Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

Pipeline leakage is the main reason that affects normal operation of the pipeline. In this paper, a feature recognition method for pipeline acoustic signals based on vocational mode decomposition (VMD) and exponential entropy (EE) is investigated, which could extract the characteristics of pipeline signals and further accurately identify the pipeline acoustic signals under different working conditions. First, the VMD is used to decompose the collected acoustic signals into a number of mode components, during which process the optimal mode number (i.e., K-value) is determined by combining local characteristic scale decomposition (LCD) and correlation analysis methods. Then, the characteristic content of each mode component is analyzed with the help of the determined correlation coefficient (CC) threshold. If the correlation coefficient of a mode component is greater than the threshold, then the mode component is selected as the feature component. Subsequently, the EE values of the selected feature components are calculated to form the feature vectors corresponding to different kinds of pipeline signals. Finally, the feature vectors are input into support vector machine (SVM) to classify and recognize the different pipeline states. The experimental results demonstrate that the proposed method can identify the pipeline signals under different working conditions, and the recognition accuracy is up to [Formula: see text]. By analyzing and comparing with methods of EE-SVM, original data-SVM, VMD-singular spectrum entropy (SSE) and VMD-information entropy (IE), it is further verified that the proposed method is feasible and superior to the methods.

A new measure of indeterminacy for uncertain variables with application to portfolio selection

Entropy is a measure for characterizing indeterminacy of a random variable or an uncertain variable with respect to probability theory and uncertainty theory, respectively. In order to characterize indeterminacy of uncertain variables, the concept of exponential entropy for uncertain variables is proposed. For computing the exponential entropy for uncertain variables, a formula is derived via inverse uncertainty distribution. As an application of exponential entropy, portfolio selection problems for uncertain returns are optimized via exponential entropy-mean models. For better understanding, several examples are provided.

Conflictividad política, pandemia de COVID-19 y nuevos paradigmas

Everything indicates that the year 2020 will end up signified by high levels of political conflict that set up an international scenario of exponential entropy with testimonies for order and global stability. Add to that the economic and social havoc generated by the pandemic of the new coronavirus, the outlook for 2021 is not encouraging at all. However, the question for social scientists and philosophers in general is these events to shape new political, economic and sociocultural paradigms in the world? The objective of reflections lies in presenting in the special issue, Vol. 38, II part, and at the same time analyzing the scenarios of political conflict in the real world in the context of COVID-19. The conclusion highlights the fact that, despite the expectations of social justice, sorrow and human rights of much of human societies, the theoretical and epistemological elements necessary to shape new or at least models of political, economic and social organization beyond social and liberal trajinisms are not apparent from what academic elites see.

AN INTEGRATED MULTI-CRITERIA DECISION-MAKING FRAMEWORK FOR SUSTAINABLE SUPPLIER SELECTION UNDER PICTURE FUZZY ENVIRONMENT

Sustainable supplier selection (SSS) is an important part of sustainable supply chain management (SSCM). In this paper, an integrated multi-criteria decision-making (MCDM) framework, based on the picture fuzzy exponential entropy, and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method, is proposed to manage SSS problems. Firstly, the evaluation criteria of SSS, including economic, environmental and social, is established. This can be evaluated in the form of the actual data or linguistic terms provided by suppliers and experts respectively in an actual decision-making process. Then, according to the translated scales, all the evaluation information can be converted into picture fuzzy numbers (PFNs). Secondly, the picture fuzzy exponential entropy is defined. Moreover, based on the entropy’s minimization principle, the defined picture fuzzy exponential entropy is used to determine the weight of the SSS’s criteria. Thirdly, the extended VIKOR method, which combines the grey correlation coefficient, is utilized to select a suitable supplier. This method avoids the shortcomings of the traditional VIKOR method in data mining and solves the conflict between SSS criteria. Finally, the feasibility and effectiveness of the proposed integrated decision framework are verified by an experiment, as well as a sensitivity analysis and comparative analysis.

Application of Exponential Jensen Picture Fuzzy Divergence Measure in Multi-Criteria Group Decision Making

A divergence measure plays a crucial part in discriminating two probability distributions and drawing inferences constructed on such discrimination. The intention of this study is to propose such a divergence measure based on Jensen inequality and exponential entropy in the settings of probability theory. Further, the idea has been generalized to fuzzy sets to familiarize a novel picture fuzzy divergence measure. Besides proposing the validity, some of its key properties are also deliberated. Finally, two illustrative examples are solved based on the proposed picture fuzzy divergence measure which shows the expediency and effectiveness of the proposed approach.

An Exponential Jensen Fuzzy Divergence Measure with Applications in Multiple Attribute Decision-Making

A divergence measure plays an important role in discriminating two probability distributions and drawing inferences based on such discrimination. This communication introduces one such divergence measure based on Jensen inequality and exponential entropy introduced by Pal and Pal in the settings of probability theory. Further, the idea has been generalized to fuzzy sets to introduce a new fuzzy divergence measure. Besides establishing the validity, some of its major properties are also studied. At last, the application of proposed fuzzy divergence measure is given in strategic decision-making.