Abstract:The switch pressing generator can convert pressing actions into electrical energy, supplying power for wireless key switches, which holds significant application value in green smart homes. This paper introduces an electromagnetic principal impact generator and proposes a bistable conversion structure. During the two steady-state transfers, the magnetic field lines in the coil undergo sudden changes in the opposite direction and the coil generates induced electromotive force pulses. Finite element software was employed to analyze the magnetic field distribution of the bistable structure, and the dimensions of the coils and iron core were optimized accordingly. The results show that the magnetic field lines in the two stable states of the coils have opposite directions, and the induced electromotive force increases almost linearly with the dimensions size. A prototype generator was fabricated and assembled. Experimental analysis reveals that the generator can produce an output voltage of up to 30 V, generating an electrical energy of 1 469 μJ. This provides sufficient power supply for wireless sensor network nodes and wireless switches, enabling green and environmentally friendly self-sufficient operation.

Abstract:In order to meet the needs of integrated and multi-functional RF front-end devices, a planar filter patch antenna with controllable radiation nulls is designed for WLAN 5 GHz band. The antenna is based on a microstrip patch antenna fed by a coaxial probe. The upper surface is a metal radiation patch fused with a ribbon and a rectangular slot, and the lower surface is trial-grounded.The symbiotic strip is located on both sides of the wide edge of the radiation patch, and the rectangular groove is etched on the radiation patch and located on both sides of the coaxial feed point. The two structures introduce a radiation null at the low frequency and high frequency of the passband respectively, which makes the antenna realize the filter response and good radiation characteristics. At the same time, the position of the two radiation nulls is freely controllable, which improves the flexibility of the design. The simulation and test results show that the center frequency of the filter antenna is 5.25 GHz, the relative bandwidth is 14% (4.89~5.62 GHz), and the two radiation zeros are located at 4.55 and 6.05 GHz respectively. The average gain in the working passband is about 7 dBi, and the out-of-band suppression level is greater than 19 dBi. The test results are consistent with the simulation results. The design of the filter antenna does not introduce additional filter network, saves the overall size of the antenna, and has low profile, light weight, compact structure, and good filtering and radiation performance.

Abstract:To deal with the issues of slow network training speed, large number of fuzzy rules and insufficient accuracy of traditional error calibration methods, a calibration method based on Principal Component Analysis and Self-Construction Fuzzy Network (PCA-SCFN) is proposed in this paper to realize real-time high-precision deformation reconstruction of integrated wing antennas. Firstly, a displacement-node degree of freedom error model is established based on the inverse finite element method (iFEM), and the inverse problem is solved by the monotone fast iterative shrinkage thresholding algorithm (MFISTA). Secondly, the PCA dimensionality reduction method is introduced to simplify the training network complexity by reducing strain dimensions. Thirdly, non-uniform rational B-spline (NURBS) fitting is applied to the small-sample training set to expand the data, enhancing network generalization and reducing the influence of noise on the training set. Finally, the SCFN is trained based on triangular membership functions (MF) and Takagi-Sugeno (T-S) fuzzy model to obtain the fuzzy rules. The results of wing loading experiment show that the PCA-SCFN-based calibration method can greatly improve the reconstruction accuracy, and at the same time, it has faster training speed and fewer rules. For a load of 80 N, the maximum reconstruction error is only 0.46 mm when the maximum deformation of the structure is -134.36 mm, and the training time of the SCFN is only 9.714 s, and the number of rules is only 121 at most. Therefore, the calibration method based on PCA-SCFN is an effective approach that can be applied to wing deformation monitoring.

Abstract:In response to the increased screening costs associated with the reduction in size and increased complexity of integrated circuits, a new screening method for integrated circuits based on single-variable test parameters has been proposed. Initially, the merge sort algorithm is used to concatenate the parameter values with the integrated circuit numbers into an array, ensuring the accuracy of subsequent screening and sorting the data according to parameter values. Then, the K-means algorithm is employed to preprocess outliers in the test data, optimizing the test data preliminarily. Finally, an innovative algorithm called D-PSO is introduced, combining derivatives and particle swarm optimization. The D-PSO algorithm enhances the sensitivity and accuracy of locating inflection points, precisely identifying these points to directly screen integrated circuits with similar parameter data. Simulation results demonstrate that this algorithm converges much faster than other algorithms and can accurately and swiftly screen integrated circuits. While maintaining test accuracy, it optimizes the test set and performs similarity screening, effectively reducing the screening costs of integrated circuits.

