Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Coordination of Regulation Devices for Damping Power Oscillations in a Dynamic Disturbance Context: A Fuzzy Logic-Based Approach Applied to the Electrical Grid of the Republic of Congo

This article presents a fuzzy logic-based approach to coordinate the control devices of the power system, such as Power System Stabilizers (PSS) and Static Synchronous Compensators (STATCOM), to damp power oscillations caused by dynamic disturbances. At first, we used the Lyapunov method to study the dynamic stability of the power grid in the Republic of Congo. This method allowed us to analyze the eigenvalues of the state variable matrix and highlight the eigenvalues in the complex plane. Secondly, we proposed a fuzzy logic-based controller to account for uncertainties existing near the thresholds. The inputs to this controller are the generator speed and generator rotor angle. We demonstrated the effectiveness and feasibility of this fuzzy control by applying it to the power grid of the Republic of Congo, with three power stabilizers and two STATCOMs. .

Enhanced Fuzzy Logic Control Model and Sliding Mode Based on Field Oriented Control of Induction Motor

In the context of induction motor control, there are various control strategies used to separately control torque and flux. One common approach is known as Field-Oriented Control (FOC). This technique involves transforming the three-phase currents and voltages into a rotating reference frame, commonly referred to as the “dq” frame. In this frame, the torque/speed and flux components are decoupled, allowing for independent control, by doing so, the motor’s speed can be regulated accurately and maintain a constant flux which is crucial to ensure optimal motor performance and efficiency. The research focused on studying and simulating a field-oriented control system using fuzzy control techniques for an induction motor. The aim was to address the issue of parameter variations, particularly the change in rotor resistance during motor operation, which causes the control system to deviate from the desired direction. This deviation implies to an increase in the magnetic flux value, specifically the flux component on the q-axis. By employing fuzzy logic techniques to regulate flux vector’s components in the dq frame, this problem was successfully resolved, ensuring that the magnetic flux value remains within the nominal limits. To enhance the control system’s performance, response speed, and efficiency of the motor, sliding mode controllers were implemented to regulate the current in the inner loop. The simulation results demonstrated the proficiency of the proposed methodology.

History of Western Philosophy and Quantum Language (Including Quantum Mechanics, Statistics, Fuzzy Logic, etc.)

Although there are many different types of philosophy, many philosophers agree that the mainstream of Western philosophy (Socrates, Plato, Aristotle, Descartes, Kant, Wittgenstein) developed toward the perfection of Socrates’ absolutism. But can the absolutism maintain its central position after analytic philosophy? There are pessimistic views on this problem, such as that of R. Rorty, the standard-bearer of neo-pragmatism. Recently, I proposed quantum language (which is including quantum mechanics, statistics, fuzzy sets, etc.). I think that that this theory is not only one of the most fundamental scientific theories, but also the scientific final destination of Western philosophy. If so, Socrates’ dream has come true. The purpose of this paper is to discuss the above and to inform readers that quantum language has the power to create a paradigm shift from the classical mechanical world view to the quantum mechanical worldview.

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud Environment

Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives.Despite the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation.Consequently,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and privacy.But the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT environment.In this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT environment.The presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT environment.To achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful format.Besides,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature vectors.Moreover,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the network.Finally,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct aspects.The experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.

Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

A Wireless Sensor Network(WSN)becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears impracticable.Concerning the energy produc-tion of the nodes,WSN has major issues that may influence the stability of the system.As a result,constructing WSN requires devising protocols and standards that make the most use of constrained capacity,especially the energy resources.WSN faces some issues with increased power utilization and an on going devel-opment due to the uneven energy usage between the nodes.Clustering has proven to be a more effective strategy in this series.In the proposed work,a hybrid meth-od is used for reducing the energy consumption among CHs.A Fuzzy Logic-based clustering protocol FLUC(unequally clustered)and Fuzzy Clustering with Energy-Efficient Routing Protocol(FCERP)are used.A Fuzzy Clustering with Energy Efficient Routing Protocol(FCERP)reduces the WSN power usage and increases the lifespan of the network.FCERP has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing capabilities.The technique creates uneven groups by using fuz-zy logic with a competitive range to choose the Cluster Head(CH).The input variables include the distance of the nodes from the ground station,concentra-tions,and remaining energy.The proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.

