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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]

[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]

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]

• 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]

• 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]

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]

• 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]

• 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]

• 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]

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]

• The reconstruction model is established for vibration signals. • The index vector is proposed to obtain all the feature coefficients. • The improve FISTA is proposed for a better performance in reconstruction. • The simulated signals and acquired signals are used to verify the effectiveness. We consider the compressive sensing reconstruction for vibration signals, which are complex due to the harsh working environment. The recent fast iterative shrinkage-thresholding algorithm (FISTA) has paved the way for the signal reconstruction with a low complexity and high efficiency. Unfortunately, when extending to the vibration signals, the current algorithm still has some drawbacks such as the bad reconstruction effect. In this paper, we propose the improved fast iterative shrinkage-thresholding algorithm (IFISTA) to improve the reconstruction effect. Under the new scheme, the reconstruction is promoted by extracting information from the unstable signals in the process of iteration. Then the feature coefficients will be protected from shrinkage during iteration. The effectiveness of the IFISTA is verified by simulated signals and acquired signals. It is showed that the proposed scheme has superior performance in reconstruction and feature protection for vibration signals. [ABSTRACT FROM AUTHOR]

Highlights • New minimum zone algorithm based on the sphere polar plane and its flatness. • Outstanding accuracy, compared with least-squares and other former methods. • Compensation of CMM error for sphericity through a new regression model. • Analytical uncertainty estimation of sphericity based on CMM error model. • Models validation by the Monte Carlo Method. Abstract Sphericity is a relevant tolerance of form in many fields of engineering. Measuring sphericity is frequently accomplished through the fitting of the point coordinates obtained from coordinate measuring machines (CMMs) to a substitution sphere. This paper presents a new algorithm for sphericity of minimum zone tolerance. It faces its resolution through the flatness problem associated with the implicit formulation of the polar plane of the sphere. The polarity transformation allows the iterative resolution of the minimum zone sphericity through a simpler flatness problem. Its accuracy is compared with the least-squares solution and some well-known minimum zone algorithms from literature. Its performance reaches or surpasses them. Next, a new error compensation model of point coordinates for sphericity is developed. A model based on the regression of the error by each CMM axis is incorporated into the sphericity measurement model. The explained error propagated allows correcting the sphericity calculated by the CMM indication, but also facilitates the uncertainty estimation by the propagation of the unexplained residual error in the regression through the measurement model. The experimental verification by the Monte Carlo Method shows efficient results. The jointly proposed method of a new algorithm with error compensation and uncertainty estimation can effectively contribute to the improvement of sphericity measurement from point coordinates in precision and accuracy. [ABSTRACT FROM AUTHOR]

• The image Hash authentication algorithm for orthogonal moments of fractional order chaotic is proposed. • The image information is authenticated by the Hamming distance. • The robustness and tampering recognition accuracy of the proposed method is tested. In this paper, the image Hash authentication algorithm for orthogonal moments of fractional order chaotic scrambling coupling hyper-complex number Tchebichef is proposed. The improved PDE function and Ring segmentation mechanism are used to process the initial image and output the secondary image to improve the robustness of Hash's scaling and rotation; and by taking full account into the color information of the image, the image R, G and B position is regarded as its imaginary part. In addition, the invariant moment of the hyper-complex number Tchebichef is desgined, and the robust characteristics of the secondary image is fully extracted to greatly improve its ability to recognize the rotation tampering. At the same time, in order to improve the security of the Hash sequence, the fractional order Logistic mapping and chaotic scrambling technique is constructed to encrypt the eigenvector; and the image information is authenticated by the Hamming distance. Finally, the robustness and tampering recognition accuracy of the proposed Hash algorithm are tested. [ABSTRACT FROM AUTHOR]

• Data loss in the process of state estimation has been addressed in this article. • The existing method based on AR model has been improved by replacing ARMA model. • A simple minimum error based mechanism is proposed to decide order of LP filter. • The computational time is reduced by avoidance of inversion of matrices. • The existing schemes are compared with the proposed one to show the performance. Data loss problems severely effect the performance of state estimation in most of communication and control systems. The normal techniques adopted for compensation in the process of state estimation are Open-loop Kalman filter and compensating closed-loop Kalman filter. The compensated closed-loop Kalman filtering scheme employs three (03) strategies namely Normal Equation, Levionson-Durbin and Leroux-Gueguen algorithms, using Autoregressive (AR) model, where only previous measurements are used. In this paper, the compensated vector is proposed based on Autoregressive Moving Average or ARMA model instead of AR model. This model contains more information than Autoregressive model, i.e. measurement and input signals, which is believed to generate more efficient results. Necessary steps including the computation of linear prediction coefficients have been taken to accommodate the input signal. Computation of this extra linear prediction coefficient however, bears an observable increase in computation time. The selection of AR and ARMA models is a trade-off between computation time and improved performance, which is the ultimate consequence of extra input signal. A standard mass-spring-damper case study is simulated to provide a comprehensive comparison in light of various parameters including state estimation, error, gain elements etc. [ABSTRACT FROM AUTHOR]

