In the acoustic detection process of buried non-metallic pipelines, the echo signal is often interfered by a large amount of noise, which makes it extremely difficult to effectively extract useful signals. An denoising algorithm based on empirical mode decomposition (EMD) and wavelet thresholding was proposed. This method fully considered the nonlinear and non-stationary characteristics of the echo signal, making the denoising effect more significant. Its feasibility and effectiveness were verified through numerical simulation. When the input SNR (SNRin) is between -10 dB and 10 dB, the output SNR (SNRout) of the combined denoising algorithm increases by 12.0%-34.1% compared to the wavelet thresholding method and by 19.60%-56.8% compared to the EMD denoising method. Additionally, the RMSE of the combined denoising algorithm decreases by 18.1%-48.0% compared to the wavelet thresholding method and by 22.1%-48.8% compared to the EMD denoising method. These results indicated that this joint denoising algorithm could not only effectively reduce noise interference, but also significantly improve the positioning accuracy of acoustic detection. The research results could provide technical support for denoising the echo signals of buried non-metallic pipelines, which was conducive to improving the acoustic detection and positioning accuracy of underground non-metallic pipelines.
This study utilized finite element simulation and experimental methods to investigate the evolution of crack detection performance of a flexible differential fractal Koch eddy current probe at different excitation frequencies as the lift-off distance increases. As the lift-off distance increased, the distribution shape of induced eddy currents changed, leading to reduced similarity in the shape of the excitation coil and an expanded distribution range of induced eddy currents, ultimately resulting in weakened output signal strength. The experimental results showed that for excitation frequencies of 10 kHz, 20 kHz, 50 kHz, 100 kHz, 200 kHz, 500 kHz, and1 000 kHz, the maximum lift distances of the real part of the output signal when cracks were detected were 5.0 mm, 7.0 mm, 8.0 mm, 8.0 mm, 8.0 mm, 6.5 mm, and 4.0 mm, respectively. The imaginary parts were 6.5 mm, 6.5 mm, 7.5 mm, 5.5 mm, 8.0 mm, 6.5 mm, and 6.5 mm, respectively.
In visual measurement, high-precision camera calibration often employs circular targets. To address issues in mainstream methods, such as the eccentricity error of the circle from using the circle’s center for calibration, overfitting or local minimum from full-parameter optimization, and calibration errors due to neglecting the center of distortion, a stepwise camera calibration method incorporating compensation for eccentricity error was proposed to enhance monocular camera calibration precision. Initially, the multi-image distortion correction method calculated the common center of distortion and coefficients, improving precision, stability, and efficiency compared to single-image distortion correction methods. Subsequently, the projection point of the circle’s center was compared with the center of the contour’s projection to iteratively correct the eccentricity error, leading to more precise and stable calibration. Finally, nonlinear optimization refined the calibration parameters to minimize reprojection error and boosts precision. These processes achieved stepwise camera calibration, which enhanced robustness. In addition, the module comparison experiment showed that both the eccentricity error compensation and the camera parameter optimization could improve the calibration precision, but the latter had a greater impact. The combined use of the two methods further improved the precision and stability. Simulations and experiments confirmed that the proposed method achieved high precision, stability, and robustness, suitable for high-precision visual measurements.
Aiming at the problems such as low reconstruction efficiency, fuzzy texture details, and difficult convergence of reconstruction network face image super-resolution reconstruction algorithms, a new super-resolution reconstruction algorithm with residual concern was proposed. Firstly, to solve the influence of redundant and invalid information about the face image super-resolution reconstruction network, an attention mechanism was introduced into the feature extraction module of the network, which improved the feature utilization rate of the overall network. Secondly, to alleviate the problem of gradient disappearance, the adaptive residual was introduced into the network to make the network model easier to converge during training, and features were supplemented according to the needs during training. The experimental results showed that the proposed algorithm had better reconstruction performance, more facial details, and clearer texture in the reconstructed face image than the comparison algorithm. In objective evaluation, the proposed algorithm's peak signal-to-noise ratio and structural similarity were also better than other algorithms.