Fuzzy Logic Based Handover Authentication in 5g Telecommunication Heterogeneous Networks

Under various deployment circumstances,fifth-generation(5G)telecommunications delivers improved network compound management with fast communication channels.Due to the introduction of the Internet of Things(IoT)in data management,the majority of the ultra-dense network models in 5G networks frequently have decreased spectral efficiency,weak handover management,and vulnerabilities.The majority of traditional handover authentication models are seriously threatened,making them vulnerable to a variety of security attacks.The authentication of networked devices is the most important issue.Therefore,a model that incorporates the handover mechanism and authentication model must be created.This article uses a fuzzy logic model to create a handover and key management system that focuses on cloud handover management and authentication performance.In order to decrease delays in 5G networks,the fuzzy logic is built with multiple criteria that aim to reduce the number of executed handovers and target cell selection.The simulation is run to evaluate the model’s performance in terms of latency,spatial complexity,and other metrics related to authentication attack validation.

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer,resulting in an inaccurate diagnosis.As a result,it needs an efficient despeckling method for ultrasound images in clinical practice and tel-emedicine.This article proposes a novel adaptive fuzzyfilter based on the direc-tionality and translation invariant property of the Non-Sub sampled Contour-let Transform(NSCT).Since speckle-noise causes fuzziness in ultrasound images,fuzzy logic may be a straightforward technique to derive the output from the noisy images.Thisfiltering method comprises detection andfiltering stages.First,image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image.Then,the system adaptively selects the better-suitedfilter for the specific image region,resulting in significant speckle noise suppression and retention of detailed features.The suggested approach uses a weighted averagefilter to distinguish between noise and edges at thefiltering stage.In addition,we apply a structural similarity mea-sure as a tuning parameter depending on the kind of noise in the ultrasound pic-tures.The proposed methodology shows that the proposed fuzzy adaptivefilter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

Cars are regarded as an indispensable means of transportation in Taiwan.Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded.In this study,a price prediction system for used BMW cars was developed.Nine parameters of used cars,including their model,registration year,and transmission style,were analyzed.The data obtained were then divided into three subsets.The first subset was used to compare the results of each algorithm.The predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural network.The second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low,medium,and high parameters of the membership function(MF)to achieve model optimization.Finally,the third subset was used for the validation set during the prediction process.These three subsets were divided using k-fold cross-validation to avoid overfitting and selection bias.In conclusion,in this study,a model combining two optimal algorithms(i.e.,random forest and k-nearest neighbors)with several optimization algorithms(i.e.,gray wolf optimizer,multilayer perceptron,and MF)was successfully established.The prediction results obtained indicated a mean square error of 0.0978,a root-mean-square error of 0.3128,a mean absolute error of 0.1903,and a coefficient of determination of 0.9249.

Design of Fuzzy Logic Control Framework for QoS Routing in MANET

Wireless networks with no infrastructure arise as a result of multiple wireless devices working together.The Mobile Ad hoc Network(MANET)is a system for connecting independently located Mobile Nodes(MNs)via wireless links.A MANET is self-configuring in telecommunications,while MN produces non-infrastructure networks that are entirely decentralized.Both the MAC and routing layers of MANETs take into account issues related to Quality of Service(QoS).When culling a line of optical discernment communication,MANET can be an effective and cost-saving route cull option.To maintain QoS,however,more or fewer challenges must be overcome.This paper proposes a Fuzzy Logic Control(FLC)methodology for specifying a probabilistic QoS guaranteed for MANETs.The framework uses network node mobility to establish the probabil-istic quality of service.Fuzzy Logic(FL)implementations were added to Network Simulator-3(NS-3)and used with the proposed FLC framework for simulation.Researchers have found that for a given node’s mobility,the path’s bandwidth decreases with time,hop count,and radius.It is resolutely based on this fuzzy rule that the priority index for a packet is determined.Also,by avoiding sending pack-ets(PKT)out of source networks when there are no beneficial routes,bandwidth is not wasted.The FLC outperforms the scheduling methods with a wide range of results.To improve QoS within MANETs,it is therefore recommended that FLC is used to synchronize packets.Thus,using these performance metrics,the QoS-responsible routing can opt for more stable paths.Based on network simulation,it is evident that incorporating QoS into routing protocols is meant to improve traf-fic performance,in particular authentic-time traffic.

Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

A Fuzzy Logic Approach to Predict Tensile Strength in TIG Mild Steel Welds

Welding defects influence the desired properties of welded joints giving fabrication experts a common problem of not being able to produce weld structures with optimal strength and quality. In this study, the fuzzy logic system was employed to predict welding tensile strength. 30 sets of welding experiments were conducted and tensile strength data was collected which were converted from crisp variables into fuzzy sets. The result showed that the fuzzy logic tool is a highly effective tool for predicting tensile strength present in TIG mild steel weld having a coefficient of determination value of 99%.

PID and Fuzzy Logic Control of Ball and Beam System Using Particle Swarm Optimization

In this research, we carried out the modeling of the ball and beam system (BBS) within the MATLAB/Simulink framework by applying both proportional-integral-derivative (PID) and fuzzy logic control strategies to govern the dynamics of this constructed model. The underlying non-linear dynamic equations adjusting the behavior of the BBS system are based on Newton’s second law of motion. The physical installation of the BBS, designed for potential real-time application, comprises a lengthy beam subject to movement through the action of a DC servomotor, with a ball traversing the beam in a reciprocating manner. A distance sensor is strategically placed in front of the beam to determine the exact position of the ball. In this system, an electrical control signal applied to the DC servomotor causes the beam to pivot about its horizontal axis, thereby enabling the ball to move freely along the beam's length. To avoid the risk of losing the ball equilibrium on the beam and to achieve precise system control, a mathematical model was devised and implemented within the MATLAB/Simulink environment. The use of the particle swarm optimization (PSO) algorithm was aimed at tackling the task of refining and optimizing the PID controller specifically designed for the linearized ball and beam control system. The presented system is controlled using both PID and fuzzy logic, and the use of the PSO algorithm enhances the system’s responsiveness efficiency.

Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and cost-effectiveness.This paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse data.When it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,respectively.We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties.We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles.These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and subtlety.We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making field.We use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted data.Additionally,we developed a novel technique by combining the IFSs with quaternion numbers.We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making problems.Using the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity ofmedical evaluation in order to thoroughly assess the relevance and efficacy of the suggested methodology.

Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs

Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

Matlab软件中的Fuzzy Logic工具箱在模糊控制系统仿真中的应用

根据作者对Matlab仿真软件包的应用研究，阐述了如何在Matlab软件中利用Simulink平台和Fuzzy Logic工具箱实现模糊控制系统的设计、仿真和动态性能分析，并以电站汽温过程控制为例进行了仿真研究，结果表明利用Matlab进行模糊控制系统仿真快速简单精确，可视化强，完全能够满足工程技术人员的需要。

基于Fuzzy Logic/Stateflow的挖掘机拟人操作技术

为提高挖掘机工作装置的自主操作水平,减轻驾驶员的劳动强度,针对PC02挖掘机工作装置在挖掘过程中的基本动作,在行为控制的基础上,提出了实现基本动作的I-FORMA-O方法以及基于FuzzyLogic/Stateflow的仿真实验模型;仿真实验实现了由基本动作对应的驱动事件使挖掘行为发生状态迁移的过程.结果表明,用模糊逻辑定义基本动作简化了计算,该仿真实验模型作为平台,直观、可靠,便于借用和修改挖掘机驾驶人员的操作经验,使开发效率明显提高,为以后使用RTW工具箱直接生成可执行代码奠定了基础.