Highlights • Thermal resistance network model based on three-dimensional structures. • Complete 3-D temperature distribution at arbitrary position. • The convection coefficient calculation based on iterative algorithm. • Accurate temperature distribution suitable for three-dimensional structures. Abstract A thermal resistance network model based on three-dimensional structure is proposed in this paper. In a chip package newly developed method (three-dimensional stacked package), the thermal distribution management is essential. In order to improve the thermal design of a chip package structure, a thermal resistance network model of temperature distribution is established based on the thermal resistance network and the principle of spherical wall heat conduction. The principle of spherical wall heat conduction is adopted to calculate the complete 3-D temperature distribution at arbitrary position. For the analysis of the thermal resistance of thermal convection, the convection coefficient is calculated based on iterative algorithm. When the heating power arises from 0.27 W to 2.18 W, the errors between the calculation results and the experimental results are less than 5%. The experimental results validate the calculation results well. [ABSTRACT FROM AUTHOR]

Highlights • The SAX algorithm is improved by investigating from multi-domain to extract comprehensive fault information. • The fault pattern is firstly presented to investigate the fault information. • The Shannon entropy is applied into symbol sequences based on local structure analysis for feature extraction. • A new machinery fault diagnosis framework based on ISAX, Shannon entropy technique and KPCA is proposed and validated. Abstract Feature extraction from vibration signals is considerably significant for condition monitoring and fault diagnosis. The Symbolic Aggregate approXimation (SAX) technique essentially transforming a real-valued time series into a symbol sequence, has been proven as a potential tool of feature extraction for machinery intelligent diagnosis. However, challenge still exists that the SAX cannot fulfill feature extraction tasks well since it is carried out only on the basis of mean value in time domain. To overcome this limitation, an improved SAX (ISAX) is proposed in this paper. This new method substitutes the feature of mean value in time domain with multiple features extracted from time, frequency and time-frequency domains in order to obtain comprehensive fault information. With the ISAX transformation, a vibration signal can be transformed into various symbol sequences according to the multi-domain features. Next the Shannon entropy technique is conducted on a symbol sequence to capture sequential patterns in local signals and then the Shannon entropy value is used as the eigenvalue of the symbol sequence. Various eigenvalues are obtained to describe a vibration signal from different perspectives, which leads to a better feature extraction. These eigenvalues are then fed into the Kernel Principal Component Analysis (KPCA) to reduce dimensions and extract principal features for classification tasks. Compared with SAX, the most significant advantage of ISAX is extracting comprehensive signal characteristics from multi-domain. Moreover, the ISAX captures fault information better considering the local fault patterns. The effectiveness and superiority of ISAX were validated by experimental studies using the fault signals of rolling bearings and reciprocating compressor valves with remarkably high classification rates. [ABSTRACT FROM AUTHOR]

Highlights • A composite vision sensing system is proposed and used in our study. • A spatial vision calibration is also proposed. • A multistep predictive model is established to estimate pool tail height. • The 3D surface of GMAW pool is estimated and reconstructed. Abstract Weld pool is the most direct signal to reflect welding quality, that means measurement and reconstruction of the pool surface is one of the most urgent task in industrial robotic welding. A lot of researches have been done in this area, but the strong welding arc and the violent oscillation of weld pool affect their effectiveness. To better resolve this problem, a composite vision sensing system was proposed in our previous study, which consists of an active vision part and a passive vision part. The geometric parameters of the weld cross section and the weld pool have been successfully extracted by using this system. Based on the obtained information, the dynamic relationship between the weld speed and the bead geometric parameters is established by multistep predictive model based on neural network. By using the established model and iterative algorithm, the pool tail height is estimated in this paper. A spatial vision calibration is also proposed to calculate the real geometric parameters of the weld pool. Then the 3D surface of the weld pool is also reconstructed by the means of space curved surface fitting. Finally, the verification experiment is also conducted to verify the feasibility of this method. [ABSTRACT FROM AUTHOR]

