Relative Entropy Model Research Articles

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

The power sector may reduce carbon emissions and reach carbon neutrality by accelerating the energy transition and lowering its reliance on fossil fuels. However, there are limitations on the new power system’s ability to operate safely and steadily due to the randomness, volatility, and intermittent nature of renewable energy supply. The key to solving this issue is to harness the flexible resources that energy storage systems (ESSs) represent; however, ESSs have more than a value for providing system flexibility. Thus, this study suggested a flexible, technical, economic, and environmental value index system based on multi-criteria decision-making models for evaluating the multi-dimensional value of ESSs. First, the objective and subjective weights are obtained using the CRITIC model and best–worst method; then, the weights are combined using the minimum relative entropy model. The complete values of five typical ESSs are then evaluated using the TOPSIS model. Three scenarios that reflect the traits of the new power system are then created and analyzed. The outcome demonstrates the following: (1) as the new power system continues to grow, the value of ESSs’ flexibility has become ever more crucial; (2) as the need for system flexibility increases, the value of electrochemical ESSs gradually manifests.

A Large Group Emergency Decision Making Method Considering Scenarios and Unknown Attribute Weights

Once an emergency event (EE) happens, emergency decision-making (EDM) plays a key role in mitigating the loss. EDM is a complex problem. Compared with conventional decision-making problems, more experts participate in decision-making. It usually has the feature of large group emergency decision-making (LGEDM). This paper proposes a large group emergency decision-making method based on Bayesian theory, relative entropy, and Euclidean distance, which is used for large group emergency decision-making with uncertain probabilities of occurrence, unknown attribute weights, and expert weights. In order to improve the accuracy of decision-making, Bayesian method is introduced into the calculation of scenario probability in the process of LGEDM. In the decision-making process, the experts’ risk preference is considered. The experts’ decision preference information is a symmetric and uniformly distributed interval value. The perceived utility values of the experts are obtained by introducing prospect theory. Euclidean distance is used to measure the contributions of experts to aggregation similarity, and different weights are given to experts according to their contributions. A relative entropy model with completely unknown weight information constraints is established to obtain attribute weights, which takes into account the differences of different alternatives under the same attribute and the differences between alternatives and the ideal solution. An example of nuclear power emergency decision-making illustrates the effectiveness of this method.

Research on the Robustness of the Chinese Input-Output Network Based on Relative Entropy Theory.

The input–output (IO) network is the quantitative description of an IO-based economy in which nodes represent industries and edges connecting nodes represent the economic connection between industries. Robustness refers to the ability of tolerating perturbations that might affect the system’s functional body. There is both practical and theoretical significance to explore the robustness of the IO network for economic development. In this paper, we probe the robustness of the Chinese IO network based on the relative entropy of the probability distribution of network parameters (node degree, strongest path betweenness, downstream closeness and upstream closeness) under random node or edge failure and intentional node or edge attack. It is found that the Chinese IO network shows relatively weak robustness when it is under intentional attack, but relatively strong robustness when it is under random failure. Our experiment also verifies the applicability and effectiveness of the relative entropy model in measuring the robustness of the IO network.

Analysis of Epilepsy Risk Levels from EEG Features through Support Vector Machine and Minimum Relative Entropy Model

The goal of this research is to attain the epilepsy risk levels from Extracted features from EEG signals. This paper analyses a two-levelmethod of classification. At level one fuzzy method were endorsed to reach a multiple sub optimal solutions. Then the SVM and MRE classifiers were used to reach the singleton results. The Classifiers were compared by means of quality values. The SVM classifier accomplisher higher Quality Value of 23.02 when compared with other classifiers.

ANALYTICAL PATH-INTEGRAL PRICING OF DETERMINISTIC MOVING-BARRIER OPTIONS UNDER NON-GAUSSIAN DISTRIBUTIONS

In this work, we present an analytical model, based on the path-integral formalism of statistical mechanics, for pricing options using first-passage time problems involving both fixed and deterministically moving absorbing barriers under possibly non-Gaussian distributions of the underlying object. We adapt to our problem a model originally proposed by De Simone et al. (2011) to describe the formation of galaxies in the universe, which uses cumulant expansions in terms of the Gaussian distribution, and we generalize it to take into account drift and cumulants of orders higher than three. From the probability density function, we obtain an analytical pricing model, not only for vanilla options (thus removing the need of volatility smile inherent to the Black & Scholes (1973) model), but also for fixed or deterministically moving barrier options. Market prices of vanilla options are used to calibrate the model, and barrier option pricing arising from the model is compared to the price resulted from the relative entropy model.

