Intelligent Fuzzy Logic Research Articles

OPTIMIZATION OF AN INTELLIGENT CONTROLLED BRIDGELESS POSITIVE LUO CONVERTER FOR LOW-CAPACITY ELECTRIC VEHICLES

This paper presents a novel approach to power conversion in electric vehicle (EV) applications. The proposed converter, a Bridgeless Single Stage Positive Luo Converter (BSPLC), is optimized to enhance efficiency and reduce losses in low-capacity EVs. Traditional converters experience higher losses due to their passive components and bridge circuits. By eliminating the bridge components, the converter achieves higher efficiency. An intelligent fuzzy logic controller is employed to provide adaptive control and stabilize output under varying input conditions, improving performance and response time. The converter design is further optimized through parameter tuning and simulation to achieve minimal ripple and maximum power efficiency at 92%. The proposed solution is ideal for low-capacity EVs, as it ensures enhanced power conversion, reduced thermal stress, and improved battery life. The study demonstrates the converter’s capability to meet the growing demands for energy-efficient solutions in modern electric mobility.

Detection of Sybil & DDoS attacks in VANET using intelligent technique

ABSTRACT This paper proposes a novel fuzzy logic controller (FLC) based model referred to as FBM to detect both the DDoS and the Sybil attacks in VANET using an intelligent fuzzy logic controller. A VANET environment is created with different numbers of attackers to check the efficacy of the proposed model using veins, which is used for simulation with an event-based network simulator OMNeT++ and a road traffic simulator SUMO. Furthermore, the performance of the proposed model is compared with a few other machine learning techniques, such as logistic regression (LR), the decision tree (DT), random forest classifier (RF), and the support vector machine (SVM). The proposed FBM model yields better performance than the existing machine-learning techniques. The proposed FBM performs better than RF in accuracy by 0.10492%, precision by 0.08065%, recall by 0.02505%, and F1 score by 0.088243% for the DDoS scenario. It is also observed that the FBM performs better than DT in accuracy by 3.91486%, precision by 14.39994%, and F1 score by 3.819618% for the Sybil scenario. The margin of error for the proposed FBM is also estimated for 95% of the confidence interval and is found to be 0.021691 for the DDoS scenario.

A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting

Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

Hypothermia Management in Newborns Artificial Intelligence Based: Systematic Review

Background: Hypothermia in newborns that is not treated properly will lead to respiratory failure and death. Hypothermia in newborns is 4 times more dangerous than hypothermia in adults. Care in the delivery room plays an important role in it. Neonatal service technology in the delivery room must be responsive, able to monitor and effective. Artificial intelligence can help determine management as needed. This study aims to analyze and examine the management of hypothermia in newborns based on artificial intelligence using the systematic review method. Subjects and Method: This study is a systematic review with PICO including: (1) Population: newborns; (2) Intervention: artificial intelligence; (3) conventional hypothermic treatment; (4) Artificial intelligence is feasible to be used in the management of neonatal hygiene. The databases used are Google Scholar, PubMed, Science Direct, and Proquest. The inclusion criteria include fulltext in Indonesian and English with the study subject of newborns with hypothermia. The exclusion criteria for this study are articles published under the last five years. Results: A total of 9 articles show that the existence of artificial intelligence can provide feedback support in the prevention of hypothermia efficiently and facilitate effective treatment. Conclusion: Hypothermic prevention feedback with artificial intelligence Fuzzy logic can save time and facilitate appropriate treatment.

Fuzzy Logic Controllers Design for the Path Tracking of an Autonomous Coaxial Octorotor

Due to the essential characteristics and utility of coaxial octocopter vehicles in various real-time applications and many actual domains, this article aims to propose intelligent fuzzy logic controllers (FLC) for a six-degrees-of-freedom coaxial octorotor with a dynamic model. Using expert knowledge, the developed control structure is applied to track reference paths in the 3D environment with sufficient stability and effectiveness. The tested octorotor dynamic model is divided into six subsystems: altitude, pitch, roll, yaw, x position, and y position. Meanwhile, a Mamdani fuzzy inference controller is designed and applied for each subsystem to generate the appropriate control action. The simulation and the evolution tasks are done using a MATLAB dynamic model of the controlled process in order to perform and evaluate the proposed intelligent control structures of the 6 DOF octorotor model. The illustrated simulation results demonstrate the designed fuzzy controllers' effectiveness in motion stability and error tracking for different types of paths and all tested variables.

