Fuzzy Logic-based Approach Research Articles

A fuzzy logic-based approach for fault diagnosis and condition monitoring of industry 4.0 manufacturing processes

Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing in the development of algorithmic diagnostic solutions for their industrial equipment, relying on measured data and process models. However, process and fault models are not usually available for complex productions plants and production data are usually unlabeled. Thus, to classify machine status, unsupervised approaches such as anomaly detection and signal processing strategies have to be employed. Due to the unsupervised nature of the problem, it is meaningful to apply several diagnostic algorithms to cover most of the process anomalous behaviors. Additionally, in some contexts, the experience of process operators in grasping the correct functioning of machines as well as their ability in understanding early signs of deterioration is relevant for the diagnosis of incoming failures. However, seldom these information can be included in failure diagnosis algorithms. In this paper, we propose a diagnostic scheme for condition monitoring of mechanical components. The proposed scheme combines anomaly detection algorithms, envelope analysis of vibration data, and eventually additional qualitative information on machine functioning. The combination of all the fault indicators is obtained leveraging on a fuzzy inference system. The proposed scheme is experimentally validated on a steel making plant with real process data, making use of heuristic information such monitoring reports of machine health status.

Sensing and Sharing of Spectrum in Cooperative Cognitive Radio Vehicular Networks Using K-tuned Classifiers and Fuzzy Logic

This paper aims to efficiently utilize the spectrum based on sensing and sharing optimal resources like relay and power on cooperative cognitive radio in a vehicular network. Resources are allocated in the V2V environment by considering the effect of the Doppler shift. Spectrum sensing is carried out using K-tuned Nearest Neighbors algorithm, in which the K parameter is tuned to avoid misclassification-related problems that enhance the spectrum sensing. After sensing the status of the spectrum, a Fuzzy logic-based approach shares the spectrum with the help of optimized resources. Simulations are carried in different vehicle scenarios in terms of bit error rate and throughput.

A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique

Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.

A Mathematical Theory of Big Data

This article presents a cardinality approach to big data, a fuzzy logicbased approach to big data, a similarity-based approach to big data, and a logical approach to the marketing strategy of social networking services. All these together constitute a mathematical theory of big data. This article also examines databases with infinite attributes. The research results reveal that relativity and infinity are two characteristics of big data. The relativity of big data is based on the theory of fuzzy sets. The relativity of big data leads to the continuum from small data to big data, big data-driven small data analytics to become statistical significance. The infinity of big data is based on the calculus and cardinality theory. The infinity of big data leads to the infinite similarity of big data. The proposed theory in this article might facilitate the mathematical research and development of big data, big data analytics, big data computing, and data science with applications in intelligent business analytics and business intelligence.

CE-oriented culture readiness: An assessment approach based on maturity models and fuzzy set theories

Moving towards a Circular Economy (CE) requires systemic changes across organizations, technological and cultural changes. However, there is a lack of studies in the literature on guidelines and tools specifically focused on helping organizations to identify the aspects that could be improved to implement a CE-oriented culture. Thus, this paper aims to propose a fuzzy logic-based approach to assess the readiness of organizations to implement a CE-oriented culture. An in-depth literature review to identify the CE-oriented culture elements, a fuzzy Delphi methodology (FDM) to select the elements, a fuzzy inference system (FIS) to classify the organization in readiness levels, and an application to clarify the proposed approach were carried out. The main result of this study is an assessment approach that, based on analyzing a set of elements representing a CE-oriented culture: classifies an organization in six levels of readiness to implement a CE-oriented culture; provides a pairwise comparison of the performance of the organization between the building blocks; and a radar chart with the overall performance of the organization regarding CE-oriented culture. These three outcomes provide organizations with specific information and guidance for decision-making regarding the changes that should be made to adapt or change the existing culture to one that comprises the specificities of CE. This study found that organizations that focus on radical innovations and balance the efforts between technical and soft aspects are more oriented towards a CE culture. It was also identified that making a more in-depth analysis of the presence of CE-oriented culture elements in the organization may contribute by mitigating possible rebound effects that could be generated from implementing CE practices. Moreover, analyzing the culture orientation for CE of the organization might create in leaders and employees a sense of urgency to move towards CE as a way to obtain environmental, social and economic benefits.