Abstract:In order to obtain a portable and simple vibration energy harvester and overcome the problem of low output of traditional energy harvesters, a double-L bracket kickback piezoelectric energy harvester was proposed, which combined the nonlinear characteristics of the tensile structure with piezoelectric technology, which could effectively improve the dynamic response and output performance of the energy harvester. The effects of external resistance, magnetic distance, excitation acceleration and angle on the output performance of the harvester were analyzed. The experimental results show that the output power reaches the peak value of the harvester at the optimal external resistance value, and the optimal external resistance value is 200 kΩ. The introduction of magnetic force can significantly improve the output performance of the energy harvester. At a magnetic distance of 18 mm, the harvester captures the most energy, and its optimal output performance reaches 1.12 mW at 12.1 Hz. In addition, the excitation acceleration has an obvious impact on the output characteristics of the energy harvesting system, and the output voltage and output power of the energy harvester also increase with the larger the excitation acceleration, and the maximum output power of the energy harvesting system reaches 1.39 mW under the condition of the excited acceleration of 0.4 g and the frequency of 12 Hz. The harvester has good output voltage and output power in the angle range of 0°~45°, and has the advantage of working under the condition of uncertain excitation direction. Practical application experiments further prove that the energy harvester can continuously output a large and stable voltage, which provides an effective solution to solve the problem of low output of traditional energy harvester.

Abstract:Oil and gas pipelines are susceptible to defects throughout their service life due to manufacturing processes and complex environmental conditions, often face incidents such as ruptures. Consequently, non-destructive testing is crucial for ensuring pipeline integrity. This study examines the impact of crack defects on the potential field through the direct current potential drop method, facilitating the development of a multi-probe defect detection technique. Finite element analysis of flat plate specimens with defects disclosed a pronounced potential difference in areas with defects compared to those without. By partitioning the specimen area according to the detection scope of multiple probes, a novel multiprobe detection method was devised. Detection of a defect triggers the calculation of the defect influence factor, k; in areas without defects, k values typically range from 0.8 to 1.2, whereas in the vicinity of defects, they exceed 2. This variance in k values aids in approximating the defect’s location and size. Additionally, a multi-probe detection experimental platform was established, confirming that potential differences near defects are significantly higher than in other areas, aligning with the findings from finite element analysis. Notably, the potential difference was substantially greater in defect areas, with k values in unaffected zones ranging from 0.75 to 1.2, and surpassing 1.5 in defect areas. The analysis of the distribution of k values offers insights into the defect’s precise location and size, with an accuracy deviation of about 3~5 mm.

Abstract:Aiming at the problem that the forward looking distance of the lateral pure tracking control algorithm is greatly affected by the vehicle speed, this paper designs an improved Salp optimization algorithm to adjust and optimize the forward looking distance in the pure tracking control in real time. Firstly, based on the pure tracking control model, the objective function of the improved Salp optimization algorithm is designed with the lateral error as the main decision parameter, and Brownian motion and adaptive weights are introduced into the algorithm to prevent falling into the local optimal solution and improve the convergence speed of the algorithm. Secondly, the longitudinal double-loop PID control algorithm is designed to track the reference speed of the vehicle. Finally, the proposed pure tracking control algorithm based on distributed longitudinal double-loop PID control algorithm and lateral forward distance optimization is verified experimentally on the actual platform of the agent vehicle, and multiple groups of comparison experiments are set up. The results show that the pure tracking trajectory tracking control algorithm based on forward looking distance optimization has the best control performance, in which the maximum lateral error is 0.068 m and the average lateral error is 0.014 m, and the control accuracy is improved by 24.73% compared with fuzzy optimization.

