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Abstract: The automatic control of product moisture content in the paper industry is based upon online moisture measurement. Furthermore, the automatic control of other quality variables such as coat weight may also rely on the moisture measurement. The integrity of infrared radiation (IR) based online moisture measurement systems was studied by performing basic calibrations at the mills with references sealed between two glass plates. The calibrations were performed according to the EN ISO/IEC 17025 standard which is a requirement for calibration laboratories. The observed measurement errors were 2.9% moisture in the first time calibrations at the 95% confidence level. It is demonstrated that these errors can be reduced to under 0.5% moisture by applying traceable methods in the basic calibration, thus improving the integrity of online moisture measurements. Also a standardised algorithm, based on the experience from the performed calibrations, is proposed to ease the adjustment of different moisture measurement systems. [Copyright &y& Elsevier]

• A hybrid broadband and narrowband active sound profiling algorithm is proposed for active hybrid noise control. • Simulations of stationary and non-stationary hybrid noise sources prove the stability and robustness of the algorithm. • The proposed algorithm can selectively control the desired narrowband noise components with specific magnitudes in hybrid noise. • Real vehicle experiments, conducted using headrests, validate the theoretical analysis. The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms' effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology. [ABSTRACT FROM AUTHOR]

A novel position estimator is proposed to address the issue of position detection for micromanipulator in surgical robots. Firstly, the single joint micromanipulator experimental prototype proposed in the paper is built based on the real micromanipulator. To identify the parameters of the position estimator dynamics model, we enhance the Honey Badger Algorithm (HBA) and utilize Improved Honey Badger Algorithm (IHBA). With global parameters, the maximum absolute estimation error of the position estimator is only 1.10 deg. To further enhance position estimation accuracy, this paper introduces an online updating parameters model and integrates it with IHBA to achieve online updating parameters. The high-precision estimation of position estimator proposed in this paper is obtained. The experiments demonstrate the effective of the position estimator with maximum absolute estimation error is only 0.28 deg and the excellent robustness. • By incorporating the Lévy flight strategy and chaotic tent map, an improve HBA algorithm named IHBA is proposed. • An online updating parameters model is proposed and integrated with IHBA, significantly enhances the estimation accuracy of the position estimator. • A high-precision position estimator that accurately represents the performance of real micromanipulator is proposed. [ABSTRACT FROM AUTHOR]

• The gated recurrent unit algorithm with fused attention mechanism is a target positioning method in a non-line-of-sight error environment. • The introduction of the attention mechanism can analyze features, select important features, and better handle non-line-of-sight errors. • Introducing the dropout layer during training reduces the risk of overfitting during training. • Through simulation and actual measurement experiments, the effectiveness of the algorithm in this paper is verified by comparing it with other famous algorithms. Amidst the increasing reliance on indoor positioning in areas such as large shopping malls, airports, and train stations, the precision of ultra-wideband (UWB) technology stands out for its centimeter-level accuracy. However, non-line-of-sight (NLOS) errors caused by people's movements significantly reduce indoor positioning accuracy. In response, this paper presents an innovative blend of gated recurrent unit algorithm with fused attention mechanism (GRU_Attention). This algorithm adeptly captures the dynamism in UWB signals and intelligently focuses on pivotal features to actualize accurate three-dimensional positioning while effectively reducing NLOS error. It has been demonstrated that GRU_Attention algorithm can reach 5.6 cm accuracy level through experiments, which improved by 74.47 %, 87.44 %, 14.46 %, 43.82 %, and 29.07 % respectively compared with the backpropagation (BP), least square (LS), GRU, convolutional neural network (CNN), and fused attention mechanism convolutional neural network (CNN_Attention) algorithms. This algorithm charts a new pathway to overcome NLOS errors in indoor personnel movement scenes. [ABSTRACT FROM AUTHOR]

• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

• An ISSS positioning algorithm based on Bluetooth AOA technology is proposed, and the Angle estimation effect of this algorithm for uniform linear array (ULA) and uniform rectangular array (URA) antennas is experiment. • To account for two common sources of indoor interference. We reduce the coherent interference with signals using the spatial smoothing technique and the interference of transient occlusion with signals using the DBSCON technique, respectively. The layout of positioning device in different indoor Spaces and the algorithm of location calculation under different layout are discussed. The positioning accuracy of these algorithms is evaluated by the experimental data. This paper investigates the Bluetooth direction finding technique and combines it with the improved signal subtraction subspace (ISSS) algorithm for precise indoor positioning. First, the phase offset caused by the electronic switch is solved by the phase compensation algorithm. Then, the ISSS algorithm is proposed by combining the spatial smoothing techniques algorithm, the density-based spatial clustering of applications with noise (DBSCON) techniques and the signal subtraction subspace (SSS), which reduces the interference of coherent signals and caused by moving between people in the room. Finally, different placement methods for receivers in different spatial shapes are proposed. In this paper, the positioning accuracy of the algorithm is tested using Silicon Labs' Bluetooth device. The results show that the average positioning errors are 0.92 m for single receivers and 0.30 m for dual receivers in a room size of 3 m * 3 m. Therefore, the method has a promising application in achieving accurate indoor positioning. [ABSTRACT FROM AUTHOR]

• Design a Fractional-order-PCA-SVM coupling algorithm for digital image recognition. • Providing an accurate digital image edge extraction algorithm. • A fractional differential mask operator is designed. This paper use the fractional differential mask operator to describe and deal with the highly self similar digital medical image, then extracting the main features of the these images with principal component analysis, and recognizing these images with SVM algorithm. To provide an efficient algorithm for medical image processing with high degree of self similarity. Based on the analysis of fractional differential theory, principal component analysis theory and support vector machine (SVM) algorithm, designing a Fractional-order-PCA-SVM coupling algorithm for digital image recognition. and taking the digital image in ORL face database as the empirical object, using PCA-NN coupling algorithm, SVM algorithm, PCA-SVM algorithm, Sobel-PCA-NN algorithm, coupling coupling Sobel-SVM coupling algorithm, Sobel-PCA-SVM algorithm, Fractional-order-PCA-NN algorithm and coupling coupling Fractional-order-SVM coupling algorithm as the comparison algorithm, creating four experiments with the same sample, regarding the speed of operation and the accuracy of recognition as the criteria of high evaluation, verifying the superiority of the Fractional-order-PCA-SVM coupling algorithm designed in this paper. The average run time of the Fractional-order-PCA-SVM coupling algorithm only 4.152 s in the four experiments, although the speed of operation is not the highest, it also meets the demand of identification. The reason that the operation speed of the algorithm slightly lower than the three control algorithms is that the three algorithms are monotonic; The average accuracy rate of Fractional-order-PCA-SVM coupling algorithm is 99.2425% in four experiments, significantly better than the eight comparison algorithm; For digital medical image recognition the coupling algorithm designed in this paper is effective. [ABSTRACT FROM AUTHOR]