Distributed and Dynamic Channel Assignment Schemes for Wireless Mesh Network

Wireless mesh network (WMN) with wireless backhaul technology provides last-mile Internet connectivity to the end-users. In multi-radio multi-channel WMN (MRMC-WMN), routers provide multiple concurrent transmissions among end-users. The existence of interference among concurrent transmissions severely degrades the network performance. A well-organized channel assignment (CA) scheme significantly alleviates the interference effect. But in trying to minimize interference, the CA scheme may affect the network connectivity. So, the CA scheme has to consider both these two conflicting issues. In this paper, as part of the initial configuration of WMNs, we propose a game theory-based load-unaware CA scheme to minimize the co-channel interference and to maximize the network connectivity. To adapt to the varying network traffic, we propose a dynamic channel assignment scheme. This scheme measures the traffic-load condition of the working channels of each node. Whenever a node finds an overloaded channel, it initiates a channel switch. Channel switching based on the fixed threshold may result in a channel over/underutilization. For optimal channel utilization, we propose a fuzzy logic-based approach to compute the channel switch threshold. The contending nodes and their densities and loads dominantly affect the network capacity and hence the performance. In the context of network capacity enhancement, we have addressed these factors and focused on increasing the network capacity. The simulation results indicate that our proposed load-unaware and load-aware CA schemes outperform the other related load-unaware and load-aware CA approaches.

A Proposed Fuzzy Logic Approach for Conserving the Energy of Data Transmission in the Temperature Monitoring Systems of the Internet of Things

One of the primary challenges facing the Internet of Things is the reservation and efficient consumption of energy resources, especially in those types of applications that require continuous monitoring or suffer from lacking ongoing energy resources. Despite this, the indoor temperature and humidity monitoring systems are unconcerned about the insignificant amount of energy consumed during critical times when sending unimportant or useless data to the control room’s servers. This paper proposes a fuzzy logic-based approach for reducing the amount of energy spent in indoor temperature and humidity monitoring systems by filtering data that is sent to servers based on several surrounding circumstances such as time of data recording and current energy consumption amount while maintaining constant monitoring. The experimental results on the Appliances Energy Prediction dataset show that the proposed fuzzy-based approach successfully reduces energy consumption in temperature and humidity monitoring systems by 11.8%.

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

This paper deals with the trajectory tracking control problem of a planar underactuated tendon-driven truss-like manipulator (UTTM) by using fuzzy logic control methods, including type-1 fuzzy and interval type-2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon-driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic-based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type-1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type-1 fuzzy controllers, an interval type-2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type-1 and interval type-2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type-2 fuzzy-based trajectory tracking controller provides better performance than the type-1 counterpart in terms of tracking accuracy and capacity against uncertainties.

Neuro-fuzzy Architecture for Handover Decision Making in Emerging Heterogeneous Networks

In emerging heterogeneous networks, seamless vertical handover is a critical issue. There must be a trade-off between the handover decision delay and accuracy. This paper’s concern is to contribute to reliable vertical handover decision making that makes a trade-off between complexity and effectiveness. So, the paper proposes a neuro-fuzzy architecture that joints the capacity of learning of the artificial neural networks with the power of linguistic interpretation of the fuzzy logic. The architecture can learn from experience how executing a handover to a particular access network affects the quality of service. Simulation results reveal that this architecture is fast, enhances the overall performance and reliability better than the fuzzy logic-based approach.