Highlight • A model updating method for building structures is presented using modal responses. • A representative value of modal responses, MPR, is proposed and used in the method. • A baseline model is obtained by minimizing MPRs between measurement and model. • Based on the damage scenario for the baseline model, damages are detected. Abstract This paper presents a model updating method for the damage detection of building structures on the basis of modal participation without the need for system identification to extract the modal parameters. The presented method uses the modal participation ratio (MPR), which is defined by a representative value of modal responses extracted from vibration responses measured by sensors mounted on a building structure, as an indicator of the extent of modal contribution. Such MPR extraction assumes that the structure is under ambient excitation; thus, no loading on the structure is required to apply the presented method. In the model updating method, the differences between MPRs extracted from the sensors and MPRs estimated from a model are established as objective functions that are generated corresponding to the number of modes considered. These functions are minimized by a multi-objective optimization algorithm while searching for the optimal properties of a dynamic system, and the finally derived optimal solution is regarded as a baseline model that reflects the actual behaviors of a structure. Through analyses of the variations of the MPRs based on the damage scenarios of the derived baseline model, specific rules for damage localization are derived. In addition, to identify the damage severity of a structure, a damage severity function that describes the relationship between damage severity and MPR variation based on the baseline model is proposed. The proposed method is employed to construct a baseline model and detect the location and severity of the damage of a multi-story structure under ambient excitation. [ABSTRACT FROM AUTHOR]

Highlights • Open source LiDAR filtering software's are well-suited UAV data. • Vegetation and artificial objects are crucial for bare earth terrain modelling. • The framework provided for classification of UAV point clouds. • Filtering methods produce different results. Abstract Nowadays, Unmanned Aerial Vehicles (UAVs) have been attracted wide attentions such as a new measurement equipment and mapping, which are capable of the high-resolution point cloud data collection. In addition, a massive point cloud data has brought about the data filtering and irregular data organization for the generation of digital terrain models. Filtering of point clouds contains vegetations and artificial objects play a crucial role for bare earth terrain modelling. Topographical maps rely on the data structures which are built on bare ground terrain points. The bare earth surface extraction is not the only crucial to the topographical maps but also decision-making processes such as natural hazards management, deformation analysis and interpretation. In order to filter a UAV-based 3D raw point cloud data, in this paper, filtering performance of four different algorithms using open source and commercial software's have been investigated, (1) curvature based (Multiscale Curvature Classification-MCC), (2) surface-based filtering (FUSION), (3) progressive TIN based (LasTool-LasGround module-commercial) and (4) physical simulation processing (Cloth Simulation Filtering-CSF). The applied filtering results were validated with the reference data set classified by operator. Although different filtering methodologies implemented on point clouds, these methods demonstrated similar results to extract ground on distinctive terrain feature such as dense vegetated, flat surface, rough and complex landscapes. The filtering algorithms' results revealed that UAV-generated data suitable for extraction of bare earth surface feature on the different type of a terrain. Accuracy of the filtered point cloud reached the 93% true classification on flat surfaces from CSF filtering method. [ABSTRACT FROM AUTHOR]

Highlights • A deep learning method for steel seismic fuse damage inspection is investigated. • Micromechanical fracture index is defined to quantify the fuse damage condition. • A saliency-based method is adopted for damage feature pattern visualization. • The effectiveness of the proposed method is demonstrated in two case studies. Abstract This paper presents a deep learning based structural steel damage condition assessment method that uses images for post-hazard inspection of ultra-low cycle fatigue induced damage in structural steel fuse members. The deep learning model – Convolutional Neural Network (CNN) model, can be trained to represent the high dimensional features in huge amount of raw data which traditional mathematical models are unable to describe. A saliency-based visualization method is employed to visualize the feature-related pattern recognized by the deep learning model. To quantify the damage condition of the inspected structure fuse members, a micromechanical fracture index is defined as the damage index and used for labeling the images in the training data set. To provide large training dataset, cumulative plastic strain contour plot images generated through finite element (FE) simulation are adopted for the training data. Parametric studies were performed to validate and optimize the deep learning based damage condition assessment method. The method and findings are further examined using real experimental images collected from cyclic testing of a steel notched plate specimen. The results suggest that the proposed method provides a promising automation tool for rapid inspection of structural steel fuse member damage condition. [ABSTRACT FROM AUTHOR]

