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To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a line-scan camera to image the fringes of sheet stack and is able to provide a real-time and noncontact measurement of their quantity. With a brief introduction of the system architecture, our main work focuses on the sheet counting algorithms. The basic principle is to identify each sheet profile from the 1-D image with a robust ridge strength measurement. First, a multiscale bi-Gaussian ridge likelihood measurement and a ridge-valley descriptor are utilized to improve adjacent objects detection by increasing local contrast around sheet fringes. Then, a sheet recognition scheme, which integrates a peak detection algorithm and the ridge region criteria for verification, is proposed to discriminate true sheets from the obtained ridgeness measure. According to experiments and tests in real production lines, our apparatus can reach a very high measuring accuracy for printing papers or cards with a thickness not <0.2 mm. [ABSTRACT FROM PUBLISHER]

In this paper, the effects of motion blur on 2-D digital image correlation (DIC) measurements are discussed, with a particular focus on the description of displacement measurement uncertainty. The research started with the simulation of motion blur with a suitable literature-based algorithm. Then the state of the art of uncertainty analysis in the context of DIC was compared with simulation results, and it was found that the effects of motion blur on uncertainty are not predicted correctly by the literature models. This phenomenon is explained by the fact that the literature models do not consider motion blur. Consequently, a simple monodimensional case is analyzed. In this way, it has been possible to evaluate the sensitivity of DIC correlation function [evaluated as the sum of squared differences (SSD)] to motion blur. Using these results, it is possible to extend the analysis to the case of a generic image with the help of a power-law model. Eventually, the model proposed in this paper is able to represent the effects of motion blur correctly. [ABSTRACT FROM AUTHOR]

Lift-off variation causes errors in eddy current measurement of nonmagnetic plates as well as magnetic plates. For nonmagnetic plates, previous work has been carried out to address the issue. In this paper, we follow a similar strategy, but try to reduce the lift-off effect on another index—zero-crossing frequency for magnetic plates. This modified index, termed as the compensated zero-crossing frequency, can be obtained from the measured multifrequency inductance spectral data using the algorithm we developed in this paper. Since the zero-crossing frequency can be compensated, the permeability of magnetic plates can finally be predicted by deriving the relation between the permeability and zero-crossing frequency from Dodd and Deeds method. We have derived the method through mathematical manipulation and verified it by both simulation and experimental data. The permeability error caused by liftoff can be reduced within 7.5%. [ABSTRACT FROM PUBLISHER]

This paper presents a wireless sensor network system (WSNS) for effective, reliable, and efficient monitoring of landslides. The system incorporates a network of wireless inertial measurement unit (IMU) sensor devices for collecting movement data, a local base station for data gathering, a capture server for data processing and storage, and a warning system. The major contributions of this paper are: 1) two approaches for defining movement thresholds; 2) landslide classification concept based on IMU sensor data patterns and magnitudes; and 3) a conceptual framework for building an intelligent and reliable wireless monitoring and warning system. The IMU sensor data collected by three-axis accelerometer and three-axis gyroscope were utilized to define the movement thresholds and classify landslides based on specially designed laboratory experiments. The performances of the IMU sensors and the base station for data collection and communication were tested through a rock-fall experiment conducted in the field conditions. The WSN-IMU system is capable of monitoring all types of slope movements independent of the triggering factors. The unique ability of the WSN-IMU system to determine landslide types allows designers and authorized personnel to predict subsequent movement pattern and duration so as to implement appropriate risk management and control measures to alleviate the socioeconomic losses. This paper outcome serves as the foundation for future studies and technological advancements that will facilitate landslide stabilization or mitigation actions as well as to predict the intensity of damages associated with those landslides. [ABSTRACT FROM AUTHOR]

Integrating new candidate sensors, such as Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU), into fail-safe train positioning systems have recently become a prominent area of research. Although there are a number of contributions related to the design of data fusion algorithms, the lack of details in raw measurements analysis has directly motivated this paper. This paper aims to record data from a variety of sensors (such as GNSS, IMU, magnetometer, barometer, tachometers, and Doppler radars) to evaluate train velocity and railway features (track slope, curve cant, and radius) extending previous works in the instrumentation and measurement field. The field test designed and concisely described in this paper presents several challenging environments, such as a tunnel, which can be used to analyze the candidate sensors limitations. In addition, a demonstration of a data fusion algorithm is presented to calculate train velocity based on measurements from the candidate sensors. The results obtained by an extended Kalman filter using GNSS and IMU are compared with velocity recorded by tachometers and Doppler radars, which is considered to be the reference value. The calculated velocity by IMU and GNSS when both sensors measurements are available presents an absolute error in velocity lower than 2 km/h in more than 90% of test duration. Finally, railway features (curve radius, cant, and slope) are calculated and analyzed according to train and railway dynamics. [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]

Lift-off variation causes errors in the eddy current thickness measurements of metallic plates. In this paper, we have developed an algorithm that can compensate for this variation and produce an index that is linked to the thickness, but is virtually independent of lift-off. This index, termed as the compensated peak frequency, can be obtained from the measured multifrequency inductance spectral data using the algorithm we developed in this paper. This method has been derived through mathematical manipulation and verified by both the simulation and experimental data. Accuracy in the thickness measurements at different lift-offs proved to be within 2%. [ABSTRACT FROM AUTHOR]

