Порівняльний аналіз деяких систем ПРО/ППО за допомогою теорії нечітких множин

The article is devoted to the problem of using air defense / missile defense systems to protect critical and military infrastructure during hostilities. Attention is drawn to the fact that these systems can be attacked and damaged, which can lead to significant losses of critical infrastructure. To prevent (or reduce) these losses, this paper proposes to use information that can be obtained on the basis of a comparative analysis of the performance characteristics of the used air defense / missile defense systems. In particular, on the example of five such systems, it is demonstrated how this comparative analysis can be carried out using the theory of fuzzy sets (for this, the work considered inclusion operations, dominance relations, calculated the so-called linear and quadratic fuzzy indices, and also constructed various sets of α-levels). Pages of the article in the issue: 116 - 122 Language of the article: UkrainianСтаття присвячена проблемі використання систем ПРО/ППО для захисту критичної та військової інфраструктури під час військових дій. Звертається увага на те, що ці системи можуть піддаватися атакам та пошкодженням, що може призводити до значних втрат критичної інфраструктури. Для запобігання (або зменшення) цих втрат у даній роботі пропонується використовувати інформацію, яку можна одержати на основі порівняльного аналізу тактико-технічних характеристик використовуваних систем ПРО/ППО. Зокрема, на прикладі п’яти систем ПРО/ППО продемонстровано як можна здійснювати цей порівняльний аналіз за допомогою теорії нечітких множин

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed

A MAGDM ALGORITHM FOR DECISION-MAKING PROBLEMS ON FUZZY SOFT SETS USING A COEFFICIENT CORRELATION AND AN ENTROPY MEASURE FOR DETERMINING THE WEIGHT OF PARAMETERS

In statistics, the correlation coefficient concept aims to show how strong the linear relationship between two variables is. Sometimes the data collected relates to everyday life problems whose value is uncertain. Therefore, the concept of correlation coefficient must be developed on the fuzzy sets and the fuzzy soft sets environment. In this study, a decision-making algorithm was designed on fuzzy soft sets using the concept of the correlation coefficient. The method used is MAGDM, where the parameter weights are determined using entropy measures. Using this method, the algorithm of our decision-making problem is more realistic and general. The final section gives an example of a decision-making problem and a numerical illustration using the designed algorithm

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

[EN] Although preterm labor is a major cause of neonatal death and often leaves health sequels in the survivors, there are no accurate and reliable clinical tools for preterm labor prediction. The Electrohysterogram (EHG) has arisen as a promising alternative that provides relevant information on uterine activity that could be useful in predicting preterm labor. In this work, we optimized and assessed the performance of the Dispersion Entropy (DispEn) metric and compared it to conventional Sample Entropy (SampEn) in EHG recordings to discriminate term from preterm deliveries. For this, we used the two public databases TPEHG and TPEHGT DS of EHG recordings collected from women during regular checkups. The 10th, 50th and 90th percentiles of entropy metrics were computed on whole (WBW) and fast wave high (FWH) EHG bandwidths, sweeping the DispEn and SampEn internal parameters to optimize term/preterm discrimination. The results revealed that for both the FWH and WBW bandwidths the best separability was reached when computing the 10th percentile, achieving a p-value (0.00007) for DispEn in FWH, c = 7 and m = 2, associated with lower complexity preterm deliveries, indicating that DispEn is a promising parameter for preterm labor prediction.This work was supported by the Spanish ministry of economy and competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220).Nieto-Del-Amor, F.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; González Martínez, M.; Monfort-Ortiz, R.; Prats-Boluda, G. (2021). Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination. SCITEPRESS. 260-267. https://doi.org/10.5220/0010316602600267S26026

Robust rank correlation coefficients on the basis of fuzzy

The goal of this paper is to demonstrate that established rank correlation measures are not ideally suited for measuring rank correlation for numerical data that are perturbed by noise. We propose to use robust rank correlation measures based on fuzzy orderings. We demonstrate that the new measures overcome the robustness problems of existing rank correlation coe cients. As a rst step, this is accomplished by illustrative examples. The paper closes with an outlook on future research and applicationsPeer Reviewe

Two fuzziness indexes proposed by Kaufmann: observations about them

Professor Arnold Kaufmann did propose at least two types of indexes for estimating fuzziness in finite standard fuzzy sets. First one has an analogue formulation to that stated by Claude Shannon for measuring uncertainty in a given system. Shannon formulation estimates one type of uncertainty classified as conflict. The present paper will reveal the inconvenience of such an index for measuring fuzziness phenomena. In addition, it is proved algebraic equivalence between another index posed by Kaufmann and a fuzziness index proposed by Ronald Yager.Facultad de Informátic

Application of Multi-environmental time similarity theory based on relative information (RI-METS) theory in durability of concrete structures in marine chloride environment

The Multi-environmental time similarity (METS) method is a testing method that establishes the similarity relationship between the indoor test environment and the on-site environment to evaluate the durability and predict service life of the proposed or under-constructed concrete structure. Based on the METS theory, a similarity ratio of chloride ion concentration and diffusion coefficient between the indoor accelerated environment and the on-site natural environment was established. Then the relative information entropy was introduced into the Multi-Environmental Time Similarity based on Relative Information (RI-METS) theory to consider the time variability of the diffusion coefficient and the surface chloride ion mass fraction. Then the service life of a component in a marine chloride environment by Monte-Carlo simulation method was predicted

A Theory for Semantic Channel Coding With Many-to-one Source

As one of the potential key technologies of 6G, semantic communication is still in its infancy and there are many open problems, such as semantic entropy definition and semantic channel coding theory. To address these challenges, we investigate semantic information measures and semantic channel coding theorem. Specifically, we propose a semantic entropy definition as the uncertainty in the semantic interpretation of random variable symbols in the context of knowledge bases, which can be transformed into existing semantic entropy definitions under given conditions. Moreover, different from traditional communications, semantic communications can achieve accurate transmission of semantic information under a non-zero bit error rate. Based on this property, we derive a semantic channel coding theorem for a typical semantic communication with many-to-one source (i.e., multiple source sequences express the same meaning), and prove its achievability and converse based on a generalized Fano's inequality. Finally, numerical results verify the effectiveness of the proposed semantic entropy and semantic channel coding theorem

Deterministic Annealing Approach to Fuzzy C-Means Clustering Based on Entropy Maximization

This paper is dealing with the fuzzy clustering method which combines the deterministic annealing (DA) approach with an entropy, especially the Shannon entropy and the Tsallis entropy. By maximizing the Shannon entropy, the fuzzy entropy, or the Tsallis entropy within the framework of the fuzzy c-means (FCM) method, membership functions similar to the statistical mechanical distribution functions are obtained. We examine characteristics of these entropy-based membership functions from the statistical mechanical point of view. After that, both the Shannon- and Tsallis-entropy-based FCMs are formulated as DA clustering using the very fast annealing (VFA) method as a cooling schedule. Experimental results indicate that the Tsallis-entropy-based FCM is stable with very fast deterministic annealing and suitable for this annealing process