Abstract:Relative localization is a prerequisite for multiple robots in unknown environments to accomplish collaborative tasks such as formation, exploration, and rescue. A relative localization method based on ultra-wideband (UWB) bearing is proposed for positioning between robots in unknown infrastructure-free environments where satellite signals are blocked. The proposed method uses a sliding window to intercept the inter-robot bearing observations and motion trajectories over a period of time, construct the bearing cost function, and estimate the relative pose between the robots by minimizing the cost function. However, the non-convexity of the function leads traditional optimization algorithms to fall into local optimal solutions. Therefore, sparrow search algorithm (SSA) is used to optimize the cost function for the relative localization between robots. To reduce the effect of UWB bearing measurement errors, the SSA-estimated pose and odometry information are fused by a back-end pose graph optimization algorithm to achieve more accurate relative positioning. The experimental results show that the method is able to achieve an average translation error of 0.32 m and an average rotation error of 2.1° in an indoor environment with a size of 12 m×6 m.

Abstract:In response to the problems of the control method used in modular multilevel converter (MMC)-unified power flow controller (UPFC), a nonlinear control strategy combining back stepping control (BSC) and sliding mode control (SMC) based on MMC-UPFC is proposed in this paper. This control strategy not only retains the strong antiinterference ability of SMC, but also has the advantages of fast response speed of BSC. Firstly, according to the topology of MMC-UPFC, the mathematical models for the parallel-side and the seriesside of MMC converter is established. Secondly, positive and negative sequence decomposition is carried out under unbalanced conditions, and the BSC-SMC controller for MMC-UPFC under unbalanced conditions is designed, and the stability of the control system is proved. Finally, a MMC-UPFC simulation model is established on the MATLAB/Simulink software platform for simulation verification, by comparing the BSC-SMC control strategy with PID control strategy under two different operating conditions: Single-phase drop of power supply voltage at the receiving end and injection voltage on the series-side of MMC, the proposed BSC-SMC control strategy not only has the strong anti-interference characteristics of SMC control strategy, but also has the characteristics of fast response speed and small overshoot of BSC control strategy, the stability and robustness of the control system are stronger, the effectiveness and superiority of BSC-SMC control strategy is verified.

Abstract:In view of the problems of large search randomness, poor target bias and path tortuousness in the current robotic arm path planning algorithm, an APF-informed-RRT* algorithm based on bidirectional target bias was proposed. Firstly, probabilistic adaptive target bias strategy is introduced based on bidirectional informed-RRT* to reduce the randomness of search and improve sampling efficiency. Secondly, for path expansion, the artificial potential field method is integrated into the two-way search tree to reduce the number of iterations of the algorithm. At the same time, in the path growth stage, the dynamic step growth strategy is adopted, that is, the step size is dynamically adjusted according to the expansion trend of the search tree, so as to avoid local optimization and speed up the path search time. Finally, the redundant nodes are removed by the principle of triangle inequality, and then the path is smoothed by B-spline curve to obtain the optimal planning path. The simulation and comparison experiments with bidirectional RRT*, bidirectional informed-RRT* and bidirectional P-RRT* are carried out in 3D environment. Compared with bidirectional RRT*, the time is saved by 41% and the number of sampling points is reduced by 63%. Compared with two-way informed-RRT*, 58% less time and 68% fewer samples are collected. Compared with bidirectional P-RRT*, it saves 30% in time and 60% in sampling quantity.

Abstract:In order to solve the problems of feature selection dependent on experience and poor robustness caused by outliers in machine learning traffic anomaly detection, a fundamental point classification method for traffic anomaly detection based on the “background relation-background distribution-background basis” system by factor space theory is proposed. Firstly, the KNN outlier detection algorithm is used to remove outliers in the data in the data preprocessing stage to reduce the influence of outliers on the subsequent background basis extraction. Secondly, the mRMR algorithm is used to sort the data features and select the most influential features for classification as category distinguishing features. Then, the background basis extraction algorithm is optimized based on the internal point discriminant method, and the background basis of different types of data in the training data is extracted, and the unit cognition package of each type is obtained. Finally, a fundamental point classification algorithm (FPCA) based on the unit cognitive packet is constructed to achieve accurate two-class classification of abnormal traffic. The proposed method attains accuracy rate of 92.48% and F1-score of 92.18% in a two-class classification task on the NSL-KDD dataset, which detection performance superior to the same type machine learning method. The test on CICIDS2017 scene data set further verifies the feasibility of the proposed method.