Feature extraction techniques are widely used in fields such as machine learning, pattern recognition, and image processing. The quality of feature extraction is crucial to the generalization ability of a model. This paper proposes a feature extraction technique based on the Shapley value method and an improved Minimum Redundancy Maximum Relevance (mRMR) analysis method. The improved mRMR algorithm enhances the recognition ability for fitting effects of multi-variable feature subsets by traversing all possible combinations of input variable subsets. That is, the result of feature subset selection with fewer input variables does not affect the feature subset selection process when there are more input variables, thus avoiding the limitations of the original algorithm. The research results indicate that using the Shapley value method in conjunction with the improved mRMR algorithm proposed in this study can select the optimal feature subset containing fewer feature variables and achieve a lower MSE value. This contributes to achieving lower computational complexity and higher data fitting accuracy. This technique is applied to the feature extraction scenarios of power battery output power signals and instantaneous fuel consumption signals in hybrid electric vehicles, and constructs the optimal feature subsets for the aforementioned signals. • Exhibits superior global search capabilities, achieving improved data fitting results. • Improves the construction efficiency of the optimal feature subset. • Helps to improve the data fitting accuracy of the plant model. [ABSTRACT FROM AUTHOR]

[Display omitted] • A machine learning-assisted Impact Localization (IL) architecture is proposed. • Adaptive Sparse Noise Reduction Algorithm is studied to extract signal features. • RIME-optimized Dual-layer Support Vector Regression (RDSVR) is developed. • RDSVR enables to select optimal parameters and accurately predict impact location. In this paper, a machine-learning architecture with the integration of two strategies including data enhancement and adaptive generation scheme for Impact Localization (IL) are developed to address the aforementioned issues for location identification of impacts on composite laminates. Two main contributions are included in this research: First, response signals collected from low-speed impact experiments under various working conditions are denoised using Adaptive Sparse Noise Reduction Algorithm (ASNRA), which aims at maximizing the preservation of the original signal amplitude, thereby avoiding the underestimation of pulse features during denoising. Then a RIME-optimized Dual-layer Support Vector Regression (RDSVR) method for the real-time update of hyperparameters is implemented in the machine-learning architecture to realize IL. The superior performances of the IL architecture over different IL models are validated throughout the numerical examples in terms of stability and efficiency. Results demonstrate that proposed architecture has the ability to realize the accurate and robust IL of composite laminates. [ABSTRACT FROM AUTHOR]

• Loaded coal-rock original temperature composition and characteristics are analyzed. • An automatic identification method of IMF attributes based on ACF is proposed. • Temperature denoising algorithm based on CEEMD and adaptive NIWT with NIWOA is got. This paper examines the composition and characteristics of loaded coal-rock temperature collected directly. And a new denoising algorithm for loaded coal-rock temperature is proposed, using complete ensemble empirical mode decomposition (CEEMD) and adaptive nonlinear improved wavelet transform (NIWT) with novel improved whale optimization algorithm (NIWOA). Then the effect of the proposed algorithm's each part is discussed. Finally, the rationale and advancement of the proposed algorithm is validated with data from uniaxial experiments. It is shown that CEEMD can entirely split the original temperature into three categories: environmental component, noise-dominated mode function, and signal-dominated mode function. And in contrast to similar, the reconstruction strategy combining CEEMD and the noise similarity based on ACF is the most effective for denoising, whose results' SNR and RMSE are greater than 52 dB and less than 0.06, respectively. In addition, both NIWOA and NIWT have significantly improved the optimization speed, solving accuracy and denoising effect compared with the original. Their combination can effectively denoise the noise-dominated modal function, and the resulting SNR and RMSE can reach 53.6831 dB and 0.0281. Furthermore, the experimental analysis demonstrates that the proposed coal-rock temperature denoising algorithm has good performance and robustness, with results that are smooth and complete and INNR can be as low as 0.00968, which is clearly superior to other similar or current ones. The research can be useful for further studies of the evolution of the temperature field and the damage location method for loaded coal-rock fracture. [ABSTRACT FROM AUTHOR]

• This paper proposes a fusion algorithm selection method for infrared image based on quality synthesis of intuition possible sets. • We develop an effective model to build mapping relationship between difference feature amplitudes of images. • Non-dominated subsets under each fusion algorithm are determined with negative entropy and credibility. • The score function of multiple fusion algorithms is constructed to select optimal fusion algorithm. Aiming at the heterogeneous features are often collaboratively optimized for fusion, and the existing feature attributes cannot be targeted to adjust algorithms to drive fusion effectively, resulting in poor fusion. We put forward infrared image fusion algorithm selection based on quality synthesis of intuition possible sets. Firstly, the fusion validity of difference feature of image is calculated. In view of intuition possible set orderings of multiple intervals of each difference feature, the degrees can be divided into three levels, and the corresponding possibility distribution subsets are obtained. Secondly, we introduce credibility and separability to weight the multiple subsets, and calculate negative entropy and credibility of each synthesized subset, in order to determine non-dominated subsets under each fusion algorithm. Thirdly, the score function of multiple fusion algorithms is constructed to select optimal fusion algorithm. Then we employ thirteen evaluation indicators and their comprehensive score to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, a point cloud matching algorithm is proposed for the scenario of measuring the dynamic deformation field of a rotating blade. Unlike conventional scheme based on Digital Image Correlation (DIC), the essence of this scheme is to reconstruct individual 3D feature points, instead of a group of feature points in a finite-size subset, on the measurement surface and then track their relative offset from reference baseline. The principles of 3D feature-point reconstruction and straddle-frame tracking are similar to those being used in the algorithm of Particle Tracking Velocimetry (PTV); therefore, no additional tags on feature points to facilitate the perspective matching and tracking is needed. Experiment test shows that this scheme works well in the scenario of high feature-point density (0.026 points per pixel), which cannot be achieved by existing feature-point-tagging methods. Both the rigid motion of the rotating blade and its dynamic deformation field can be reliably obtained. Comparing to DIC-based scheme, the benefit of the present proposed scheme includes the improvement of spatial resolution, the insensitivity to feature-point density, as well as the avoidance of outliers due to insufficient texture information. • Proposes a point cloud matching scheme for measuring fast-rotating propeller blade deformation. • Reconstructs 3D feature points on the surface and tracks their offset from a reference baseline. • Validated empirically in high feature-point density scenarios (0.026 points per pixel). • Superior to DIC-based schemes in resolution, feature-point density insensitivity, and outlier robustness. [ABSTRACT FROM AUTHOR]