The parametric estimation of stochastic error signals is a common task in many engineering applications, such as inertial sensor calibration. In the latter case, the error signals are often of complex nature, and very few approaches are available to estimate the parameters of these processes. A frequently used approach for this purpose is the maximum likelihood (ML), which is usually implemented through a Kalman filter and found via the expectation-maximization algorithm. Although the ML is a statistically sound and efficient estimator, its numerical instability has brought to the use of alternative methods, the main one being the generalized method of wavelet moments (GMWM). The latter is a straightforward, consistent, and computationally efficient approach, which nevertheless loses statistical efficiency compared with the ML method. To narrow this gap, in this paper, we show that the performance of the GMWM estimator can be enhanced by making use of model moments in addition to those provided by the vector of wavelet variances. The theoretical findings are supported by simulations that highlight how the new estimator not only improves the finite sample performance of the GMWM but also allows it to approach the statistical efficiency of the ML. Finally, a case study with an inertial sensor demonstrates how useful this development is for the purposes of sensor calibration. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection is an effective way to find defects and diagnose the insulation condition of power equipment. During manufacturing and operating, there may exist multi-source PD signals in the equipment, which could seriously affect the accuracy of subsequent defect analyses. In this paper, a universal separation methodology using linear prediction analysis (LPA) and the isolation forest algorithm (IFA) is introduced. By approximating the present waveform point by a linear combination of several points in the past, a 12-D linear prediction cepstrum coefficient (LPCC) feature space can be established which can accurately characterize PD waveforms. In this paper, we applied principal component analysis (PCA) to reduce the feature space to 2-D space. The IFA was adopted to separate multi-source PD signals which quantified the degree of clustering and added one more parameter based on the original features. Thus, the proposed method can separate multiple PD sources effectively even if the difference in the features of different types of PDs is small. In addition, the proposed algorithm easily removed the noise points during the separation process. The algorithm was applied to different types of PD signals on 35-kV transformers and a three-source PD data set on a 252-kV gas-insulated switchgear (GIS) platform. The separation results confirm that the proposed algorithm can effectively separate and distinguish various PD signals. [ABSTRACT FROM AUTHOR]

Fasteners are an important part of railway security. The application of computer vision and image processing on fastener detection has become a research hot spot. However, most of the researches have focused on the missing and damage detection of fasteners up to now. In this paper, a method based on image processing is proposed to locate the center of the hexagon nut in the railway fastener. The precise position of the hexagon nut can be used for the automatic replacement of the nut, which is of great significance for the automatic railway construction and maintenance. The locating method proposed in this paper can be divided into two steps. First, the improved Canny detector which combines surrounding suppression and iterative excitation is used to extract the contours of the fastener. Then, a multifeature hierarchical locating (MHL) algorithm is proposed, which integrates the circle contour of the gasket and the hexagon features of the nut. The experimental results show that the improved Canny detector suppresses noises effectively and highlights the contours of fastener. The MHL algorithm in the next step improves the locating accuracy remarkably while guaranteeing the robustness meantime. [ABSTRACT FROM AUTHOR]

Generally, the positioning system is based on the wearable inertial and magnetic measurement unit (IMMU), but its positioning algorithm relies heavily on the wearing position, and there is no adaptive positioning algorithm for the wearable position. To solve this problem, this paper proposes a human body topology recognition (BTR) algorithm based on gait information, which can accurately and on real time identifies six typical wearing positions. The average recognition rate is 99.11%, and the real-time performance is controlled within 1.2 s. As a priori information, a gait detection algorithm based on the human BTR is extended. Experimental results show that the proposed algorithm can accurately classify the gaits of different wearing positions, achieving 99.39% average accuracy. This method realizes the flexibility, diversity, and portability of wearable IMMU. It can be widely used in attitude calculation, posture recognition, and dead reckoning of human or robot. [ABSTRACT FROM AUTHOR]

This paper introduces a new attitude estimation algorithm for pitch and roll angles. Pitch and roll angles are represented by a unit vector, and its estimation error is estimated in the Kalman filter. The main theoretical contribution is that the error covariance equations are simplified to scalar equations. Thus, the proposed algorithm is computationally efficient. The proposed algorithm is also applied to vertical movement estimation. Simulation and experiment results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a method to detect and evaluate surface and subsurface cracks in aluminum multilayer structures using a planar eddy current testing (ECT) probe and processing an inverse problem algorithm. The proposed excitation method using this ECT probe allows the induction of eddy currents with different orientations on the metal surface without rotating the probe during the scan. An inversion algorithm was applied to evaluate the geometry of the cracks. The main result of this inversion algorithm is the determination of the shape of the cracks using the reconstructed eddy current pattern inside the specimen. These reconstructed patterns give the indication of the length, orientation, and geometry profile of the crack. In this paper, complex geometry cracks at different depths are inspected. Experimental data were obtained around a star crack at different depths in a stack of four aluminum plates where each plate has a thickness equal to 1 mm. The presented work shows that the distance between the sensor and the layer under analysis must be adjusted in the inversion process in order to obtain the best reconstructed images when subsurface cracks are under study. This consideration affects the quality of the resulting images. [ABSTRACT FROM PUBLISHER]

