Search Results (469)

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the equipment, but also effectively reduce energy consumption and emissions. Particle swarm optimization (PSO) fuzzy PID control algorithms have been widely used in many complex engineering problems due to their powerful global optimization capability and excellent adaptability. Currently, PSO-based fuzzy PID control research mainly integrates hybrid algorithmic strategies to avoid the local optimum problem, and lacks optimization of the dynamic noise suppression of the input error and the rate of change of the error. This makes the algorithm susceptible to the coupling of the system uncertainty and measurement disturbances during the parameter optimization process, leading to performance degradation. For this reason, this study proposes a new framework based on the synergistic optimization of the untraceable Kalman filter (UKF) and PSO fuzzy PID control for the speed control system of a single-cylinder diesel engine. A PSO-optimized fuzzy PID controller is designed by obtaining accurate speed estimation data using the UKF. The PSO is capable of quickly adjusting the fuzzy PID parameters so as to effectively alleviate the nonlinearity and uncertainty problems during the operation of diesel engines. By establishing a Matlab/Simulink simulation model, the diesel engine speed step response experiments (i.e., startup experiments) and load mutation experiments were carried out, and the measurement noise and process noise were imposed. The simulation results show that the optimized diesel engine speed control system is able to reduce the overshoot by 76%, shorten the regulation time by 58%, and improve the noise reduction by 25% compared with the conventional PID control. Compared with the PSO fuzzy PID control algorithm without UKF noise reduction, the optimized scheme reduces the overshoot by 20%, shortens the regulation time by 48%, and improves the noise reduction effect by 23%. The results show that the PSO fuzzy PID control method with integrated UKF has superior control performance in terms of system stability and accuracy. The algorithm significantly improves the responsiveness and stability of diesel engine speed, achieves better control effect in the optimization of diesel engine speed control, and provides a useful reference for the optimization of other diesel engine control systems. In addition, this study establishes the GT-POWER model of a 168 F single-cylinder diesel engine, and compares the cylinder pressure and fuel consumption under four operating conditions through bench tests to ensure the physical reasonableness of the kinetic input parameters and avoid algorithmic optimization on the distorted front-end model. Full article

The most important control parameters in the methanol distillation process, which are directly related to product quality and yield, are the temperature, pressure and water content of the finished product at the top of the column. In order to adapt to the development trend of modern industrial technology to be more accurate, faster and more stable, the fusion of multi-sensor data puts forward higher requirements. Traditional control methods, such as PID control and fuzzy control, have the disadvantages of low heterogeneous data processing capability, poor response speed and low control accuracy when dealing with complex industrial process detection and control. For the control of tower top temperature and pressure in the methanol distillation industry, this study innovatively combines generative artificial intelligence and a type II fuzzy neural network, using a GAN for data preprocessing and a type II fuzzy neural network for steady-state inverse prediction to construct the GAN-T2FNN temperature and pressure control model for an atmospheric pressure tower. Comparison experiments with other neural network models and traditional PID control models show that the GAN-T2FNN model has a better performance in terms of prediction accuracy and fitting effect, with a minimum MAE value of 0.1828, which is more robust, and an R² Score of 0.9854, which is closer to 1, for the best overall model performance. Finally, the SHAP model was used to analyze the influence mechanism of various parameters on the temperature and pressure at the top of the atmospheric column, which provides a more comprehensive reference and guidance for the precise control of the methanol distillation process. Full article

Tourism is a dynamic industry that significantly contributes to the global economy, driven by the increasingly diverse preferences of tourists. Addressing these preferences requires sophisticated decision-making models capable of handling the uncertainty and subjectivity of human judgments. This study proposes sustainable models for effectively capturing and evaluating individual tourist preferences using the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). These models leverage the strengths of the AHP to construct a flexible decision-making framework that adapts to diverse tourist preferences, offering personalized recommendations. In this study, three main criteria are considered: types of tourism, tourism facilities, and tourism areas. Tourists are encouraged to provide their preferences for these criteria and sub-criteria, enabling the AHP and Fuzzy AHP to recommend suitable destinations. An analysis was conducted with 30 respondents providing pairwise comparisons of the tourism criteria, which were then used to generate tourist attraction recommendations using both the AHP and Fuzzy AHP. The study assessed respondents’ satisfaction with the recommendations, finding that both methods were effective, with a slight preference for the Fuzzy AHP due to its ability to better capture individual preferences. The results underscore the potential of these models in sustainably enhancing decision support systems in the tourism industry, offering tailored recommendations that align more closely with tourist expectations. Full article

In this paper, we provide an overview of type-3 fuzzy logic systems (T3FLSs) and their applications in a general way. The contribution of this paper is to analyze and review, in the best way possible, applications in several fields utilizing type-3 fuzzy logic systems. Recently, many algorithms are receiving more and more attention in this area, and for this reason, an overview of this field is important. This article provides an overview of the most important applications in which intelligent computing methods based on T3FLSs are used. The main goal of this paper is to thoroughly explore these applications and identify emerging scientific trends in the adoption of intelligent methods, particularly those involving T3FLSs. To achieve this, we use the VosViewer software to construct and visualize bibliometric networks. VosViewer is a free, Java-based tool designed for analyzing and visualizing bibliometric data. This program is used for the creation of maps of papers, authors, etc., and the development of maps for keywords, countries, research groups, and more. Full article