Blade tip timing (BTT), as an effective non-contact measurement technology, is widely used for health monitoring of rotating blades. Due to the installed number of probes is limited by the engine structure, the requirement of BTT sampling frequency is difficult to meet the Nyquist sampling theorem, which leads the BTT signal is severely under-sampled. Therefore, it is difficult to extract vibration parameters, such as frequency, amplitude. Many algorithms was proposed to solve the undersampling problem. In this paper, a new BTT signal processing algorithm is proposed based iterative signal space, which expands individual data into subspace level data geometrically. Compared to some previous methods, the iterative signal space algorithm can identify the blade vibration parameters more accurately, which is verified in both numerical simulation and experiment. Furthermore, different degrees of noise is used to show the feasibility and robustness. The results are very significant for online blade fault diagnosis and health monitoring of turbines. • A novel parameter identification algorithm based on iterative signal space for blade tip timing (BTT) signal analysis, which improves the frequency and amplitude analysis capability of BTT. • Geometrically speaking, one-dimensional signal measurement vectors are expanded into signal matrices. • ISS is based on continuously updating a set of orthogonal basis forms a signal space, calculating correlation with dictionary atoms to achieve parameter identification. [ABSTRACT FROM AUTHOR]

• The characteristics of sferic depend more on the propagation distance than on the lightning source and can be used for pulse pairing. • The effect of the treatment of propagation velocity and time of arrival on the long-range lightning algorithm is discussed. • Improvements in location accuracy of the order of a few kilometers can be achieved by using the time of arrival that is minimally affected by sky wave. The propagation velocity (v) and time of arrival (T a) are the primary factors affecting long-range lightning location. In this paper, we have enhanced the Very Low Frequency Long-range Lightning Location Network (VLF-LLN) in China in terms of both pulse pairing methods and location algorithms. Then, the algorithm utilized in the VLF-LLN, and four other long-range lightning location algorithms were compared using lightning strike accidents, simulated lightning strike, and other lightning data as the reference values. Building on this foundation, the discussion centers on the effect of v and T a on location accuracy, with the results showing that an appropriate T a yields a more significant improvement in location accuracy compared to v. VLF-LLN uses the peak time of the ground wave as T a avoids the significant lag caused by sky wave, which is present in other algorithms, and therefore has the best location accuracy with a median location accuracy of 1.81 km. [ABSTRACT FROM AUTHOR]

• A novel technique to determine the capacitance for capacitor-run single phase motor has been proposed. • Measurement of current in both main winding and auxiliary winding, has been performed. • A novel technique to measure the roundness of flux trajectory has been proposed using roundness algorithm. This research paper presents a novel technique to determine the required value of capacitor used in a capacitor-run single phase induction motor. Dynamic analysis of main winding and auxiliary winding currents are performed and the electromagnetic flux revolving in the stator-rotor air gap is studied. In capacitor-run single phase induction motors, the most suitable capacitor is one that produces an electromagnetic flux that has a uniform trajectory (distribution) along the air gap. The novel technique presented in this research paper is the application of roundness algorithm to determine the most uniform trajectory of electromagnetic flux produced by varying auxiliary winding capacitors. The value of capacitance thus found from the proposed technique is compared with the motor manufacturer recommended capacitance. The agreement of the comparison validates the usefulness of the proposed technique. [ABSTRACT FROM AUTHOR]

Highlights • An unique robust subdivision algorithm for single-track absolute encoder is proposed. • Robustness relies on decomposition of correlated information from multiple grid lines. • Standard deviation of subdivision output is improve from 5. 7 ″ to 0. 9 ″ by test. Abstract To improve precision performance of angular sensor, in this paper, research is performed on subdivision technology for a single-track absolute photoelectrical encoder. Firstly, a robust subdivision algorithm is proposed by means of processing a feature matrix of measurement which is created from multiple grating lines' image. Secondly, simulation of anti-noise and anti-spot is established to prove that the proposed algorithm is robust theoretically. Finally, the algorithm is applied in a photoelectrical encoder for real tests, experiment result shows that the subdivision algorithm proposed in this paper is more accurate and adaptive than the traditional way. To reduce a negative precision effect caused by noises and spots, it measures angle according to multiple grating lines by assigning different weights, making those grating lines deeply noised with lower weight, and higher weight with minor noise, thus the overall accuracy is improved. With the proposed subdivision algorithm, test encoder's standard deviation of error can be increased to 1. 6 ″ and precision achieves up to ± 0. 9 ″. [ABSTRACT FROM AUTHOR]

• The AICKF for dual foot-mounted sensors pedestrian positioning is proposed. • Estimating the magnetic disturbance in real time during the pedestrian walking. • We illustrate the theoretical findings using experiments based on the MTI-G710. The advantages of foot-mounted inertial pedestrian position system are passive, full-autonomous and not affected by environment. However, the disadvantage of the accumulating pedestrian position error, which is introduced by Micro Inertial Measurement Unit (MIMU) noise, limits its application. Zero Velocity Update (ZUPT) algorithm is proposed to correct the accumulating error. But ZUPT has poor performance due to the unobserved position error and azimuth misalignment angle, which results in a continuous increase in azimuth error and an increase in positioning error. To solve the problem, the improved method for dual foot-mounted Inertial/Magnetometer pedestrian positioning based on adaptive inequality constraints Kalman Filter is proposed in this paper. Our first contribution in this paper is studying the adaptive inequality constraints Kalman Filter algorithm for dual-foot inertial position algorithm. In this method, the adaptive inequality constraint is introduced in the Kalman Filter of ZUPT, and the constraints parameter can be online adaptive adjusted based on the character of pedestrian step length. Therefore, the accumulating position error can be decreased and limited in a range. As our second contribution, we propose an approach for estimating the magnetic disturbance in real time during the pedestrian walking period. After studying the observability properties of magnetic disturbance parameters, the magnetic disturbance can be estimated when the foot satisfied the conditions of static and angular motion. Then, the azimuth misalignment angle can be corrected by magnetometer during the ZUPT period. We illustrate the findings of our theoretical analysis using experiments based on the MTI-G710 MIMU. The achieved performance indicates that our proposed method can conveniently be used in consumer products. [ABSTRACT FROM AUTHOR]

• The four-point calibration algorithm is done through the self-developed human-computer interaction touch platform. • The single-touch and multi-touch performance can be accurately achieve through the proposed 3D interactive software system. • The proposed calibration algorithm can achieve precise positioning and real-time interactive control. In the depth image interaction system, there is a deviation between the detection range of the Kinect sensor and the projection interaction surface. Aiming at this problem, this paper proposes a trapezoidal correction algorithm based on the three-dimensional sensor Kinect, and performs the calibration on the interaction space to achieve precise interactive control. The performance of the algorithm is verified by the human-computer interaction control platform, which is developed independently. The algorithm proposed in this paper was applied to the 3D interactive software system, which can achieve accurate single-touch and multi-touch performance. Compared with the traditional optical trapezoidal calibration algorithm, the algorithm tracking accuracy rate and the tracking time have been greatly improved, and a good interactive effect can be well obtained. The presented method can be widely applied to various interactive systems such as image interaction systems, human-computer interaction systems, and multi-touch systems, etc. [ABSTRACT FROM AUTHOR]