The synchronization accuracy of the nodes of a wireless sensor network (WSN) can be perturbed by the plug-in of nonsynchronized nodes (NSNs). In the case of peer-to-peer synchronization algorithms, the reference time of the WSN is established on the basis of the clock time of all nodes. Therefore, each NSN changes the reference time to synchronize all nodes with the new reference time interval needs. In this time interval, the synchronization accuracy can degrade, i.e., the delay among node clocks overcomes the admissible range. In the case of only one or many NSNs, it was assessed in previous papers that by filtering the message of each NSN, the synchronization accuracy of the already synchronized nodes (ASNs) is preserved. However, the spatial distribution of the NSNs can fool the ASNs, foiling the effect of the message filtering. This paper presents a procedure that overcomes this inconvenience. The new fully distributed and consensus-based procedure iteratively filters the messages of communicating NSNs that would increase the time delay over the admissible range. As a consequence, the synchronization accuracy is preserved whatever the spatial distribution of ASNs and NSNs. Numerical and experimental tests are performed to validate the proposed procedure. [ABSTRACT FROM PUBLISHER]

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]

Smartphones have become an indispensable tool in the work and life of most people. Given the many kinds of sensors built into smartphones, solving indoor navigation problems using these phones has become feasible. This paper proposes such a solution, using a particle-filter-based indoor positioning method. This method improves the positioning accuracy and helps improve the user experience. The major features of the method are that it leverages and fuses the advantages of two different indoor positioning methods [pedestrian dead reckoning (PDR) and received-signal-strength-indicator-based indoor positioning], and considers the influence on positioning of the indoor spatial map. The smartphone built-in sensors are used as data sources and constitute a feasible method for PDR. The iBeacon devices are also used—because of the many advantages, they present in indoor environments, such as low energy consumption, convenient deployment, and popular applications—and their received signal strength indicator values are used as observations in the particle filter. The devices used in this paper are conventional, without any special equipment, and the experimental sites are common work environments. Therefore, we believe that the proposed method has a wide application potential for indoor navigation applications (e.g., museum guides), and is especially suitable for working with indoor navigation maps. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stabilization problem of a class of networked control systems, where the plant measurements and the control signals are transmitted over a network and encounter both time delays and packet losses. To deal with the nonzero disturbance rejection issue, two kinds of networked optimal state feedback integral control (SFIC) methods are proposed in this paper: One is a networked guaranteed-cost SFIC method, and the other one is an SFIC method based on estimation of a distribution algorithm. It has been shown that the proposed methods have better nonzero disturbance rejection capability and can achieve zero steady-state error. Simulation and experimental results are given to demonstrate the effectiveness and applicability of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of measuring the delay of a packet in a network with an associated error bound, but without having a need for clock synchronization and for any form of bidirectional messaging between the sender and receiver. A novel lightweight technique is proposed that aims at keeping the actual error made in the delay estimation very low while simultaneously providing a good error bound for each individual estimated packet delay. One-way delay measurement without clock synchronization and messaging cannot guarantee an error bound on delay estimations in general; however, we show that this is possible using periodic probe packets and appropriate assumptions that are compliant with the physical conditions of the environments within which the sender and receiver operate. Although we calculate an error bound for all our delay estimates, the main purpose is to have a much smaller actual error in these delay estimates in comparison with the computed error bound and other methods existing in the literature. The proposed method is evaluated against a recently reported technique of the same category and is shown to be much more superior overall. [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]

Oil-impregnated paper (OIP) bushing is essential equipment in power substations, and moisture intrusion is one of the major faults during its operation. In this article, a method is proposed to diagnose the moisture intrusion fault by the hidden Markov model (HMM) and the shuffled frog-leaping algorithm (SFLA). First, HMM is employed to characterize the probabilistic relationship between the moisture states and frequency-domain spectroscopy (FDS). Then, SFLA is adopted to optimize the locatable results of moisture intrusion point (MIP) in the bushing. Next, extensive simulated data are used for parameter training in the HMM-SFLA model library, with cross-validation for optimal parameters’ configuration of HMM and SFLA. Finally, the proposed method was validated by experiment in laboratory and field measurements. The results show that the proposed method can effectively diagnose the MIP of OIP bushings. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of 1-D and 2-D Otsu’s thresholding methods, a 3-D Otsu method has been introduced. While yielding satisfactory segmentation results for images with a low signal-to-noise ratio (SNR) and poor contrast, it has the downside of high computational complexity. In this paper, the cuttlefish algorithm (CFA)-based 3-D Otsu thresholding method is proposed to pace up the conventional 3-D Otsu thresholding for color image segmentation. In order to decrease the effects of noises and weak edges, an optimally selected multilevel 3-D Otsu image thresholding technique is brought into the proposed segmentation scheme. The CFA is a newly developed stochastic meta-heuristic optimization algorithm based on observing, mimicking, and modeling the camouflaging feature of cuttlefish. It is used to simplify the problem of exhaustive search for the optimal threshold vector in 3-D space. Experimental results, when compared to 1-D Otsu, 1-D Otsu-Cuckoo search (CS) algorithm, 1-D Otsu-lightning search algorithm (LSA), 1-D Otsu-CFA, conventional 3-D Otsu, 3-D Otsu-CS, and 3-D Otsu-LSA, indicate that the proposed algorithm CFA-based 3-D Otsu thresholding is superior to all the other multilevel thresholding algorithms. The proposed 3-D-CFA method produces promising segmentation results from the objective and subjective aspects. [ABSTRACT FROM AUTHOR]