In this article, a fuzzy controller mathematical model synthesising method that uses cognitive computing and a genetic algorithm for automated tuning and adaptation to changing environmental conditions has been developed. The technique consists of 12 stages, including creating the control objects’ mathematical model and tuning the controller coefficients using classical methods. The research pays special attention to the error parameters and their derivative fuzzification, which simplifies the development of logical rules and helps increase the stability of the systems. The fuzzy controller parameters were tuned using a genetic algorithm in a computational experiment based on helicopter flight data. The results show an increase in the integral quality criterion from 85.36 to 98.19%, which confirms an increase in control efficiency by 12.83%. The fuzzy controller use made it possible to significantly improve the helicopter turboshaft engines’ gas-generator rotor speed control performance, reducing the first and second types of errors by 2.06…12.58 times compared to traditional methods. Full article

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability ($P_{rk}$) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for $P_{rk}$ and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters. Full article

This work is devoted to the development of a biometric authentication system and the generation of a cryptographic key or a long password of 1024 bits based on a voice password, which ensures the protection of a biometric template from compromise. A new hybrid neural network model based on two types of trigonometric correlation neurons was proposed. The model is capable of recording correlation links between features and is resistant to data extraction attacks. The experiments were conducted on our own AIC-spkr-130 dataset and the publicly available RedDots, including recordings of user voices in different psycho-emotional states (sleepy state, alcohol intoxication). The results show that the proposed neural fuzzy extractor model provides an equal error probability level of EER = 2.1%. Full article

In certain stages of the application of a type-2 fuzzy logic system, it is necessary to perform operations between input or output fuzzy variables in order to compute the union, intersection, aggregation, complement, and so forth. In this context, operators that satisfy the axioms of t-norms and t-conorms are of particular significance, as they are applied to model intersection and union, respectively. Furthermore, the existence of a range of these operators allows for the selection of the t-norm or t-conorm that offers the optimal performance, in accordance with the specific context of the system. In this paper, we obtain new t-norms and t-conorms on some important subfamilies of the set of functions from $[0,1]$ to $[0,1]$. The structure of these families provides a more solid algebraic foundation for the applications. In particular, we define these new operators on the subsets of the functions that are convex, normal, and normal and convex, as well as the functions taking only the values 0 or 1 and the subset of functions whose support is a finite union of closed intervals. These t-norms and t-conorms are generalized to the type-2 fuzzy set framework. Full article

The fusion of multi-polarized petrographic images of rock thin sections involves the fusion of feature information from microscopic images of rock thin sections illuminated under both plane-polarized and orthogonal-polarized light. During the fusion process of rock thin section images, the inherent high resolution and abundant feature information of the images pose substantial challenges in terms of computational complexity when dealing with massive datasets. In engineering applications, to ensure the quality of image fusion while meeting the practical requirements for high-speed processing, this paper proposes a novel fast fusion Transformer. The model leverages a soft matching algorithm based on intuitionistic fuzzy sets to merge redundant tokens, effectively mitigating the negative effects of asymmetric dependencies between tokens. The newly generated artificial tokens serve as brokers for the Query (Q), forming a novel lightweight fusion strategy. Both subjective visual observations and quantitative analyses demonstrate that the Transformer proposed in this paper is comparable to existing fusion methods in terms of performance while achieving a notable enhancement in its inference efficiency. This is made possible by the attention paradigm, which is equivalent to a generalized form of linear attention, and the newly designed loss function. The model has been experimented on with multiple datasets of different rock types and has exhibited robust generalization capabilities. It provides potential for future research in diverse geological conditions and broader application scenarios. Full article

An enhanced whale optimization algorithm (WOA) through the implementation of a generalized type-2 fuzzy logic system (GT2FLS) is outlined. The initial idea is to improve the efficacy of the original WOA using a GT2FLS to find the optimal values of the ${\overrightarrow{\mathit{r}}}_{\mathbf{1}}$ and ${\overrightarrow{\mathit{r}}}_{\mathbf{2}}$ parameters of the WOA, for the case of optimizing mathematical functions. In the WOA algorithm, ${\overrightarrow{\mathit{r}}}_{\mathbf{1}}$ is a variable that affects the new position of the whale in the search space, in this case, affecting the exploration, and ${\overrightarrow{\mathit{r}}}_{\mathbf{2}}$ is a variable that has an effect on finding the local optima, which is an important factor for the exploration. The efficiency of a fuzzy WOA with a GT2FLS (FWOA-GT2FLS) is highlighted by presenting the excellent results of the case study of the benchmark function optimization. A relevant analysis and comparison with a bio-inspired algorithm based on artificial bees is also presented. Statistical tests and comparisons with other bio-inspired algorithms and the initial WOA, with type-1 FLS (FWOA-T1FLS) and interval type-2 FLS (FWOA-IT2FLS), are presented. For each of the methodologies, the metric for evaluation is the average of the minimum squared errors. Full article

