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The dominating set problem (DSP) is one of the most famous problems in combinatorial optimization. It is defined as follows. For a given graph G = (V , E) , a dominating set of G is a subset S ⊆ V such that every vertex in V ∖ S is adjacent to at least one vertex in S. Furthermore, the DSP is the problem of finding a minimum-size dominating set and the corresponding minimum size, the domination number of G. In this, work we investigate a variant of the DSP, the super dominating set problem (SDSP), which has attracted much attention during the last years. A dominating set S is called a super dominating set of G , if for every vertex u ∈ S ¯ = V ∖ S , there exists a v ∈ S such that N (v) ∩ S ¯ = N (v) ∖ S = { u }. Analogously, the SDSP is to find a minimum-size super dominating set, and the corresponding minimum size, the super domination number of G. The decision variants of both the DSP and the SDSP have been shown to be NP -hard. In this paper, we present tight bounds for the super domination number of the neighbourhood corona product, r -clique sum, and the Hajós sum of two graphs. Additionally, we present infinite families of graphs attaining our bounds. Finally, we give the exact number of minimum size super dominating sets for some graph classes. In particular, the number of super dominating sets for cycles has quite surprising properties as it varies between values of the set { 4 , n , 2 n , 5 n 2 − 10 n 8 } based on n mod 4. [ABSTRACT FROM AUTHOR]

Given a non-decreasing sequence S = (s 1 , s 2 , ... , s k) of positive integers, an S -packing coloring of a graph G is a partition of the vertex set of G into k subsets { V 1 , V 2 , ... , V k } such that for each 1 ≤ i ≤ k , the distance between any two distinct vertices u and v in V i is at least s i + 1. In this paper, we study the problem of S -packing coloring of cubic Halin graphs, and we prove that every cubic Halin graph is (1 , 1 , 2 , 3) -packing colorable. In addition, we prove that such graphs are (1 , 2 , 2 , 2 , 2 , 2) -packing colorable. [ABSTRACT FROM AUTHOR]

A matching in a graph is a set of pairwise nonadjacent edges. A maximum matching is a matching which covers as many vertices as possible. In this paper, using the Gallai–Edmonds Structure Theorem, we obtain a characterization of graphs each of whose edges belongs to at most one maximum matching. [ABSTRACT FROM AUTHOR]

In computational and theoretical chemistry, the eccentricity matrix of graph has widely applications due to its effect of eccentricity in molecular graph. Since a molecular structure is modeled by hypergraph which has a higher accuracy than by graph, it is also interesting to study eccentricity matrix of hypergraph. The eccentricity spectral radius of H is the spectral radius of its eccentricity matrix. In this paper, some properties on eccentricity spectral radius of hypergraphs are obtained and some extremal structure of hypertrees are characterized. [ABSTRACT FROM AUTHOR]

Boolean functions used in symmetric-key encryption should have high higher-order nonlinearity to resist several known cryptographic attacks, such as algebraic attacks and low-degree approximation attacks. The higher-order nonlinearity also plays an important role in coding theory and theoretical computer science, since it relates to the covering radius of Reed–Muller codes and the Gowers norm, respectively. It is well-known that bent functions have the highest nonlinearity in an even number of variables and thus they possess the best ability to withstand fast correlation attacks and best affine approximation attacks. However, there is currently limited knowledge regarding the higher-order nonlinearity of bent functions because computing the higher-order nonlinearity, or even providing tight lower bounds, is an extremely hard task. In 1974, Dillon proposed two well-known classes of bent functions based on partial spread (in brief, PS), called PS − and PS + , respectively. He also exhibited a subclass of bent functions in PS − , known as partial spread affine plane(PS a p for short). In this paper, we provide a lower bound on the third-order nonlinearity of the simplest PS a p bent functions in n variables, where n ≥ 6 is even, by calculating the nonlinearities of all second-order derivatives of this kind of bent functions. Compared to the two known lower bounds on the third-order nonlinearity given by Carlet and Tang et al. respectively, our lower bound is much better than these two ones. [ABSTRACT FROM AUTHOR]

A dominating broadcast of a graph G is a function f : V (G) → { 0 , 1 , 2 , ... , diam (G) } such that f (v) ⩽ e (v) for all v ∈ V (G) , where e (v) is the eccentricity of v , and for every u ∈ V (G) , there exists a vertex v with f (v) ⩾ 1 and d (u , v) ⩽ f (v). The cost of f is ∑ v ∈ V (G) f (v). The minimum cost over all the dominating broadcasts of G is called the broadcast domination number γ b (G) of G. In this paper, we give new upper and lower bounds for γ b (G). We show that both the bounds are tight. Also, we improve the upper bound for a subclass of regular graphs. Further, we explore the broadcast domination numbers of generalized Petersen graphs and a subclass of circulant graphs. [ABSTRACT FROM AUTHOR]

A set of n drones with limited communication range is deployed to monitor a terrain partitioned into n pairwise disjoint and closed convex trajectories, one per drone. There is exactly one communication link between two trajectories if they are close enough, and drones can communicate provided they visit the link at the same time. If each robot flies around an assigned area and shares information with the neighbors periodically the system is said to be synchronized. Over time, one or more drones may fail and the ability to survey, communicate, and stay connected decreases, thus the robustness against drone failure becomes crucial. In this paper we study various problems related to the proper functioning of a synchronized system under drone failure. First, we provide efficient algorithms, both centralized and decentralized, for determining the connected components induced by the set of surviving drones. Second, we study coverage, isolation, and connectivity under a probabilistic failure model and show that, in the case of grids, the system is quite robust in the sense that it can tolerate a large probability of failure before drones fail to completely cover the terrain, become isolated, or the system loses full connectivity. [ABSTRACT FROM AUTHOR]

