In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improvedartificialfish sw...
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In view of the classification of corrosion defects of well controlled manifold pipelines, an ultrasonic defect recognition method based on the combination of support vector machine(SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed. Firstly, perform wavelet packet decomposition on the ultrasonic defect signal waveform to obtain the characteristic vector of characterizes the defect type;Then establish the support vector machine defect classification model, and use the improved artificial fish swarm algorithm to optimize the support vector machine parameters. Finally, a software and hardware experimental platform for the classification of pipeline corrosion defects of the well control manifold is built to carry out software simulation and experimental analysis. The experimental results show that the recognition rate of the defect classification model based on improvedartificialfishswarm optimization support vector machine parameters is 94.67% for ultrasonic defect signals at different depths.
The number of fragments and the variety of primitive cultural relics unearthed in archaeology, especially the mixed fragments of several dynasties unearthed in Qinglong town, Shanghai, pose a great challenge to the ma...
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The number of fragments and the variety of primitive cultural relics unearthed in archaeology, especially the mixed fragments of several dynasties unearthed in Qinglong town, Shanghai, pose a great challenge to the manual splicing. The traditional manual comparison method is easy to cause the second damage to the cultural relics. In this paper, the edge feature is extracted based on removing the noise of point cloud, a bilateral filtering point cloud denoising algorithm based on salient features is proposed. By changing the step size and field of view, the improved artificial fish swarm algorithm is used to get the matching strategy, and the point cloud is used to reconstruct 3D model by the Dual Quaternion Transformation method. The pairing of fragments and virtual reconstruction can effectively avoid the secondary damage of cultural relic fragments. It provides a feasible artificial intelligence solution for the protection and restoration of similar archaeological excavations.
This research develops a novel hybrid model for multi-point deformation monitoring of super-high arch dams in operating conditions. The weighted distances are established to characterize deformation similarity degree,...
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This research develops a novel hybrid model for multi-point deformation monitoring of super-high arch dams in operating conditions. The weighted distances are established to characterize deformation similarity degree, and then observation point groups with similar deformation regularities are produced using the bottom-up hierarchical clustering. The hybrid hydrostatic seasonal time (HHST) panel model is proposed, and the artificialfishswarm (AFS) algorithm is improved to optimize the undetermined parameters. The confidence ellipsoid criteria are established by applying multivariate statistic and principle of small probability event. According to the example analysis, the HHST panel model achieves a better fitting performance than the HST panel model;the applicability of HHST panel model is wider than that of HHST model;the optimization performance of the improved AFS is superior to that of the conventional AFS;confidence ellipsoid compared with confidence interval possesses a stricter identification for abnormal deformations and a clearer physical significance.
Selection of the kernel function by the support vector regression (SVR), for the purposes of load forecasting, is affected by the power load characteristics. The non-ideal SVR with a kernel function has low forecastin...
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Selection of the kernel function by the support vector regression (SVR), for the purposes of load forecasting, is affected by the power load characteristics. The non-ideal SVR with a kernel function has low forecasting accuracy and poor generalization ability. A novel load forecasting method combining SVR and stacking is proposed in this paper. Base models are constructed based on SVRs with different kernel functions, then multiple base models are merged to obtain a base model layer via stacking algorithm. Finally, an SVR is connected as the meta-model layer. The stacking fusion model is composed of base model layer and meta-model layer. This model is trained with k-fold cross validation to enhance its generalization ability. An improved artificial fish swarm algorithm is employed to optimize the parameters to improve the forecasting accuracy of the stacking fusion model;speed variables are introduced to replace step lengths and improve the convergence speed and search ability. The forecasting accuracy and generalization ability of the proposed method are verified by comparative analysis.
This paper presents an adaptive stochastic resonance method based on the improved artificial fish swarm algorithm. By this method, we can enhance the weak characteristic signal which is submerged in a heavy noise. We ...
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This paper presents an adaptive stochastic resonance method based on the improved artificial fish swarm algorithm. By this method, we can enhance the weak characteristic signal which is submerged in a heavy noise. We can also adaptively lead the stochastic resonance to be optimized to the greatest extent. The effectiveness of the proposed method is verified by both numerical simulation and lab experimental vibration signals including normal, a chipped tooth and a missing tooth of planetary gearboxes under the loaded condition. Both theoretical and experimental results show that this method can effectively extract weak characteristics in a heavy noise. In the experiment, each weak fault feature is extracted successfully from the fault planetary gear. When compared with the ensemble empirical mode decomposition (EEMD) method, the method proposed in this paper has been found to give remarkable performance.
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