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• Novel rapid moisture test for oil-paper insulation. • Microstrip ring resonator analyzes dielectrics properties. • Precise moisture level distinction found. • New model assesses the moisture content accurately. Moisture ingress significantly accelerates the aging of oil-paper insulated equipment, making accurate assessment of moisture content critical for the operation and maintenance of oil-immersed insulated power equipment. In this paper, a novel method for rapid moisture content evaluation is proposed based on a microstrip ring resonator (MRR) designed with a fundamental frequency of 1.16 GHz. A calculation model for the dielectric response analysis using MRR is established to characterize dielectric properties of the material layered on the substrate. ANSYS HFSS electromagnetic simulation software was used to study the impact of dielectric material thickness and the substrate on the MRR test results. Experimental measurement demonstrated that the maximum deviation in resonance frequency was merely 0.542 %. The dielectric properties of uniformly and non-uniformly damp insulating pressboard were analyzed across the frequency band from 1 to 6 GHz. The MRR test results were compared with those of the standard resonant cavity method and frequency domain dielectric spectroscopy (FDS) test. Using the regression algorithm and curve analysis, moisture assessment models for both uniformly and non-uniformly damp oil-paper insulation were established. The experimental results show that the MRR test can clearly distinguish between the pressboard samples with varying moisture levels under uniform conditions, with a maximum moisture evaluation error of 8.19 %. The results demonstrate that the MRR test method, requiring only 56 s, can quickly and effectively evaluate the moisture content of insulating pressboard. [ABSTRACT FROM AUTHOR]

• This paper conducted an error analysis of the mass transfer visual positioning system and concluded that the error between the wafer plane and the camera projection plane is the primary source affecting the accuracy of the visual positioning system. • A novel method for calculating camera poses is proposed, which is a self-calibration algorithm that eliminates the need for preliminary calibration work. • An improved Kalman filter with a forgetting factor is introduced to proposed method to update the camera pose parameters better. The accuracy of the visual positioning system directly impacts the production efficiency of Mini/Micro LED display panels. This paper aims to enhance the precision of the mass transfer visual positioning system to meet the increasing demands of industrial production. Firstly, we conducted an error analysis of the mass transfer visual positioning system to understand the impact of different error sources on positioning accuracy. Subsequently, based on the results of the error analysis, we proposed an error compensation method that utilizes a high-precision motion positioning platform and laser displacement sensor to calculate the pose of the camera. Additionally, we introduced a Kalman filter with a forgetting factor to iteratively optimize the subsequent calculation of camera pose parameters. Ultimately, through simulation experiments and transfer experiments, we demonstrated that our proposed method can effectively enhance the accuracy of the visual positioning system for mass transfer devices while maintaining high robustness. [ABSTRACT FROM AUTHOR]

• Introduces theories related to Rayleigh wave in pavement inspection. • Highlight the main findings of Rayleigh wave in pavement inspection. • Motivations, challenges and issues in applying Rayleigh waves to pavement assessment. • Present research gaps and provide options for other interested researchers. To maintain optimal serviceability and control maintenance costs, regular inspections of pavements are vital. Compared to other non-destructive methods, Rayleigh wave techniques are particularly effective in evaluating layered materials and show significant potential and advantages in assessing road pavements. This method not only provides modulus variation with depth but also reveals pavement crack distribution with higher precision. Moreover, it can detect in-situ damage and predict impending cracks. This paper, presents a comprehensive summary of Rayleigh wave methods, including their processes, principles, frequency dispersion curve extraction methods, forward methods, and inversion methods. The application of Rayleigh waves in pavement assessment is also illustrated, covering the thickness and moduli of each layer and the distribution and development status of internal cracks. Furthermore, the paper highlights challenges, trends, and future perspectives of Rayleigh wave techniques in asphalt pavement evaluation. [ABSTRACT FROM AUTHOR]

• To eliminate the influence of CO 2 and H 2 O on NH 3 and NO concentration measurements, this paper proposes a machine-learning modeling and concentration measurement method using combined spectral signals from reference gases. • Before the concentration measurements, the direct absorption spectroscopy (DAS) signals of NH 3 and NO underwent noise reduction using the variational mode decomposition algorithm optimized by the artificial bee colony algorithm and wavelet threshold denoising (ABC-VMD-WTD) method. The signal-to-noise ratio (SNR) of NH 3 and NO increased from 17.2 and 46.9 before denoising to 167.8 and 532.4 after denoising. • After testing, the average relative errors of NH 3 and NO concentrations measured by the particle swarm optimization-support vector machine (PSO-SVM) model are 4.0 % and 0.8 %, respectively, with measurement precision of 0.05 ppm and 0.42 ppm, which is better than the conventional integral absorbance method. • By analyzing the Allan deviation, the minimum detection limit (MDL) for NH 3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. Accurate measurements of NH 3 and NO are essential for controlling NO x emissions from coal-fired power plants, reducing ammonia slip, and improving the efficiency of selective catalytic reduction (SCR) operation. We propose a method for measuring NH 3 and NO concentrations based on machine learning algorithms to address the challenges posed by overlapping interference of CO 2 and H 2 O spectral lines in flue gas. Our approach introduces a novel method for acquiring mixture spectra for the model. Initially, we measure the spectra of individual components under different concentrations and temperatures. Subsequently, mixed spectral samples are generated by combining the measured spectra of the individual components. This approach simplifies the spectral measurement process while preserving accuracy. The particle swarm optimization support vector machine (PSO-SVM) algorithm is leveraged, providing a reliable foundation for the continuous and synchronous measurement of NH 3 and NO. Upon testing, the PSO-SVM demonstrates average relative errors of 4.0 % and 0.8 % for NH 3 and NO concentrations, respectively. The corresponding measurement precision is 0.05 ppm for NH 3 and 0.42 ppm for NO, better than the conventional integral absorbance method. The minimum detection limit (MDL) for NH 3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. The methodology of this paper is expected to play an important role in reducing the influence of interfering components and improving the accuracy of field measurements. [ABSTRACT FROM AUTHOR]

• Thermal array sensors accurately recognize OWTPF flow patterns. • Use AOK-TFR, URP to realize the distinction of flow patterns. • First application of SSA-SVM for flow patterns segmentation. To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow. [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]

• The impact of the covariance matrix of process noise on the estimation of the target state is scrutinized. • A model is developed to estimate the covariance of process noise, taking into account factors such as the distance between the target and the UAV, the image miss distance, and the stability of the optical axis. • An AEKF algorithm for localizing targets is proposed, specifically designed for use on airborne electro-optical stabilized platforms. • The practicality and efficacy of the algorithm presented in this manuscript have been substantiated through actual flight data. To address the problem of inaccurate process noise affecting the accuracy of target positioning in airborne electro-optical stabilized platforms, a novel adaptive extended Kalman filter is proposed. Firstly, this paper employs a classification method to estimate of process noise covariance based on the range values between the target and the unmanned aerial vehicle. Secondly, this noise estimation strategy is combined with the extended Kalman filter algorithm, resulting in an adaptive extended Kalman filter based on uncertain process noise estimation. Finally, the proposed algorithm is validated through simulations and flight tests. The simulation results demonstrate that the accuracy of the algorithm proposed in this paper is respectively improved by 50 % compared to the least squares algorithm and by 20 % compared to the Kalman filter algorithm. The results of actual flight tests reveal that this algorithm significantly improves the positioning accuracy of static ground targets. The error rate is 25 % lower than that of the other current algorithms. It has enormous guiding significance for engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel electromagnetic coil design method is proposed, which has a large uniform region for calibrating array optically pumped magnetometers (OPMs). The weighted Tikhonov regularization method, whose weight matrix is optimized by particle swarm optimization, is to balance the field error distribution in the uniform region. The cube coils system with a 25 cm side length can produce uniform fields in a cube uniform region with a 15 cm side length. The simulation result shows that the maximum field error ɛ m a x of the cube coils system is 1% in the uniform region, which is greatly reduced compared with Ruben's coils system(4.8%) and bi-planar coils system (22.7%). Due to the optimized weight matrix, the errors of the coils are mainly distributed in the range of 0–0.5%. The testing results were consistent with simulation results. The result of calibration experiment proves the superiority of cube coil in this application. • The ill-posed problem in the target field method is mainly due to the distribution of the coils on two finite planes. Therefore, the cube coils designed in this paper are innovatively designed on six planes of a cube. • In order to expand the volume of the uniform region, the weighted least squares method is used to balance the distribution of field errors in the uniform region, which is combined with particle swarm optimization algorithm. • Because the cube coil system designed in this paper has a large uniform region and high uniformity, it can obtain a higher-precision gain coefficient than the on-board coil of OPM, which is crucial for MCG and MEG experiments. [ABSTRACT FROM AUTHOR]