The topological structure of a recursive interconnection network can be modeled by an n -dimensional graph G n. A faulty vertex set (resp. edge set) of G n is called a t -embedded vertex (resp. edge) cut if the remaining network is disconnected and each vertex in the residual network is in a copy of fault-free t -dimensional graph G t. The t -embedded vertex connectivity ζ t (G n) (resp. the t -embedded edge connectivity η t (G n)) is defined as the minimum cardinality over all t -embedded vertex (resp. edge) cuts of G n. The m -ary n -dimensional hypercube G (n , m) has an elegant recursive structure and is also a generalized variant of the classical hypercube. This paper studies the reliability of the G (n , m) and generalizes some known results in ternary n -cubes. Specifically, we prove that ζ t (G (n , m)) = (m − 1) (n − t) m t for t ≤ n − 2 and η t (G (n , m)) = (m − 1) (n − t) m t for t ≤ n − 1. [ABSTRACT FROM AUTHOR]

In this paper, we study the distribution of parallelograms and rhombi in a given set in the plane over arbitrary finite fields F q 2. As an application, we improve the recent result due to Fitzpatrick, Iosevich, McDonald, and Wyman on the Vapnik–Chervonenkis dimension of the induced distance graph. [ABSTRACT FROM AUTHOR]

• A WGM-FP hybrid microcavity for dual-parameter measurement is presented. • The two signal components could be well demodulated. • The crosstalk between concentration and temperature of NaCl solutions is reduced. Whispering gallery mode (WGM) resonator is sensitive to the external medium change due to its strong light-matter interaction. However, the temperature sensitivity of silica-based WGM resonators would cause crosstalk, which is a fatal shortcoming for those high-quality (Q) resonators. In this paper, a hybrid microcavity including WGM resonance and Fabry–Pérot (FP) interference is proposed for simultaneous measurement of NaCl concentration and temperature, using the same microcavity in the same system to obtain two types of optical signals. The experimental results indicate that the maximum measurement error of NaCl concentration and temperature without compensation is about ± 0.026 % and ± 1.1 °C, while they would be ± 0.012 % and ± 0.3 °C after compensation. Sharing one microcavity enables temperature compensation to achieve temperature synchronization, and sharing of one system ensures the simplicity and stability of the sensing platform. [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]

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

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]

• Proposed an adaptive exposure adjustment algorithm, eliminating the need for manual intervention during the calibration process, making the calibration process more robust to environmental changes. • Proposed an adaptive blur adjustment algorithm, enhancing the clarity of calibration images, addressing the issue of uncontrollable blurring in previous algorithms, and improving the accuracy of camera intrinsic parameter calibration. • Designed a semi-automatic high-precision calibration strategy that addresses significant discrepancies in calibration results obtained from different calibration processes in traditional algorithms, thus improving robustness. Camera calibration is a prerequisite for many computer vision tasks, such as visual measurement and visual localization. This paper proposes a new method for high-precision calibration of camera intrinsic parameters. By dynamically adjusting image brightness and blur to cope with changes in external lighting conditions and camera working distance, high-precision internal parameter measurement is achieved. This method solves the problems of the traditional camera intrinsic parameter calibration process being cumbersome and susceptible to changes in the placement of calibration boards and fluctuations in external lighting conditions. Firstly, an adaptive exposure adjustment algorithm is proposed to adjust the camera exposure value by statistically analyzing the average gray value of randomly selected rectangular regions on the calibration board, thereby solving the problem of the camera calibration accuracy being affected by overly bright or dark environments. Subsequently, an evaluation criterion and adaptive adjustment strategy for adjusting image blur are proposed. By extracting edge points of feature circles and obtaining the slope of the fitted curve, the relationship between edge sharpness and slope is utilized to adjust the image blur. Finally, the proposed method achieves high-precision semi-automatic calibration. Experimental results demonstrate that the proposed method has significant advantages in terms of correctness, robustness, and flexibility, with an average reprojection error of 0.0167. [ABSTRACT FROM AUTHOR]

Wi-Fi-based gesture recognition represents an emerging paradigm of human–computer interaction. While deep learning-based and model-based solutions are crucial for improving accuracy and generalization performance, they often depend heavily on extensive datasets and the widespread deployment of sensing devices. In this paper, we address the challenge involving limited sensing data and devices. We propose a signal-based gesture recognition method that leverages gesture coding based on stroke trajectory and feature representation based on dynamic phase changes. Specifically, we develop an endpoint detection algorithm to ensure precise gesture recognition. Additionally, a subcarrier selection algorithm is designed to select optimal subcarriers, capturing comprehensive gesture information. Extensive experiments are conducted to evaluate the performance. Results demonstrate an average accuracy of 93.67% for six gestures. This approach effectively mitigates the impact of location and environment factors on gesture characteristics, and reduces dependence on large quantity of samples and transceiver devices, obviating the need for model training. • An endpoint detection method is proposed to accurately extract the gesture information. • A subcarrier selection algorithm is proposed to select the subcarriers carrying a higher proportion of gesture-related information while being less susceptible to noise interference. • A gesture coding-based recognition method that leverages the stroke trajectory and unique feature representation is presented. This approach eliminates the need for model training and mitigates the dependence on quantity of samples and sensing devices. • Improve generalization performance of Wi-Fi-based gesture recognition system. [ABSTRACT FROM AUTHOR]