Highlights • Comparison and application of data-driven methods for operational modal analysis. • Damping ratios extraction using Hilbert Transform and Random Decrement Technique. • Proposed methods are validated by a discrete system and a continuous steel plate. • Dynamic parameters (modal shapes, frequencies, damping ratios) are identified. • Present the results of research from the field of signal theory and engineering. Abstract The operational modal parameters identification under ambient excitation has been widely applied in structural dynamics analysis, where the input force is difficult to measure. This paper makes comprehensive and systematic comparisons of four statistical learning algorithms (PCA, ICA, SOBI, LLE) on analyzing their performance for resolving operational modal parameters identification. It mainly focuses on the comparison and evaluation of the four data-driven methods based for operational modal analysis and explores the use of Hilbert Transform and Random Decrement Technique for the identification of modal damping ratios. The further tests, the robustness of four algorithms, are conducted for investigating the influences of external noise to the performance of the algorithms. On the basis of the modal expansion theory, the vibration response signals could be decomposed into the inner product of modal shapes matrix and modal responses vector in the modal coordinate, from which the modal shapes, modal natural frequencies and modal damping ratios can be well estimated. Hence, a one-to-one mapping between the mathematical model of four statistical learning algorithms and physical model of dynamic systems is established. To validate the effectiveness of the proposed methods, performance and comparison of the proposed methods are studied using a discrete three degree-of-freedom (DOF) system and continuous cantilever steel plate. The simulation and experimental results show that, for the same experimental data, the SOBI algorithm has better performance than other algorithms, and it is more suitable for operational modal identification. Finally, the characteristics of four algorithms and further directions are concluded and discussed in this paper. [ABSTRACT FROM AUTHOR]

Highlights • A novel digital analysis system is proposed to measure and identify the fibers. • K-means is used to realize automatic recognition and reduce the subjective error. • The recognition rate has an improvement compared with the traditional methods. Abstract The identification of wool and cashmere is always considered as a challenge in the field of textile and clothing industry. A novel digital analysis method based on fractal algorithm, parallel-line algorithm, and K-mean clustering algorithm was proposed in the paper to improve the accuracy of fiber measurement and identification. First, the original images of 600 cashmere fibers and 600 wool fibers were captured by optical microscope attached with a digital camera, the self-developed image preprocessing was carried out to obtain the binary image and contour image of cashmere and wool fibers. Then, the fractal algorithm was used to calculate the box-counting and information dimension of fiber binary images, and the parallel-line algorithm was used to measure the fiber fineness of the contour image. These features can be used as the representation of fiber surface morphological features. Finally, the cluster analysis of the extracted feature set was carried out by k-means algorithm, the specimens used for the identification include three sets of wool and cashmere fibers blended with different proportions. The experimental results showed that the accuracy of the fractal-based and diameter-based identification of three sets with different fiber blending proportions was higher than that of traditional methods, the average identification accuracy rate was 97.47%. Through this investigation, the novel digital analysis method proposed in the paper was proved to be feasible to measure and identify the wool and cashmere fibers, it had the protentional application of digital and automatic fiber classification in the future, in terms of both equipment, method and standard. [ABSTRACT FROM AUTHOR]

• An algorithm of dexterity evaluation for continuum robots based on IK is proposed. • An IK model for continuum robots is established using an oval curve equation. • Length distribution of the robot is optimized based on dexterity indices. Performance evaluation and inverse kinematics (IK) for continuum robots are always hard and time-consuming. In this paper, an efficient dexterity evaluation algorithm based on IK (DEAIK) for continuum robots is proposed. The IK model is established using an oval curve equation to improve computational efficiency. The relationship between length distributions and dexterity distribution is obtained by the simulation in this paper. Length distribution of the two-segment continuum robot is optimized under the guidance of the dexterity indices using the fruit fly algorithm. Theoretical analysis and numerical simulations demonstrate that the dexterity of the structure-optimized continuum robot is better than that of the traditional continuum robot. The simulation shows that the DEAIK algorithm is 3.68 times faster than the algorithm based on forward kinematics. The IK algorithm in this paper is 3 203 times faster than the IK algorithm based on the Levenberg-Marquardt algorithm in the same accuracy. This work is significant for designing a high-performance continuum robot. [ABSTRACT FROM AUTHOR]

To augment situational awareness capabilities for high-frequency disturbances, the modern power system necessitates the implementation of high-sampling rates for real-time Synchro-Waveform (SW) measurements. Nevertheless, the nonlinear nature of data collected from SW measurement devices poses significant challenges to the communication and storage capacities of data centers. To effectively tackle this issue, this paper proposes a Multi-stage Hybrid Coding (MHC) method for the lossless compression of SW data. The MHC method utilizes a well-structured two-stage approach to achieve efficient compression. In the first stage, a variable frame-based method is designed to reduce redundant information and achieve initial compression using multiple periodic recursive compression. Subsequently, the dictionary-based method, specifically the Lempel–Ziv–Markov chain algorithm, is seamlessly integrated to further compress the SW data. Experimental and numerical analyses are conducted using both simulated and real-world source data, with real-time performance validation in SW measurement units. The obtained results convincingly demonstrate that the proposed method enables online compression of both event and non-event SW data, achieving an impressive reduction of over 73.4% in data space, while maintaining a data loss ratio lower than 0.4% for high-density SW data. • Multiple periodic recursive method is proposed to eliminate repetitive information. • A multi-stage hybrid coding method is implemented for the lossless compression. • A series of synchro-waveform events are collected to evaluate the performance. [ABSTRACT FROM AUTHOR]

• Nonuniform E-field is restored by measuring E-FISH signals along probing laser. • Convergence of inversion algorism related to measuring system parameter is given. • Accuracy of inversion algorism is validated via experimental investigations. • Robustness of inversion algorism in resisting measurement noise is confirmed. The electric field-induced second-harmonic generation (E-FISH) method can offer high spatiotemporal resolution in electric field measurement, but faces challenge in reconstructing the inhomogeneous electric field along the probing laser. This paper presents an inversion method to reconstruct the electric field profile by using E-FISH signals measured from multiple points along the probing laser. The relationship between the condition number of the inversion coefficient matrix and the parameters of E-FISH measurement system is clarified. The inversion method is validated through experimental investigations conducted in three different electrode configurations, namely the plate-to-plate gap, the rod-to-rod gap, and the rod-plate gap. With the inhomogeneity coefficient varies from 1 to 10.7, the maximum deviation between the measured electric field and the finite element simulation is less than 6%. This method does not rely on priori assumptions and electrode configurations, and exhibits good robustness in resisting the effect of measurement noise on the inversion accuracy. [ABSTRACT FROM AUTHOR]