The onsite partial discharge (PD) detection of cable insulation is of great importance for power system maintenance. To preserve more basic features of denoised PD signal, in this paper, a PD denoising by a new class of affine projection algorithm (APA) is proposed based on the high-order error power (HOEP) criterion. We propose a new affine projection least mean absolute third algorithm (APLMATA), which exploits the HOEP criterion ($p=3$) and can achieve reliable performance under Gaussian interference. We further develop a modified APLMATA (MAPLMATA) to reduce the misadjustment with moderate computational burden. Analytical models are derived for MAPLMATA for Gaussian signals. To enhance the stability of the algorithm in generalized Gaussian noise scenarios, an affine project least mean $p$ th power algorithm (APLMPA) that includes the $p$ th power dependence on the error is presented. In particular, we apply the proposed APA-based algorithms to suppress the noise from PD signal in power cables. Simulation and experimental results demonstrate the effectiveness of the proposed algorithms for system identification and PD denoising. [ABSTRACT FROM AUTHOR]

An improved algorithm with feature selection and neural network classification is proposed in this paper to investigate the fault diagnosis problem of rotating machinery. The feature vectors are constructed by extracting the time- and frequency-domain characteristics of the overall machine under multiple operating conditions. To strengthen the fault diagnostic ability, an improved sensitive feature selection algorithm is proposed by improving the distance evaluation (DE) method and reconstructing a low-dimensional sensitive feature sample with selectively chosen parameters from multidimensional feature vectors. The recursive least square backpropagation (RLS-BP) neural network algorithm is used for fault diagnosis by classifying the feature vectors of normal signal and faulty signals. The effectiveness of the proposed method is verified via hardware experiments using wind turbine drivetrain diagnostics simulator (WTDDS) by comparing with conventional feature vector construction methods and neural network algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a semicool temperature compensation algorithm based on the double exponential model of the transient temperature rise curve to improve the performance of the 3-D ultrasonic positioning system. The error model of the 3-D ultrasonic positioning system is derived and the importance of temperature to the positioning accuracy is demonstrated. Then, the double exponential model of the transient temperature rise curve derived by the difference principle is established through a dynamic system identification method. On this basis, the semicool temperature compensation algorithm is proposed, which includes the estimation of the initial time $t_{0}$ , the principle of the choice of parameter $k$. The effectiveness of the algorithm is verified by temperature measurement experiments. The impact of the ambient temperature on the algorithm is also analyzed qualitatively. In the end, the semicool temperature compensation algorithm is proved to be able to improve the performance of the 3-D ultrasonic positioning system by the comparison experiments respectively under static measurement of a fixed point and dynamic measurement of a fixed line. [ABSTRACT FROM AUTHOR]

The Cole model is widely used in bioimpedance spectroscopy (BIS) applications for evaluating the contents and status of biological tissues. Traditional frequency-domain fitting methods for Cole parameter estimation (CPE) often need wideband direct or indirect BIS measurements. The newly emerged time-domain fitting methods do not require BIS measurement, but they usually have unsatisfactory estimation accuracy and heavily rely on some specific excitations. This paper proposes a novel time-domain fitting method based on the fractional operational matrix (FOM) for CPE. First, the Cole model is represented as a FOM-based algebraic equation. Then, the model-based voltage response can be calculated as the product of the sampled current excitation and the FOM-based Cole equation. Finally, the Cole parameter can be estimated by minimizing the sum of squares error between the sampled voltage response and the model-based voltage response using an iterative nonlinear least-squares fitting algorithm. Experimental measurements are verified on three 2R1C circuits and in vivo leg muscle of a healthy volunteer. The results show that the Cole parameter can be accurately estimated by the proposed FOM-based fitting method directly using the sampled current and voltage signals. This paper establishes an effective time-domain fitting method for CPE, which could greatly simplify the hardware and software designs without the need for the BIS measurement. [ABSTRACT FROM AUTHOR]

This paper introduces a novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for electrocardiogram (ECG) arrhythmias classification. The LSTM-based AE network (LSTM-AE) is used to learn the features from ECG arrhythmias signals, and the SVM is used to classify those signals from the learned features. The LSTM-AE consists of an encoder model, which extracts high-level feature information from ECG arrhythmias signals through LSTM network, and a decoder model which outputs reconstruct ECG arrhythmias signals from high-level features through LSTM network. Experiments show that the proposed method can learn better features than the traditional method without any prior knowledge, presenting a good potential for the ECG arrhythmias classification. In the classification of five heartbeats types, including normal, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complexes (APC), premature ventricular contractions (PVC), the proposed method achieved average accuracy, sensitivity, and specificity of 99.74%, 99.35%, and 99.84%, respectively, in the beat-based cross-validation approach, and 85.20%, 62.99%, and 90.75%, respectively, in the record-based cross-validation approach, in public MIT-BIH Arrhythmia Database. While based on the Advancement of Medical Instrumentation (AAMI) standards, the proposed method achieved average accuracy, sensitivity, and specificity of 99.45%, 98.63%, and 99.66%, respectively, in the beat-based cross-validation approach. [ABSTRACT FROM AUTHOR]