Highlights • Method simulates effects of launch conditions on rail-gun current and armature speed. • The input five parameters are closely related to the rail-gun launchment. • Three neural networks predict the rail-gun current and armature speed with low error. • General Regression neural network is optimal on error, time cost, parameter design. Abstract In this paper, based on the advantages of artificial neural network, such as good tolerance of data noise, strong ability of nonlinear mapping, multi-dimensional input variables, fast operation, low error, etc., a method of using artificial neural network for data prediction is proposed for the research of rail-gun. The results show that it is feasible to use the Back Propagation Neural Network, the Radial Basis Function Neural Network and the General Regression Neural Network to realize the method of prediction and simulation of the rail-gun current and the armature speed curve through relevant parameters. The General Regression Neural Network has superiority in error performance and time cost of neural network training and simulation. [ABSTRACT FROM AUTHOR]

Highlights • The position relationship between the rotary and linear axis is characterized. • A method for identifying the position relationship by laser tracker is proposed. • Simulations and experiments verify the feasibility of the proposed method. Abstract In the multi-axis machining process, the relative position relationship between the linear and rotary axis also has certain influence on the machining accuracy in addition to 6 geometric errors of each axis. How to accurately detect and compensate for this error will have a positive effect on further improving the machining of multi-axis machine tool. The laser tracker is adopted multi-station and time-sharing measurement to identify the relative position relationship between the rotary and linear axis on the basis of global positioning system principle in this paper, which will provide data support for accuracy compensation for multi-axis machine tool. The relative position relationship between the rotary and linear axis is characterized by the homogeneous transformation matrix, and its influence on the motion accuracy of machine tool is also analyzed. Meanwhile, the mathematical model concerning on identifying the relative position relationship between the rotary and linear axis with laser tracker is established, and the corresponding measurement algorithm of machine tool motion and identification algorithm of the relative position relationship are derived respectively. The feasibility and accuracy of the derived algorithms are demonstrated in simulations. Meanwhile, the experiment further validates the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • Uncertainty of the Unified Method for TEMs characterization is carried out. • The uncertainty is Evaluated by means of a Monte Carlo Simulation. • A sensitivity analysis, gives a comprehensive insight into the most critical issues. • Experimental results show that two proposed stimuli provide compatible estimates. Abstract In this paper the uncertainty evaluation of the recently proposed Unified Method for Thermo-Electric Module characterization is carried out. The measurement model is detailed and individual uncertainty contributions are highlighted, with close reference to the instrumentation and measurement setup. The uncertainty evaluation is performed by means of a Monte Carlo Simulation. The same algorithm is used to perform a sensitivity analysis, giving a comprehensive insight into the most critical issues of the proposed method and assessing the performance of the two adopted electrical stimuli. The experimental results thereby obtained are discussed and improvements to the measurement setup and technique are finally proposed. [ABSTRACT FROM AUTHOR]

Highlights • A three-stage system of algorithms for pavement lane marking detection using 2D laser images with 1-mm resolution. • Newly developed matched filter for a better match with the lane markings. • Novel hybrid thresholding algorithm that preserves lane markings efficiently after matched filtering. • Shape Examination that increases the confidence on the geometric shapes of detected patterns. • High accuracies on six real pavement sections with pavement lane markings of varying styles and conditions. Abstract This paper proposes novel algorithms to detect pavement lane markings on two-dimensional (2D) laser images via three stages. First, a new matched filter is developed to generate strongest responses at pavement lane markings. Second, a novel hybrid thresholding method is proposed to preserve strong responses while eliminating weak responses. Last, the Shape Examination is conducted to verify if the geometric shapes of detected objects are similar to the pattern of pavement lane markings, resulting in a higher confidence level. The experimental results on six real pavement sections demonstrated that the proposed algorithms achieved high F-measures greater than 92% for pavement lane markings in excellent or fair conditions. In addition, the F-measures were higher than 82% even for the two sections with lane markings in severe conditions, which should reflect the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The use of photovoltaic (PV) systems as electrical power generator is increasing rapidly and this growth is expected to be continuous. As well known in PV systems the tracking of the maximum power point (MPP) is fundamental. For this reason, many testing systems and maximum power point tracking (MPPT) algorithms have been developed. Testing systems are used for the development as well as for assessing the quality of PV products. The typical problem related to the evaluation of PV system performances and MPPT algorithm comparison is the guarantee of the repeatability of the testing conditions (e.g. temperature and radiation). In this paper, a testing system for PV applications has been developed and characterized. Flexibility, ease of use, low cost, capability to evaluate the characteristic curves of more than one PV panel simultaneously (same environmental conditions), the capability of algorithms simulation, and capability of tracking MPP on the base of different MPPT algorithms are some of the main characteristics. Experimental tests have been carried out to show the capability of the developed architecture. Different MPPT algorithms have been compared in the same environmental conditions both in the steady and dynamic state. [ABSTRACT FROM AUTHOR]