In this paper a new instrument specifically designed for the compliance check of windings for Medium Voltage/Low Voltage (MV/LV) cast resin insulated power transformers is presented. The proposed instrument, based on the SFRA (Sweep Frequency Response Analysis) measuring technique, has been designed to carry out automatic measurements during the industrial production of the windings. In this paper the main hardware and software instrument features are presented, with particular consideration of the algorithm adopted for the compliance check. Finally, results obtained during both the lab instrument characterization and the on-field validation at the BTicino winding production factory is also presented. [ABSTRACT FROM AUTHOR]

• The bearingless permanent magnet slice motor control system for artificial hearts. • To improved (SMO) and high frequency injection method was proposed in this paper. • GAPSO algorithm was adopted the critical point of low-speed domain and medium. • High-speed domain to realize smooth switching between different estimation methods. Increase in volume in the bearingless permanent magnet slice motor (BPMSM) control system for artificial hearts is the major problem in the existing works. A sensorless control method of the BPMSM for the artificial heart is proposed in the study based on an improved sliding mode observer (SMO) and improved high-frequency injection method. Also, a full-speed rotor position detection method combining the high-frequency injection (FHI) method and sliding mode observer is designed. In this method, an improved high-frequency injection method was designed in the low-speed domain, and an improved sliding-mode observer was designed in the medium–high-speed domain. Besides, the speed domain switching method based on the genetic particle swarm optimization (GAPSO) algorithm was adopted at the critical point of the low-speed domain and the medium and high-speed domain to realize the smooth switching between different estimation methods, and then realize the sensorless control of the rotor in the full speed domain. Secondly, the simulation study by Simulink was used to compare the detection effects of the BPMSM rotor position and speed of the artificial heart pump under different methods are compared in this work. The results show that the speed estimation value and rotor position estimation value of the new method in this paper were closer to the actual value, the position estimation error is 3%, and the speed estimation error was within 1.5%. Further, the experimental study was carried out on the principal prototype of the sensorless control part of the artificial heart to verify the effectiveness of the proposed method. The method proposed in this paper has certain generality in the field of motor sensorless control technology, and has important reference value for researchers in this field. [ABSTRACT FROM AUTHOR]

• Proposes an Adaptive Kernel Correlation Filter algorithm improving tracking precision. • A binocular error correction method improves the synchronization precision of cameras. • A motion capture system evaluates and compensates for binocular measurement errors. • The reconstructed models' average error and similarity are 1.352 mm and 0.996, respectively. • The results contribute to the deformation measurement of flexible mechanisms. In order to measure the kinematic parameters and deformation of flapping wings precisely, this paper presents a wing motion reconstruction method utilizing a binocular vision system (BVS) comprising two action cameras. We designed an Adaptive Kernel Correlation Filter algorithm based on target scaling prediction for wing reconstruction to improve target tracking accuracy and robustness. A binocular error correction method with a startup time difference-induced error model was proposed, which helps the action cameras achieve the same high-precision synchronization as the industrial cameras with external trigger mode. Moreover, an infrared motion capture system is applied to assess and compensate for errors in the BVS. The experimental results show that the maximum value of the average errors of the BVS is 1.352 mm, and the minimum similarity value of the two wings is 0.996. The results contribute to the deformation measurement and optimization of soft materials such as the flapping wings. [ABSTRACT FROM AUTHOR]

The acquisition of large atomic-force-microscopy (AFM) scans at nanoscale resolutions can take hours and produce datasets with millions of pixels, which is time consuming and computationally expensive to analyze. In this paper, we present an approach to speedup this process by using a computer-vision algorithm, namely the Noise2Noise algorithm, to reconstruct high-resolution, low scan speed AFM data from high-speed, noisy, sparsely sampled AFM data. This algorithm is trained on various noise types to reproduce different sources of experimental noises encountered during the acquisition of AFM data. Our results demonstrate that a sparse, uniform AFM scan of 20 × 20 μ m at 128 × 128 pixel resolution can be processed within seconds, and the output image is comparable to a higher quality raw AFM data scan which required 30 minutes or more to collect and process manually, reducing not only the acquisition and analysis time, but also the size of the data being collected. • High resolution, low scan speed AFM images are reconstructed from higher scan speeds data. • The Noise2Noise algorithm is used to map noisy AFM images to clean AFM images. • Several noise modes are used to train the algorithm to mimic realistic noises in AFM data. • Process of raw and fast scan data corrections leads to up to 70% improvement in image quality. [ABSTRACT FROM AUTHOR]

• A spatial ray tracing method in non-uniform refractive index field based on Hamiltonian algorithm was proposed. • The non-uniform refractive index field was reconstructed by multi-angle Tomography Schlieren method. • Error compensation method for visual measurement in the non-uniform refractive index space was realized. In complex large-scale industrial environments, light rays no longer propagate in a straight line due to the influence of non-uniform refractive index, which will lead to large measurement errors in visual measurement. In order to accurately compensate the error, this paper firstly quantitatively analyzes the influence of non-uniform refractive index distribution on visual measurement. Then the symplectic matrix ray-tracing method of Hamiltonian optics is used to reconstruct spatial light in the reverse direction with known light propagation end point and spatial refractive index distribution. Thus the traditional model of light propagation along a straight line in visual measurement can be changed and the measurement error introduced by non-uniform refractive index field can be corrected. Finally, the proposed Hamiltonian ray tracing method was verified through the spatial non-uniform refractive index field reconstructed by Background Oriented Schlieren(BOS) method. The experimental results verified the effectiveness and feasibility of this method. [ABSTRACT FROM AUTHOR]

