Fractal characteristics of rocks and mesoscopic fractures at different loading rates

Multi-sensor data fusion method based on divergence measure and probability transformation belief factor

To realize aerial target recognition in a complex environment, we propose a multi-source Takagi–Sugeno (T-S) intuitionistic fuzzy rules method (MTS-IFRM). In the proposed method, to improve the robustness of the training process of the model, the features of the aerial targets are classified as the input results of the corresponding T-S target recognition model. The intuitionistic fuzzy approach and ridge regression method are used in the consequent identification, which constructs a regression model. To train the premise parameter and reduce the influence of data noise, novel intuitionistic fuzzy C-regression clustering based on dynamic optimization is proposed. Moreover, a modified adaptive weight algorithm is presented to obtain the final outputs, which improves the classification accuracy of the corresponding model. Finally, the experimental results show that the proposed method can effectively recognize the typical aerial targets in error-free and error-prone environments, and that its performance is better than other methods proposed for aerial target recognition.

Multi-Source T-S Target Recognition via an Intuitionistic Fuzzy Method

The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in D–S evidence theory is an open issue. This paper proposes a new negation of BPA, logarithmic negation. It solves the shortcoming of Yin’s negation that maximal entropy cannot be obtained when there are only two focal elements in the BPA. At the same time, the logarithmic negation of BPA inherits the good properties of the negation of probability, such as order reversal, involution, convergence, degeneration, and maximal entropy. Logarithmic negation degenerates into Gao’s negation when the values of the elements all approach 0. In addition, the data fusion method based on logarithmic negation has a higher belief value of the correct target in target recognition application.

/pdf/logarithmic-negation-of-basic-probability-assignment-and-its-3c243f6n.pdf

Logarithmic Negation of Basic Probability Assignment and Its Application in Target Recognition

Extending the service life of asphalt pavements was an effective way to be a green environment and conserve resources. Gradation significantly affects the service life of asphalt mixtures. This paper aimed to investigate the influence of gradation on micro properties and the engineering performance of the mixture. Four representative gradations in the gradation range of AC-16 were selected. The fractal dimensional was used to characterize the quality distribution properties of the aggregates. The sensitivity of the Marshall index of the different graded mixtures to the asphalt aggregate ratio was investigated. CT scan tests were carried out to study the distribution pattern of microscopic properties. The high-temperature performance, low-temperature performance, water stability performance, indirect tensile strength, and fatigue performance of the four mixtures were also tested. The findings suggest that the effective asphalt aggregate ratio range differed for different gradation mixtures. The optimum asphalt aggregate ratio should be determined by settings the appropriate asphalt aggregate ratio step size. The fractal model can well characterize the quality distribution of aggregates. The larger the fractal dimension value, the smaller the air voids, and the higher the number of contact points of the mixture, high-temperature performance, strength, and fatigue performance. The low-temperature performance showed the opposite pattern. The water stability performance was not linearly related to the fractal dimension value. The performance of the mixture can be preliminarily predicted by analyzing the fractal dimension of the gradation. 

Influence of gradation on volume characteristics and engineering performance of asphalt mixture

Field Investigations on Rock Fragmentation Under Deep Water Through Fractal Theory

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Field Investigations on Rock Fragmentation Under Deep Water Through Fractal Theory