An Intelligent Fuzzy Logic Automobile Fault Diagnostic System

The advent of intelligent transportation systems and the growing complexity of modern automobiles necessitated innovative approaches to fault detection and diagnostics. This study presents an Intelligent Fuzzy Logic Automobile Fault Diagnostic System. It is designed to enhance the safety and reliability of automotive systems. Fuzzy logic with its ability to handle imprecise and uncertain data is harnessed to develop a robust model capable of identifying and classifying faults in real-time. The system incorporates a hybrid computerized fuzzy system, to aid vehicle owners in identifying issues with their vehicles and providing sound repairs recommendations for any malfunctioning parts. The system was implemented using web technologies; ASP.Net, Bootstrap 3.5, CSS, JavaScript, JQuery and SQL server. The results indicate its potential for widespread adoption, with the ability to reduce accidents and maintenance costs while enhancing the driving experience and provides an accuracy of 73.14% in performance. The Precision 100% and F1 Score 75.72%.

Intelligent fuzzy logic based intrusion detection system for effective detection of black hole attack in WSN

Intelligent fuzzy logic based intrusion detection system for effective detection of black hole attack in WSN

A new power management strategy for PV-FC-based autonomous DC microgrid

Solar energy is widely available in nature and electricity can be easily extracted using solar PV cells. A fuel cell being reliable and environment friendly becomes a good choice for the backup so as to compensate for continuously varying solar irradiation. This paper presents simple control schemes for power management of the DC microgrid consisting of PV modules and fuel cell as energy sources and a hydrogen electrolyzer system for storing the excess power generated. The supercapacitor bank is used as a short term energy storage device for providing the energy buffer whenever sudden fluctuations occur in the input power and the load demand. A new power control strategy is developed for a hydrogen storage system. The performance of the system is assessed with and without the supercapacitor bank and the results are compared. A comparative study of the voltage regulation of the microgrid is presented with the controller of the supercapacitor bank, realized using a traditional PI controller and an intelligent fuzzy logic controller.

FUZZY FRACTIONAL ORDER SLIDING MODE CONTROL FOR OPTIMAL ENERGY CONSUMPTION-TRANSIENT RESPONSE TRADE-OFF IN ROBOTIC SYSTEMS

This work presents a new fuzzy logic parameter tuning-based fractional order sliding mode control (F-FOSMC) strategy for the trajectory tracking of robotic systems. The primary objective is to achieve an optimal control compromise seeking to minimize energy consumption for required motions, all while increasing both accuracy and rapidity of the transient response, addressing therefore a critical challenge in the field of robotics. Our approach integrates a sophisticated fuzzy logic fractional order mechanism to dynamically fine-tune the noninteger derivation parameter of the FOSMC, offering adaptability to varying operational conditions. This intelligent fuzzy logic controller tuning optimizes the FOSMC performance, demonstrates a remarkable capacity to handle uncertainties and disturbances intrinsic to robotic applications and allows to optimize the trade-off between the energy consumption and transient response efficiency throughout the robot’s motion. The effectiveness of the proposed study is thoroughly examined via simulations conducted on a 3 DOF manipulator robotic system. Results showcase that F-FOSMC significantly reduces control energy consumption while preserving rapid and efficient transient response characteristics.

Application of an intelligent fuzzy logic based sliding mode controller for frequency stability analysis in a deregulated power system using OPAL-RT platform

Owing to unpredicted power demand, integration of distributed generators and parametric disparity in the system cause the power system more intricacy which in turn affects the frequency and power fluctuation critically. To mitigate these phenomena, an intelligent and efficient automatic generation control (AGC) is indispensable. Therefore, in this paper, an intelligent and robust controller named fuzzy sliding mode controller (FSMC) has been recommended to tackle the AGC problem in a deregulated power system in the presence of distributed generators effectively. Again, to manifest its dominance over SMC, fuzzy-PID and PID controllers, dynamic response of the deregulated system under Poolco, Bilateral and Contract violation conditions has been evaluated and compared. Further, to support the FSMC controller’s ability, another test model has been taken for the AGC study. The gains of these controllers have been enumerated by Gannet Optimization Algorithm (GOA) subjecting to a minimization problem. Exploration phase of GOA involves two steps, namely U- & V-shaped diving patterns of Gannets and exploitation phase again involves two steps namely sudden rotation and random walk of Gannets. These four steps ensures quick convergence of the objective function to its optimal value. Apart from transient response under different power transaction conditions, parametric uncertainties of the system, solar and wind power variation and abrupt load change have been considered to support the robustness & credibility of FSMC controller. In addition to this, the stability of the proposed model has been analyzed in the frequency domain. Finally, the MATLAB/SIMULINK based transient response has been validated by a real-time simulator using OPAL-RT-4510, Xilinx Kintex-7 FPGA software.

