Search Results (2,399)

The management of time is essential in most AI-related applications. In addition, we know that temporal information is often not precise. In fact, in most cases, it is necessary to deal with imprecision and/or uncertainty. On the other hand, there is the need to handle the implicit common-sense information present in many temporal statements. In this paper, we present FTCProlog, a logic programming language capable of handling fuzzy temporal constraints soundly and efficiently. The main difference of FTCProlog with respect to its predecessor, PROLogic, is its ability to associate a certainty index with deductions obtained through SLD-resolution. This resolution is based on a proposal within the theoretical logical framework FTCLogic. This model integrates a first-order logic based on possibilistic logic with the Fuzzy Temporal Constraint Networks (FTCNs) that allow efficient time management. The calculation of the certainty index can be useful in applications where one wants to verify the extent to which the times elapsed between certain events follow a given temporal pattern. In this paper, we demonstrate that the calculation of this index respects the properties of the theoretical model regarding its semantics. FTCProlog is implemented in Haskell. Full article

The novelty of this work is that it is the first to introduce complex-valued suprametric spaces and apply it to Fractal Generation and mixed Volterra–Fredholm Integral Equations. In the realm of fuzzy logic, complex-valued suprametric spaces provide a robust framework for quantifying the similarity between fuzzy sets; for instance, utilizing a complex-valued suprametric approach, we compared the similarity between fuzzy sets represented by complex-valued feature vectors, yielding quantitative measures of their relationships. Thereafter, we establish related fixed point results and their applications in algorithmic and numerical contexts. The study then delves into the generation of fractals, exemplified by the Barnsley Fern fractal, utilizing sequences of affine transformations within complex-valued suprametric spaces. Moreover, this article presents two algorithms for soft computing and fractal generation. The first algorithm uses complex-valued suprametric similarity for fuzzy clustering, iteratively assigning fuzzy sets to clusters based on similarity and updating cluster centers until convergence. The distinctive pattern of the Barnsley Fern fractal is produced by the second algorithm’s repetitive affine transformations, which are chosen at random. These techniques demonstrate how well complex numbers cluster and how simple procedures can create complicated fractals. Moving beyond fractal generation, the paper addresses the solution of mixed Volterra–Fredholm integral equations in the complex plane using our results, demonstrating numerical illustrations of complex-valued integral equations. Full article

The output processer of interval type-2 fuzzy logic systems (IT2FLSs) is a complex operator which performs type-reduction plus defuzzification (TR+D) tasks. In this paper, a complexity-reduced yet high-performance TR+D for IT2FLSs based on Maclaurin series approximation is utilized within a feedback-error-learning (FEL) control structure for controlling linear move stages. IT2FLSs are widely used for control purposes, as they provide extra degrees of freedom to increase control accuracies. FEL benefits from a classical controller, which is responsible for providing overall system stability, as well as a guideline for the training mechanism for IT2FLSs. The Kalman filter approach is utilized to tune IT2FLS parameters in this FEL structure. The proposed control method is applied to a linear stage in real time. Using an identification process, a model of the real-time linear stage is developed. Simulation results indicate that the proposed FEL approach using the Kalman filter as an estimator is an effective approach that outperforms the gradient descent-based FEL method and the proportional derivative (PD) classical controller. Motivated by the performance of the proposed Kalman filter-based FEL approach, it is used to control a linear move stage in real time. The position feedback of the move stage is provided by a precision laser interferometer capable of performing measurements with an accuracy of less than $1\mathsf{\mu }\mathrm{m}$. Using this measurement system in a feedback loop with the proposed control algorithm, the overall steady state of the system is less than $20\mathsf{\mu }\mathrm{m}$. The results illustrate the high-precision control capability of the proposed controller in real-time. Full article

Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modeling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel intelligent traffic light controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and leveraging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the design and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results demonstrate the effectiveness of the ITLC system in improving traffic flow and reducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated the accuracy and reliability of our controller. Full article

