基于二维经验模态分解的五步非等步在线三维检测

The edge feature extraction of image is one of the research fields of image processing theory and application. The traditional methods of image feature extraction are simple and proficient, but the accurate performance of the feature extracted is not even higher. As such, a new method for extracting image edge feature was proposed by using the two-dimensional Empirical Mode Decomposition (2-D EMD) and Riesz Transform. Firstly, the image was decomposed to multi-level Intrinsic) Mode Function (IMF) by 2-D EMD, and then the Riesz Transform with stronger local preserving ability was used for analyzing their local properties instead of the Hilbert Transform, by which the edge feature extraction with higher resolution was carried on. Finally, simulation shows that the proposed method is feasible and valid.

Traditional two-dimensional empirical mode decomposition (2D-EMD) algorithms generate multiple subband signals, each having the same size of the original signal. Thus, huge amounts of data to be stored may be generated. Moreover, the computational load is massive as the decomposition levels increase. This paper introduces a method to reduce the data generated (i.e. reduce storage requirement) by incorporating decimation into the 2D-EMD, while maintaining perfect reconstruction. Furthermore, it is well established that traditional EMDs can be thought as having the structure of a single dyadic filter bank. The proposed algorithm is applicable into any arbitrary tree structures including octave filter banks, 2D-EMD packets when applied to a full binary tree, etc. The methodology hereby presented builds on the algorithm introduced by the authors in [8].

Pattern recognition is a key issue in the field of textile computer aided analysis and design. Focusing on the non-stationarity of the fabric texture structure, a hybrid method combining two-dimensional mode decomposition with mean shift segmentation is proposed in this paper. Two-dimensional empirical mode decomposition is an adaptive image analysing technique, which uses intrinsic spatial scales instead of predefined basis functions to decompose a given image into a number of intrinsic modes. The first intrinsic mode preserves multiple patterns for an underlying fabric image, which is subsequently submitted to the mean shift clustering process in this paper. Simulation results have proved that the proposed mean shift algorithm in empirical mode domain is in accordance with human visual effect, and possesses the capability of clustering diverse patterns inherent in the textile in a fully unsupervised way.

Objective. The aim of this work was to study the cerebral protective effect of craniotomy hematoma removal under propofol anesthesia based on the artificial intelligence algorithm analysis of the changes in imaging characteristics of chronic subdural hematoma (CSDH) patients. Methods. A total of 60 CSDH patients who were treated in hospital were recruited and were randomly rolled into an experimental group and a control group, with 30 people in each group. Patients in the experimental group were treated with propofol anesthesia + craniotomy hematoma removal, while those in the control group were treated with conventional anesthesia + craniotomy hematoma removal. Head CT examinations were performed on the next day, one week, one month, three months, and six months after the operation. A two-dimensional empirical mode decomposition (BEMD) algorithm was used for edge detection and denoising of brain CT images of CSDH patients. Then, the amount of hematoma was calculated, and the Markwalder grading was performed to evaluate the neurological function. The number of recurrence and reoperation cases within six months of follow-up was collected. Results. 1. The quality of CT images was remarkably improved after processing with artificial intelligence algorithms. 2. The amount of hematoma in the experimental group was remarkably lower than that in the control group at January, March, and June after surgery (12.89 ± 2.12 VS 20.32 ± 16.41; 7.55 ± 4.13 VS 15.88 ± 14.22; 3.39 ± 3.79 VS 6.55 ± 3.69, P < 0.05 ). 3. The experimental group was remarkably better than the control group in Markwalder grading three months and six months after the operation ( P < 0.05 ). Conclusion. Artificial intelligence algorithm had good effect on the brain CT image processing of CSDH patients, and craniotomy hematoma removal under propofol anesthesia had an ideal brain protection effect.

An adaptive denoising method based on 2D empirical mode decomposition (EMD) is proposed to improve the infrared image quality of inspection UNMANNED aerial vehicles (UAVs) and provide guarantee for improving the inspection level of distribution network. Through rapid adaptive two-dimensional empirical mode decomposition algorithm decomposition of a UAV collected for distribution network inspection original noise of infrared image, get more than the IMF component and the residual amount, a forecast noise dominated the IMF component parameters such as threshold value and the variance of noise, using the estimated parameters in combination with the optimal linear interpolation algorithm of noise threshold function of leading the IMF component implementation of threshold denoising. After the denoised IMF component is obtained, the denoised infrared image is obtained after reconstruction with the signal-dominated IMF component, and the adaptive denoising of the infrared image of the distribution network inspection UAV is realized. The experimental results show that the method in this paper can maintain the details of the image, improve the definition, significantly improve the visual effect, the overall denoising performance is stable and feasible, and ensure the quality inspection UAV to collect infrared images.

In 3D shape measurement, because deformed fringes often contain low-frequency information degraded with random noise and background intensity information, a new fringe-projection profilometry is proposed based on 2D empirical mode decomposition (2D-EMD). The fringe pattern is first decomposed into numbers of intrinsic mode functions by 2D-EMD. Because the method has partial noise reduction, the background components can be removed to obtain the fundamental components needed to perform Hilbert transformation to retrieve the phase information. The 2D-EMD can effectively extract the modulation phase of a single direction fringe and an inclined fringe pattern because it is a full 2D analysis method and considers the relationship between adjacent lines of a fringe patterns. In addition, as the method does not add noise repeatedly, as does ensemble EMD, the data processing time is shortened. Computer simulations and experiments prove the feasibility of this method.