Time-varying (TV) nonlinear systems widely exist in various fields of engineering and science. Effective identification and modeling of TV systems is a challenging problem due to the nonstationarity and nonlinearity of the associated processes. In this paper, a novel parametric modeling algorithm is proposed to deal with this problem based on a TV nonlinear autoregressive with exogenous input (TV-NARX) model. A new class of multiple beta wavelet (MBW) basis functions is introduced to represent the TV coefficients of the TV-NARX model to enable the tracking of both smooth trends and sharp changes of the system behavior. To produce a parsimonious model structure, a locally regularized ultraorthogonal forward regression (LRUOFR) algorithm aided by the adjustable prediction error sum of squares (APRESS) criterion is investigated for sparse model term selection and parameter estimation. Simulation studies and a real application to EEG data show that the proposed MBW-LRUOFR algorithm can effectively capture the global and local features of nonstationary systems and obtain an optimal model, even for signals contaminated with severe colored noise. [ABSTRACT FROM AUTHOR]

One of the key techniques toward energy efficiency and conservation is nonintrusive load monitoring (NILM) which lies in the domain of energy monitoring. Event detection is a core component of event-based NILM systems. This paper proposes two new low-complexity and computationally fast algorithms that detect the variations of load data and return the time occurrences of the corresponding events. The proposed algorithms are based on the phenomenon of a sliding window (SW) that tracks the statistical features of the acquired aggregated load data. The performance of the proposed algorithms is evaluated using real-world data and a comparative analysis has been carried out with one of the recently proposed event detection algorithms. Based on the simulations and sensitivity analysis, it is shown that the proposed algorithm can provide the results of up to 93% and 88% in terms of recall and precision, respectively. [ABSTRACT FROM AUTHOR]

In a smart house connected to a smart grid via advanced metering infrastructure, a nonintrusive load monitor (NILM) that identifies individual appliances by disaggregating composite electric load signal from the minimal number of sensors installed at the main distribution board in the field can be regarded as a part of a home/building energy management system. This type of load monitoring technique, not only for domestic but also for industrial applications, is relevant to electricity energy management and conservation issues. In this paper, an improved time–frequency analysis-based NILM composed of three system components, including data acquisition, transient feature extraction, and load identification, is proposed. The improved NILM proposed in this paper incorporates a multiresolution S-transform-based transient feature extraction scheme with a modified 0–1 multidimensional knapsack algorithm-based load identification method to identify individual household appliances that may either be energized simultaneously or be identified under similar real power consumption. For the load identification process, an ant colony optimization algorithm is employed to perform combinatorial search that is formulated as a modified 0–1 multidimensional knapsack problem. As shown from the experimental results, the improved NILM strategy proposed in this paper is confirmed to be feasible. [ABSTRACT FROM PUBLISHER]

Distribution system state estimation (DSSE) is an essential tool for the management and control of future distribution networks. Distribution grids are usually characterized by a very large number of nodes and different voltage levels. Moreover, different portions of the system can be operated by different distribution system operators. In this context, multiarea approaches are key tools to efficiently perform DSSE. This paper presents a novel approach for multiarea state estimation in distribution systems. The proposed algorithm is based on a two-step procedure, where the first-step local estimations are refined through a newly designed second step that allows the integration of the measurement information available in the adjacent areas. The main novelty in this paper is the mathematical analysis of the impact brought by possible measurements shared among different areas, which drives the design of a new efficient weighted least squares formulation of the second step to maximize the achievable estimation accuracy. Tests performed on the unbalanced IEEE 123-bus network prove the goodness of the new multiarea estimator proposed and show the accuracy and efficiency enhancements obtainable with respect to previous literature. [ABSTRACT FROM PUBLISHER]

Oil-impregnated paper (OIP) bushing is essential equipment in power substations, and moisture intrusion is one of the major faults during its operation. In this article, a method is proposed to diagnose the moisture intrusion fault by the hidden Markov model (HMM) and the shuffled frog-leaping algorithm (SFLA). First, HMM is employed to characterize the probabilistic relationship between the moisture states and frequency-domain spectroscopy (FDS). Then, SFLA is adopted to optimize the locatable results of moisture intrusion point (MIP) in the bushing. Next, extensive simulated data are used for parameter training in the HMM-SFLA model library, with cross-validation for optimal parameters’ configuration of HMM and SFLA. Finally, the proposed method was validated by experiment in laboratory and field measurements. The results show that the proposed method can effectively diagnose the MIP of OIP bushings. [ABSTRACT FROM AUTHOR]

Commercial time-of-flight (TOF) ultrasonic flowmeters (UFM) are rapidly expanding in the general industry. Among the different techniques that can be applied to determine the TOF of ultrasonic waves, the cross-correlation method presents numerous advantages, such as robustness for weak signals and noise suppression. However, the selection of an appropriate reference wave is presumably a key element in the precise measurement of TOF. In this paper, an algorithm to compute an accurate TOF is proposed. The form of the electric signal received by the transducer is obtained from an acoustically forced underdamped oscillator model, and the analytical solution of the model is proposed as a reference wave. In order to validate the effectiveness of this procedure, a UFM system is designed and tested in a flowmeter calibration test rig. It is demonstrated that the use of the presented scheme overcomes the average method limitations, and turns out to be a convenient solution in a wide range of conditions. Robust measurements of near-zero flow values are acquired, which allow the achievement of a high dynamic range. The error curve of the proposed system have been obtained, revealing that the absolute value of the relative errors is lower than 2% within all the spectrum of flow rates considered (from 0.2 to 150 $\text{m}^{3}$ /h). Results demonstrate that the algorithm provides high-precision measurements within a wide dynamic range. The algorithm is portable and versatile: it can be adapted to different types of transducers without the need of additional measurements, allowing adjusting parameters on-the-fly for an optimal performance of the ultrasonic flowmeter (UFM) system. [ABSTRACT FROM AUTHOR]

