However, SWT still suffers from TF blurs for signals with fast varying IF. Moreover, the variable operating conditions of the aeroengine always make the vibration a signal with fast varying IF, especially when it comes to significant speed changes, which results in the obscure TFR for aeroengine vibration monitoring. In this paper, the MSWT introduces a chirp rate estimation into a comprehensive IF estimation to match the TF structure of the signals with fast varying IF and thus to achieve a highly concentrated TFR as the standard TF reassignment methods. Most importantly, the MSWT retains the reconstruction benefit like the SWT. The proposed MSWT is validated by both numerical simulation and applications in a bat echolocation signal analysis. Finally, a case study of a dual-rotor turbofan engine is given to illustrate the effectiveness of the proposed method for aeroengine vibration monitoring. [ABSTRACT FROM AUTHOR]

The availability of real-time information about the biological activity is a valuable matter in wastewater treatment employing activated sludge processes, and this is represented by the oxygen uptake rate (OUR). In these processes, OUR measurements have been used as a fast indicator of changes in waste load or influx of toxic organic compounds and for monitoring of the process efficiency. In this paper, we propose a method for OUR estimation by using Kalman filters and PWM control of the dissolved oxygen (DO) concentration in the activated sludge reactor. The proposed method presents a shorter response time than the standard online procedure and a lower measurement uncertainty compared to a previous feedback control method, which estimates the OUR from values of the applied control signal and the DO concentration measurements. Simulations are presented to illustrate the method and experiments are performed in a bench scale activated sludge reactor to show the applicability of the proposed estimator in a real system. The results confirm the fast response of the proposed method and its consistency with respect to the standard procedure for OUR computation. [ABSTRACT FROM AUTHOR]