The distribution of heavy metals in agricultural soils is affected by various anthropogenic activities and environmental factors occurring at different spatial scales. This paper introduced the two-dimensional empirical mode decomposition (2D-EMD) to separate the spatial variability in soil heavy metals into different scales. Geostatistics and multivariate analysis were also utilized to quantify their spatial structure and identify their potential influencing factors. The study was conducted in an arable land in southeastern China where 260 surface soil samples were collected and measured for total contents of cadmium (Cdtotal), mercury (Hgtotal), and sulfur (TS); pH; and soil organic carbon content (SOC). The results showed that both Cdtotal and Hgtotal had high coefficients of variation. The overall variation in all five soil variables was separated into three intrinsic mode functions (IMFs) and spatial residues. All three IMFs had short-range spatial correlations (1-8km), while the spatial residues had moderate-large spatial ranges (13-39km). IMF1 of Cdtotal was strongly correlated with IMF1 of SOC and TS, which was consistent with the principal component analysis. This indicated that IMF1 of Cdtotal represented local variations which were influenced by agricultural activities. IMFs of Hgtotal showed clustered distributions in the study area, with IMF1 and IMF2 of Hgtotal correlated in one principal component, and IMF3 of Hgtotal and IMF3 of soil pH in another component. This indicated that all three IMFs of Hgtotal might be influenced by different industrial activities or different pathways of the same industrial activities. The residues of Cdtotal and Hgtotal, representing the regional trends, only accounted for 26% of the total variance, whereas IMF1 contributed about half of the total variance. It can be concluded that agricultural activities and industrial activities were the dominant contributors of the overall variations in Cdtotal and Hgtotal in the study area, respectively.

This study addresses the challenges of partial discharge ultra-high frequency pattern recognition under complex data sources. A systematic framework integrating data preprocessing and convolutional neural network techniques is proposed. First, a hierarchical broadcast synchronization algorithm balances synchronization precision and power consumption for sensor networks. Non-structured data such as images and waveforms are unified into phase-resolved pulse sequence matrices through grayscale conversion, binarization, and segmentation. Two-dimensional empirical mode decomposition and Kalman filtering suppress noise, enhancing data quality. Second, an improved LeNet-5 CNN model initializes convolutional layer parameters via unsupervised autoencoder pre-training, strengthening feature extraction. Validated on a 145kV real-type gas-insulated switchgear platform simulating five insulation defects, results demonstrate a minimum measurable discharge magnitude of 60 pC. Training and testing accuracies reach 93.54% and 91.26%, respectively, confirming high robustness for power equipment condition monitoring.

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSROri or PLSROri), partial least squares regression based on bi-dimensional intrinsic mode function (PLSRBIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMDPM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMDPM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

We introduce and demonstrate the bivariate two-dimensional empirical mode decomposition (bivariate 2D-EMD) for the decomposition of a turbulent instantaneous velocity field to separate spatial large-scale organized motion from random turbulent fluctuations. To validate this approach, it was applied to an experimental homogeneous and isotropic turbulent flow (HIT), perturbed by a synthetic Lamb–Oseen vortex that mimics the feature of organized motion. Through different test cases, the scale, the amplitude and the position of the synthetic vortex with respect to the turbulent velocity field were changed. By applying an energy criterion on the modes which resulted from the decomposition process, the initial HIT flow was separated from synthetic perturbation. It is important to point out that in this approach the decomposition as well as the distinction of different parts of the flow are free from any prior and objective assumptions and it requires just one instantaneous velocity field of the flow under analysis. The proposed methodology could be used for analyzing 2D velocity fields obtained from experimental measurement or CFD in different configurations (in-cylinder flow, channel flows, etc.).

Bidimensional empirical mode decomposition (BEMD) for extraction of gravity anomalies associated with gold mineralization in the Tongshi gold field, Western Shandong Uplifted Block, Eastern China

This paper presents a new method for BEMD. BEMD can decompose a source image into several two-dimensional intrinsic mode functions. During the image decomposition process, it is required to interpolate and draw the upper and lower envelopes. However, these interpolations and drawing the enveloping surface require a large amount of computing time and artificial screening. Thus, some scholars proposed the rapid realization of BEMD. Further, the window size being fixed during the decomposition process led to losses in the data-driven characteristics, adaptability and dimension consistency of the original BEMD. Therefore, this paper proposes a simple but effective means of keeping the original BEMD method features. The estimate reconstruction method is used to replace surface interpolation, and the variable neighborhood window method is adopted to replace the fixed neighborhood window method. In this article, an order filter is used to reconstruct the upper and lower envelopes, and then the filter size is obtained by using the fact that the image information itself is adaptively variable. Through an empirical analysis, this paper shows that this method can keep the original BEMD method’s rapid decomposition, data-driven characteristics, adaptivity and consistency of scale.