Abstract:Aiming at the problem of high-precision control of rotational speed in the rotary system of hydraulic anchor drilling rigs in the presence of parameter uncertainty and nonlinear constraints, a model-free adaptive iterative learning-based rotational speed control scheme for the rotary system of hydraulic anchor drilling rigs is proposed by taking advantage of the repetitive nature of the drilling rig operation. First, the state space model of the drill rig slewing control system about the rotational speed is constructed. Secondly, the dynamic linearization technique is used to construct the equivalent linear mapping relationship between the hydraulic motor and the servo valve current in the iterative domain of the drilling rig slewing system, and the model-free adaptive iterative learning speed control design method is proposed based on the historical servo valve current input and hydraulic motor rotary angle output data collected by the system. The asymptotic convergence of the rotational speed tracking error of the hydraulic anchor drilling rig slewing system along the data direction as well as in the direction of repeated operations is then given theoretically. Finally, the effectiveness of the algorithm is verified by joint simulation using MATLAB software and AMEsim platform. The results show that compared with the traditional PID algorithm and the iterative learning control algorithm, the proposed algorithm can realize the high-precision control of the drilling rig speed by using only the measurable data without the need of a known anchor drilling rig system model, and it still has a good adaptive and anti-jamming ability in the face of the sudden external disturbances and the fluctuation of the oil temperature.

Abstract:Phase-sensitive optical time-domain reflectometer (Φ-OTDR) utilizes a laser as the detection light source for detecting vibration signals along the optical fiber. However, the spontaneous emission of the laser can lead to phase fluctuations in the optical field, directly impacting the signal-to-noise ratio (SNR) of the phasedemodulated signal. To tackle this issue, a block adaptive denoising method in wavelet domain noise estimation is proposed. The characteristics of phase noise caused by laser spontaneous emission has been analyzed. The phase noise levels at different decomposition scales are extracted using continuous wavelet transform (CWT). By combining with unbiased risk estimation to adjust the block length and threshold of the wavelet coefficients, adaptive denoising for different input signals is achieved. Experimental results demonstrate that compared to untreated signals, at 4.5 km of optical fiber, the SNR of single-frequency signals improves from 40.01 dB to 54.60 dB, and the system’s strain resolution optimizes from 66.15 pε/√Hz to 11.69 pε/√Hz. The SNR of linear swept-frequency signals improves from 18.31 dB to 26.40 dB. In comparison with other denoising algorithms, for single-frequency signals, root mean square error (RMSE) reduces to 0.009 6 with an SNR gain of 14.59 dB; for linear swept-frequency signals, RMSE decreases to 0.080 9 with an SNR gain of 8.09 dB. The study demonstrates that this method suppresses phase noise while preserving effective signals, thereby improving the accuracy of phase recovery.

Abstract:With the rapid development of China’s civil aviation industry, the safety of aircraft takeoff and landing is an important consideration and challenge faced by the airport authorities. Friction, which is generated on the contact surface between the tire and runway, is a crucial factor in ensuring the safe landing of aircraft. Measuring the friction coefficient is a vital task for resolving the issue of measuring runway friction coefficient in China. In this paper, we present a finite element method to quantify the runway friction coefficient. We perform a multi-physical field coupling analysis of tire-runway interactions utilizing ABAQUS. This allows us to obtain the correlation between the friction coefficient and the tread friction (shear) stress under varying load, pressure, and speed conditions. By analyzing the trend of friction stress variation under both univariate and multivariate operating conditions, and subsequently employing the fitting method to reverse solve the friction coefficient, it is possible to establish models for estimating the friction coefficient under differing operating conditions. This leads us to the achievement of a measurement method that solely relies on tread friction stress to assess friction coefficient within specific operating conditions. Finally, a tester for determining the friction coefficient of airport runways has been employed to validate an estimation model in accordance with standard working conditions. Six experiments were conducted at a distance of 3 000 metres, as a result, the discrepancies between the estimation outcomes and the actual measurements ranged from 2.47% to 4.13%. This study affirms the validity and accuracy of the finite element method-based estimation model and presents a stimulating framework for investigating the measurement technique of runway friction coefficient.