• Cube satellites are rapidly deployed in low earth orbit (LEO) to provide high data rate service to users. Cube satellites have been constellated to offer a fast and wide area coverage. • Due to the constellation of the cube satellites, a congested RF environment is created, causing signal interference between Rx and Tx systems. Along with the wide bandwidth, circular polarization, and high gain, a compact bandpass filtering system is required in the antenna to reject out-of-band noisy signals. • Artificial magnetic conductors (AMCs) are periodic elements located in the x and y direction that possess characteristics such as phase reflection between ± 90O while crossing 0O for incoming EM waves with different incidence angles. • A carefully constructed AMC can enhance wideband performance in terms of in-phase reflection and gain. To address the challenges as stated above, an angularly stable AMC-loaded antenna array and filter combined structure is modeled in this paper. This paper investigates an angularly stable artificial magnetic conductor (AMC) loaded circularly polarized (CP) antenna array and filter co-design for the K-band satellite application. The AMC array is constructed from a periodic array of square metal patch unit cells with rectangular and annular slots. The AMC unit cell has good angular stability under various incidence angles and operates in a frequency range of 18 GHz to 21 GHz. The AMC-surrounded and bandpass filter (BPF) loaded antenna array is simulated and fabricated on the single side of a 0.508 mm thick Roger/5880 substrate. The impedance matching, gain, directivity, and efficiency (%) are enhanced due to the constructive interference between AMC and antenna mutual coupling. In contrast, the axial ratio (AR) is preserved. The manufacturing of the proposed structure is comparatively less complex. Finally, based on the simulation and measured results, the suggested AMC-loaded compact, wideband, and filter-inserted array with enhanced gain and efficiency can be a good choice for the 3U satellite application working in K-band. [ABSTRACT FROM AUTHOR]

• A novel general cascaded convolutional neural network (CNN) reading method for pointer reading is presented. • Proposes PMA method, which can increase the data samples significantly without introducing redundant background information. • Proposes ADFPN for feature fusion, learns branch importance, performs fusion at various levels. Enhances fusion efficiency. • Construct a dataset for pointer meter readings that contains images of common pointer meters in substations. Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for "large background and small target" is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts. [ABSTRACT FROM AUTHOR]

Despite the prevalence of well-established and explored navigation systems, alternative localization methods are currently the focus of intensive research. This interest is driven by geopolitical challenges and increasingly sophisticated applications of mobile robots and uncrewed aerial vehicles. This study investigates the problem of real-time positioning in GPS-denied environments. Based on the mapped magnetic anomaly field and using Bayesian formalism for data fusion, the localization obtained from embedded sensors is corrected to reduce cumulative errors. The proposed method has minimal computational cost and a minimal number of tunable parameters. The paper introduces it and demonstrates its effectiveness in a laboratory study. Experimental tests, using a system equipped with an Inertial Measurement Unit, demonstrated a significant reduction in localization uncertainty. The improvement was especially notable in areas with large, smooth variations in the magnetic field. Finally, the accuracy of the method is analyzed, and its performance is compared to a particle filter. • The paper proposes a framework for a magnetic navigation module. • The module utilizes local anomalies of the magnetic field. • In GPS-denied environments, magnetic corrections improve the IMU-based localization. • When the reference position is available, the module maps the magnetic anomaly field. • A lab-scale verification and a comparison to the particle filter are provided. [ABSTRACT FROM AUTHOR]

• A novel leakage model test system was proposed. • Deformation, uneven settlement and deterioration of the segment were simulated during service. • A non-linear growth trend in water leakage was observed. • Decreased contact stress and gasket aging aggravate leakage. With the increasing mileage and operation time of China's subsea tunnels, tunnels are entering a period of high disaster. Water leakage is one of the most common disasters. To study the mechanism of water leakage in subsea shield tunnels during the service period, a model test study was carried out by proposing a novel tunnel seepage leakage model test system in this paper. The system consists of a model frame, ground stress loading system, hydraulic pressure loading system, temperature control system, and monitoring system. Through 3D printing, the tunnel segments were innovatively made of resin. The process of uneven settlement, seawater erosion, joint deformation, gasket stress relaxation, and water leakage during the service period were successfully simulated by heating the model segments and local overloading. The test results showed that the joint deformation with service life could be approximately divided into three stages: non-linear rapid growth, linear growth, and non-linear slow growth. And the joint deformation and the aging of the gasket resulted in the decrease of the contact stress of the segment joint. Both exacerbated the leakage in the joint. By the time the service life reaches 16th year, the waterproofing performance no longer met the waterproofing standard for subsea tunnels. The developed test device and method are practical in predicting water surges in subsea tunnels during service life. This paper provides new insights into long-term waterproofing performance testing of shield tunnels under complex conditions. [ABSTRACT FROM AUTHOR]

Rotating machines are extensively utilized in diverse industries, and their malfunctions can result in significant financial consequences and safety risks. Consequently, there has been growing research interest in the intelligent fault diagnosis of rotating machines, particularly through the utilization of multi-sensor condition monitoring data. However, a comprehensive review focusing on multi-sensor data fusion methods is lacking. To bridge this gap, this paper provides a comprehensive analysis of the existing literature on the application of multi-sensor data fusion techniques to diagnose faults in rotating machines. Basic concepts of multi-sensor data fusion are first provided, establishing a robust foundation for subsequent discussions. The review then provides an in-depth analysis of the applications of multi-sensor data fusion in intelligent diagnosis for rotating machines. Furthermore, this review paper highlights the current challenges encountered in multi-sensor data fusion for intelligent fault diagnosis of rotating machines. By considering these challenges and consolidating knowledge from various sources, this paper proposes future research directions in this field. This review article serves as a valuable resource for researchers, practitioners, and decision-makers in the domain of intelligent fault diagnosis of rotating machines. The review provides comprehensive insights into the latest advancements of multi-sensor data fusion techniques and guiding future research directions in the measurement sciences. [ABSTRACT FROM AUTHOR]

• This paper proposes an online Condition Monitoring (CM) and Fault Detection (FD) algorithm for Permeant Magnet synchronous Machine. • The developed algorithm has been implemented in motor control software, make it suitable for critical mission applications. • The proposed remedy is non-intrusive and is based on current and voltage signature analysis. It operates concurrently with the motor driver and does not compromise the functioning of the motor operation control. • The proposed method has a high detection accuracy of 98%, is computationally efficient and can detect and classify the fault accurately. • Only the motor drive's built-in sensors, such as current and position sensors, are used to detect three types of faults: inter-turn short circuit, partial demagnetization, and static eccentricity. • The developed algorithm has been fully tested and validated using Simulation and experimental data, demonstrated the efficiency of the FD algorithm for online identification and classification of machine faults. The machine's health should be continuously monitored, or a test should be applied on a regular basis to predict failure before fatal damage is incurred. Unexpected failures, particularly in mission-critical applications, can cause irreversible damage to the system, or even human life. This paper introduces a novel non-intrusive, real-time, online Condition Monitoring (CM), and Fault Diagnosis (FD) system for Permanent Magnet Synchronous Machines (PMSMs). Only the motor drive's built-in sensors, such as current and position sensors, are used to detect three types of faults: inter-turn short circuit, partial demagnetization, and static eccentricity. It encompasses the implementation of algorithms within a motor drive system and the creation of failure mode models. The proposed solution adopts a hardware-free approach, utilizing current/voltage signature analysis for cost-effectiveness. It requires a small memory and short execution time, allowing it to be implemented on a simple motor controller with limited memory and calculation power. The drive system is intended for mission critical applications, therefore, computation load, code size, memory allocation, run-time optimization, etc. are the key focuses for real-time operation. It offers immediate insights into motor's health without interrupting the drive operation. Additionally, it ensures rapid processing with modest computational requirements, making it adaptable for implementation on any PMSM controller. The non-intrusive nature of this diagnostic approach has the potential to enhance safety in systems reliant on PMSM drives. The proposed method has a high detection accuracy of 98%, is computationally efficient and can detect and classify the fault accurately. Simulation and experimental results demonstrate the efficiency of the FD algorithm for online identification and classification of machine faults. Theoretical hypotheses are proven based on experimental data. [ABSTRACT FROM AUTHOR]