A fuzzy cascaded PI−PD (FCPIPD) controller is proposed in this paper to optimize load frequency control (LFC) in the linked electrical network. The FCPIPD controller is composed of fuzzy logic, proportional integral, and proportional derivative with filtered derivative mode controllers. Utilizing renewable energy sources (RESs), a dual-area hybrid AC/DC electrical network is used, and the FCPIPD controller gains are designed via secretary bird optimization algorithm (SBOA) with aid of a novel objective function. Unlike the conventional objective functions, the proposed objective function is able to specify the desired LFCs response. Under different load disturbance situations, a comparison study is conducted to compare the performance of the SBOA-based FCPIPD controller with the one-to-one (OOBO)-based FCPIPD controller and the earlier LFC controllers published in the literature. The simulation’s outcomes demonstrate that the SBOA-FCPIPD controller outperforms the existing LFC controllers. For instance, in the case of variable load change and variable RESs profile, the SBOA-FCPIPD controller has the best integral time absolute error (ITAE) value. The SBOA-FCPIPD controller’s ITAE value is 0.5101, while sine cosine adopted an improved equilibrium optimization algorithm-based adaptive type 2 fuzzy PID controller and obtained 4.3142. Furthermore, the work is expanded to include electric vehicle (EV), high voltage direct current (HVDC), generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD). The result showed that the SBOA-FCPIPD controller performs well when these components are equipped to the system with/without reset its gains. Also, the work is expanded to include a four-area microgrid system (MGS), and the SBOA-FCPIPD controller excelled the SBOA-CPIPD and SBOAPID controllers. Finally, the SBOA-FCPIPD controller showed its superiority against various controllers for the two-area conventionally linked electrical network. Full article

Searchable encryption (SE) allows users to efficiently retrieve data from encrypted cloud data, but most of the existing SE solutions only support precise keyword search. Fuzzy searchable encryption agrees with practical situations well in the cloud environment, as search keywords that are misspelled to some extent can still generate search trapdoors that are as effective as correct keywords. In scenarios where multiple users can search for ciphertext, most fuzzy searchable encryption schemes ignore the security issues associated with malicious cloud services and are inflexible in multi-user scenarios. For example, in medical application scenarios where malicious cloud servers may exist, diverse types of files need to correspond to doctors in the corresponding departments, and there is a lack of fine-grained access control for sharing decryption keys for different types of files. In the application of medical cloud storage, malicious cloud servers may return incorrect ciphertext files. Since diverse types of files need to be guaranteed to be accessible by doctors in the corresponding departments, sharing decryption keys with the corresponding doctors for different types of files is an issue. To solve these problems, a verifiable fuzzy searchable encryption with blockchain-assisted multi-user scenarios is proposed. Locality-sensitive hashing and bloom filters are used to realize multi-keyword fuzzy search, and the bigram segmentation algorithm is optimized for keyword conversion to improve search accuracy. To realize fine-grained access control in multi-user scenarios, ciphertext-policy attribute-based encryption (CP-ABE) is used to distribute the shared keys. In response to the possibility of malicious servers tampering with or falsifying users’ search results, the scheme leverages the blockchain’s technical features of decentralization, non-tamperability, and traceability, and uses smart contracts as a trusted third party to carry out the search work, which not only prevents keyword-guessing attacks within the cloud server, but also solves the verification work of search results. The security analysis leads to the conclusion that the scheme is secure under the adaptively chosen-keyword attack. Full article

The objective of this paper is to compare a fuzzy implication produced by t-conorm probor with three other fuzzy implications constructed by t-conorms max, Einstein, and Lukasiewicz. Firstly, in methodology, six pairs of combinations of five t-conorm comparisons are performed in order to find the ranking order of five fuzzy implications. Moreover, the evaluation and calculation of the four fuzzy implications (probor, max, Einstein, and Lukasiewicz) are made using meteorological data, fuzzifying the crisp values of temperature and humidity, constructing four membership degree functions, and inserting as inputs the membership degrees of meteorological variables into the two variables of the fuzzy implications. Finally, extensive tests are made so as to find which membership degree function and which fuzzy implication receives the best and the worst results. The key findings are that the application of isosceles trapezium to the fuzzy implications of Probor and Einstein gives the best values, while fuzzy implication Lukasiewicz, although it was found to be first in the ranking order, is rejected due to unreliable results. As a result, the crucial role of these implications lies in the fact that they are non-symmetrical, i.e., there is a clear difference between the cause and the causal. Full article

In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child’s fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy. Full article

The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recognition. In response to the above issues, by introducing object detection technology in computer vision, this article proposes a building shape recognition and classification method based on the YOLO object detection model. Firstly, for different types of buildings, four levels of building training data samples are constructed, and YOLOv5, YOLOv8, YOLOv9, and YOLOv9 integrating attention modules are selected for training. The trained models are used to test the shape judgment of buildings in the dataset and verify the learning effectiveness of the models. The experimental results show that the YOLO model can accurately classify and locate the shape of buildings, and its recognition and detection effect have the ability to simulate advanced human visual cognition, which provides a new solution for the fuzzy shape recognition of buildings with complex outlines and local deformation. Full article