The multimode thru–reflect–line (TRL) calibration technique is applied to perform on-wafer mixed-mode scattering-parameter (S-parameter) measurements for differential circuits. This paper describes the first design and realization of a coupled coplanar waveguide (CCPW) calibration kit on a quartz substrate in the ground–signal–ground–signal–ground configuration. CCPW integrated verification elements such as attenuators, and matched and mismatched transmission lines are simulated and measured to validate the design of the multimode TRL calibration kit as well as the calibration algorithm itself. With the advent of four-port vector network analyzers supporting true mode stimulus, at least two approaches are available for mixed-mode S-parameter measurements. The first one is the one-tier measurement using the single-ended stimulus with the multimode TRL calibration technique. The second one relies on the two-tier measurement with a short-open-load-reciprocal thru calibration at the first tier and the multimode TRL calibration at the second tier. From the viewpoint of metrology, the one-tier approach is preferable as it requires fewer standards and connections. The good agreement between simulation and measurement results performed on different verification elements demonstrates the validity of both methods up to 40 GHz. [ABSTRACT FROM AUTHOR]

This paper presents a modified sine-fitting algorithms which can be used for high-accuracy sampling measurements of complex voltage ratio of sinusoidal signals. The proposed algorithms provide a significant reduction in the measurement uncertainty of phase shift when synchronizing the sampler and signal source is impossible. [ABSTRACT FROM AUTHOR]

Tribological tests are adopted to estimate the kinetic friction coefficient of the material of interest according to the ASTM standard. Typically, for the measurement process, several replicas as well as a postprocessing data treatment are necessary to take into account the observed casual variability of the measurand. This paper describes a novel statistical approach aiming to highlight the most significant time intervals during each test replica. The experimental activity has been carried out with reference to the ball-on-flat configuration of the tribometer in order to verify the suggested procedure in terms of both accuracy and reliability. Numerical simulation and comparison of the measurement results with the output of a different outlier detection algorithm are also included to show the improvement allowed by the authors’ proposal about the dry friction coefficient measurement. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the solar radiation level on the available output power of moving photovoltaic (PV) installations with the help of PV simulation models and real-world environmental data. For moving PV installations, for example, on top of hybrid electric vehicles and battery-powered electric vehicles, the sampling rate was 6000 samples per second. We analyze the changes in the amount of solar radiation that can influence on the control of the operating voltage of PVs within maximum power point tracking (MPPT) algorithms. We present recommendations for the sampling rate of environmental data, which is used for PV simulation models. Furthermore, we discuss the update frequency of vital parameters of different MPPT techniques for controlling moving PV installations. Here, we concentrate on the degree of efficiency of the perturb and observe algorithm. In addition, we show how the sampling rate of environmental data influences the test criteria for MPPT algorithms. [ABSTRACT FROM AUTHOR]

This paper presents two algorithms, both based on the time of flight (ToF) of scattered waves, to locate defect in an anisotropic woven-fabric carbon fiber reinforced polymer (CFRP) plate. The first algorithm uses a probabilistic approach by constructing a probability matrix. Each element of this matrix is associated with a location and represents the probability of existing a defect at the corresponding spatial coordinates of the plate. For the probability matrix method, localization results are influenced by manually chosen parameters and by the anisotropy of the CFRP plate. The second algorithm, based on artificial neural networks (ANNs), enables us to improve the accuracy of locating defects. With this ANN method, the anisotropic feature can be “learned” by the neural network from training data. The ToF of scattered waves obtained from three sensor pairs were used directly as inputs of the neural network. The spatial coordinates of the defect are the ANN outputs. The difficulty of obtaining sufficient experimental data for ANN training was surpassed by using added blocks on the surface of the plate under test to simulate defects. This scheme was validated and proved to be effective by comparing the scattered waves from a delamination and from an added block. Conclusions have been drawn by comparing the two methods. The localization results obtained by the ANN algorithm are proved to be better. [ABSTRACT FROM AUTHOR]

Target tracking is an important field of investigation in wireless sensor networks. When multiple targets are closely spaced, their measurement points are mixed together due to the insufficient accuracy of the sensor. This may bring some difficulties in determining the positions of observation points required by subsequent algorithms and applications. Most of the known tracking algorithms are derived from the Kalman filter, extended Kalman filter, and particles filter. In this paper, a novel measurement data classification algorithm based on support vector machine (SVM) is provided. SVM and Kalman filter are combined to obtain the updated classification line at each sampling period, and the sampling points would be classified by the updated classification line to calculate the coordinates of the corresponding observation points, which are then used to estimate the precise positions of two targets. A series of simulations and experiments are carried out to validate the presented algorithm on classifying and tracking two targets moving closely. Simulation results for a maneuvering targets classification example illustrate the feasibility of the new algorithm, as well as experimental and quantitative results from the practical data validate the effectiveness and stability of our proposal in contrast with existing methods. [ABSTRACT FROM AUTHOR]