With the development of concrete structure health monitoring (SHM), the utilization of acoustic emission (AE) as an effective non-destructive testing method has attracted increasing attention among researchers. However, the acquisition of concrete AE signals in real-world scenarios is often limited, which poses challenges to the training as well as the practical application of deep learning (DL) models. To address this problem, a concrete acoustic emission signal augmentation method based on generative adversarial networks (GAN) is proposed in this paper. This method features a unique architecture where both the generator and discriminator are built upon a Bidirectional Long Short-Term Memory (BiLSTM) network. Additionally, the generator integrates an attention mechanism between the BiLSTM layers, enhancing its ability to capture the temporal dynamics of AE signals. To improve the local details of the generated signals, a temporal difference mean square error (TD-MSE) regularization is incorporated into the loss function. Experimental results demonstrate that the method not only substantially increases the amount of data available but also significantly enhances the model's generalization capabilities and classification accuracy. • Use BiLSTM in GAN to capture signal timing information. • Add an attention mechanism to BiLSTM layers for signal focus. • Employ temporal difference regularization for smoother signal generation. • The discussion analyzes improved model generalization from the augmented dataset. [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]

Spatiotemporal signals are currently gaining wide popularity owing to their richness in terms of information content from both spatial and temporal aspects. Climatic studies involve the usage of such signals from different spatial locations to understand the climatic conditions, anomalies and for prediction of similar climatic zones. Measurement of the complex inter-relationships in the spatiotemporal processes is of vital importance in this regard. The predominant frequency components associated with annual, semi-annual, and quarterly seasonal variations exhibit interchangeability across spatial and temporal dimensions, thereby contributing significantly to the variability within the spatiotemporal region under investigation. Employing frequency domain analysis enables the examination of these crucial seasonal components, even amidst noise, provided effective frequency domain-based techniques are employed. This paper is based on the measurement of the variability of spatiotemporal climatic variables considered from different geographic zones using multivariate variational mode decomposition. The main advantage of the proposed index lies in the ability to measure the variability of the signals acquired from either spatially close or far-off locations on Earth. Also, the index harnesses the hidden frequency components in the spatiotemporal signals under test by comparing the aligned periodic components across different zones. The correctness of the proposed index is tested through exhaustive simulation studies and also against the weather measurements like the Earth skin temperature, humidity and precipitation acquired from spatially close and far-off locations over a period of 40 years. • Spatiotemporal variability index is a novel metric that quantifies the variability in spatiotemporal climatic signals. • The index considers predominant seasonal frequency component analysis across spatial and temporal dimensions. • Multivariate variational mode decomposition is used to get hidden signal components in spatiotemporal signals. • The proposed index is evaluated against simulated and on real world climatic signals. • The climatic signals tested are Earth skin temperature, specific humidity and precipitation signals. [ABSTRACT FROM AUTHOR]

[Display omitted] • A reliable fault diagnosis method based on SPV noise signal is established. • The number of sensors is drastically reduced by using the noise emission of SPVs. • The coherence analyses are conducted to validate effectiveness of the approach. • The number of workers is greatly reduced, especially in aging Chinese plants. The solenoid pulse valve (SPV) is a key component of dust collector. The frequent switch of SPV accelerates its damage, subsequently decrease operation efficiency of dust collector. The traditional manual inspection methods lack reliability and efficiency, while vibration-based condition monitoring is costly and sophisticated in acceleration sensor distribution and signal processing. This paper proposes a fault diagnosis approach based on SPV noise. The average coherence coefficient of SPV noise and vibration signals is above 0.8 and close to 1, which demonstrates that there is a close relationship between SPV noise and its structural vibration. Furthermore, the validation experiments indicate that the SPV fault can be clearly distinguished by its noise signal, confirms the effectiveness of proposed approach. By utilizing this noise monitoring method, the number of sensors can be reduced by about 95 %, the work intensity of the staff is greatly reduced, and production efficiency and safety are guaranteed. [ABSTRACT FROM AUTHOR]

• An unstressed antenna sensor is proposed for passive wireless crack sensing. • The crack width is encoded in both the amplitude and phase of the retransmission signal. • Cross polarization technique is employed in sensor's wireless interrogation to avoid signal self-jamming. To address the insufficient strain transfer ratio of monolithic antenna sensors and signal self-jamming in wireless interrogation, this paper presents a cross-polarized antenna sensor based on a U-shaped resonator for crack sensing. The sensor comprises an Rx antenna, a Tx antenna, and a sensing element. Employing cross polarization between Rx and Tx antennas mitigates ambient reflection, and the channel model of the sensing system based on cross polarization technique is demonstrated to reveal the working mechanism. Within the sensing element, a U-shaped resonator and movable shorted unit form an unstressed resonator, encoding the crack width in both the amplitude and phase in the retransmission signal, which are utilized to extract the sensor's resonant frequency. The equivalent circuit model is established to analyze the monitoring mechanism comprehensively. The operational performance of the sensor is simulated in CST and validated by experiment. Results demonstrate a good linear relationship between the sensor's resonant frequency and the crack width. [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]