• A novel iteratively reweighted accurate sparse low-rank (IRASLR) matrix estimation algorithm is proposed. • An equivalent variant of minimax concave penalty (MCP) is constructed. • The equivalent weighted MCP and equivalent weighted matrix γ - n o r m are defined with their proximal operators provided. • The simulation and experiment signal proved that the proposed IRASLR method shows better reconstruction accuracy. Bearing fault diagnosis is significant to ensure the safe functioning of mechanical systems. One of the key issues in bearing fault diagnosis is the accurate and effective extraction of fault characteristic features. In order to further explore the construction of penalty function, further alleviate the problem of bias estimation and enhance the capabilities of the sparse low-rank (SLR) method in effectively diagnosing bearing faults, in this paper, an iteratively reweighted accurate sparse low-rank (IRASLR) matrix estimation algorithm is proposed. Taking the sparse and low-rank properties of fault transient signals in time–frequency domain into account, an optimization problem is formulated. Two penalty functions, derived from a core minimax-concave penalty (MCP), induce sparse and low-rank properties, respectively. Specifically, to facilitate the design of weighting schemes, we construct an equivalent variant of MCP (EVMCP). By separately extending EVMCP to matrix elements and singular values, we obtain equivalent weighted MCP and equivalent weighted matrix γ - n o r m. Finally, based on the proximal operators of the two penalty functions, we deduce an IRASLR algorithm under the alternating direction method of the multipliers (ADMM) framework. The performance of IRASLR is verified by processing simulated and experimental signals, while the superiority of the algorithm is confirmed through comparative experiments. [ABSTRACT FROM AUTHOR]

• L-S/DSC algorithm realizes real-time 3D strain/displacement field reconstruction. • This algorithm has good applicability and suits beam and bracket arm structures. • This algorithm avoids the difficulty of selecting the mode order in traditional modal theory. • Reconstructed full-field strains and displacements with very high accuracy. The bracket arm structure, subjected to long-term vertical levitation and transversal guidance forces, is a key load-bearing component of high-speed maglev trains. It is crucial to monitor strain, displacement , and load in real time to evaluate structural health. However, it is almost impossible to measure directly at the service site of trains due to the intricacy of the bracket arm structure and the constrained installation area. Hence, it is an effective and feasible approach to reconstruct the strain/displacement field using a limited amount of strain information from FBG sensors. In order to reconstruct the strain/displacement field and identify the load on complicated structures, a load–strain/displacement superposition conversion algorithm is proposed in this paper. The modal coordinate and the mode-shape matrix are swapped out for the load weight and the load–strain/displacement matrix, respectively. Thus, it gets around the difficulty of choosing the mode order in traditional modal theory. In addition, the algorithm can achieve full-field reconstruction of both strain and displacement by measuring only part of the nodal strain response, reducing the redundancy of the traditional algorithm. Finally, numerical simulations and experimental tests were carried out to verify the feasibility of the proposed method for field reconstruction and load identification. [ABSTRACT FROM AUTHOR]

A fast iterative algorithm for frequency estimation is developed in this paper to improve the frequency tracking performance. If the signal is transformed by a mathematical tool, the signal to noise ratio (SNR) should not be greatly reduced after the transformation. The analysis presented in this paper showed that the traditional method for frequency estimation causes large noise at high frequency range, therefore, the suitable estimation range of traditional method is only from 0 to fs /6 Hz ( fs is the sample frequency). In order to overcome this limitation, a new structure of iterative algorithm is established to extend the upper bound frequency from fs /6 to fs /2 Hz. The experimental noisy sinusoid signal frequency estimation and chirp signal frequency tracking confirmed that the novel algorithm showed improved performance. Furthermore, the average estimation error was decreased over 30% (under SNR = 15 dB) when applying the novel iterative algorithm. The novel iterative algorithm will have broad applications in fields of signal processing and communication systems. [ABSTRACT FROM AUTHOR]

The extraction of repetitive impacts from vibration signals plays an essential role in bearing fault detection. Among different signal processing algorithms, morphological filter (MF) has attracted lots of attention because it could directly extract the geometric structure of the impulsive feature and only needs little computation. However, the conventional MF and some current improvements are based on the local optima of the raw signal to de-noise the noisy signal and its faulty feature extracting capability would be greatly affected by the noise. In this paper, a new improved MF algorithm is proposed to overcome such deficiency. Firstly, morphological gradient (MG) operator is selected in this paper due to its capability of picking up both positive and negative impulses. Then, based on the relationship between the defect induced impulse and a harmonic function with the resonant frequency, the harmonic waveform in a period is adopted to instruct the construction of structuring element (SE). The improved MF can obtain the fault feature from low SNR signals. The processing results of a simulation signal and two sets of experimental signals and a set of comparisons verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive fuzzy logic controller (FLC) design technique for controlling an induction motor speed drive using backtracking search algorithm (BSA). This technique avoids the exhaustive traditional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive MFs are implemented in speed controller design for input and output based on the evaluation results of the fitness function formulated by the BSA. In this paper, the mean absolute error (MAE) of the rotor speed response for three phase induction motor (TIM) is used as a fitness function. An optimal BSA-based FLC (BSAF) fitness function is also employed to tune and minimize the MAE to improve the performance of the TIM in terms of changes in speed and torque. Moreover, the measurement of the real TIM parameters via three practical tests is used for simulation the TIM. Results obtained from the BSAF are compared with those obtained through gravitational search algorithm (GSA) and particle swarm optimization (PSO) to validate the developed controller. Design procedure and accuracy of the develop FLC are illustrated and investigated via simulation tests for TIM in a MATLAB/Simulink environment. Results show that the BSAF controller is better than the GSA and PSO controllers in all tested cases in terms of damping capability, and transient response under different mechanical loads and speeds. [ABSTRACT FROM AUTHOR]

Time synchronization is a key feature of Wireless Sensor Networks (WSNs). In low cost WSNs this is a very challenging issue since the general employment of a large number of nodes, characterized by low power and limited energy resources. Therefore, in such contexts, the adoption of synchronization algorithms is preferable to the adoption of GPS-based solution because of its high energy consumption, high cost and poor performance in indoor environment. An even increasing number of papers dealing with the design and the implementation of protocol-based techniques for synchronization can be found in literature. These synchronization algorithms can be divided into three main categories: one way messaging, two ways messaging and consensus based synchronization. Among the others, those based on the one way messaging and, in particular, on the adoption of regressive algorithms are widely used in many application contexts. Focusing the attention on this class of synchronization algorithms, this paper proposes a deep performance analysis aimed at highlighting the sensitivity of the regression-based algorithms to some factors that influence the accuracy in typical low cost applications, such as the finite resolution of the local timing clock, the presence of clock drift, clock high frequency noise and low frequency noise, the presence of latencies due to the radio devices, the presence of latencies due to the microcontroller device. The main goal is to evaluate the effect of each one of these factors of influence on the overall synchronization performance. To these aims, suitable analyses both in simulation environment and on real nodes have been carried out. [ABSTRACT FROM AUTHOR]