Contact force sensing is an important means to ensure the safe operation of surgical robots. This paper presents a miniature triaxial force sensor based on fiber Bragg grating (FBG) for detecting the interaction forces between the tip of flexible endoscopic surgical robots and tissue. The proposed sensor mainly consists of a force-sensitive flexure structure, a support body, and four FBG sensors. A composite hollow structure with a parallel helical slot and elliptical hinge has been utilized to design the force-sensitive flexure with an excellent linear measurement range. The four optical fibers arranged along the circumference of the flexure are configured as a tightly stretched status with their two points fixed. This configuration can improve sensitivity and avoid the drawbacks of FBG chirping and low repeatability. Finite element method-based simulation has been implemented to optimize the flexure's structural parameters for balancing the force sensor's axial and radial resolution. The prototyped sensor has been calibrated and achieves a high resolution of 0.77 mN and 0.79 mN for both lateral forces within −1 to 1 N and 1.25 mN for axial force within 0 to 2 N. Loading experiments have been conducted to validate the effectiveness of the proposed designs. The results show that the sensor can provide a high-accuracy measurement of three force components with an average RMS error of less than 3% in the total operating ranges. Such a result validates the feasibility of the designed sensor to measure the robot-tissue interaction forces. • This paper presents a three-axis force sensor for endoscopic surgery. • The detailed structural parameters are optimized to maintain similar resolutions. • This paper establishes a force sensing model and decoupling model. [ABSTRACT FROM AUTHOR]

• Dynamic characteristics testing and evaluation of electromagnetic devices. • A digital modeling approach to evaluate dynamic characterization is proposed. • Multiphysics modeling approach for FEM description of the electromagnetic devices. • The nonlinear effects of temperature on the dynamic characteristics are considered. • Transmission line parallel algorithm improves the efficiency of solving FEM models. Electromagnetic systems of Electromagnetic devices involve multiphysics coupling of electromagnetic fields, electrical circuits, mechanical motion, and temperature fields. When the digital prototype model has a large number of meshes, the commercial finite element software also has the disadvantage of long computation time in the simulation of dynamic characteristics of electrical apparatus. This paper proposes a transmission line method (TLM) based digital modeling method for the electromagnetic-thermal-force coupling to test and evaluate the static and dynamic characteristics of electromagnetic devices more accurately and efficiently. The case study is developed on a direct current (DC) contactor, and the dynamic characteristics testing and modeling in the electromagnetic-thermal-force coupling of the contactor are investigated. The numerical results of the finite element method (FEM) model for the electromagnetic system of the contactor are compared with the commercial FEM software Altair Flux and the experimental results, which demonstrate the exceptional accuracy of the proposed digital modeling method for the multiphysics coupling of the DC contactor. Meanwhile, the dynamic characteristics of the DC contactor at different ambient temperatures are measured and evaluated, and the comparison with the experimental measurements shows the applicability of the proposed digital prototype model at different ambient temperatures. Compared with the conventional Newton-Raphson (NR) iteration, when dividing fine mesh and extremely fine mesh, the TLM parallel computing significantly improves the efficiency of solving the dynamic characteristics of multiphysics coupling by about 35 % and 44 % with the same computational accuracy, respectively. The dynamic characteristics evaluation method combined with digital modeling technology and experimental measurements proposed in this paper demonstrates high accuracy and solving efficiency, making a notable contribution to the field of design and optimization of electromagnetic devices. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of measurement uncertainty in science, engineering, and technology, to ensure accurate and reliable measurements. A bibliometric analysis that examines current trends, challenges, and future research directions in measuring uncertainty, based on the analysis of selected papers published between 2011 and 2022 sourced from the Scopus database, has been conducted. This study also classifies the papers based on the different approaches identified and the different methods of calculating uncertainty used by the authors. From the results of co-occurrence keyword analysis of the filtered 406 papers, six clusters corresponding to six research themes were obtained. Although the Law of Propagation of Uncertainty (LPU) method is still the most widely used method, it is also the most frequently criticised for its limitations, prompting many authors to compare it with other methods or propose improvements. This review also concludes that the Monte Carlo method (MCM) is a method for evaluating measurement uncertainty other than LPU whose use has increased significantly in the last decade. • Depicts 12 years of uncertainty research, sourced systematically from Scopus. • Identifies publication trends, affiliations, subjects, and leading countries in the field. • Classifies measurement uncertainty articles by calculation methods and approaches. • Offers insights into exploring uncertainty methods beyond the LPU. [ABSTRACT FROM AUTHOR]

Differential mode attenuation of RF suppression filters can be measured directly. Alternatively, this attenuation can be derived as modal S-Parameters from the S-Parameters measured for the single multiport paths which in fact the filter is. This paper outlines the differences between the two approaches. The first consists in direct measurement with separation transformers and the second with calculation from single ended measurement. In addition, the paper presents the method of determining the differential mode attenuation for the case of input and output impedance different from those used in direct measurements, i.e. by 50 Ω / 50 Ω. At the same time, the presented method of determining the differential mode attenuation for different input and output impedances will require a change in the assumptions of the CISPR 17 document entitled "Methods for measuring the attenuation characteristics of passive EMC filtering devices", since the modal S-Parameter formulas presented in this document are only valid for the case when the RF suppression filter is in perfect balance. Otherwise, for different impedances it should be done taking into account the modification of the modal S-Parameters, as shown in this paper. The inclusion of this innovative change in the CISPR 17 document will allow the approach presented in this document to be used for various cases of the RF suppression filter, which is undoubtedly a significant progress in determining its attenuation. Frequency span by measurement of attenuation of RF Suppression filter is spanned from 10 kHz to 30 MHz. The comparison presented in this paper confirms that the S parameters are identical only if the filter is in equilibrium ( Z L = Z N ). From the results, three frequency areas can be distinguished. In the first interval between 10 kHz and 150 kHz the filter is in balance, which was demonstrated with identical measurement results. Between 150 kHz and 1. 5 MHz, the attenuation is superimposed on the dynamics of the measurement system and therefore on the random frequency dependence of both measurements. Above 1. 5 MHz there is a clear filter asymmetry, so the curves move away from each other. • The differences between measured directly and modal S-Parameters. • The method of the attenuation referred a different impedance than by measurement. • The presented method will require a change in the future edition of the CISPR 17. • The modal S-Parameter are only valid when the filter is in perfect balance. [ABSTRACT FROM AUTHOR]

• Propose a pseudo measurement update method for head pose determination. • A robust detector combined with heuristic wavelet denoising is designed. • A head pose determination method incorporating a robust strategy is proposed. The human head often shifts abruptly after a prolonged inactivity. When the head remains motionless, pseudo measurement correction approaches can increase the accuracy of head pose determination (HPD). However, existing detectors based on the general likelihood ratio test (GLRT) framework cannot identify zero angular rate events and are insensitive to rotational motion. In this paper, a heuristic wavelet denoising approach is designed to pre-process the input signals of a GLRT-based moving variance detector, thereby enhancing the precision of the detector. Furthermore, an approach for head pose determination based on the error-state Kalman filter (ESKF) is presented. Finally, simulation tests, attitude estimation studies of head motion mimicked by a turntable and actual head tracking experiment are used to validate the performance of the suggested method. The experimental results demonstrate that the suggested detector is more precise. The HPD approach described in this paper can achieve greater estimation precision than previous methods. [ABSTRACT FROM AUTHOR]

