Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
The article deals with the extensions of discrete-time games with infinite time horizon and their application in a fuzzy context to fishery models. The criteria for these games are the total discounted utility and the average utility in a fishing problem. However, in the fuzzy case, game theory is not the best way to represent a real fishing problem because players do not always have enough information to accurately estimate their utility in the context of fishing. For this reason, in this paper, trapezoidal-type fuzzy utility values are considered for a fishing model, and the terms of the Nash equilibrium are given in the fuzzy context, i.e., this equilibrium is represented using the partial order of the α-cuts of the fuzzy numbers; to the best of the authors’ knowledge, there is no work with this type of treatment. To obtain each equilibrium, a suitable fully determined fuzzy game is used in combination with the dynamic programming technique applied to this game in the context of fishing. The main results are (i) the Nash equilibria of the fuzzy games coincide with the Nash equilibria of the nonfuzzy games and are explicitly determined in a fishery model and (ii) the values of the fuzzy games are of trapezoidal type and are also explicitly given in the fishery model....
In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders....
With the rapid advancements in automated driving technologies, there is a growing demand for the commercialization of advanced automated vehicles. Through these technologies, we envision enjoying various types of entertainment in automated vehicles, apart from manual driving. To achieve widespread acceptance of automated driving, appropriated interactions between users and automated driving systems must occur. From users’ perspective, automated driving vehicle must be operated within users’ comfort, safe, and satisfying perception based on their personal driving style such as aggressive and defensive driving. Thus, during the motion planning phase of automated driving, consideration should be given to the implementation of a behavioral algorithm based on user propensity. However, user preferences for automated driving patterns exhibit considerable variation, making it essential to conduct an in-depth investigation into the preferred automated driving patterns corresponding to users’ propensity. In this study, we confirmed that the characteristics of preferred automated driving patterns can be deduced from comprehensive driving propensities, which were derived by combining inherent driving propensities with simulator-based driving behavior characteristics using the fuzzy logic method. This study confirmed that in the era of automated driving, the preferred automated driving patterns may vary depending on the propensity from the user’s perspective. Considering these differences, it is meaningful in which it suggests the need for automated driving motions to be implemented based on individual preferences that appear according to human factors such as user propensity....
In this work, we discuss solutions to the abstract Cauchy problem of fuzzy conformal fractional. In addition, the method of fuzzy fractional semigroups is used to obtain analytical solutions to the fractional differential equation. We use the concept of Krasnoselskii’s fixed point theorem to determine the existence and uniqueness of the solution. An application is also given to illustrate our main abstract results....
Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings’ polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = −5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data....
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