Maximum Correlation Criterion Research Articles

3D suitability evaluation of urban underground space using a variable weight method and considering ground restrictions

The evaluation of urban underground space (UUS) suitability involves multiple indicators. Assigning weight to these indicators is crucial for accurate assessment. This paper presents a method for spatially variable weight assignment of indicators using the order relation analysis method (G1-method), the entropy weight method, an improved grey relational analysis (GRA) and a set of spatial weight adjustment coefficients. First, the subjective and objective weights of indicators for engineering geological and hydrogeological conditions were determined by the G1-method and entropy weight method, respectively, and their combined weights were then obtained using the principle of minimum discriminatory information. This study highlighted the impact of surface restrictions, such as buildings, on UUS, and the degree of the influence of these buildings gradually decreased from very significant to zero with increasing depth, which resulted in changes in weights of indicators with depth. To address this issue, a coefficient was defined as the standardized value of the ratio of additional stress applied by restrictions to the self-weight stress of soil at the same depth to modify the combined weights so that all weights of indicators could vary in space. Finally, an improved GRA was used to determine the suitability level of each evaluation cell using the maximum correlation criterion. This method was applied to the 3D suitability evaluation of UUS in Sanlong Bay, Foshan City, Guangdong Province, China, including 16 evaluation indexes. This study comprehensively considered the influence of multiple factors, thereby providing reference values for evaluating the suitability of UUS in big cities.

A robust spatio‐temporal prediction approach for wind power generation based on spectral temporal graph neural network

At present, the penetration of wind power generation is increasing remarkably worldwide, and the accurate wind power forecasting (WPF) is essential to ensure the reliability and economy of the power system. Most of the current work of WPF only capture temporal correlation in the time domain but ignore the spatial correlation. In this study, a spectral time graph neural network based on the maximum correlation criterion (MCC-Stem-GNN) is proposed to improve the accuracy of WPF for multiple sites and horizons. The self-attentive mechanism in the MCC-Stem-GNN automatically learns the correlations between the multivariate sequences. Besides, this model combines the Graph Fourier Transform (GFT) to model spatial correlation and the Discrete Fourier Transform (DFT) to model temporal correlation. The effectiveness of the proposed robust deep learning framework is verified on the simulated wind energy dataset over 16 locations in Ohio, US through considering different sample contamination types and levels, comprehensive case study is carried out to show the superiority of the MCC-Stem-GNN over the benchmarks.

Supervised sparse preserving projections model based on distributed principal component analysis for chemical process monitoring

AbstractFor the monitoring of large‐scale chemical processes, the distributed method is often used to extract local feature information and model the extracted local feature information to obtain a process monitoring model. But the distributed process monitoring model often contains more process variables, which makes the local information of the process data flooded. To make up for the insufficient extraction of local information in traditional distributed process monitoring, supervised sparse preserving projections model based on distributed principal component analysis (DPCA‐SSPP) is proposed in this paper. First, the process data are decomposed by the PCA algorithm, and the principal component space and residual space are obtained. Second, the variables of each sub‐block are selected according to the maximum correlation criterion, and the SSPP process monitoring model is established for each sub‐block. Finally, the monitoring results of each sub‐block are combined together to form a global monitoring result through the Bayesian information fusion strategy. The proposed scheme can be proved to be effective through the simulation on a nonlinear numerical example and the Tennessee Eastman benchmark (TE) process.