The existing multi-view subspace clustering algorithms based on tensor singular value decomposition (t-SVD) predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples, while neglecting the correlation among the samples within different views. Moreover, the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function. Treating different singular values equally may result in suboptimal tensor representation. A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant (HRMSC-DTL) was proposed. The algorithm used subspace learning in each view to learn a specific set of affinity matrices, and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness. It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space. Furthermore, it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor. At the same time, an alternating direction of multipliers method (ADMM) was also designed to solve non-convex optimization model. Experimental evaluations on seven widely used datasets, along with comparisons to several state-of-the-art algorithms, demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.
In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging, a new algorithm based on multi-scale morphological reconstruction with adaptive transmittance and atmospheric light correction was proposed. Firstly, the algorithm used the open operation under morphological reconstruction to replace the minimum filter operation in the dark channel, and used the morphological edge to set the scale of the open operation structure elements, and constructed a multi-scale open operation fusion dark channel. After morphological noise reduction, the exact initial transmittance was obtained. According to the relationship between brightness and saturation difference and transmittance, an adaptive transmittance correction model was fitted with Gaussian function to correct the initial transmittance of the sky fog map. Then the local atmospheric light was improved according to the image brightness information and morphology closure operation. Finally, the proposed algorithm was combined with the atmospheric scattering model to obtain an accurate fog free image. The experimental results showed that the proposed algorithm was suitable for fog image restoration under various scenes, the restoration effect was good, and the brightness was suitable.
In order to solve the problems of artifacts and noise in low-dose computed tomography (CT) images in clinical medical diagnosis, an improved image denoising algorithm under the architecture of generative adversarial network (GAN) was proposed. First, a noise model based on style GAN2 was constructed to estimate the real noise distribution, and the noise information similar to the real noise distribution was generated as the experimental noise data set. Then, a network model with encoder-decoder architecture as the core based on GAN idea was constructed, and the network model was trained with the generated noise data set until it reached the optimal value. Finally, the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network. The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training, removed image noise and artifacts, and reconstructed image with rich texture and realistic visual effect.
In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details, a super resolution (SR) method based on generative adduction network (GAN) was proposed. This method reconstructed the detail texture of mural image better. Firstly, in view of the insufficient utilization of shallow image features, information distillation blocks (IDB) were introduced to extract shallow image features and enhance the output results of the network behind. Secondly, residual dense blocks with residual scaling and feature fusion (RRDB-Fs) were used to extract deep image features, which removed the BN layer in the residual block that affected the quality of image generation, and improved the training speed of the network. Furthermore, local feature fusion and global feature fusion were applied in the generation network, and the features of different levels were merged together adaptively, so that the reconstructed image contained rich details. Finally, in calculating the perceptual loss, the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation, while avoiding artificial interference. The experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms, with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity, and the proposed method had better visual effects.
In order to solve the problem of path planning of tower cranes, an improved ant colony algorithm was proposed. Firstly, the tower crane was simplified into a three-degree-of-freedom mechanical arm, and the D-H motion model was established to solve the forward and inverse kinematic equations. Secondly, the traditional ant colony algorithm was improved. The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function. Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability. In addition, the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points. Finally, according to the position of the hook, the kinematic inversion of the tower crane was carried out, and the variables of each joint were obtained. More specifically, compared with the traditional ant colony algorithm, the simulation results showed that improved ant colony algorithm converged faster, shortened the optimal path length, and optimized the path quality in the simple and complex environment.