Empowering Financial Management in Educational Institutions: A Multi-Objective Decision-Making System using Intelligent Fuzzy Logic Algorithm and Digital Marketing

Empowering Financial Management in Educational Institutions: A Multi-Objective Decision-Making System using Intelligent Fuzzy Logic Algorithm and Digital Marketing

A Centralized Stage-Wise Control Approach for Frequency and Voltage Regulation in PV–Wind–Storage Based Islanded Microgrid

In a microgrid, perturbations stemming from uncertain renewable sources and sudden load fluctuations can result in deviations in system voltage, frequency, and more, leading to instability. Thus, focusing on achieving stable microgrid operation, this paper proposes a novel stage-wise control approach for regulating the frequency and voltage of islanded microgrids. The approach employs an intelligent fuzzy logic controller with 64 rules. Specifically, the control approach is divided into three stages. In stage 1, the proposed approach manages the system under minor perturbations to regulate all system parameters within a feasible operating range, utilizing a battery as ancillary support. Furthermore, during significant perturbations, stages 2 and 3 of the proposed approach involve percentage-wise shedding non-critical loads to enhance stable microgrid operation. The load-shedding process continues until the system parameters stabilize within an acceptable operating range. The performance of the proposed approach is validated using MATLAB software and is compared against existing topologies. Notably, it has been observed that in the presence of significant small and large perturbations, the system demonstrates considerably lower deviations, following the recommended standards, i.e., 0.57 and 0.72% voltage deviation, including 0.14 and 0.17% frequency deviation. This observation emphasizes the superiority of the proposed control approach.

Empowering Financial Management in Educational Institutions: A Multi-Objective Decision-Making System using Intelligent Fuzzy Logic Algorithm and Digital Marketing

Empowering Financial Management in Educational Institutions: A Multi-Objective Decision-Making System using Intelligent Fuzzy Logic Algorithm and Digital Marketing

Optimal design, intelligent fuzzy logic and model predictive control for high-purity ethyl-methyl carbonate and diethyl carbonate production using reactive dividing wall column

Methyl carbonate (EMC) and Diethyl carbonate (DEC), as excellent solvents for lithium-ion electrolytes, are synthesized by transesterification, and consecutive reaction seriously complicate optimization and control issues. Process synthesis, parameters optimization and advanced control are crucial points that should be addressed for this ill-conditioned and highly nonlinear system, which is the motivation and novelty of present work. With single-objective and multi-objective genetic algorithm, intensified processes for EMC and DEC production were optimized respectively with heterogeneous catalyst. And reactive dividing wall column achieved 23% and 13% total annualized cost reductions compared with reactive distillation for two target carbonates. Based on optimal steady-state solution, intelligent fuzzy logic and model predictive control of EMC and DEC production is conducted to clarify their respective strengths in intensified process with consecutive reaction. Sequential optimization for parameters of FLC was carried out with Integral of Squared Error (ISE) as objective. Finally, control performance of FLC and LPMC are compared with that under PID control, and results show that overshoot and transition time under FLC and LMPC are obviously mitigated, along with evident reduction in ISE.

Model of the Performance Based on Artificial Intelligence-Fuzzy Logic Description of Physical Activity.

The aim of the study was to build a fuzzy model of lower limb peak torque in an isokinetic mode. The study involved 93 male participants (28 male deaf soccer players, 19 hearing soccer players and 46 deaf untraining male). A fuzzy computational model of different levels of physical activity with a focus on the lower limbs was constructed. The proposed fuzzy model assessing lower limb peak torque in an isokinetic mode demonstrated its effectiveness. The novelty of our research lies in the use of hierarchical fuzzy logic to extract computational rules from data provided explicitly and then to determine the corresponding physiological and pathological mechanisms. The contribution of our research lies in complementing the methods for describing physiology, pathology and rehabilitation with fuzzy parameters, including the so-called dynamic norm embedded in the model.