• The noise-induced phase error of the multi-frequency phase-shifting algorithms is analyzed and compared. • The phase unwrapping reliability of the multi-frequency phase-shifting algorithms is analyzed and compared. • The results of this paper can provide a reference for algorithm selection in 3D measurement applications. For fringe projection profilometry (FPP), phase extraction accuracy determines three-dimensional (3D) measurement precision. The combination of phase-shifting (PS) and multi-frequency temporal phase unwrapping (MTPU) can reconstruct discontinuous or isolated surfaces with high accuracy and robustness. However, due to the introduction of inevitable random noise, the phase extraction is biased. To this end, this paper firstly compares the wrapped phase errors based on various PS methods in detail. Then, the phase errors and unwrapping reliability based on various dual-frequency temporal phase unwrapping methods are compared. Finally, the effects of three-frequency temporal phase unwrapping and reasonable selection of fringe frequency on phase extraction accuracy are further discussed. The accuracy of the wrapped and unwrapped phase extraction and the phase unwrapping reliability are compared experimentally. This paper can provide a reference for algorithm selection in 3D measurement applications. [ABSTRACT FROM AUTHOR]

The paper deals with measurement and control aspects of active power filtering at ship systems. A main focus of the paper is concentrated on the analysis of the solutions for the improvement of algorithms of compensating current generation. Two aspects of the problem are discussed. Firstly, a choice of an appropriate algorithm focused on hardware and software complexity. Secondly, an assessment of the quality of compensation by analysis of resulting current waveforms on power supply side, using various distortion indices and considering various frequency bands. [ABSTRACT FROM AUTHOR]

• Performance analysis of different video-based eye blinking detection. • Development of a low-cost imaging acquisition system. • Quality metrics analysis of proposed algorithms. • Performance comparison between RGB and Grayscale images. • The red channel of the RGB camera achieves in our experiments a 100 % eye blink detection rate for event detection. The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions. Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions. In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used. [ABSTRACT FROM AUTHOR]

Low-amplitude and wide-frequency bands characterize microvibrations and can severely degrade the working accuracy of sensitive payloads. Notably, active microvibration isolation systems (AMISs) are more effective for expanding the effective bandwidth and improving the vibration isolation performance than other approaches, such as passive and semi-active methods. Accordingly, our research has focused on the key technologies used in AMISs over recent decades and their further development. In this study, we review the key technologies, such as the systematic dynamics methodology, sensing, actuation, and controller design. The approaches of dynamics modeling and decoupling determine the controller design for increased control accuracy. Sensors and actuators are the sensing and actuation elements of AMISs, and their intrinsic properties significantly affect the microvibration isolation performance. The authors believe that smart material actuators, such as piezoelectric, magnetostrictive, and dielectric elastomer actuators, will be popular research topics for a long time. For the controller design, control strategies, control architectures, and advanced control algorithms, such as fuzzy logic and neural networks, determine the controller performance with regard to control accuracy, reliability, and stability. This work presents a state-of-the-art review of the key technologies with active vibration control that can be employed in microvibration rejection. To conclude the paper, the current challenges, future opportunities, and directions are presented. [ABSTRACT FROM AUTHOR]

• A fast digital lock-in algorithm combining undersampling and oversampling is proposed. • A current source combined with the load characteristic is designed. • A low-cost respiratory monitoring system based on the magnetic eddy current sensing principle is designed. To design a low-cost non-contact respiratory monitoring system based on the magnetic eddy current sensing principle, a fast digital lock-in algorithm combining undersampling and oversampling is proposed in this paper. This algorithm enables direct digital sampling and fast demodulation of high-frequency respiratory modulated signals, decreasing the performance requirements of the analog-to-digital converter (ADC) and the microcontroller (MCU). In addition, a current source combined with the load characteristic is designed to reduce the cost of waveform generation. The experimental results show that the low-cost amplitude modulation (AM) system constructed based on the above studies can detect respiratory signals in different states with the signal-to-noise ratio (SNR) higher than 92 dB. Compared with the results of the medical monitor, the relative error range of the respiratory rate (RR) measured by the AM system is 1.15%–3.12%, and the measurement accuracy is higher than 98.9%. [ABSTRACT FROM AUTHOR]

The spatial object detection for autonomous navigation is an active research topic with many works published about it, many of them use object detection and stereo vision systems to fulfill this task. One of the main tasks of the stereo vision system is to find the corresponding object on the image pair in order to compute the object three-dimensional coordinates, the most used methods to perform this task is to use disparity maps. The disadvantages of these methods are that they are time consuming with a high computational cost which is not ideal for real-time applications. Other methods to perform this task are template match algorithms like SAD, SSD or NCC. These algorithms have a low computational cost, but the problem is that they are time consuming, making them not suitable for real-time applications. This is why this paper proposes a novel template match algorithm called SoRA with faster run-times and low computational cost. The performed experiments demonstrate that the proposed algorithm outperforms existing template match algorithms maintaining accuracy and a low computational cost which is ideal for the intended application. • Template match algorithm to enhance object detection using stereo vision. • The algorithm finds object correspondence in stereo image pair for spatial location. • Fast template match algorithm with low computational cost. • Fast template match algorithm with low execution time. • Template match algorithm optimizes dissimilarity measures by reducing operations. [ABSTRACT FROM AUTHOR]

• A small sample pavement crack detection framework based on GAN and CNN. • The image generated by the proposed GAN model can retain the high-frequency information and be directly used in CNN model. • The detection method based on CAM enables the classification model to provide semantic information and expands the detection ability of the classification model. Pavement crack detection methods based on deep learning and computer vision can greatly improve detection efficiency and accuracy, but in many cases the data in training set is lacking or uneven, making it insufficient to train an accurate detection model. This paper proposes a detection method under small samples, which is composed of two steps. First, with a generative adversarial network (GAN) constructed, the small sample data set of pavement cracks taken by unmanned aerial vehicle (UAV) is used as the training set and the GAN model is trained. The best trained model is used for generation of new images. Second, original small-sample data set is expanded by images generated by the GAN model, and a convolutional neural network (CNN) model is constructed at the same time. Then, data set before and after the expansion is trained and tested by the method of transfer learning to verify the effectiveness of expanded data separately. It has been proved that, compared with the unexpanded data set, CNN model trained after expansion improves the test set detection accuracy from 80.75% to 91.61%, which is regarded as a significant improvement. In addition, this paper also uses class activation map (CAM) to visually evaluate CNN model, and expands the detection ability of classification model. [ABSTRACT FROM AUTHOR]