Precisely controlled digital pulse plays a crucial role in various cutting-edge testing applications. This paper introduces an innovative approach to digital pulse generation utilizing an FPGA transceiver, with the AMD evaluation board ZCU102 serving as a demonstrative platform. In the normal mode, the transmitter generates a serial bit stream, enabling the generation of single or repeated pulses with configurable width and period. In the high precision mode, an internal dedicated phase interpolator enhances the configuration step size for width and period by up to 64 times, providing finer phase shift capability. The demonstration system operates at a line rate of 10 Gbps, achieving a resolution of 100 ps in normal mode and 1.5625 ps in high precision mode. This paper details the design methodology, presents test results for pulse width uncertainty and non-linearity, and showcases achieved period jitter and time interval error jitter as small as around 1 ps. • A novel digital pulse generator is implemented with GTH transceiver in AMD FPGA. • Transceiver can produce digital pulse by controlling the sent bits with low jitter. • Resolution can be further enhanced by phase interpolator in the transceiver. [ABSTRACT FROM AUTHOR]

• You only need to train twice (DKNET) has been designed. • Multi-scale segmentation head based on dynamic kernel. • Multi-scale depth supervision loss function. • Processing the uncertainty of prediction results based on entropy theory. • High-resolution dynamic kernel defect detection model, RSU_DKNet. • Experimental results show that the model designed has the lowest algorithm complexity (0.836 MB). Deep learning usually needs a lot of marking training data, but in the industrial field, it is very difficult and costly to obtain large-scale marking data of weld defects. Therefore, this paper puts forward a new model of industrial small sample weld defect detection, YOTT (You only training twice), to solve this problem by minimizing the use of marking data. Firstly, a multi-scale segmentation head based on a dynamic kernel is designed, which can perceive defect features in advance and fuse multi-scale defect information, to enhance its ability to identify defect information. Secondly, combined with the multi-scale prediction information, a multi-scale depth supervision loss function is designed. In addition, to measure the randomness of the model output, the uncertainty processing of the prediction results based on the entropy model theory is designed, and the uncertainty quantification of the prediction output is obtained at the same time as the model. Finally, to further improve the performance on high-resolution sets, an RSU_DKNet model suitable for high-resolution data sets is designed, which can capture more context information without significantly increasing the computational cost. The experimental results on 9 public data sets show that the performance of the DKNet model designed in this paper is 100 times better than that of the existing advanced models. Moreover, the algorithm complexity of the method designed in this paper is the lowest, and the model size is only 0.836 MB, which has potential application value in solving the problem of industrial small sample defect detection. [ABSTRACT FROM AUTHOR]

• An optical fiber temperature sensor employing the vernier effect is presented. • The sensor has low production cost, simple fabrication, and small volume. • The sensor has good stability and repeatability. • It provides a new approach for in-depth research on temperature monitoring. This paper introduces a optical fiber temperature sensor based on vernier effect. The sensor's design consists of two integrated interferometric structures: the Mach-Zehnder interferometer (MZI) and the Fabry-Perot interferometer (FPI). Both interferometric structures exhibit symmetry, but they employ different types of optical fibers. Specifically, the MZI employs dual side hole fiber (DSHF), while the FPI is constructed using hollow-core optical fibers.Given the MZI's superior sensitivity to temperature changes compared to the FPI, it is chosen as the sensing arm. The paper conducts theoretical research and experimental validation of the optical fiber sensor's sensing principles. The findings reveal a sensor sensitivity of −305.42 pm/°C when measuring temperatures within the 32–50 °C range. Stability testing, repeatability testing, and response time testing are carried out using a constant temperature chamber and a water bath, affirming the sensor's excellent repeatability and stability, along with a response time faster than that of conventional thermometers. [ABSTRACT FROM AUTHOR]

This paper investigates using an Iterated Bayesian Focusing Algorithm and out-duct array measurements to identify multiple broadband sources placed in a cylindrical duct. The problems of large main lobe and low resolution due to localization of sources using conventional methods are investigated in this paper. To solve these problems, an Iterated Bayesian Focusing (IBF) method is utilized to localize the broadband sources. To evaluate and confirm the efficacy of the method, simulation tests and experimental validation are performed. In our experiments, we used a 56-channel dobby spiral array for out-duct measurements and beamforming method, equivalent source method (ESM), deconvolution approach for the mapping of acoustic sources (DAMAS), and Iterated Bayesian Focusing (IBF) method to localize broadband sources inside the duct. Simulation and experimental results demonstrate that the Iterated Bayesian Focusing (IBF) method significantly reduces the main lobe width and minimizes the number of minor lobes. This leads to improved accuracy in localizing double sources. • Broadband localization of stationary sources in a cylindrical duct is investigated. • Investigated using an out-duct microphone array for localizing induct sound sources. • Localization of a single sound source versus two sources is investigated. • Iterated Bayesian Focusing (IBF) is applied to improve the resolution of the results from the conventional methods. [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]

NP3L (Non perspective-three-line) aims to estimate the poses of three cameras from three line correspondences, which can be applied into multi-camera systems for localization. Existing methods lack a deep insight for different line distributions, resulting in the inaccurate pose estimation. This paper focuses on the robust and accurate solution for different line distributions. First, for the general situation, an algebraic method is proposed to establish two intermediate coordinates to simplify the rotation matrix. Subsequently, a polynomial is constructed for rotation and translation estimation. Second, this paper divides line distributions into six categories. Based on the proposed method, a specific simplified edition is designed for each distribution. Third, this paper provides insightful analyses of the solution number, polynomial order, existences of closed-form solutions for all line distributions. In experiments, the comparative results verify that the proposed methods can estimate relatively accurate camera pose, and outperform state-of-the-art methods on the synthetic and real-world datasets. • Algebraic methods are proposed for NP3L problem in general and special distributions. • This paper analyzes the properties of the proposed methods for special distributions. • The proposed methods offer accuracy and precision compared to the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Blade Tip Timing (BTT) has become a promising method for online monitoring of rotating blades due to its non-contact measurement characteristics. However, due to the undersampling and non-uniform characteristics of blade tip timing signals, there are few studies on using them to diagnose rub-impact fault. This paper focuses on this topic and proposes a rub-impact diagnosis process. The contents of this paper are as follows: (1) A review of the existing rub-impact diagnosis technology and signal processing methods, then the advantages of diagnosis rub-impact using BTT are given. (2) The digital twin model framework for rotating blades has been established, including physical entity, digital entity, and the connections between them. (3) Dynamics analysis of both single blades and entire blade disks under rub-impact states has been carried out for the digital twin model, and six signal characteristics of blade tip under rubbing condition are summarized which provide prior knowledge for subsequent diagnosis. (4) Aiming at that traditional Enhanced Sparse Decomposition (ESD) method is prone to falling into local optimal solutions for BTT undersampled data, an envelope spectrum kurtosis enhanced sparse decomposition (ESKESD) method is proposed. (5) A complete rub-impact fault diagnosis process using digital twin is proposed. Finally, the effectiveness of the proposed method and diagnosis process are verified through simulation and experimental data. • Reviewed rub-impact diagnosis methods. • Established digital twin model for rotating blades. • Analyzed dynamics response of blades under rub-impact states. • Proposed envelope spectrum kurtosis enhanced sparse decomposition method for BTT data. • Proposed complete rub-impact diagnosis with Digital Twin and BTT. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm that aims to estimate an accurate dynamic model for a vehicle suspension system. The proposed method addresses the uncertainties associated with the system model and unknown road input by incorporating complementary terms. These terms are calculated using information from accelerometers installed on both the sprung and unsprung masses. To correct measurement errors, including bias and noise, the paper suggests fusing the accelerometer data with displacement data. The paper further introduces an adaptive solution for adjusting the estimator coefficients, enabling effective data fusion. An experimental implementation is conducted on a quarter-car suspension platform, equipped with additional sensors to verify the estimated responses. Comparative results with the unknown-input Kalman filter highlight the superior accuracy of the proposed estimator in constructing a precise dynamic model for the system. Additionally, the results demonstrate the effectiveness of data fusion-based adaptive tuning of the estimator coefficients in correcting measurement errors. • Novel estimation of accurate dynamic model for vehicle suspension system. • Suggesting data fusion to correct measurement errors of accelerometers. • Adaptive solution for adjusting the estimator gains for effective data fusion. • Stochastic analysis of the proposed estimation algorithm. • Experimental implementation of the estimator on a quarter-car suspension platform. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper proposes a fast dynamic pose estimation method for industrial robots. • The encoder-driven error-state kinematics for industrial robots is reconstructed. • This method has robust estimation performance in complex desired trajectories. • This method reduces estimation error by about 50% compared to existing methods. • This method significantly improves the trajectory tracking accuracy of robots. Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector's pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation). [ABSTRACT FROM AUTHOR]