This paper studies the problem of adaptive fuzzy control based on command filtering for a class of nonlinear systems characterized by an input dead zone, input saturation, and unknown control direction. First, this paper proposes a novel equivalent transformation technique that simplifies the design complexity of multiple input constraints by converting the input dead zone and saturation nonlinearities into a unified functional form. Subsequently, a fuzzy logic system is utilized to handle the unknown nonlinear functions, and the command-filtering method is employed to address the issue of complexity explosion, while the Nussbaum function is utilized to resolve the challenge of an unknown control direction. Based on Lyapunov stability, it is proven that the tracking error converges to a small neighborhood around the origin, and all closed-loop signals are bounded. Finally, a numerical simulation result and an actual simulation result of a pendulum are presented to verify the feasibility and effectiveness of the proposed control strategy. Full article

The study aimed to address the complex and critical issue of surface water quality monitoring by proposing a diversified approach that incorporates a range of chemical indicators. (1) Background: the purpose of the study was to address the problem of surface water quality monitoring in relation to the toxic effects of ammonium on aquatic ecosystems by developing predictive models using fuzzy logic and artificial neural networks. (2) Water samples from the Styr River, influenced by the Rivne Nuclear Power Plant, were analyzed using certified standard methods and measured parameters, while fuzzy logic and artificial neural network models, including Mamdani’s algorithm and various configurations of activation functions and optimization algorithms, were employed to assess water quality and predict ammonium toxicity. (3) A fuzzy logic system was developed to classify water quality based on ammonia content and other parameters, and six Artificial Neural Network (ANN) models were tested, with the ANN#2 model (using ReLU activation and ADAM optimizer) showing the best performance. (4) This study emphasizes the critical need for precise monitoring and modeling of total ammonium in surface water, considering its variable toxicity and interactions with environmental factors, to effectively protect aquatic ecosystems, namely ichthyofauna. Full article

Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda’s C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR’s performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats. Full article

Most populated developing countries having water resources, like Egypt, are interested in aquaculture since it supplies around 30% of the cheap protein consumed by customers. Increasing the production of aquaculture, specifically fish farming, in such countries represents an essential need. One candidate water resource for freshwater fish farming in Egypt is the Nile River (1530 km long). Yet, this represents a challenging task due to the existing variations in its water quality (WQ) parameters, such as dissolved oxygen, acidity, and temperature, at different sites. Climate change and pollution negatively affect many water quality parameters. This work provides a fuzzy-based approach for modeling WQ requirements for a set of fish types and evaluates the suitability of a water site for farming them. Thus, it greatly helps managing and planning fish farming in a set of water sites. It benefits from the flexibility of fuzzy logic to model the farming requirements of each fish type. Consequently, it evaluates and clusters the water sites with respect to their degrees of suitability for farming various fish types. The illustrative case study considers 27 freshwater sites spread along the Nile River and 17 freshwater fish types. The result incorporates a set of suitable clusters and a set of unsuitable ones for farming each fish type. It greatly helps managing and planning fish farming, to maximize the overall productivity and prevent probable catastrophic damage. In addition, it shows how to enhance each unsuitable site. We believe that eliminating the causes of pollution in the polluted freshwater sites along a water source could cause a significant boom in the cultivation of multiple freshwater fish types. Full article

Cardiac pacemakers are used for handling bradycardia, which is a cardiac rhythm of usually less than 60 beats per minute. Therapeutic dual-sensor pacemakers aim to preserve or restore the normal electromechanical activity of the cardiac muscle. In this article, a novel intelligent controller has been developed for implanted dual-sensor cardiac pacemakers. The developed controller is mainly based on intuitionistic fuzzy logic (IFL). The main advantage of the developed IFL controller is its ability to merge the qualitative expert knowledge of cardiologists in the proposed design of controlled pacemakers. Additionally, the implication of non-membership functions with the uncertainty term plays a key role in the developed fuzzy controller for improving the performance of a cardiac pacemaker over other fuzzy control schemes in previous studies. Moreover, the proposed pacemaker control system is efficient for managing all health-status conditions and constraints during the different daily activities of cardiac patients. Consequently, the healthcare of patients with implanted dual-sensor pacemakers can be efficiently improved intuitively. Full article