• 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]

[Display omitted] • A novel focusing magnetic field excitation device for electromagnetic torque sensor. • Magnetic path analysis about focusing magnetic field distribution of C-shaped core. • Structure optimization about magnetic core shields. • Torsional angle measurement using focusing magnetic field torque sensor. To enhance accuracy and sensitivity of electromagnetic torque sensor, a highly focusing magnetic field is essential. This paper proposes a C-shaped excitation device designed to focus the magnetic field. The device is enclosed by dual layers of ferromagnetic metal shields. The C-shaped structure directs most of the magnetic field lines through its magnetic core, enhancing the focusing performance. Theoretical analysis of the device's focusing magnetic field distribution was conducted under three conditions: unshielded, with a single-layer shield, and with a dual-layer shield, using magnetic path analysis techniques. Using COMSOL 6.1 to examine various configurations, we identified the optimal structure for the device. Subsequently, a physical prototype was fabricated, and the magnetic field distribution was measured. Furthermore, an analysis was conducted on the phase differences of the induced voltage generated under its influence. The nonlinearity of the measured phase differences was4.37%. [ABSTRACT FROM AUTHOR]

• The GARCH family models from time series analysis is introduced for the first time into machine condition monitoring. • Health indicators constructed based on GARCH and EGARCH models demonstrate the capability to detect incipient faults in bearings, verified through two experimental cases. • In accelerated degradation tests, it is established that acoustic emission signals are closely associated with damage evolution. • Acoustic emission technology can be employed for quantitative assessment of bearing damage. Recent progress in digital twin (DT) has significantly contributed to the advancement of predictive maintenance. The interaction of data between physical and virtual models is facilitated through carefully designed health indicators (HIs). Conventional condition monitoring HIs are inadequate for early-stage fault detection and lack the capacity to quantitatively assess defects. In light of this, the paper proposes HIs based on the generalised autoregressive conditional heteroskedasticity (GARCH) family time series model to characterise the evolution of bearings dynamic response, specifically the cyclostationarity of the repetitive transients. The verification of the proposed indicators is assessed using a publicly available vibration dataset and acoustic emission signals acquired from accelerated bearing degradation tests. The result shows that the GARCH oriented indicators can determine the incipient failure earlier than traditional statistical HIs and have the ability to quantify defects. Significantly, the improved Exponential GARCH model-based indicator demonstrates heightened stability throughout the monitoring process. [ABSTRACT FROM AUTHOR]

• The FSW welding process is monitored based on multi-sensor fusion. • Wigner-Ville distribution algorithm is optimized by smooth pseudo for SPWVD. • The smooth function in SPWVD is fine-tuned by wavelet window function. • ResNet is improved by 1D-conv and SE modules to learn components in the spectrum. As one of the important development directions in modern railway and automobile, lightweight requires reliable connection technology of dissimilar materials. Thermal-assisted friction stir lap welding (TA-FSLW) is an available welding technology for the joining of dissimilar materials, which can solve the problems of physical and chemical properties in the welding process of dissimilar materials. Unfortunately, defects may occur during the welding process. This paper proposes a defect diagnosis method based on smooth pseudo Wigner-Ville distribution (SPWVD) and ResNet18 for Al-CFRTP TA-FSLW. Firstly, the acoustic emission (AE) signal and the temperature of the weld nugget are acquired by the AE sensor and thermography camera in the FSLW process. Then, the time–frequency domain features of AE signal are extracted by SPWVD, and the maximum temperature of the weld nugget is extracted by exponential moving average (EMA) from thermography. Moreover, the dataset is established by SPWVD features, maximum temperature of welding nugget, and several time–frequency features. Finally, the ResNet18-attention network is employed to classify welding defects and stages. The Al-CFRTP TA-FSLW experiments are carried out based on the proposed method. Experimental result indicates that the SPWVD spectrum can effectively extract the feature frequency of deep and shallow hole defects in the welding process. In the section of classification, the accuracy of the dataset with SPWVD features is improved by 2% to 4% compared to other methods, which demonstrates that the SPWVD feature is superior in defect feature extraction. [ABSTRACT FROM AUTHOR]

• The workpiece coordinate system of face gears is iteratively established. • The measured points are corrected by reconstructing complicated tooth surfaces. • Multi-objective optimization comprehensively compensates for measurement errors. • The proposed measurement method is effective and more robust than the previous ones. In contact coordinate measurement, measurement error sources would affect the establishment of the workpiece coordinate system (WCS) and the exact measurement for the points on the complicated surfaces. This issue would be more complicated for face gears with complex features, especially when having huge machining errors. To address the above problem, this paper proposes a novel precise mathematical model to measure face gears by considering the geometric deviations of multiple teeth, including the deviations of tooth surfaces, pitch, and tooth thickness. An iterative method is presented to establish the WCS on machines by considering multiple reference points on different teeth. Subsequently, the correction model of measured points is established, and the sensitivity analysis is conducted. Further, a multi-objective optimization is proposed to comprehensively compensate for measurement errors, and the algorithm is given. The experiment of a face gear with huge overcut tooth surfaces validates the proposed method is effective and robust. [ABSTRACT FROM AUTHOR]