Fast inspection of a defect is a challenging task in mass production of curved surfaces, and photometric stereo (PS) utilizing multiple images from a single camera under a number of different illumination directions is a promising technique for this task due to its high sensitivity to surface normal perturbations. This paper adapts conventional PS and extends the technique to the inspection of non-Lambertian surfaces with high accuracy and efficiency. A data-driven PS is presented by establishing the Gaussian process (GP) model to represent the nonlinear reflectance behavior of various materials based on measured reflectance data sets. With the trained GP model, the surface normal can be estimated in two steps: prediction of bidirectional reflectance distribution function values under different light directions and the subsequent least-squares estimation of a surface normal. Comparison tests with other algorithms on the Mitsubishi Electric Research Laboratories data set and real workpieces with non-Lambertian materials show the superior accuracy and efficiency of the proposed method in surface normal estimation. After the surface normal of the workpiece is recovered, the defects can be detected by filtering out the perturbation of the surface normal. Experiments on steel and glossy polyester workpieces validate the efficacy of the proposed approach in detecting defects on curved non-Lambertian surfaces. [ABSTRACT FROM AUTHOR]

The availability of low-power wide area networks solutions opens new scenario in many application sectors. Among these technologies, long-range wide area network (LoRaWAN) was originally designed for consumer internet-of-things systems, but now it is investigated for distributed measurement systems applied to more demanding applications, including industry. In order to fulfill real-time requirements, the LoRaWAN end devices (EDs) are frequently requested to share a common timebase (i.e., to be synchronized). This paper deals with the tradeoff between the desired synchronization uncertainty and the energy available in the ED, which is usually battery powered. A new approach based on two algorithms for a posteriori synchronization and uncertainty estimation is proposed. After experimental validation of the proposed algorithms, a set of optimal energy/uncertainty tradeoff curves is obtained with the help of a specific tradeoff algorithm. Finally, the proposed tradeoff methodology is applied to two use cases concerning the design of: an industrial time division multiple access system and a predictive maintenance framework. [ABSTRACT FROM AUTHOR]

Fast and accurate measuring motion with multi-degrees-of-freedom (multi-DOFs) and high resolution has been highly demanded in the development of nanopositioning systems in recent years. This paper develops an efficient and practical image correlation method capable of measuring micromotions of the planar 3-DOF ($x,y,\theta _{z}$) nanopositioners. In this method, the measurement of 3-DOF micromotion is first transformed into a problem of three-variable optimization by utilizing the correlation function of sum of squared difference. Subsequently, an optimal algorithm based on the inverse compositional Gaussian–Newton searching is developed to instantaneously solve these three variables. Simulation results show that in the image space, accuracy of translational components ($x$ , $y$) and rotation component ($\theta _{Z}$) of the proposed algorithm can reach to 0.02 pixels and 0.01°, respectively. Experimental results demonstrate that the proposed method can realize real-time motion tracking of a 3-DOF nanopositioner with the performance similar to commonly used multicapacitive sensor scheme. [ABSTRACT FROM AUTHOR]

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to identify and classify the data obtained by the sensor network (SN) for the detection of mines. This method is used for the identification of antitank and antipersonnel mines and classification of buried objects within a target region. In this paper, a mobile SN is used to detect mines and some other objects buried and creating magnetic anomaly in and around the region where they are found, with the behavior of the individual sensors swarming onto the area under which a mine or any other object is buried. The process of collecting data by the SN and modeling it mathematically are explained in detail. The SN is modeled as a fictitious two-dimensional spatial impulse sampler. This paper is motivated by clearing the territories of mine fields to open them to agriculture. It is very important because, currently, in some countries, very fertile territories around the borders are covered by buried mines. The approach is basically based on magnetic anomaly measurements, which directly tackles the subregions corresponding to buried objects whether they represent objects that are separately located or occluded by other objects. It is based on a new developed method that is called “the back-most object detection and identification algorithm.” This method is fully automatic, and there is no human intervention throughout the process. In this paper, classification of objects is based on their well-known shapes and dimensions. Therefore, there is no need for sophisticated learning algorithms to achieve classification. The experimental results are given both for detection and identification of a single mine and classification of a number of mines and any other objects that have a potential of giving false alarms in a target region. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive genetic algorithm (AGA) for an active noise control (ANC) system. The conventional ANC system often implements the filtered extended least mean square (FXLMS) algorithm to update the coefficients of the linear finite-impulse response (FIR) and nonlinear Volterra filters, owing to its simplicity; meanwhile, the FXLMS algorithm may converge to local minima. In this paper, the FXLMS algorithm is replaced with an AGA to prevent the local minima problem. Additionally, the proposed AGA method does not require identifying the secondary path for the ANC, explaining why no plant measurement is necessary when designing an AGA-based ANC system. Simulation results demonstrate that the effectiveness of the proposed AGA method can suppress the nonlinear noise interference under several situations without clearly identifying the secondary path. [ABSTRACT FROM PUBLISHER]