Abstract:Phased array ultrasonic technology can be used to detect lead seal defects in high-voltage cable terminals. In order to solve the problem that conventional ultrasonic quantitative methods cannot effectively detect and quantify lead seal defects, this paper proposes a quantitative detection method for lead seal defects in high-voltage cables based on ultrasonic fanscan images. The method takes the longitudinal wave fanscan image of the lead seal defect as the object and combines the threshold segmentation and corrosion algorithm to obtain other relevant information such as the cross-sectional area of the defect and the height of the defect in real time. Firstly, an ultrasonic testing platform for lead seal defects of high-voltage cable terminals was built to detect lead seal defects of different diameters represented by holes and slag inclusions. Then, threshold segmentation and corrosion algorithm processing were performed on the collected fan-scan images of lead seal ultrasonic testing, and quantitative analysis of defects was carried out and compared with the traditional -6 dB quantitative results. Finally, the influence of different diameter defects on ultrasonic quantitative results was discussed. The results show that compared with the traditional -6 dB method, the distance error measured by this method is reduced by 5%, the defect size accuracy is improved by 10%, and the accuracy rate is more than 85%. However, when the diameter of the lead seal defect increases, the defect measurement error also increases. This method verifies that the phased array ultrasonic technology can detect and quantify the lead seal defects of high-voltage cables efficiently and intuitively. It provides an important reference value for the engineering application of lead seal defects of high-voltage cables, which is helpful to improve the quality of lead seal process of high-voltage cable terminals and ensure the safety of power grid operation.

Abstract:Microseismic monitoring technology can give the spatial location of rock body rupture or instability in real time and accurately, and has become one of the important means of early warning for disasters such as coal and gas herniation and tunnel rock explosion. Aiming at the problem of complex environment and weak signals difficult to be recognized effectively in underground engineering, a microseismic signal recognition method combining convolutional neural network and Transformer (T_CNN) is proposed. Six kinds of signals in tunnel engineering in a western region are collected by using fiber-optic acceleration sensors, and the signals are input into the model for training and verification after band-pass filtering for noise reduction and Fourier transform. Convolutional neural network in the model is utilized for feature extraction, focusing on the key information based on Transformer, and the final multi-classification results are derived by multilayer perceptron. The results show that the classification accuracy of the T_CNN-based model reaches 98.09% and converges faster. Compared with the current state-of-the-art residual neural network, the accuracy is improved by 6.2%, and the precision, recall, and F1 score are improved by 0.036, 0.023, and 0.033, respectively, which confirms the superiority of the algorithm in practical engineering applications. In addition, the energy of the fiber microseismic signal can also be estimated more accurately after the fiber microseismic signal is input into the model after the feature transformation, which further verifies that the model has good application prospects.

Abstract:Aiming at the difficulty of extracting features and classifying 3D point clouds due to their irregularity and disorder, a 3D point cloud classification method that fuses deep learning and ensemble learning is proposed. Firstly, the deep learning model PointCloudTransformer is trained to extract point cloud features and train base classifiers to establish a base classifier set. Subsequently, a base classifier selection model is designed for ensemble learning, and optimization objectives of the model are diversity and average overall accuracy of base classifiers. To reduce ensemble scale, binary multi-objective beluga optimization algorithm based on improved beluga optimization algorithm is proposed to optimize the base classifier selection model and obtain an ensemble pruning scheme set. Finally, the majority voting is used to ensemble the classification results of each base classifier combination on the test set to obtain the optimal base classifier combination, and an ensemble learning model of point cloud classification based on multi-objective optimization ensemble pruning is obtained. Experimental results on the point cloud classification dataset demonstrate that the method in this paper achieves higher ensemble accuracy with a smaller ensemble scale and can accurately classify multi-class 3D point clouds.

Abstract:A flexible LC wireless humidity sensor based on GO/Mxene is investigated. Its purpose is to compensate for the limitations of a single material and meet the requirements for passive sensing and bending performance in applications. The principle of the sensor is analysed, and a flexible cross finger electrode antenna based on polyimide is designed. The resonant frequency of the antenna is 146 MHz, and the electric field distribution on the antenna is obtained using simulation software. GO/MXene is prepared. The surface morphology and microstructure of GO/MXene are characterized using scanning electron microscopy and energy spectrum analyze, and its structure and constituent elements of GO/MXene are verified. The humidity sensor is fabricated by placing the prepared GO/MXene as a humidity sensitive material at the strongest field strength of the antenna. The performance of the sensor is tested. The results showed that the sensor has high sensitivity. In the relative humidity（RH）range of 20~70% RH, the sensitivity of the sensor reached 90.51 kHz/% RH, and in the relative humidity range of 70~95% RH, the sensitivity reached 651.86 kHz/% RH. At the same time, the sensor has good stability and response time performance, which can monitor human respiration. The Sensor has considerable application prospects in fields such as health detection and robot skin.