• Building point cloud contour is widely used in urban planning and building identification, considering the low point cloud accuracy of UAV, and noise impact, the existing method cannot work well in dealing with UAV point cloud data in contour feature detection and extraction. In this paper we propose the feature recognition method of adjacent points distribution to conduct the extraction of UAV building point cloud contour. The main contributions of this paper can be concluded as: • A fold points extraction based on histogram detection is proposed, which judges fold by corner feature and extracted fold points by furthest bar in distance histogram. • A four-quadrant analysis method is introduced to detect boundary points, which use the information entropy to measure the distribution of adjacent points and decides the boundary points by Information entropy threshold. • The overview of the proposed method is shown in Fig. 1. Building point cloud contour is widely used in urban planning and building identification. The existing method cannot work well in the extraction of UAV building point cloud contour points because the low point cloud accuracy of UAV, and effect of noise. In this paper we propose extraction method of UAV building point cloud contour based on the adjacent points distribution. Firstly, we construct an information entropy model for determining optimal neighborhood points, and then use the eigenvectors corresponding to the maximum and minimum eigenvalues obtained by PCA to construct initial projection plane Π 1 and Π 2. Secondly, we propose the fine-tuning model of the projection plane Π 2 by using the aggregation characteristics of neighborhood points, and construct the extraction model of fold points with three parameter constraints. Finally, we propose the information entropy model of points probabilities in different quadrants on the plane Π 1 to extract boundary points. Real UAV point cloud data is used to test the performance and parameters of the proposed method, experiment results show that the proposed method is superior to OPAHT, 2D line detection and region clustering segmentation methods in the performance of UAV building point cloud contour extraction. The proposed method can accurately extract the point cloud contour points of UAV buildings. [ABSTRACT FROM AUTHOR]

• A kinematic analysis method of FCM based on CGA and equipment pose fusion is proposed. • The monitoring system in longwall mining is improved based on infrared range sensor. • The DEF/DPS-VRF is proposed to improve the comprehensiveness of straightness monitoring. Full-pose monitoring of the Floating Connection Mechanism (FCM) still exists blind zone, unreliable sensing data, etc., this paper proposes a digital twin bi-directional deduction method for the pose of starting/end point of the FCM. Firstly, considering the pose of the connection equipment, an optimization of the motion law of the FCM based on Conformal Geometry Algebra (CGA) is realized; secondly, the virtual application of optimization motion laws of the FCM is achieved by marking key points and extracting key information; then, referring to the information enhancement scheme obtained by adding digital twin infrared range sensors and a bi-directional deduction method is proposed for the full-pose of the FCM; finally, the experimental validation shows that the motion law of the FCM proposed in this paper is in line with the actual motion and generates better analytical results, the pose deduction accuracy is over 87%, providing new ideas for equipment pose monitoring. [ABSTRACT FROM AUTHOR]

• A BIW weld quality inspection method using vibration response signals has been proposed. • The LGHGNN model is designed for intelligent detection of BIW weld quality. • The cluster upgrade graph pooling is proposed to elevate node features. • Hierarchical information interaction and collaborative output. • Parallel anomaly detection for multi-label corresponding to multiple regions. To enhance the assembly quality in Body-in-White (BIW) assembly, this paper proposes an intelligent detection method for the nugget quality of Resistance Spot Weld (RSW) based on weld joint vibration excitation response signals. The method proposes a novel deep learning model, the Local-Global Hierarchical Graph Neural Network (LGHGNN). LGHGNN can automatically construct graph structures and, by introducing a newly designed upgrade pooling operation, extends the traditional flat structure of graph networks into a hierarchical structure within three-dimensional space. Therefore, LGHGNN achieves layered interaction of local-global information, enabling the model to focus on local details while gaining a broader learning perspective. Additionally, this paper proposes a strategy for multi-label unsupervised anomaly detection that involves layered interaction and collaborative decision-making for local and global graphs. The effectiveness of LGHGNN is demonstrated through its application in the BIW right front door assembly, achieving a remarkable 97.5% average accuracy in multi-region parallel anomaly detection. [ABSTRACT FROM AUTHOR]

• The two phases of invasion behavior were divided. • A hazard-based duration was carried out to model the invasion duration. • The Kaplan–Meier analysis identifies the effect of categorical variables on the invasion duration. • The log-logistic distribution provided the best fit to the invasion duration. • The model identified the significant covariates that affected the invasion duration. Electric two-wheeler riders' invasion behavior is a violation and a significant contributor to traffic safety concerns. Invasion duration, an essential parameter in invasion behavior, measures the travel time of an ETW rider in a motorized lane and represents the rider's risk exposure time. This paper endeavors to elucidate the characteristics of invasion behavior and model invasion duration. Using trajectory data, it addresses questions about the length, degree, duration, and two phases of invasion behavior. The hazard-based duration model is employed to fit the duration and analyze the factors influencing it. The results indicate that characteristics exhibit mean values of 3.23 s for duration, 8.313 m for length, and 0.629 m for degree. The log-logistic Accelerated Failure Time (AFT) model exhibits better performance for fitting invasion duration than the Weibull and the Log-normal AFT models. The findings of this paper can provide a reference for enhancing traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

• Blade tracking system, overcoming rotational interference. • Developed computer vision system for motion vibration tracking. • Rotation center identification markers optimize optical flow tracking. • Center markers optimize "small displacement" assumption with phase mapping. • Enhances speed and accuracy of blade target point detection. Based on the computer vision, optical non-contact measurement has been widely applied in the field of mechanical vibration measurement. However, existing computer vision methods are primarily suitable for on-site vibration scenarios, which pose significant technical challenges for combined scenarios involving both rotation and vibration. When measuring high-speed test points, the simplest and most direct approach is to use high-frame-rate image acquisition devices. This paper adopts a gradient-based optical flow tracking and measurement technique to track points of interest on rotating structures. In order to overcome the limitation of optical flow method's "spatial continuity assumption" on the measurement speed of rotating structures, a novel optical tracking and measurement method for periodically rotating blades based on center marker recognition in reorganized videos is proposed. Building upon the optical flow-based target tracking and detection algorithm, an improved method based on Quick Response Code（QR Code） marker tracking and Phase Mapping Method (PMM) is proposed. The optical flow method for rotating structures achieves an increased maximum detection speed, thereby reducing the cost of image sampling devices. In theory, the maximum detectable rotational speed that can be increased depends on the ratio of the length of the blade (R) to the adopted two-dimensional side length (r), denoted as R / r. In this paper, the theoretical maximum value for the increased rotational speed is R / r ≈4. Compared to high-frame-rate cameras with costs ranging from tens of thousands to even several hundred thousand dollars, the commercial camera devices used in this paper are priced at around a few thousand dollars. On the other hand, the use of QR Code for marking and tracking improves the accuracy of the detection algorithm and allows for the inclusion of more information within the QR Code. The paper introduces and analyzes the fundamentals and principles of this algorithm, conducts experimental measurements, and verifies the proposed improvement through theoretical calculations. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel indoor PDR method, which uses wearable devices to collect pedestrian acceleration, gyroscope, geomagnetic and electromyographic signals, and other motion data in real-time. An adaptive Gaussian bilateral filtering (AGBF) algorithm is proposed for preprocessing the collected data. An improved step length estimation (SLE) model is also established based on surface electromyography and acceleration signals. The quaternion method is employed to calculate the heading angle, and the angle increment is acquired by integrating the inertial data, and then the heading information at the next moment. By improving the Unscented KF optimized by the particle filter, the heading angle deviation is corrected, reducing gyroscope drift errors. The current position of indoor pedestrians is estimated based on gait detection, SLE, and heading angle estimation. • The main contributions of this work are listed below:(I) This paper proposes an AGBF algorithm, which uses a numerical filter that adaptively adjusts the standard deviation and adopts a targeted denoising strategy, further enhancing the denoising performance. (II) The gait detection method based on the generalized likelihood ratio multi-threshold detection (GLR-MTD) algorithm can adaptively adjust the threshold and is suitable for pedometers, indoor positioning, and other fields. It has extensive universality and meets the needs of engineering applications. (III) We constructed a novel SLE method for the Linear multi-source information fusion (LM-SIF) model, which can provide accurate SLE for the PDR algorithm. (IV) An improved particle filter-unscented Kalman filter algorithm (IPF-UKF) is proposed to estimate the heading angle error. This algorithm can effectively improve the positioning accuracy of real-time pedestrian inertial navigation. (V) Due to infrastructure and historical data independence, PDR methods based on MEMS-IMU and surface electromyography (SEMG) sensors are very easy to integrate with any application that requires high-precision location services. To solve the problems of severe error accumulation and low accuracy of pedestrian trajectory estimation in traditional Pedestrian Dead Reckoning (PDR) positioning technology, this paper proposes a multi-sensor fusion indoor PDR algorithm. Firstly, a generalized likelihood ratio multi-threshold detection algorithm is employed to detect the gait of pedestrians. Then, a linear multi-source information fusion model is constructed for step length estimation. Next, the quaternion strap-down attitude solution is utilized and coupled with an improved particle filter-unscented Kalman filter algorithm to correct heading angle deviations. Finally, integrate them into the PDR algorithm to estimate the pedestrian's position. The proposed PDR method's relative positioning errors for indoor two-dimensional plane and three-dimensional space walking are 0.36 % and 0.435 %, respectively. Compared to four traditional positioning algorithms, it reduces errors by approximately 0.77 %∼1.18 % and 5.42 %∼11.69 %, respectively. Experimental results indicate that the proposed PDR method effective suppression of error accumulation, achieving more accurate indoor PDR results. [ABSTRACT FROM AUTHOR]