Aiming at the industry cyber-physical system (ICPS) where Denial-of-Service (DoS) attacks and actuator failure coexist, the integrated security control problem of ICPS under multi-objective constraints was studied. First, from the perspective of the defender, according to the differential impact of the system under DoS attacks of different energies, the DoS attacks energy grading detection standard was formulated, and the ICPS comprehensive security control framework was constructed. Secondly, a security transmission strategy based on event triggering was designed. Under the DoS attack energy classification detection mechanism, for large-energy attacks, the method based on time series analysis was considered to predict and compensate for lost data. Therefore, on the basis of passive and elastic response to small energy attacks, the active defense capability against DoS attacks was increased. Then by introducing the cone-complement linearization algorithm, the calculation methods of the state and fault estimation observer and the integrated safety controller were deduced, the goal of DoS attack active and passive hybrid intrusion tolerance and actuator failure active fault tolerance were realized. Finally, a simulation example of a four-capacity water tank system was given to verify the validity of the obtained conclusions.
The DC microgrid has the advantages of high energy conversion efficiency, high energy transmission density, no reactive power flow, and grid-connected synchronization. It is an essential component of the future intelligent power distribution system. Constant power load (CPL) will degrade the stability of the DC microgrid and cause system voltage oscillation due to its negative resistance characteristics. As a result, the stability of DC microgrids with CPL has become a problem. At present, the research on the stability of DC microgrid is mainly focused on unipolar DC microgrid, while the research on bipolar DC microgrid lacks systematic discussion. The stability of DC microgrid using CPL was studied first, and then the current stability criteria of DC microgrid were summarized, and its research trend was analyzed. On this basis, aiming at the stability problem caused by CPL, the existing control methods were summarized from the perspective of source converter output impedance and load converter input impedance, and the current control methods were outlined as active and passive control methods. Lastly, the research path of bipolar DC microgrid stability with CPL was prospected.
To reduce thrust ripple and cost and improve the average thrust of permanent magnet linear motors, a modular dual-field modulation permanent magnet linear motor was studied, and the parameters were optimized. First, sensitive parameters were selected using the Taguchi method, and then the optimal variables were sampled using the optimal Latin hypercube experimental design method and an ensemble of surrogates model of optimization objectives, and its accuracy was verified. Next, a multi-objective particle swarm optimization algorithm was used to optimize the purpose of “maximum average thrust and minimum thrust ripple”, and the Pareto front of average thrust and thrust ripple was obtained. Finite element analysis showed that the optimized modular dual flux-modulation permanent magnet linear motor (MDFMPMLM) had a 29.5% reduction in thrust ripple and a 5% increase in average thrust compared to the original motor. This study provided an effective method for improving the performance of permanent magnet linear motors.
In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions, an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images. Firstly, VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal. On this basis, the current signal was transformed into a two-dimensional graph suitable for convolutional neural network, and the data set was classified by convolutional neural network and softmax classifier. Secondly, the infrared image was segmented and the fault features were extracted, so as to calculate the similarity with the infrared image of the fault bearing in the library, and further the sigmod classifier was used to classify the data. Finally, a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight, and the motor bearing fault diagnosis result was obtained. Through experimental verification, the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation, and the accuracy of fault diagnosis can reach 98.85%.
The contact network dropper works in a harsh environment, and suffers from the impact effect of pantographs during running of trains, which may lead to faults such as slack and broken of the dropper wire and broken of the current-carrying ring. Due to the low intelligence and poor accuracy of the dropper fault detection network, an improved fully convolutional one-stage (FCOS) object detection network was proposed to improve the detection capability of the dropper condition. Firstly, by adjusting the parameter α in the network focus loss function, the problem of positive and negative sample imbalance in the network training process was eliminated. Secondly, the generalized intersection over union (GIoU) calculation was introduced to enhance the network’s ability to recognize the relative spatial positions of the prediction box and the bounding box during the regression calculation. Finally, the improved network was used to detect the status of dropper pictures. The detection speed was 150 sheets per millisecond, and the MAP of different status detection was 0.951 2. Through the simulation comparison with other object detection networks, it was proved that the improved FCOS network had advantages in both detection time and accuracy, and could identify the state of dropper accurately.