The broad acceptance of sustainable and renewable energy sources as a means of integrating them into electrical power networks is essential to promote sustainable development. Microgrids using direct currents (DCs) are becoming more and more popular because of their great energy efficiency and straightforward design. In this work, we discuss the control of a PV-based renewable energy system and a battery- and supercapacitor-based energy storage system in a DC microgrid. We describe a hierarchical control approach based on sliding-mode controllers and the Lyapunov stability theory. To balance the load and generation, a fuzzy logic-based energy management system has been created. Using a neural network, maximum power defects for the PV system were determined. The global asymptotic stability of the framework has been verified using Lyapunov stability analysis. In order to simulate the proposed DC microgrid and controllers, MATLAB/SimulinkR (2019a) was utilized. The outcomes show that the system operates effectively with changing production and consumption. Full article

The orange (Citrus sinensis) is a fruit of the Citrus genus, which is part of the Rutaceae family. The orange has gained considerable importance due to its extensive range of applications, including the production of juices, jams, sweets, and extracts. The consumption of oranges confers several nutritional benefits, including flavonoids, vitamin C, potassium, beta-carotene, and dietary fiber. It is crucial to acknowledge that the primary quality criterion employed by consumers and producers is maturity, which is correlated with the visual quality associated with the color of the epicarp. This study proposes the implementation of a computer vision system that estimates the degree of ripeness of oranges Valencia using fuzzy logic (FL); the soluble solids content was determined by refractometry, while the firmness of the fruit was evaluated through the fruit firmness test. The proposed method was divided into five distinct steps. The initial stage involved the acquisition of RGB images. The second stage presents the segmentation of the fruit, which entails the removal of extraneous noise and backgrounds. The third and fourth steps involve determining the centroid of the fruit, and five regions of interest were obtained in the centroid of the fruit of the Citrus Color Index (CII), ranging from 3 × 3 to 11 × 11 pixels. Finally, in the fifth step, a model was created to estimate maturity, °Brix, and firmness using Matlab 2024 and the Fuzzy Logic Designer and Neuro-Fuzzy Designer applications. Consequently, a statistically significant correlation was established between maturity, degree Brix, and firmness, with a value greater than 0.9, using the Citrus Color Index (CII), which reflects the physical–chemical changes that occur in the orange. Full article

The fuzzy transportation problem (FTP) represents a significant extension of the classical transportation problem (TP) by introducing uncertainly and imprecision into the parameters involved. Various algorithms have been proposed to solve the FTP, including fuzzy linear programming, metaheuristic algorithms and fuzzy mathematical programming techniques combined with artificial neural networks. This paper presents the application of trigonometric acceleration coefficients-PSO (TrigAC-PSO) to solve the FTP. TrigAC-PSO is a variation of the classical particle swarm optimization algorithm, which has already been applied to solve the TP showing remarkable success. This fact constitutes the main reason that drives the utilization of TrigAC-PSO in current contribution to further investigate its performance in solving the FTP. TrigAC-PSO’s adaptability to handle fuzzy data by solving the FTP via instances with classic fuzzy numbers and generalized fuzzy numbers is explored through a comprehensive comparison between TrigAC-PSO and established methods applied to solve the FTP. The comparative analysis, with recent state-of-the-art algorithms, demonstrates the efficiency and robustness of the proposed method in solving the FTP across various scenarios. Through experimental results and performance metrics, the superiority of the proposed method is presented by achieving optimal solutions. The innovation of current research contributes to advancing the field of fuzzy optimization while providing variable insights into the application of TrigAC-PSO in real-world scenarios. Full article