Artificial Intelligence Fuzzy Logic Modeling of Surface Roughness in Plasma Jet Cutting Process of Shipbuilding Aluminium Alloy 5083

In this paper the influence of different process parameters on surface roughness responses in plasma jet cutting process was investigated. Experimentations were conducted on shipbuilding aluminium 5083 sheet thickness 8 mm. Experimental work was performed according to Taguchi L27 orthogonal array by varying four parameters such as gas pressure, cutting speed, arc current and cutting height. Due to complexity of manufacturing process and aim to cover wide experimental space few constraints regarding cutting area were defined. Surface roughness parameters Ra and Rz were analysed as cut quality responses. In order to define mathematical model that will be able to describe effects of process parameters on surface roughness artificial intelligence (AI) fuzzy logic (FL) technique was applied. After functional relations between input parameters and surface roughness responses were defined prediction accuracy of developed fuzzy logic model was checked by comparison between experimental and predicted data. Mean absolute percentage error (MAPE) as well as coefficient of determination (R2) were used as validation measures. Finally, optimal process conditions that lead to minimal surface roughness were defined by creating response surface plots.

Optimization and Validation of Model Predictive Controller (MPC) Approach for Wind Turbine Energy System in Domestic Loads

HIGHLIGHTS A novel MPC controller for Wind turbine system (WTS) is proposed to control the turbine actuation system using intelligent fuzzy logic. The controller proposed with the small actuator that defines with WTS will tackle losses associated during operation and rotation of WTS. The simulation outcomes suggest the proposed controller and further reduce ripples with various operating ranges in domestic loads holds better results compare to conventional one. Insert a highlight no longer than 85 characters.

An Experimental Testing of Optimized Fuzzy Logic‐Based MPPT for a Standalone PV System Using Genetic Algorithms

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)‐based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions.

Implementation of Quality of Service-Oriented Distributed Routing Protocol using Fuzzy Logic in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a type of wireless network made up of nodes that are both wireless and mobile. This type of wireless network is a non-infrastructure network (without a central manager such as router, servers, etc.) that is hard to manage; providing the Quality of Service (QoS) is also difficult. The fuzzy logic is one of the effective methods required to achieve high QoS in MANET. We propose an Ant-colony Based Cluster-head (ABC) selection a protocol based on artificial intelligence fuzzy logic techniques in this paper. With the proposed protocol, we divide the network into clusters, then, choose a cluster head which is a node having high residual energy, the most trusted; this is achieved using Ant-colony optimization (ACO) algorithm thanks to the use of a probability function which determines the likelihood of various nodes to be selected as cluster-heads. Using the Network Simulator Version-2 (NS-2), we evaluated our Proposed Protocol with ones existing in literature; Quality of Service-Ad-hoc On-Demand Distance Vector (QoS-AODV), Bandwidth aware Multi-path Routing protocol in mobile Ad Hoc networks (BMR) and Dynamic Multi-Path Source Routing Method (MSR). We conducted various experimental evaluations varying both the number of the total mobile nodes in the network and the number of receivers from a single sender; all simulation outcomes revealed that the Proposed protocol; Quality of Service-Oriented Distributed Routing Protocol using Fuzzy Logic and clustering techniques in Mobile Ad Hoc Networks (QODFL) outperformed the existing ones as compared to existing protocols, it was able to increase both the Packet Delivery Ratio by 10.5% and throughput by 6.7 % , and the End-to-End Delivery ratio by 7.7%.

Comparative Study between Type-1 and Interval Type-2 Fuzzy Systems in Parameter Adaptation for the Cuckoo Search Algorithm

The objective of this work is focused on improving the optimization ability of the cuckoo search algorithm (CS), and, for this reason, a comparison is made between type-1 and interval type-2 fuzzy logic to look for more promising results in the cuckoo search algorithm (CS), and to help performance, we dynamically adjust the alpha parameter. The idea is to enable CS in leaving the local optima, and then be able to reach the global optima. Currently, there are good results in improving the optimization of algorithms through intelligent fuzzy logic computing after finding the best adjustment parameters. The approach is based on finding the ideal rules with their respective linguistic variables to represent the real world as is perceived by humans. The membership functions that the fuzzy system uses are symmetrically defined for reducing the search space, and this symmetry is what makes the algorithm efficient. We plan to test the proposal in future works in the optimal design of control systems. In the present study, we use five benchmark mathematical functions with variation in the number of dimensions to validate the approach and perform the comparison of interval type-2 and type-1 fuzzy systems in parameter adaptation. For the dynamic adjustment of the parameters, we select the alpha parameter, and the values of Pa and Beta are defined based on the analysis of their behavior in previous works.