• Augmented resolution to improve Digital Image Correlation results. • Quartic polynomial form to interpolate correlation maps for subpixel accuracy. • Larger number of points for correlation map interpolation. • Novel AR-DIC algorithm based on augmented resolution and better polynomial fitting. • Comparison of AR-DIC with literature algorithms in the case of vibration data. Digital Image Correlation (DIC) is a widespread technique, and many different implementations were developed in the literature to optimize the algorithm performance. In this paper, a new DIC algorithm is optimized for vibration measurements, where the displacement amplitude drastically decreases with the increase in frequency. Even if high-resolution cameras are used, it is mandatory to use a DIC algorithm with reliable sub-pixel performance. DIC algorithms are generally optimized for large complex displacement fields, and they solve the sub-pixel problem by fitting correlation data through a second-order polynomial function. An innovative solution is proposed to push the sub-pixel accuracy and increase the signal-to-noise ratio of the measurement, by exploiting image resizing to achieve virtual higher resolutions and by adopting a quartic polynomial function to interpolate the displacement map with an increased number of interpolation points. The proposed algorithm is validated through synthetic and experimental data and compared to literature DIC algorithms. [ABSTRACT FROM AUTHOR]

Optically pumped atoms have essential applications in magnetic sensing, physics research, and inertial navigation. The polarization gradient caused by light absorption dramatically influences the performance of quantum sensors based on optical pumping. This paper presents a novel approach to address the trade-off between polarization gradient and sensitivity by introducing the pulsed optical pumping method. The interplay of pulsed optical pumping, relaxation, and diffusion processes leads to a more homogeneous distribution of electron spin. Experimental results indicate that the pulsed optical pumping technique increases the optically pumped magnetometer (OPM) signal strength by 12.8% and increases the nuclear spin coherence time by 12.29% in optically pumped co-magnetometer (OPCM). This technology has broad applications in sensors based on the optical pumping effect, such as OPM, OPCM, and nuclear magnetic resonance gyroscope, and holds immense promise for future advancements in the optical pumping field. • Novel Approach: Pulsed optical pumping for homogeneous polarization. • Improved Performance: Improved Performance: Increases OPM signal strength and nuclear spin coherence time. • Broad Applications: Applicable to OPMs, OPCMs, NMRGs, et al. [ABSTRACT FROM AUTHOR]

When using vision systems that employ multiple spectral bandwidths, such as visible and Long-Wave Infrared (LWIR) bands, the calibration object's appearance can significantly impact the matching of common features in the images. To address this issue, a robust calibration technique is necessary to work with reflections and the degraded appearance of the calibration pattern over time. In this paper, we propose a novel stereo calibration technique that can be used for visible-thermal imaging systems for remote sensing applications (e.g., mounted on Unmanned Aerial Vehicles, UAVs). Proposed method can be used on a generic calibration pattern, and we exploit computer vision algorithms to accurately estimate the intrinsic and extrinsic parameters. The algorithm can mitigate specular reflections and contrast inversion issues that may degrade the calibration accuracy. The calibration technique is designed to be versatile as it operates also in outdoor environments, hence it can be especially useful for UAV applications since on-field re-calibration is often needed. The proposed method has been validated using two different calibration patterns and compared with state-of-the-art techniques. • A stereo visible-thermal calibration procedure for multi-modal vision system. • A novel feature extraction pipeline that mitigates the contrast inversion and is robust to specular reflections. • An iterative optimization procedure that helps the detection of the thermal pattern. • An optimized use of low-cost and easy-to-build calibration targets for the visible-thermal range. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feed system thermal error model based on positioning error decoupling is proposed. • Thermally induced error is affected by motion and non-motion heating. • Feed system already has thermal errors when it is not working. • Thermal error model based on MLR has physical meaning and good robustness. • MLR has better accuracy than data-driven models over long time prediction. Thermal error modeling and compensation considering of the measured positioning error decoupling for the lathe with a slant bed is proposed. Due to the complex coupling factors of thermal error, the current compensation methods exhibit limited applicability and generalization. The linear fitting method is used to decouple geometric error and thermal error from the positioning error. The motion heating and non-motion heating experiments of X-direction feed system were carried out to investigate the principle of thermal error generation and the impact factors. The multiple linear regression model of the thermal error was established based on the analysis of the thermal error measurement results. To verify the effectiveness of the proposed model, the thermal error at two working conditions was predicted, and then the error compensation was carried out. The results show that the positioning error is reduced by more than 84% and 92% with the present method compared with that without compensation. This research provides effective method for the generation principle and compensation of thermally induced errors of lathe with a slant bed. [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]

• A new type of high-conductivity layer structure is proposed for magnetic shielding devices. • The AC magnetic shielding factor of magnetic shielding device for SERF magnetometer is improved. • Modified the theoretical calculation method for AC magnetic shielding factor and improved the accuracy of the results. • Promote the performance improvement and application of ultra-high sensitivity atomic sensors. Since the frequency of the biological magnetic field is DC to 500 Hz, it is important to improve the ability of the magnetic shielding device within this range for the measurement of the biological magnetic field and the reduction of the magnetic noise of the SERF magnetometer. At present, the optimization of AC magnetic field shielding in the frequency range of 0.01 to 500 Hz is rarely considered. To optimize the AC shielding factor, this paper analyzed the influence of the position and structure of the high-conductive layer in the magnetic shielding device and proposed an optimized structure of the high conductive layer. What's more, the theoretical formula was improved by introducing frequency coefficients, solving the problem of significant differences between theoretical calculation results, FEM results and measurement results. Finally, it was verified by experiments that the optimized high-conductivity layer structure can improve the AC shielding factor of magnetic shielding device. [ABSTRACT FROM AUTHOR]