• The initial measurement point is optimized, and the wavelet packet decomposition method is used to decompose and reconstruct the original measurement signal to realize the preprocessing of the data. Its main contributions include removing the DC component of the signal and reducing the non-stationarity and inconsistency of the sampled signal. • In view of the mode aliasing phenomenon caused by using EMD mode to analyze the signal, this paper uses VMD self-use segmentation technology to realize the adaptive segmentation of each component in the signal field, which effectively avoids the mode aliasing phenomenon, has stronger robustness, and effectively reduces the endpoint effect. At the same time, aiming at the problem that the grayscale map is not enough to visually express the deep features in the process of signal conversion to a grayscale map, this paper uses an SDPI diagram to display signal characteristics, and uses information entropy (IE) to select the optimal component after VMD decomposition to enhance the characteristics of symmetry point mode image (SDPI), and finally fuses the signal corresponding to the optimal component to generate a snowflake map, so as to display the changes between different vibration states simply, clearly and intuitively, and enhance the recognition ability of the image, and the image accuracy of the method is higher than the traditional sample accuracy. • Aiming at the phenomenon that the traditional SoftMax classification ability in convolutional neural network is not high, firstly, the convolutional neural network optimization is realized by adjusting the parameters of the CNN model, adding regularization, adjusting the hyperparameters, etc., and at the same time, the combined model combining SVM and CNN is used to replace the SoftMax classifier with the SVM classifier, and the results show that the method can improve the classification ability of images. • To verify the effectiveness and superiority of the proposed method, the proposed method is compared with the 1DCNN method, grey image-CNN method, SDPI-CNN, SDPI-SVM, and other methods, and the comparative results show that the model has good predictive performance and generalization ability, and verifies that the proposed signal picture establishment method and network improvement optimization model method are feasible for lead screw fault classification. The failure of the ball screw in the machine tool presents various types and complex coupling characteristics, which pose challenges in extracting fault features from vibration signals and lead to decreased accuracy in fault diagnosis classification. To tackle this issue, an improved intelligent fault diagnosis method based on enhanced CNN is proposed by optimizing the variational mode decomposition and symmetrized dot pattern image features. Initially, VMD is employed to decompose the preprocessed signals, incorporating information entropy for selecting decomposition layers to enhance accuracy. Different image features are generated using SDPI, and differential information is introduced to enhance feature expressive capability through fusion. Subsequently, considering the limited classification ability of traditional SoftMax classifiers in CNNs, SVM is integrated for classification purposes to optimize convolutional neural networks' structure and improve their classification performance. Finally, the proposed method was ultimately validated by means of comparative analysis of screw vibration signals, thereby affirming its exceptional accuracy in fault diagnosis and classification. [ABSTRACT FROM AUTHOR]

• Motion correction method is proposed to deal with real-time camera motion in all directions during the structural drift identification using SFM method. • Motion correction method is verified by two single-degree-of-freedom experiments with slight and huge rotation. • Error of identified maximum drift decreased to 7.3% after correcting camera motion. • SFM method is verified by a multi-degree-of-freedom experiment with an average error of 4.5%. Structure from Motion (SfM) method can reconstruct the story drift ratio, but the camera motion produced by earthquakes reduces the measuring accuracy. This paper improves the applicability of the traditional method by correcting camera motion according to the identification of translation and rotation of camera. Experiments at different levels are designed to prove the proposed method. First, one set of experiments proves the motion correction method by the Single-degree-of-freedom (SDOF) system. The error of maximum response is 4.5% for the case with less rotation. As for the camera motion with larger rotation, the average error increases to 7.9%, which still meets the practical utilization. After that, the accuracy of using SfM method is confirmed by the Multi-degree-of-freedom (MDOF) system with the average error of 4.9%. This paper is expected to extend approaches for the application of the SfM method in the case of huge earthquakes. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel, non-destructive and high-throughput testing technique to evaluate the film thickness of corrosion products and the corrosion surface damage. • The difference between the measurement result and the conventional value is only 0.03 μm. • The new measurement method can accurately characterize corrosion heights of approximately 0.2 µm and above. • By using the established technology, we found that there is a certain correlation between the corrosion rate and the product film thickness, but the film thickness is not the only factor determining the corrosion rate. This paper proposes an accurate and high-throughput testing technique to characterize the film thickness of corrosion products and the corrosion surface damage. This method first selected the optimal instrument parameters and sample preparation methods. Secondly, three approaches were implemented to improve test accuracy: eliminating initial errors, optimizing post-processing processes, and excluding the impact of critical data. Finally, this technology is applied to evaluate the corrosion resistance of B10 pipes. The results in the range of 4 mm × 4 mm indicate that when the fluctuation range is −400 μm ∼ 100 μm and the magnification 20 × 1, the optimal combination of acquisition time and image quality could be achieved. After implementing three processing methods to improve accuracy, the difference between the measurement result and the conventional value is reduced to 0.03 μm, and the variance is reduced to 0.002. This method is verified to accurately characterize a corrosion height of approximately 0.2 µm. [ABSTRACT FROM AUTHOR]

• Improves the U2-Netp algorithm to detect graphite ball surface defects. • A skip connection based on attention mechanism and feature fusion. • Predicted output of Sigmoid_Threshold with Log_Dice_focal loss function design. • The models constructed in this paper can obtain better detection performance. Graphite balls are used as matrix materials of nuclear fuel. To ensure the safe use of nuclear energy, it is necessary to detect the surface defects of graphite balls to screen out the balls that do not meet safety requirements. However, due to the small surface defects and complex background of graphite balls, the detection accuracy based on traditional detection methods is difficult to meet engineering requirements. To solve the above problems, this paper designs an image acquisition device for graphite balls based on line scanning to collect spherical images and designs an improved algorithm based on the U2-Netp network to solve the problem of low accuracy of defect detection. Firstly, the algorithm replaces the up-sampling in the RSU module with cubic linear interpolation to enhance the smoothness of the image. Secondly, spatial channel attention and multi-feature fusion modules are built between the encoder and the decoder to improve the accuracy of the model to obtain interesting information. Finally, the prediction output of Sigmoid and Threshold fusion is designed to solve the problems caused by the gradient disappearance and the uneven polarity of positive and negative samples. Tested on the actual collected data set, the improved U2-Netp algorithm improved the average accuracy of detecting surface defects on graphite balls by 8.1% compared to the original U2-Netp algorithm, reaching 85.3%. [ABSTRACT FROM AUTHOR]