Abstract:Aiming at the problem of difficult feature extraction of rolling bearing vibration signals in the strong noise background, based on the basic theory of sparse representation, a multi-regularized sparse reconstruction noise reduction model using multi-factor evolutionary algorithm is proposed. Firstly, the solution of the multi-regularization model is divided into three more objective subtasks, the l0-paradigm constrained optimization main task and the l1 and l1/2-paradigm regularization additional tasks, and the above tasks constitute three different objectives of the sparse reconstruction algorithm for multi-factor optimization; secondly, according to the priority of different regularization tasks in the evolutionary process, the golden segmentation search strategy is used to ensure that each community contains individuals with similar fitness, and the sparsity characteristics of the samples are guaranteed by the two-point crossover genetic operator; lastly, the thresholding iterative algorithm is applied to the local search process to accelerate the population convergence in the subtask. On this theoretical basis, the feasibility of this method is verified by simulation signal and actual bearing data respectively, and it is found that the signal to Interference plus noise ratio（SNR）of the reconstructed signal still reaches 5 dB under the interference of Gaussian noise of -10 dB. The experimental results show that this method can effectively extract the impact features under the background of strong noise, and provide reliable a priori knowledge for further fault diagnosis.

Abstract:Aiming at the low efficiency of fault diagnosis caused by the diversity and uncertainty of fault occurrence in the ZPW-2000A jointless track circuit, this paper proposes a deep convolutional neural network (DCNN)-based fault diagnosis method for jointless track circuit from the perspective of fault feature extraction and fault classification. Twelve rail circuit fault states are summarized through fault analysis, and the monitoring data of rail circuits under different fault states are standardized as inputs to the DCNN model. The model adopts the convolution-pooling structure to extract the key features of the rail circuit and filter out the redundant features. The back propagation neural network (BPNN) is used as the fully connected layer of the model and combined with the Softmax function for fault classification. The model structure is optimized by the k-fold cross-validation method to determine the best model. The experimental results show that the track circuit fault diagnosis model with a four-layer convolution-pooling layer structure achieves 98.48% in diagnosis accuracy, which is 6.06%, 6.06%, 3.33%, and 2.27% higher than the optimal models of long short-term memory (LSTM), deep feedforward network (DFN), bidirectional long and short-term memory (BiLSTM), and the combination of CNN-LSTM, respectively. Additionally, the training convergence speed is about 1 250, 4 250, 1 250, and 1 450 times faster, respectively, with less loss fluctuation during training. This study improves the efficiency of fault diagnosis of rail circuits and provides a new, effective method for the task of fault diagnosis of rail circuits.

Abstract:Tunnel maintenance is crucial for averting safety incidents triggered by tunnel structural issues. Effective maintenance depends on thorough and accurate detection of tunnel diseases. Traditional methods of tunnel inspection rely on manual detection, which are hindered by factors like inadequate tunnel illumination and limited inspection time, leading to inefficiencies and inaccuracies. To tackle these challenges in tunnel disease detection, a subway tunnel disease monitoring system has been devised, taking into account the uniformity of tunnel structures and the fixed nature of track lines. This system integrates laser scanning technology with photogrammetry, employing laser scanners to capture threedimensional point cloud data of tunnels and multi-line array cameras to obtain tunnel imagery. Laser trackers are utilized for calibrating the spatial coordinates of cameras and scanners to ensure a unified coordinate reference system. Adopting a distributed software architecture, the system develops data acquisition software for sensor communication and data storage. In addition, the system uses an external trigger mechanism to synchronize the sensor data acquisition rate with car speed. Finally, a data management platform, built upon the Cesium framework, is employed for the organization and manage of tunnel data. The system is applied to a tunnel to verify data accuracy. The experiment shows that the rail tunnel disease detection system can detect cracks with a width of 0.2 mm and misalignment with a size of 0.5 mm in the actual tunnel, and has the disease detection ability of subway tunnel. Finally, by applying the data collected in the experiment to tunnel crack detection, misalignment detection, deformation detection, etc., and locating the detected diseases, the practicability of the rail tunnel disease detection system is proved, and a reliable solution is provided for the subway tunnel disease detection.