• A self-calibration method for a five-axis dispensing machine based on monocular vision and the product of the exponentials formula. • A ChArUco marker-based pose detection system is designed to achieve a wide range of calibration space for a five-axis dispensing machine. • Significantly improve the motion accuracy of five-axis dispensing machine after self-calibration. Geometric error identification and kinematic calibration are common and effective methods for improving the machining accuracy of five-axis machines. Compared with the traditional measurement methods characterized by complicated inspection processes and high cost, this paper proposes a self-calibration method for a five-axis dispensing machine based on monocular vision and the product of the exponentials formula. The proposed method can automatically identify geometric errors and complete kinematic calibration, even for simultaneous five-axis motion. First, a ChArUco marker-based pose detection system is designed to achieve a wide range of calibration space for a five-axis dispensing machine. Based on this, the proposed sub-pixel checkpoint detection framework has higher pose detection performance accuracy at different depths of the field. Then, a general kinematic error model of five-axis machines is established using screw theory to avoid singularities. Moreover, the identification method of its geometric error is proposed for an RRTTT-type five-axis dispensing machine. Finally, an arc-shaped dispensing trajectory for the 3D glasses' inner wall is used for experimental validation. The experimental results show that the average orientation quantization error decreases from 0.0112 rad to 0.0039 rad. In contrast, the average position error decreases from 0.1627 mm to 0.0473 mm before and after the self-calibration, respectively. Thus, the convenience, effectiveness, and accuracy of the self-calibration method are validated on this five-axis dispensing machine. [ABSTRACT FROM AUTHOR]

In the development of smart home products, energy monitoring, and control systems, there is a significant need for precise and reliable electric current measurement. Most domestic appliances, whether residential or commercial, are powered by power cords with two wires. A reliable and accurate current probe would be required to detect current in two-wire power cables. Existing current sensors for two-wire power cables are complex, bulky (they use a magnetic core), and need full access to the power cord. In this paper a coreless, contactless current probe that requires only limited access of the space around the two-wire power cable is proposed. The sensor unit presented here measures the current accurately even for certain types of misalignments of the power cord. This makes the current measurement less sensitive to minor mechanical displacements, radius of the conductors, and the overall insulation thickness of power cord. In the proposed current probe, one-of-a-kind and highly reliable arrangement of magnetic field sensors, are used to measure the magnetic flux density caused by the current passing in the power cord. The current is computed using a derived mathematical formula which is expressed in terms of the voltage outputs of the magnetic sensors. To provide a clear understanding of the measurement process, an analysis using the Finite Element Method (FEM) is performed. The functionality of the proposed current probe is theoretically tested. Considering different sources of errors, an accuracy of less than 1.5% is obtained. [Display omitted] • The proposed current probe does not require 360° space around the power cord, this method requires only partial space by the side of the power cord. • The proposed probe enables distance-independent current measurement, varying based on power cord insulation thickness and diameter. • The probe offers efficient, simple, cost-effective current measurement in power cords with three magnetic sensors. • To keep up with the errors, mathematical analysis was done with finite length of the power cord. • In addition, FEM analysis validated in presence of ferromagnetic materials and with interference due to external magnetic field. [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]

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]

• Propose a new non-invasive air gap flux density measurement method for PMSLM. • Introduce GAF method to show 1D magnetic density signals features obviously. • Introduce image matching method to obtain the best TMR sensor measurement position. • Integrated design of TMR sensor hardware circuit and PMSLM mover mounting module. • Establish CNN-LSTM model to accurately calculate PMSLM air gap magnetic density. This paper presents a new non-invasive air gap flux density measurement method for permanent magnet synchronous linear motor (PMSLM) using tunneling magnetoresistance (TMR) sensor and convolutional neural networks-long short-term memory (CNN-LSTM) regression modeling. First, the analytical and finite element models of the air gap magnetic field of PMSLM are established as the data basis. Second, TMR sensor is used to measure the external stray magnetic density. Gramian Angular Field method combined with image similarity matching technology are used to obtain the optimal measurement position of the TMR sensor. Then, a new deep learning regression method as CNN-LSTM is introduced to establish a high-precision mapping model to realize non-invasive high-precision measurement of the air gap magnetic density by "substituting external to internal." Finally, PMSLM prototype experimental platform with TMR sensor hardware acquisition circuit is built. Comparison confirmatory experiments with Gauss meter can verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The parachute recovery system requires continuous improvement to meet the increasingly stringent performance requirements. Measuring parachute morphology and motion parameters during descent is essential for parachute performance and stability analysis. This paper presents a vision-based method for measuring the morphology and motion parameters of parachutes and designs a self-observing system for parachute testing. The system employs four synchronized cameras, with two cameras capturing the performance of the parachute during its operation, and the other two estimating the 6D pose of the load. By integrating the data from four synchronized cameras, we can achieve a more accurate and comprehensive understanding of the parachute's working condition during descent. Through the accuracy verification experiments and outfield airdrop experiments, the system is verified to have high measurement accuracy and practicability. The experiments show that the RMSE of the feature point location of the parachute measured by our method is 2.78 mm. • A novel vision-based method for measuring parachute morphology and motion parameters. • The method effectively integrates parachute measurements with load pose information. • A system suitable for small parachute self-observing was designed. • An air-drop experiment was implemented to verify the validity of our proposed method. [ABSTRACT FROM AUTHOR]