The objective of this study is to design a security control for ensuring the stability of systems, maintaining their state within bounded limits and securing operations. Thus, we enhance the reliability and resilience in control systems for critical infrastructure such as manufacturing, network bandwidth constraints, power grids, and transportation amid increasing cyber-threats. These systems operate as singularly perturbed structures with variables changing at different time scales, leading to complexities such as stiffness and parasitic parameters. To manage these complexities, we integrate type-2 fuzzy logic with Markov jumps in dynamic event-triggered protocols. These protocols handle communications, optimizing network resources and improving security by adjusting triggering thresholds in real-time based on system operational states. Incorporating fractional calculus into control algorithms enhances the modeling of memory properties in physical systems. Numerical studies validate the effectiveness of our proposal, demonstrating a 20% reduction in network load and enhanced stochastic stability under varying conditions and cyber-threats. This innovative proposal enables real-time adaptation to changing conditions and robust handling of uncertainties, setting it apart from traditional control strategies by offering a higher level of reliability and resilience. Our methodology shows potential for broader application in improving critical infrastructure systems. Full article

For atypical traditional villages, their invaluable historical traces and cultural memories are preserved in the existing village landscapes. Rapid and accurate acquisition of the spatial information of various surface elements in a village is an important prerequisite for a scientific, reasonable, feasible planning and design scheme for conserving, progressing, and developing atypical villages. Taking Qianfeng Village as an example, this research proposes the atypical traditional village landscape classification model based on unmanned aerial vehicle (UAV) imagery (ATVLUI) by virtue of the UAV RGB images and the object-oriented fuzzy logic membership classification technique that extracts objects according to their spectrums, textures, geometries, and context relationships, aiming at precise extraction of atypical traditional village landscapes. Based on the landscape information, the landscape pattern indexes are calculated to explore the spatial distribution characteristics of different landscapes and analyze the current conditions of Qianfeng Village as the epitome of atypical traditional villages. Accordingly, suggestions for protecting, planning, and developing atypical villages are proposed. The results show that: (1) the ATVLUI boasts excellent identification for village landscapes in a complex scenario, with a classification accuracy for traditional structures of 84%, an overall accuracy of 93%, and a Kappa coefficient of 0.89. This model is proven superior to K-nearest neighbors (KNN), decision tree (DT), and random tree (RT); (2) according to the area and proportion calculations, the structures account for 33.94% of Qianfeng Village’s total area, in which 29.69% and 4.25% are modern and traditional structures, respectively. The number of traditional structures is 202, accounting for 13% of the total number of structures; (3) within the village, connectivity between and extension of the modern structures can be recognized, suggesting a trajectory where the traditional structures are being gradually substituted by modern ones. The ecological environment at the periphery of the village is favorable. The building-to-building common boundaries are long. The modern structures are densely distributed. The discretely distributed traditional structures gather as small clusters. In general, different structures are highly interlaced to form a fragmented distribution pattern. Full article

A novel combination of three control systems is presented in this paper: an adaptive control system, a type-two fuzzy logic system, and a super-twisting sliding mode control (STSMC) system. This combination was developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE). This controller incorporates two methods to calculate the gains of the switching term in the STSMC utilizing the particle swarm optimization algorithm: (1) adaptive gains and (2) optimized gains. This methodology was applied to a nonlinear model of the Cessna Citation X business jet aircraft generated by the simulation platform developed at the LARCASE in Simulink/MATLAB (R2022b) for aircraft lateral motion. The platform was validated with flight data obtained from a Level-D research aircraft flight simulator manufactured by the CAE (Montreal, Canada). Level D denotes the highest qualification that the FAA issues for research flight simulators. The performances of controllers were evaluated using the turbulence generated by the Dryden model. The simulation results show that this controller can address both turbulence and existing uncertainties. Finally, the controller was validated for 925 flight conditions over the whole flight envelope for a single configuration using both adaptive and optimized gains in switching terms of the STSMC. Full article