Abstract:Planar electrical capacitance tomography (ECT) is suitable for non-destructive testing applications such as surface/subsurface defect detection of composite materials, or limited sensor installation. A new planar capacitive sensor with a new structure array is proposed aiming at the problems of poor sensitivity distribution uniformity and low imaging quality of the central region of the square array sensors used in conventional ECT systems. The sensor consists of a honeycomb array of seven sensing electrodes. The new structure array sensor is modeled and analyzed by COMSOLTM professional software, and the electrostatic field boundary conditions are set, and the electric field distribution, electrode capacitance value and sensitivity matrix of the sensor are obtained. By comparing with the 3×3 square array sensor of the same size, the key performance of the new structure array sensor, that is, the change value of the electrode capacitance and the uniformity of the sensitivity distribution, is evaluated. What’s more, the physical sensor is manufactured using printed circuit board technology combined with linear back-projection algorithm for image reconstruction, and the imaging performance of the two structures of sensors is verified. The results of numerical imaging experiments and test of reconstructed images show that the new structure array sensor is beneficial to the detection of capacitive sensing signals, improves the uniformity of sensor sensitivity distribution, as well as the imaging quality of the central area of the ECT system.

Abstract:In order to overcome the deficiencies of the traditional low ripple DC regulated power supply’s main circuit topology, such as complex circuit structure and large size, a new type of adjustable DC regulated power supply topology based on Buck-Boost inverter circuit is proposed to replace the two links of high frequency inverter and transformer step-up in the traditional structure with the Buck-Boost inverter link, which can be significantly simplified in terms of the circuit structure compared with the traditional structure. Compared with the conventional structure, this structure can be simplified significantly in terms of circuit structure. In order to realize the requirement of low ripple and high stability DC voltage output from this new type of adjustable DC regulated power supply, a composite control method based on proportional-vector proportional-integral is proposed for the Buck-Boost inverter circuit, that is, the capacitor voltage and inductor current in the Buck-Boost inverter circuit are taken as the state variables, and the inductor current is taken as the inner loop of the control, and the capacitor voltage is the outer loop of the control, and the two closed loops are decoupled by adopting proportional-vector proportional-integral composite control method. The decoupling control of the two state variables is realized by adopting the proportional-integral-vector proportional-integral composite control method for the two closed loops, and finally the effect is verified by simulation and experiment, and at the same time compared and analyzed with the traditional low ripple DC regulated power supply. The results show that the new adjustable DC regulated power supply topology based on Buck-Boost inverter circuit proposed in the paper and the control method proposed for the topology not only have small output DC voltage ripple, high steady-state accuracy and good dynamic performance, but also have the features of simple circuit structure and arbitrary adjustable output DC voltage, which are of good application value.

Abstract:In the scenario with dynamic varying loads, for the traditional VSG secondary frequency modulation strategy, the integrator continuously accumulates due to the continuous change of frequency deviation, which may lead to poor frequency adjustment effect and even frequency oscillation. For improvement, a secondary frequency modulation method of isolated microgrid, which based on the identification of load disturbance modes and adaptive adjustment of VSG, is proposed in this paper. Firstly, the issues in the traditional VSG secondary frequency regulation strategy of dynamic varying loads are analyzed. Then, the power characteristic data is used to discern the modes of load disturbances in real time. When the secondary frequency modulation is requisite for the microgrid, and the load disturbance is classified the mode of dynamic change, the parameters and virtual governor structure of VSG are changed by an adaptive adjustment. The adjustment is predicated on the deviations of frequency and bounded by the thresholds of secondary frequency regulation and active power change, thus enabling the frequency tracking in the microgrid. Further, a small signal model is constructed to evaluate the implications on system stability prompted by the incorporation of an integrator and the flexible adjustment of VSG parameters. Finally, the feasibility of the proposed frequency modulation strategy is verified by the results of simulation testing. When compared with the other existing strategies under the same conditions, the proposed strategy limits the range of secondary frequency fluctuation of microgrid within 0.017 3 Hz, which exhibits significant merits in decreasing the magnitude of frequency offsets.