Abstract: The usage of eddy current probes (ECP) with a single magnetic field sensor represents a common solution for defect detection in conductive specimens but it is a time consuming procedure that requires huge amount of scanning steps when large surface specimens are to be inspected. In order to speed-up the nondestructive testing procedure, eddy current probes including a single excitation coil and an array of sensing coils present a good solution. The solution investigated in this paper replaces the sensing coils for giant magneto-resistors (GMRs), due to their high sensitivity and frequency broadband response. Thus, the ECP excitation coil can be driven at lower frequencies than the traditional ones allowing defects to be detected in thicker structures. In this work an optimized uniform eddy current probe architecture including two planar excitation coils, a rectangular magnetic field biasing coil and a GMR magnetometer sensor array is presented. An ac current is applied to the planar spiral rectangular coil of the probe, while a set of GMR magnetometer sensors detects the induced magnetic field in the specimens under test. The rectangular coil provides the DC uniform magnetic field, assuring appropriate biasing of the GMR magnetometers of the probe, setting-up the functioning point on the linear region and at the same branch of the GMR static characteristics. The differences on the images obtained for the same specimen for each GMR are reduced if all sensors are biased on the same working point. Elements of the automated measurement system used to inspect the plate under test using the proposed eddy current probe, including a validation procedure based on a 2D template matching algorithm and the corresponding experimental results are included in the paper. [Copyright &y& Elsevier]

Abstract: Modelling of microburst is significant for flight simulations. The most widely used model of microburst is the one based on the principle of multiple vortex ring. The characteristics of the microburst are directly determined by the parameters of the model. The conventional method to fix the parameters of multiple vortex ring microburst model is using experiential formula, which is not flexible enough. In this paper, we treat the parameters estimation of multiple vortex ring model as an optimization problem, and introduce the differential evolution algorithm to solve it. In the process of estimating the parameters by the maximum wind velocity, there are two interrelated optimization processes. Therefore, we improved the standard differential evolution algorithm in this paper. A nested differential evolution algorithm is proposed to accomplish the two optimization process, intermediate optimization and objective optimization. The numerical experiments show that this method can flexibly generate microburst with any maximum wind velocity, and the error meet the requirement set by the user. [Copyright &y& Elsevier]

Abstract: This paper deals with the calibration of hysteretic transducers. The limits of the traditional approach are explained and a new method based on the Maximum Likelihood algorithm is proposed, overcoming the mentioned limits. It is also shown that the features of the input to the transducer are important to determine in which situation the proposed method is needed. The harmonic and random cases are analysed, showing that different calibration strategies are required in the two different situations. The calibration of a hysteretic transducer is therefore much more complex than that of a non-hysteretic one. The analysis is carried out numerically, adopting a model to simulate the behaviour of hysteretic load cells and then a real case is analysed at the end of the paper. [Copyright &y& Elsevier]

Abstract: Vibration analysis of machines operating under non-stationary operational conditions requires special attention. This refers to the necessity of use of order tracking algorithms together with additional advanced signal processing methods, especially when faults indicators of interest are located in resonance frequency ranges. Nowadays, cyclostationarity analysis becomes more popular in the field of mechanical systems signal processing. It has proven its functionality in diagnostics of rotating machinery in numerous cases. The paper introduces the method called averaged instantaneous power spectrum as a time–frequency representation of selected cyclic components. The purpose of proposed tool is to provide supplementary information about the character of the cyclic component of interest and to enhance conclusions drawn from classical vibrodiagnostics approach. The method is tested on a selected case of wind turbine drive train fault as typical example of machinery operating under non-stationary operational conditions. The averaged instantaneous power spectrum is applied to vibration signal components originated from rolling element bearings. In the paper, the capabilities and limitations of the proposed method calculated in angular domain are presented with the main focus on variable operational conditions. [Copyright &y& Elsevier]

Abstract: With the rapid development of deepwater exploration and development, more and more deepwater offshore engineering such as gas pipeline, riser system, and mooring system will apply to deep seabed. The measurement of relative posture of submarine structures is one of the key for these engineering. Paper takes measurement of relative posture for two seabed pipeline as an example to design a deepwater pipeline flanges measuring system which is based on the stretching wire. After establishing the mathematical model, using the robot posture theory, the transition matrix theory and the principle of vector operations, using transition matrix pose algorithm, paper solves posture parameter’s solution of two pipelines such as relative distance and relative angles. For the complexity of the marine environment, paper carries out experimental studies measuring system to verify the accuracy of measurement method. Replace the positioning base of deepwater pipeline flanges measuring system can also be applied to the measurement of other submarine structures, which provides a valid method and approach for deepwater measurement techniques. [Copyright &y& Elsevier]

Abstract: This paper deals with the implementation of an innovative measurement instrument for power measurement in Digital Video Broadcasting Terrestrial (DVB-T) systems. In previous researches the authors have designed and tuned the measurement method. It is based on a digital signal processing section, which properly estimates the power spectrum density (PSD) of the input DVB-T signal. The peculiarities of the selected measurement algorithm have suggested the development of the instrument on a suitable FPGA platform. In this paper the hardware and the software architectures of an innovative instrument implementing the cited method are proposed. It is characterized by high transportability and exhibits metrological performance comparable with that peculiar to state-of-the-art. In particular, both the firmware solutions and optimizations, proposed to limit the resource demanding and making use of the massive parallelism offered by the FPGA architecture, are described in detail. A number of experimental tests carried out on actual DVB-T signals are also presented, whose results show the good metrological performance of the realized instrument. All measurement results concur with those provided by competitive commercial solutions. [Copyright &y& Elsevier]

Abstract: In this paper two different low-cost eddy current testing (ECT) systems used in detecting and measuring defects in conductive surfaces are evaluated and its performance compared with a commercial equipment. Both developed systems include a probe with a giant magnetoresistance (GMR) as the magnetic field sensor and a computer mouse pointer as the positioning system. This configuration, despite its low cost, allows better performances than commercial equipments because deeper defects can be detected due to the higher sensitivity of the GMR sensor, which is constant in a very large frequency range (10Hz–1MHz), but also because a precisely located graphical representation of the defect is delivered to the user due to the incorporated positioning system. Although having the same goal, both developed ECT system implementations differ on their architectures and signal processing algorithms. One system is based on a digital signal processor (DSP) where raw data is digitally processed and the other system uses analog circuits to process the acquired signals. This paper includes a detailed description of each implementation, the obtained results and a performance comparison with commercial equipment. [Copyright &y& Elsevier]

Abstract: The paper presents a new method of elimination of influence of drift-like errors in so called intelligent cyclic A/D converters, in particular, errors caused by drifts (droops) of voltage at the output of sample-and-hold blocks. The method is based on application of the extended multi-dimensional algorithm, which estimates simultaneously values of the input sample and drift rate. Implementation of the extended algorithm in the intelligent cyclic A/D converters requires only insignificant changes in the digital part of the converter and does not increase their production costs. The motivations to these investigations resulted from practical realizations of the intelligent cyclic A/D converter in CMOS technology and difficulties in design of a precise sample-and-hold circuit. The results of selected simulation experiments related to analysis of influence of a droop rate on the final performance of the intelligent cyclic A/D converters employing the standard (one-dimensional) and extended algorithms are discussed and compared in the paper. The results of experiments show that application of the proposed solution enables efficient functioning of the converters even in the presence of relatively large drifts. [Copyright &y& Elsevier]