• A novel methodology integrating percussion and CNN model is established to detect concrete voids. • The CNN model trained by both CWT and MFCC show satisfying categorizing performances. • The methodology proves its good noise-immune capacity in processing percussion sound signals. • The superiority of the proposed method is well demonstrated by comparison with ML methods. As a fundamental construction material, concrete commonly encounter void defects, which could significantly impair the integrity of concrete members and seriously weaken their load-bearing capacity. Worse still, void defects typically exist inside concrete members, making it difficult to detect them in real time. Therefore, conducting regular inspections for internal void defects of concrete structures is of great importance to maintain structural health. This paper investigates an integrated method to detect concrete voids based on percussion approach and a convolutional neural network (CNN). Firstly, percussion-induced sound signals were collected from 18 concrete specimens with different precast void defects. Each specimen was tapped 100 times, for a total of 1,800 percussion-induced acoustic signals. Subsequently, continuous wavelet transform (CWT) and Mel frequency cepstral coefficient (MFCC) were introduced to convert the collected acoustic signals into two types of images. The converted images were divided into testing and training datasets according to the ratio of 1:3, and 600 images were input to the CNN as the test dataset and 1200 images as the training dataset. The CNN was then trained and verified by the joint input of CWT and MFCC images to perform concrete voids classification. The result shows that the proposed method presents a satisfying classification accuracy of 100 %. Next, percussion tests under 5 levels of noisy environments were conducted, and the results prove the remarkable anti-noise performance of the methodology by achieving 88.12 % accuracy even at the strongest signal-to-noise ratio of 0 dB. Finally, the superiority of the proposed method was further demonstrated through the accuracy comparison with machine learning (ML) methods, with the accuracy consistently outperforming over 25 %. In conclusion, the proposed method has a great potential for future in-situ applications in concrete void 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]

[Display omitted] Crystal measurement, especially on-line measurement, in industrial crystallization is always a challenging topic in the past years, which is crucial to precisely understand crystal dynamic behavior and product quality control. With the development of photoelectric sensing technology, various on-line/in situ crystal measurement technologies (CMTs) have been developed to extract crystal morphological information, detect crystal form and even microstructure. This paper critically reviews the principles, advantages, and disadvantages of various existing CMTs, and their applications in exploring new crystal forms, characterization, prediction, control of shapes and size of crystals. The future research directions are proposed from the aspects of improving measurement accuracy, developing novel technologies for complex crystal behavior measurement, and promoting the application of CMTs in industrial crystallization. On balance, this review aims to enhance the awareness of scientists and engineers across industry and academia on CMTs, further promoting the development of automation and intellectualization of industrial crystallization processes. [ABSTRACT FROM AUTHOR]

• A fast CWE entity calculation method based on the local Boolean operation is clarified. • A 2D image mapping method is proposed to achieve the rapid extraction of engagement region. • A simulation platform embed with kernel program of CWE extraction is developed. • An accurate and fast prediction of instantaneous milling force during multi-axis machining has been achieved. The paper proposes a fast extraction method for cutter-workpiece engagement (CWE), which greatly facilitates the milling force prediction during multi-axis machining. One of the key cores of the method is to reconstruct the nearby cut-off material of workpiece using a set of past neighboring cutter location points (CLPs), and obtain the CWE entity through local Boolean operation among the current tool, workpiece blank, and reconstructed entity. Moreover, by performing 2-dimensional (2D) image mapping, the engagement boundary of CWE entity will be quickly obtained. Combining with the analytical formula of instantaneous undeformed chip thicknesses (IUCTs), the composite IUCT for tool side and bottom cutting edges during actual immersion is further derived. Finally, accurate and fast prediction of milling force with consideration of tool attitude, corner feature and bottom cutting effect is achieved. The results reveal that the method can predict milling force with an excellent performance of first-order computation complexity. [ABSTRACT FROM AUTHOR]

• A new blind T60 estimation method using a denoising-aided multi-task learning network is proposed for accurate T60 estimation. • The noise-robustness and applicability of the proposed model are comprehensively investigated. • Audio datasets with unknown RIRs and noise recorded in real-world classrooms are used to further validate the effectiveness of the proposed method. The noise in reverberant speech severely limits the estimation accuracy of reverberation time T60 using current deep learning (DL) methods. To address this issue, this paper proposes a denoising-aided multi-task learning (DAMTL) method for blind T60 estimation. Specifically, speech denoising, as an auxiliary module, is conducted joint training with T60 estimation for more accurate prediction accuracy. These two tasks are integrated into one DL network by sharing the same encoder network, where the complex-valued spectrum is introduced to extract comprehensive high-dimensional features from noisy reverberant speech. Subsequently, complex operation of 2-D convolutional neural network (Conv2d), batch normalization and long short-term memory (LSTM) are formulated. Furthermore, the noise robustness and applicability of the DAMTL are fully discussed by comparison with state-of-the-art DL-based methods using simulated data and real-world recorded data. The results prove the effectiveness and superiority of the proposed DAMTL, especially in low signal-to-noise ratio (SNR) scenarios and practical applications. [ABSTRACT FROM AUTHOR]