ГЕОДИНАМІКА

The differential and source terms locations of a series of small (1.0<ML<2.5) similar (recurrent) earthquakes that occurred during 2013-2015 near the village of Trosnyk in the south of Transcarpathians were determined. Adaptive filtering was proposed to reduce the effect of correlated noise in records with very low signal-to-noise ratio and to improve the reliability of differential arrivals. The maximum correlation criterion was modified to include the minimum departure from the calculated arrival times. Analysis of the intervals between phase arrivals at pairs of stations was proposed to further reduce the number of problematic arrivals. The sensitivity of the final solution to the network configuration was assessed using the jack-knife principle, when the coordinates are calculated, each time removing one station from the full set. The focal mechanism common to all earthquakes in the series was defined using the polarities of P-wave arrivals at 16 stations. Based on the results of the 3D interpretation of the differential hypocenters, the nodal plane with a strike of 150° was identified as the rupture plane, and the mechanism itself was classified as left-lateral slip with a component of thrust. The epicenter of the strongest earthquake was located almost exactly on the fault of the pre-Neogene basement with a strike parallel to the Carpathian arc, almost the same as the strike of the rupture plane. The axis of compression in the focal mechanism is directed to the east, which is fully consistent with the northeast direction of the general regional field.

A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

IMAGE THRESHOLDING USING FUZZY CORRELATION CRITERION AND HARMONY SEARCH ALGORITHM

This paper reports a novel image thresholding method based on fuzzy set theory and maximum correlation criterion using harmony search algorithm. In this study, the maximum fuzzy correlation criterion is defined using Z- and S- fuzzy member function on image gray level histogram. Then fuzzy correlation criterion image segmentation based on harmony search algorithm is implemented. The experimental studies were conducted on a variety of images by testing the proposed method and some classical thresholding methods. The experimental results demonstrate that the proposed method can select the threshold automatically and effectively. Compared with the exhaustive search method, the harmony search algorithm can give high degree of accuracy while needing less search time and has good search stability in the segmentation experiments.

METHOD FOR RECOGNITION OF INDEPENDENT SOURCES OF COMPLEX SYSTEM UNDER STRONG INTERFERENCES

In order to identify independent sources from a complex system under strong noisy environment, a method for sources recognition based on a compound PCA-ICA network is proposed, by use of such features as principal components projection with PCA and redundancy reduction with ICA. By combining PCA and ICA, their advantages are brought into play well, and every independent sources embedded into multi-channel observation by sensors are separated. By use of an adaptive revision based on fast fourier transform (FFT) and maximum correlation criterion (MCC), blind indeterminacy of the estimated sources by ICA is eliminated effectively, and the sources and their mixing matrix are restored correctly. Thus, different independent sources are recognized. The result of simulation imply that the new method is not only effective, but also of great potential in sources recognition of complex system.

Models of Planetary Nebulae based on deprojection

Density distributions of four planetary nebulae have been calculated in cylindrical coordinates (r,z,ϕ) using the iterative deprojection algorithm by Bremer (1995). Narrowband filter CCD images of NGC 2346, NGC 6563, NGC 6826 and NGC 7009 in the Hα line (6562.8 å) were corrected for cosmic ray hits, background/foreground stars were removed, and the position angles PA of their symmetry axes were determined with a maximum correlation criterion. For NGC 6563, the axis was shifted manually to the south-east to put it on the central ridge (the PA was conserved). For deprojections in cylindrical coordinates, an inclination angle i (angle between the object axis and the line of sight) must be specified. Literature values based on optical spectra were available for NGC 2346 (Walsh 1983) and NGC 7009 (Preston and Willson 1990), for NGC 6563 and NGC 6826 lower limits were derived from deprojections.

A new criterion for automatic multilevel thresholding

A new criterion for multilevel thresholding is proposed. The criterion is based on the consideration of two factors. The first one is the discrepancy between the thresholded and original images and the second one is the number of bits required to represent the thresholded image. Based on a new maximum correlation criterion for bilevel thresholding, the discrepancy is defined and then a cost function that takes both factors into account is proposed for multilevel thresholding. By minimizing the cost function, the classification number that the gray-levels should be classified and the threshold values can be determined automatically. In addition, the cost function is proven to possess a unique minimum under very mild conditions. Computational analyses indicate that the number of required mathematical operations in the implementation of our algorithm is much less than that of maximum entropy criterion. Finally, simulation results are included to demonstrate their effectiveness.