Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach....
Quadcopter drones have been extensively researched due to their flexibility and suitability for diverse tasks. In this study, a control strategy tailored for scenarios with restricted network bandwidth is developed. An event-triggered control approach was used to minimize network bandwidth load. Also, a robust fuzzy controller was integrated to enhance the system’s resilience and efficiency. The simulation results confirmed that the developed control strategy fosters stable performance, even under constrained network conditions....
Individual Pitch Control (IPC) is a crucial mechanism for mitigating asymmetric loads in offshore floating wind turbines (OFWTs). Conventional IPC systems face significant limitations in wind speed estimation accuracy and control strategy robustness, leading to load fluctuations and power degradation. To address these challenges, this study proposes a novel IPC system incorporating an innovative effective wind speed estimation method and a fuzzy PID control strategy. The wind speed estimation is achieved using polynomial fitting of the tip speed ratio and pitch angle. The fuzzy PID control strategy for IPC employs variable control gains calculated based on wind speed, azimuth angle, and blade root loads. To verify the performance of the proposed control system, it is compared against the baseline control system implemented in the OpenFAST software v1.0.0 by a case study of the NREL 5MW OFWT. Results demonstrate that the proposed system has high accuracy in wind speed estimation and maintains rated power output while reducing blade flapwise and pitching moments. Notably, the proposed EWSE has a 53.1% improvement in median error and a 19.23% improvement in data error threshold compared with a reference EWSE. Under strong turbulent conditions (15% turbulence intensity), the proposed system achieves a reduction of 17.9% in flapwise moment and 12.9% in pitching moment compared with a baseline controller....
To address the poor trajectory tracking of mining trucks in narrow, high-curvature paths, this study explores the impact of four-wheel steering (4WS) and direct yaw moment control (DYC) on vehicle stability. A validated two-degree-of-freedom 4WS vehicle model was developed. A fuzzy logic controller with dual inputs (yaw rate and yaw angular acceleration) and a single output (compensatory yaw moment) was designed, alongside an optimal torque distribution controller based on tire friction circle theory to allocate the resultant yaw moment. A co-simulation platform integrating TruckSim and MATLAB/Simulink was established, and experiments were conducted under steady-state and double-lane-change conditions. Comparative analysis with traditional front-wheel steering and alternative control methods reveals that the 4WS mining truck with fuzzy-controlled optimal torque distribution achieves a reduced turning radius, enhancing maneuverability and stability. Hardware-in-the-loop (HIL) testing further validates the controller’s effectiveness in real-time applications....
This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out....
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