• A novel pressure distribution testing method of piston/cylinder interface. • A piston pump test rig has been set up. • Experimental investigations are conducted and a hydrodynamic lubrication model is developed. • The experimental results closely match the numerical simulations. The piston/cylinder lubricating interface is one of the most critical components in piston pumps. The characteristics of the interface, such as pressure distribution, leakage flow and viscous friction, etc., have significant effect on the performance of the piston pump. Previous experiments obtained data distribution by installing pressure sensors on a fixed cylinder block. However, too many measurement points on the cylinder block may disrupt the lubricating film; insufficient measurement points will result in incomplete oil film pressure distribution. This paper presents a novel time domain location testing method which a measuring point is placed on each cylinder surface as a time-domain calibration reference, enabling the acquisition of the pressure distribution over one shaft revolution. Real-time pressure between the piston and cylinder is measured under various operating conditions. Further research results show that the pressure distribution obtained through this method exhibits a high degree of conformity with the simulation results. [ABSTRACT FROM AUTHOR]

• A theoretical model is proposed to explore current disturbance mechanism for subsurface cracks at different frequencies. • The variation pattern of Bx signal with frequency is discovered and employed for the identification of subsurface cracks. • A novel transition frequency is proposed and utilized for assessing the burial depth of subsurface cracks. Subsurface cracks are more challenging to detect and evaluate compared to surface cracks. This paper introduces a novel transition frequency to identify and evaluate subsurface cracks using alternating current field measurement (ACFM) technique. A theoretical model is proposed to explore current disturbance mechanism for subsurface cracks under different frequencies. A 3D simulation model is established to validate the proposed theoretical model and investigate the effects of crack length, shape and specimen thickness on transition frequency. The ACFM testing system is used to detect subsurface cracks with different burial depths and explore the effect of lift-off on transition frequency. The results demonstrate that, with increasing frequency, the shape of Bx signal for subsurface cracks transitions from a trough to a peak, which can be utilized for identifying subsurface cracks. The burial depth can be accurately assessed by transition frequencies. The transition frequency is hardly affected by crack length, shape and lift-off. [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 dual-camera computer vision monitoring system is proposed and applied in displacement monitoring of an underground prefabricated subway station for 5 months during construction and post-construction phases. • Field calibration experiment is carried out using movable targets equipped with spiral micrometers and the measurement errors are discussed. • The potential correlation between uneven displacement and some minor structural defects, as well as the temperature-induced deformation fluctuations are investigated. The increasing applications and structural vulnerability of prefabricated metro stations have resulted in a growing demand for monitoring their displacements during the construction and post-construction phases. This paper proposes a dual-camera computer vision monitoring system consisting of a dual-camera base station, a series of monitoring prisms and two reference prisms. The displacement and rotation of the camera station are considered to reduce measurement errors caused by camera motion. The system has been applied in the displacement monitoring of the 164 m assembly section of an underground station in Shenzhen during the construction and post-construction periods. A movable target equipped with a spiral micrometer is used for field calibration experiments, and the results show that the average measurement deviation is 0.01 mm and the standard deviation is 0.068 mm. During the 5-month monitoring, displacement caused by backfilling, rainfall, and other construction conditions are precisely reflected, which shows a potential correlation between uneven displacement and some minor structural defects. At the post-construction period, settlement variance is relatively small, and the temperature effect causes horizontal convergence to fluctuate. [ABSTRACT FROM AUTHOR]

During the measurement of structured light, different types of ambient light may cause some interference with the imaging process of grating projection. Therefore, this paper provides a descriptive method of the intensity of ambient light based on the polarization information. This method describes the degree of influence of ambient light on the grating projection system with the Mueller matrix of the target object and then processes the grating image in the frequency domain to reduce the influence of ambient light on the grating projection system. The final experimental results indicate the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

Schematic representation of phospholipid-modified polyethersulfone membrane surface resist non-specific adhesion. [Display omitted] • The phospholipid biomimetic PES membranes are prepared by simple in-situ blending under mild conditions. • The biomimetic membranes possess excellent anti-protein adhesion and anticoagulation properties. • The complement activation and biofilm formation are efficiently inhibited on the membrane surface. Hemodialysis (HD) is one of the most important treatments for patients suffering from kidney failure, and polyethersulfone (PES) is the most common type of hemodialysis membrane. However, owing to insufficient biocompatibility and lending to the dilemma of its coagulation and bleeding risk in clinical applications, it limited the service life of hemodialyzer and seriously affected the dialysis safety of patients. In this paper, phospholipid biomimetic PES membranes were prepared by in situ blending between the hydrophilic polymer (PMAPC) rich in amino groups and the chloromethyl functional polyethersulfone (CMPES), which made up for the shortcomings of the hydrophilic additives that were easy to elate. Characterization and evaluation showed that the modified PES membrane had excellent hydrophilicity and stability. Furthermore, it could also effectively inhibit platelet adhesion activation and fibrinogen adsorption denaturation, suppress complement activation, and prolong coagulation time. The in vitro blood circulation experiment further confirmed the excellent anticoagulant activity of the modified membrane. The modification and preparation methodology described in this paper was simple and practical under mild reaction conditions, low cost, and easy to industrialize. This research lays the foundation for potential applications of phospholipid-mimicking PES membranes in hemodialysis. [ABSTRACT FROM AUTHOR]