From physical to social interactions: The relative entropy model

Complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.) are formed and maintained through verbal interactions. Therefore, the ability to (1) model these interactions and (2) to use models of interaction for identifying significant relations may be of interest to the social sciences. Adopting the perspective of social physics, we present a general approach for modeling interactions through relative entropy. For illustrating the benefits of the approach, we derive measures of “perspective-taking” and use them for identifying significant-romantic relations in a data set composed of the verbal interactions taken place at the famous TV series “Sex and the City”. Using these measures, we show that significant-romantic relations can be identified with success. These results provide preliminary support for the benefits of using the proposed approach.

Relative Entropy Model of Uncertain Random Shortest Path

The shortest path problem is one of network optimization problems. This paper considers a shortest path problem under the situation where lengths of arcs in a network include both uncertainty and randomness, and focuses on the case that the lengths of arcs are expressed by uncertain random variables. This paper presents a new type of model: relative entropy model of shortest path. By the definition of relative entropy of the uncertain random variables, relative entropy model of shortest path problem is proposed to find the shortest path which fully reflects uncertain and random information. This model is formulated to find a shortest path whose chance distribution minimizes the difference from the ideal one. A numerical example is given to illustrate the model's effectiveness.

Combination Weighting Method Based on Generalized Mahalanobis Distance and Weighting Relative Entropy

Aimed at combination weighting in multiple attribute decision making, a new approach for combining different weighting vectors is proposed. The proposed approach considers the randomicity of weights themselves and the consistency among weighting vectors, constructs a constrained weighted relative entropy model. Aimed at the disadvantage in the TOPSIS based on Euclidean distance, the TOPSIS based on Mahalanobis distance is adopted to solve the coefficients of optimal weight vector. Finally, an example is conducted and the results show the proposed approach is effective and is more reasonable than three other combination approaches.

Protein-protein relationship measurement based on relative entropy

Protein-protein relationship (or evolutionary distance) measurement is an important research topic in phylogenetics. Currently, most of these methods are based on the sequence alignment process. However, traditional sequence alignment is an NP-hard problem and most algorithms have large scale of computations. We build the relative entropy model to measure evolutionary distances among proteins directly. Since the sequence alignment process is not necessary our model can greatly speed the computation, meanwhile maintaining the accuracy. Experimental results show this method outperforms many traditional sequence-alignment-based methods and thus is reliable. Key words: Relative entropy, evolutionary distance, protein-protein relationship.

Several appropriate background distributions for entropy-based protein sequence conservation measures

Amino acid background distribution is an important factor for entropy-based methods which extract sequence conservation information from protein multiple sequence alignments (MSAs). However, MSAs are usually not large enough to allow a reliable observed background distribution. In this paper, we propose two new estimations of background distribution. One is an integration of the observed background distribution and the position-specific residue distribution, and the other is a normalized square root of observed background frequency. To validate these new background distributions, they are applied to the relative entropy model to find catalytic sites and ligand binding sites from protein MSAs. Experimental results show that they are superior to the observed background distribution in predicting functionally important residues.

Improving agent-based negotiation efficiency in construction supply chains: A relative entropy method

This research presents a relative entropy method for improving agent-based negotiation efficiency (REANE) in a construction supply chain. REANE provides a path forward to help negotiators reach an acceptable solution when other methods fail; the key insight is the use of relative entropy to measure the relative degree of consensus among parties and hence minimize necessary compromises. An illustrative example is presented to show the application of REANE. The REANE method aggregates the preference information of negotiators in CSC twice. First, a compromise group preference order is ascertained using preference information, which is newly provided by negotiators, and compromise preference information (formed previously in the agent-based auto-negotiation process). Second, group preferences are aggregated by employing a relative entropy model, which is established based on entropy theory, while, considering the multiple attributes of negotiation in a construction supply chain. The method of REANE fulfills the necessary requirement of group decision-making, i.e., maximizing the preference consistency of decision-making groups and minimizing the utility gaps between every negotiator's preference and the group's preference.