• Binocular vision based approach for cost-effective and efficient deformation monitoring. • Precise calculation of displacements combining SIFT, gauss–newton, and interpolation. • Enhanced SGM for stereo matching, yielding precise vertical displacement measurement. • Comparative assessment proves superior accuracy and efficiency over traditional methods. Monitoring the deformation of critical structures such as tunnel、buildings and bridges is critical to ensure the safety of infrastructures. Non-contact deformation monitoring technology based on digital imagery has emerged as a prevalent and effective monitoring method, which enables cost-effective and efficient tracking of structural deformations. This paper aims to develop an automated deformation monitoring methodology using the binocular vision. First, standard preprocessing of digital images is performed through the integration of filtering algorithms and image enhancement techniques. Furthermore, by combining the Scale Invariant Feature Transform (SIFT) feature matching algorithm, the inverse compositional gauss–newton iterative algorithm, and interpolation methodologies, the planar displacement of the monitored targets can be calculated precisely, with sub-pixel precision being achieved. Subsequently, by adopting an enhanced Semi-Global Matching (SGM) algorithm, stereo matching of binocular images were accomplished. This process yields a disparity map, from which the depth map is computed. Then, precise measurement of the vertical displacement of the monitoring target can be obtained. This enables accurate measurement of three-dimensional displacements of the monitored targets. Finally, the developed methodology undergoes a comparative assessment against traditional measurement methods and existing algorithms to validate its measurement accuracy. The results prove that the deformation measurement technique based on binocular vision proposed in this paper captures structural deformation effectively. The accuracy surpasses that of existing measurement algorithms, and its efficiency outperforms traditional measurement methods. [ABSTRACT FROM AUTHOR]

• Machine learning has significantly enhanced optical fiber sensor performance. • Reviewed the latest progress in machine learning applied to optical fiber sensors. • More comprehensive classification introduction from point/discrete sensors, distributed sensors, image processing, and others. • A comprehensive overview of machine learning methods applied to optical fiber sensors was provided. In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction. [ABSTRACT FROM AUTHOR]

• The true temperature solution method presented in this paper not only does not require any hypothetical spectral emissivity and preset multi-spectral data. • The proposed method can reconstruct the spectral emissivity based on the measured brightness and known wavelength quantities, and the reconstructed model matches the actual spectral emissivity distribution. • Spectral emissivity reconstruction using sparse representation algorithm can be better adapted to the emissivity distribution states of different targets. With the aim of solving the problem of detecting the true temperature of objects using multi-spectral radiation thermometry, this paper proposes a multi-spectral radiation thermometry method based on the reconstructed spectral emissivity model. Compared with traditional hypothetical spectral emissivity models, this model is a reconstruction emissivity model based on actual measured brightness and known wavelength quantities, which can improve the characterization of the real emissivity state of the measured object and not be affected by any hypothetical conditions or artificial preset parameters. In order to verify the effectiveness of the proposed method for detecting the temperature of real problems, a total of eight typical spectral emissivity models, spectral data of Si 3 N 4 ceramics and ZrO 2 ceramics, and measurement data of rocket motor casings are selected for experiments. The experimental results indicate that the true temperature calculated by the proposed method has a relative error of less than 1 %, and the calculation time is kept at the millisecond level, which meets the real-time multi-spectral temperature measurement requirements. [ABSTRACT FROM AUTHOR]

Regular measurement and inspection of whisky casks are crucial for ensuring quality and effective inventory management. This process helps detect issues such as leaks, damage, and contamination that can harm the whisky's condition. Additionally, monitoring the whisky level provides valuable insights into maturation progress, evaporation rates, and production planning to account for expected losses. Currently, the industry relies on dipsticks and pressure sensors to measure liquid levels in casks. Dipsticks require moving casks from high shelves, risking human safety, the utilisation of extensive resources and necessitating cask opening, which has inherent issues. Pressure sensors, installed inside casks, are sensitive to temperature, limiting their accuracy and suitability for warehouse testing. This paper presents a new non-invasive method using ultrasound to measure liquid levels in wooden casks. An external ultrasonic probe glides along the surface of the cask. By leveraging the distinct ultrasonic acoustic properties in the liquid and the air, the sensor can detect the upper boundary of the liquid, thereby indicating the whisky level. Since this new method only needs to access a small portion of the cask to measure the level, it enables distilleries to accurately monitor cask contents on-site, reducing the costs and risks associated with moving casks. The paper also includes quantitative data on ultrasonic properties such as moisture, velocity, and signal attenuation for various cask samples. The prototype system successfully demonstrated this approach on a retired Ex-Bourbon whisky cask, providing precise liquid level measurements on different surfaces and storage orientations without the need for cask opening. It achieved centimetre-level accuracy compared to dipstick reference readings. • Proposed a new, non-invasive method to measure liquid levels in wooden casks. • Quantitatively assessed ultrasonic properties in two cask samples. • Demonstrated and verified the method using a retired Ex-Bourbon cask. • Achieved centimetre-level accuracy independent of the cask storage orientation. [ABSTRACT FROM AUTHOR]

• The proposed unsupervised method merges anomaly detection and fault tracing. • Signal decomposition-based energy features are extracted to make the diagnosis more robust. • The model training process only requires normal data. • The diagnostic process can be performed without labeled fault samples. The absence of detailed fault data presents a significant challenge in diagnosing faults of rotating machinery. This paper focuses on addressing abnormal detection and fault localization challenges in drivetrain components under the assumption that only normal data is available. Specifically, this paper proposes an unsupervised hypersphere description (UHD) framework by seamlessly integrating abnormal detection and localization. By constructing a one-class description hypersphere, this UHD method achieves fault detection without special fault samples. Abnormal samples can be detected based on their distance from the center of hypersphere, enabling accurate localization of the underlying abnormality. Validation of experimental data demonstrates that the UHD approach effectively detects anomalies and accurately localizes faults within specific locations. Remarkably, the UHD approach surpasses several mainstream unsupervised methods in terms of fault tracing accuracy. This shows the potential of the UHD approach in abnormal detection and localization in a drivetrain, particularly when only normal data is available. [ABSTRACT FROM AUTHOR]

• Derive trajectory and vibration equations in fractal dimension. • The designed simulation surface has self-affinity and directionality. • Reduction of Sa and Sq errors in simulation models. • The study can provide simulation data support for machine vision measurement. The grinding surface's 3-D topography has self-affinity. To more accurately simulate this characteristic, this paper proposes for the first time to construct a grinding surface simulation model based on fractional derivatives, including: utilizing the Weierstrass-Mandelbrot function to simulate the point cloud of the grinding wheel surface; further deriving the trajectory equation based on the He's fractal derivative; the semi-inverse method is employed to construct the fractional-order variational principle of the vibration equation. Experiments show that the simulation model designed in this paper can predict the surface height more accurately, and is better than the traditional model in the calculation of roughness parameters Sa and Sq. The model is helpful to thoroughly understand the formation mechanism of grinding surface topography, and provides simulation data guidance for machine vision inspection of grinding surface in micron scale. [ABSTRACT FROM AUTHOR]