Abstract:Cement concrete is a composite material employed in a multitude of engineering construction projects. The setting state of cement concrete exerts a profound influence on the advancement and security of such endeavours. It has been demonstrated that the setting time of cement concrete is contingent upon the water content. Therefore, a water content sensor based on the principle of capacitance detection is designed to measure the water content of cement concrete. According to the dielectric properties of cement concrete and the detection principle of capacitive sensors, and utilising the edge electric field to extend the measuring range, capacitive edge electric field sensors of parallel plate and cylindrical types were designed. A finite element simulation analysis was conducted using COMSOL software, and the cylindrical structure was selected based on a comparison of the penetration depth, sensitivity and signal strength of the two sensor structures. Subsequently, the performance of the cylindrical sensors with different parameter combinations was compared by orthogonal experiments, and the optimal parameter combinations of the sensors were determined as electrode spacing of 5 mm, electrode width of 50 mm, and electrode radius of 15 mm. This configuration enabled the sensors to reach a depth of penetration of 66.86 mm, a signal strength of 11.387 pF, and a sensitivity of 0.267. Finally, the developed sensor was utilised for the actual measurement of cement concrete with varying water contents, and the output capacitance value exhibited a satisfactory linear relationship with the water content, with a non-linear error of ±1.276% and a maximum relative error of 1.533% in comparison with that of the weighing method, which demonstrated a satisfactory measurement effect.

Abstract:In the era of information big data, the dependence degree on analog circuits is getting more severe, which results in the requirement for diagnosis accuracy of analog circuits grow with every passing day. However, analog circuits are very difficult to diagnosis, as a result, it is the bottleneck of electronic system diagnosis and maintenance. In this paper, an IHHO-SVM combining AVMD and PE and manifold learning is put forward. Firstly, adaptive variational modal decomposition AVMD is used to obtain IMF signals from observable signals of circuit under test, which could not only suppress noises disturbance, but also adaptively determine the number of IMF signals and improve the decomposition accuracy. Then IMF signals are computed with permutation entropy (PE) to construct fault features in order to fully reflect the local characteristic of IMF signal at different time span. Based on all these works, t-distributed stochastic neighbor embeddings(t-SNE) is combined to realize dimensionality reduction while remaining excellent discrimination power of fault features vectors, with the new feature vector formed at last. Finally, Harris Hawks algorithm is combined to optimize the support vector machine, which is called HHO-SVM here, for fault classification. The simulation tests show that the algorithm proposed in this paper has an excellent accuracy of 100%.

Abstract:The traditional point cloud registration algorithm achieves corresponding point pairing through features between two-point cloud datasets. This method requires point clouds to possess distinct features, yet it suffers from issues such as high computational complexity, long matching time, and low registration accuracy. Although the ICP algorithm is widely used, it is sensitive to initial values. To address these challenges, we propose a multi-view point cloud registration method based on pose parameter estimation (PPE-ICP). Firstly, by analyzing the distribution characteristics of errors, we demonstrate the existence of error minima. The A* search algorithm is then employed to locate these minima, reducing the impact of error propagation and providing improved initial values for subsequent parameter estimation. Secondly, we introduce total least squares estimation into point cloud registration, which, without relying on point cloud data, utilizes a limited number of reference points to obtain the transformation matrix from the target coordinate system to the Northeast-Up (ENU) coordinate system. This accomplishes point cloud pose correction, and in combination with the Iterative Closest Point (ICP) algorithm, achieves precise point cloud registration. Comparative experiments were conducted with five methods: FGR-ICP, FPFH-ICP, NDT-ICP, RANSAC-TrICP, and KSS-ICP, using both publicly available datasets and point clouds collected from a self-made experimental setup. When dealing with a point cloud dataset of 20 000 points, our PPE-ICP achieves registration in just 6.55 seconds, significantly reducing the time cost for point cloud registration with large datasets. In field applications, the maximum translation error is less than 0.03 m, and the rotation error is controlled within 0.07°. The experimental results demonstrate that PPE-ICP exhibits strong robustness against similar transformations, incomplete point clouds, and low repetition rates, achieving high registration efficiency and accuracy in multi-view point cloud registration.