Electrical capacitance tomography (ECT) attempts to image the permittivity distribution of an object by measuring the electrical capacitance between sets of electrodes placed around its periphery. Image reconstruction in ECT is a nonlinear ill-posed inverse problem, and regularization methods are needed to stabilize this inverse problem. The reconstruction of complex shapes (sharp edges) and absolute permittivity values is a more difficult task in ECT, and the commonly used regularization methods in Tikhonov minimization are unable to solve these problems. In the standard Tikhonov regularization method, the regularization matrix has a Laplacian-type structure, which encourages smoothing reconstruction. A Helmholtz-type regularization scheme has been implemented to solve the inverse problem with complicated-shape objects and the absolute permittivity values. The Helmholtz-type regularization has a wavelike property and encourages variations of permittivity. The results from experimental data demonstrate the advantage of the Helmholtz-type regularization for recovering sharp edges over the popular Laplacian-type regularization in the framework of Tikhonov minimization. Furthermore, this paper presents examples of the reconstructed absolute value permittivity map in ECT using experimental phantom data. [ABSTRACT FROM AUTHOR]

A computer-based automatic alternating current (ac) bridge for impedance measurement is proposed. The problem of impedance measurement is formulated as a linear-in-parameter estimation one, and a suitable algorithm is proposed that ensures high measurement accuracy under a high convergence rate. In particular, the optimum experimental conditions regarding the reference resistance and the sampling time are developed. These conditions guarantee that the estimation is performed with possibly high accuracy. This paper also shows how to use the proposed approach for fault detection, which is very important from the point of view of modern control and fault diagnosis. The final part of this paper shows a number of illustrative experiments that justify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

An algorithm that is suitable for the computation of symmetrical components is presented in this paper. The structure of the algorithm consists of four decupled linear modules: the first for adaptive filtering of input signals, the second for frequency estimation, the third for symmetrical components estimation, and the fourth for the amplitudes of the component estimation. Filters are used to minimize the noise effect and to eliminate the presence of harmonics. A simple method for online designing of digital filters of sinusoidal signals is used in this paper. This method uses closed forms for calculation of filter coefficients and requires modest computations. The symmetrical components estimation through adaptive transformation matrix of phase shifters used in this paper makes it possible to get instantaneous symmetrical components independently on the frequency variation with modest-computation requirements. The technique used in this paper provides accurate frequency estimates with the maximum error of 0.002 Hz and symmetrical components estimation with the maximum error of 0.03% for SNR = 60 dB in about 25 ms. The algorithm is not sensitive to power-system frequency changes and to the harmonic distortion of the input signals. To demonstrate the performance of the developed algorithm, computer-simulated data records have been processed. It has been found that the proposed algorithm is suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Recently, more and more research has been done on micro air vehicles (MAVs). An autonomous flight control system is necessary for developing practical MAVs to be used for a wide array of missions. Due to the limitations of size, weight, and power, MAVs have the very low payload capacity and moments of inertia. The current technologies with rate and acceleration sensors ape plied on larger aircrafts are impractical to MAVs, and they are difficult to be scaled down to satisfy the demands of MAVs. Since surveillance has been considered as the primary mission of MAVs, it is essential for MAVs to be equipped with on-board imaging senson such as cameras, which have rich information content. So vision-based techniques, without increasing the MAVs payload, may be a feasible idea for flight autonomy of MAVs. In this paper, a new robust horizon extraction algorithm based on the orientation projection method is proposed, which is the foundation of a vision-based flight control system. The horizon extraction algorithm is effective for both color images and gray images. The horizon can be extracted not only from fine images captured in fair conditions but also from blurred images captured in cloudy, even foggy days. In order to raise the computational speed to meet real-time requirements, the algorithmic optimization is also discussed in the paper, which is timesaving by narrowing the seeking scope of orientations and adopting the table look-up method. According to the orientation and position of the horizon in the image, two Important angular attitude parameters for stability and control, the roll angle and the pitch angle, could be calculated. Several experimental results demonstrate the feasibility and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

A fast wind turbine abnormal data cleaning algorithm via image processing for wind turbine power generation performance measurement and evaluation is proposed in this paper. The proposed method includes two stages, data cleaning and data classification. At the data cleaning stage, pixels of normal data are extracted via image processing based on pixel spatial distribution characteristics of abnormal and normal data in wind power curve (WPC) images. At the data classification stage, wind power data points are classified as normal and abnormal based on the existence of corresponding pixels in the processed WPC image. To accelerate the proposed method, the cleaning operation is executed parallelly using graphics processing units (GPUs) via compute unified device architecture (CUDA). The effectiveness of the proposed method is validated based on real data sets collected from 37 wind turbines of two commercial farms and three types of GPUs are employed to implement the proposed algorithm. The computational results prove the proposed approach has achieved better performance in cleaning abnormal wind power data while the execution time is tremendously reduced. Therefore, the proposed method is available and practical for real wind turbine power generation performance evaluation and monitoring tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for localization optimization of control points for robust calibration of a pinhole model camera. Instead of performing accurate subpixel control point detection by specialized operators, which are normally adopted in conventional work, our proposed method concentrates on estimating the optimal control points in regions of plausibility determined by distortion bias from perspective distortion, lens distortion, and localization bias from out-of-focus blurring. With this method, the two main strict preconditions for camera calibration in conventional work are relieved. The first one is that the input images for calibration are assumed to be well focused and the second one is that the individual control point needs to be detected with high accuracy. In our work, we formulate the accurate determination of control points’ localization as an optimization process. This optimization process takes determined control points’ uncertainty area as input. A global searching algorithm combined with Levenberg–Marquardt optimization algorithm is introduced for searching the optimal control points and refining camera parameters. Experimental results show that the proposed method achieves higher accuracy than the conventional methods. [ABSTRACT FROM AUTHOR]