• In this paper, we proffer an enhanced YOLOv7 and DeepSORT algorithm for the purpose of tracking foreign entities in the coal domain. The YOLOv7 network model undergoes enhancements through the reduction of Backbone convolutional layers, the introduction of the COTN module, and the incorporation of a compact target detection layer. • The DeepSORT tracking algorithm is refined by substituting the re-recognition network structure of DeepSORT with the MTL framework and replacing the DeepSORT foreign object tracking algorithm with the OSA module. Notably, the OSA module supplants the WAR stacked residual block in DeepSORT. • The algorithm attains a foreign object detection accuracy of 91.3% and a recall rate of 90.6%. In addition, it achieves a tracking accuracy of 76.1% for MOTA, and a precision rate of 80.6% for MOTP. Given the indistinct dissimilarities between foreign matter and coal in terms of their physical characteristics, the utilization of machine vision detection technology for foreign matter tracking yields suboptimal accuracy and precision, thereby failing to satisfy the exigencies of coal mine production. In this paper, we proffer an enhanced you only look once version 7 (YOLOv7) and simple online and realtime tracking with a deep association metric (DeepSORT) algorithm for the purpose of tracking foreign entities in the coal domain. The YOLOv7 network model undergoes enhancements through the reduction of Backbone convolutional layers, the introduction of the context overlap and transition network (COTN) module, and the incorporation of a compact target detection layer. Concurrently, the DeepSORT tracking algorithm is refined by substituting the re-recognition network structure of DeepSORT with the machine translation interface (MTL) framework and replacing the DeepSORT foreign object tracking algorithm with the occlusion-aware spatial attention (OSA) module. Empirical findings substantiate the efficacy of the proposed algorithm, as it successfully achieves foreign object detection and tracking. Specifically, the algorithm attains a foreign object detection accuracy of 91.3% and a recall rate of 90.6%. In addition, it achieves a tracking accuracy of 76.1% for multiple object tracking accuracy (MOTA), and a precision rate of 80.6% for multiple object tracking precision (MOTP). Notably, in comparison to the DeepSORT tracking algorithm, the proposed algorithm exhibits a significant improvement of 6 percentage points in MOTA and 3.9 percentage points in MOTP. [ABSTRACT FROM AUTHOR]

Low-dose CT examination will reduce the radiation risk to patients, but noise and artifacts in CT images will increase. This paper proposes a Denoising Swin Transformer (DnST) model for low-dose CT image denoising. DnST model modifies the network structure based on Swin Transformer and introduces a perceptual loss function and residual mapping into the network. In addition, this paper also designs a new image quality assessment (IQA) metric, PPSNR (Perceptual Peak Signal-to-Noise Ratio), which measures the difference of higher-level information of image and optimizes the problem that the scores of PSNR are inconsistent with the visual quality seen by human eyes. The experimental results show that compared with the CNN method, DnST achieves good results in noise suppression, structure protection and lesion detection. The image quality is improved by about 2.28% ∼ 15. 95 %. Not only noise is suppressed, but also the structure and critical information are protected. [Display omitted] • Proposing a Denoising Swin Transformer (DnST) for low-dose CT image denoising. • Designing a new image quality assessment (IQA) metric, PPSNR. • Image quality of low-dose CT after denoising by DnST is close to the normal-dose CT. • PPSNR scores of images are consistent well with human perception of images. [ABSTRACT FROM AUTHOR]

• DFOSS for detailed tie strain profiling. • Optimized B-spline function improves strain profile fitting. • Accurate predictive under-tie force continuous distribution. Previous studies have attempted to determine the under-tie support condition using strain gauges and stress cells. However, there have been limited successes due to either poor sensor accuracy or insufficient measuring points. The objective of this paper is to examine a new method using distributed fiber-optic strain sensors (DFOSSs) for predicting the tie force distribution and assessing the under-tie support condition through tie strain profile. A high-definition DFOSS was instrumented along a scale wood tie to collect strain profiles. This method includes two steps: (1) fitting the strain profiles by a customized high-order B-spline function; and (2) deriving the B-spline functions of tie bending moment, shear force, and force distribution based on Euler–Bernoulli beam theory. Test results obtained on ties with various predetermined support settings demonstrate the algorithm's ability to accurately predict support conditions. Moreover, the paper discusses the effectiveness of using shear force profiles for calculating applied loads. [ABSTRACT FROM AUTHOR]

• This article realizes the simultaneous output of high-voltage high-frequency excitation for transmission line measurement. • The proposed differential linear cascaded amplifier topology breaks through the limitation of gain bandwidth product. • Designing appropriate isolation voltage for cascaded power supply can meet different testing voltage requirements. • The parallel fiber optic isolation cascade method breaks through the kHz limitation of high-voltage amplifiers. In this paper, a high-frequency and high-voltage linear cascaded amplifier based on parallel optical fiber isolation is proposed. The electro-optical synchronous drive of the excitation signal is realized through equal proportional laser splitting, which achieves the same linear gain and output phase shift in each photoelectric conversion unit. The differential linear cascade topology designed in this paper effectively solves the gain bandwidth product limitation and achieves a high-voltage linear output in a high-frequency range. The new amplifier solves the problem that due to the low AC excitation voltage of traditional impedance analyzer, the signal attenuation is severe in long distance and high insulation distributed electrical parameter network. It can be widely used to measure high-frequency dielectric loss and impedance spectrum for dielectric materials and electrical components in industry and laboratory. By cascading n -stage amplifier units, the voltage gain can reach 6n times. Finally, a 16-stage cascade amplifier is built, and the response characteristics are tested with the distributed electrical parameter network loads. The output characteristics show that the expected amplifier gain can be obtained accurately in the high-frequency range of 100 kHz–100 MHz, the output voltage can reach 192Vpp, and the peak current can reach ±500 mA. The maximum of the curve about THD and noise versus frequency could be limited to 0.49 %, which could meet the needs of on-site measurement. [ABSTRACT FROM AUTHOR]

• This paper addresses aero-engine sensor data from the view of non-stationarity. • A new difference transformer network is described. • A novel time series decomposition strategy is proposed. • Our method outperforms the current state-of-the-art methods on the aero-engine public and engineering datasets. Remaining useful life (RUL) estimation has been widely concerned, given its significant role in prognostics and health management of industry systems. This paper focuses on the non-stationarity of real-world aero-engine sensors data under multiple operating conditions. Previous methods accomplish the normalization operation to attenuate the inherent non-stationarity of raw series for better predictability, which can be less instructive for bursty RUL prediction tasks. To tackle the limitations of direct normalization, we propose a difference transformer network (DFormer). First, we present a series decomposition module to extract predictable components from different series, which can meticulously perceive the non-stationary information of each component. Then the difference attention is proposed to approximately obtain attention without normalization to maintain the non-stationarity of the time series. Furthermore, DFormer consists of two complementary parts: the encoder for incorporating the non-stationary information of the original series and the decoder for stable distribution of time series. We have conducted extensive experiments on the CMAPSS and N-CMAPSS datasets. The results show that DFormer can significantly improve prediction performance compared to existing state-of-the-art methods. In addition, the non-stationary feature learning performance of the model is validated on QAR engineering data, demonstrating the capability of practical engineering applications. [ABSTRACT FROM AUTHOR]

• Summarize the mainstream methods of BSS at this stage. • Discuss and illustrate the challenges faced when applying BSS algorithms to the field of mechanical system vibration analysis, providing an overview of the current solutions. • Conduct a systematic analysis and explanation of a large body of literature on mechanical system vibration analysis based on BSS, with a specific focus on fault diagnosis, operational modal analysis, and load identification. • Study the limitations of the current application of BSS in engineering vibration analysis. • Provide an outlook on potential future research directions. Vibration is a widespread phenomenon in mechanical systems, and vibration analysis is imperative for assessing the dynamic characteristics of mechanical structures. Vibration analysis extensively relies on analyzing the vibration signals. Blind source separation (BSS) was introduced as a pattern separation technique in vibration signal analysis in the 21st century. BSS has provided innovative solutions for various vibration problems by effectively recovering desired source signals from mixed signals despite limited prior information. This paper reviews the application research of BSS in vibration analysis in the past decade, focusing on detailed explanations in the areas of fault diagnosis, operational modal analysis (OMA), and load identification. Firstly, the paper presents the fundamental theory of BSS, encompassing several prominent algorithms: independent component analysis (ICA), non-negative matrix factorization (NMF), sparse component analysis (SCA), bounded component analysis (BCA) and deep learning-based BSS. Subsequently, corresponding to the three key elements of structural dynamics, namely, response, system, and excitation, the paper categorizes and organizes the research on the application of BSS in the areas of fault diagnosis, OMA, and load identification, summarizing the methodological approaches in relevant literature for specific problems in practical applications. Finally, the paper presents a future perspective on the application prospects of BSS in vibration analysis. [ABSTRACT FROM AUTHOR]