A Novel Method to Obtain the Flux-Linkage Characteristics of Switched Reluctance Motors

Measuring and quantifying the electromagnetic characteristics of switched reluctance motors (SRMs) are indispensable for advanced motor control and performance enhancement. This article reports on a novel method in obtaining the flux-linkage characteristics of an SRM without rotor clamping devices and position sensors. This approach combines measurement with data reconstruction. For the measuring step, flux-linkage samples at four torque-balanced positions were measured indirectly. Considering the magnetic coupling among phases, two different excitation forms were adopted to further increase accuracy. Then, for the reconstructing step, a fuzzy logic-based approach was applied to reconstruct the complete flux-linkage characteristics with measurement results. The construction results were compared to measurements results from the traditional rotor clamping method, and good agreements can be found, demonstrating the effectiveness of the proposed method. Furthermore, based on reconstructed electromagnetic characteristics, dynamic simulation of an SRM prototype under different control modes was carried out and analyzed in MATLAB. Comparison with dynamic experiments further verifies the accuracy and effectiveness of the proposed method.

A Study on Fuzzy Query with Weights Based on Fuzzy Logic

Natural Language Processing (NLP) is the electronic tactic to analyzing text that is depends on both a set of ideas and a set of technologies. Natural Language Processing (NLP) is a subfield of artificial intelligence and etymology it thinks about the issues of computerized era and comprehension of regular human dialects. Common dialect era frameworks change over data from PC databases into ordinary sounding human dialect, and normal dialect understanding frameworks change over specimens of human dialect into more formal representations that are less demanding for PC projects to control. The Fuzzy logic-based approach provides another alternative for effective natural language analysis. It is commonly recognized that many phenomena in natural language lend themselves to descriptions by Fuzzy mathematics, including Fuzzy sets, Fuzzy relations and Fuzzy logic. By defining a Fuzzy logic system and acquiring proper rules, we hope that difficulties in analysis of speech can be alleviated. The goal of NLP is to enable communication between people and computers without resorting to memorization of complex commands and procedures.

A Fuzzy Logic-Based Approach for Humanized Driver Modelling

All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

Prioritizing facilities linked to corporate strategic objectives using a fuzzy model

PurposeLimited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.Design/methodology/approachA Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.FindingsResults of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.Originality/valueThis paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic-Based Approach

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise.

Analysis and experimental evaluation of novel hybrid fuzzy‐based sliding mode control strategy for performance enhancement of PV fed DSTATCOM

This article shows the motivation towards the development of a novel hybrid fuzzy-based sliding mode control scheme for photovoltaic tied DSTATCOM topology for eliminations of perturbations in source currents and reactive power compensation. A LCLC type passive filter is employed at the output of PV fed DSTATCOM, which helps to connect the solar system to a grid. The presence of the series capacitor in the passive filters decreases dc link voltage, which helps in reducing the ratings of DSTATCOM in the proposed system. The switching signals for the PV fed DSTATCOM is generated by the utilization of novel hybrid fuzzy-based sliding mode control scheme. To improve the efficiency of dc link voltage controller and to regulate the dc link voltage, fuzzy logic-based approach has been utilized. The controller performance has been compared with conventional algorithms of reference signal generation to analyze the efficiency. An experimental prototype has been developed in the laboratory and results are analyzed to validate the efficiency of the proposed controller.

A novel neuro-fuzzy-based localisation system for WSN using node proximity

Localisation is a challenging issue in wireless sensor network. This paper describes a neural network and fuzzy logic-based approach for localisation in wireless sensor network (WSN). The received signal strength indicators (RSSIs) of some anchor nodes are used as basic parameter to estimate the location of sensor nodes. Using fuzzy logic the RSSI values of anchor nodes are categorised into some predefined regions and RSSI patterns are generated using fuzzy inference rules. These patterns are then used as input to a trained neural network (NN) for estimating a proximity factor of sensor nodes which in turn is used to calculate their positions. The RSSI patterns are used to find out the weighted position of the anchor nodes which when divided by the proximity factor gives the estimated position of the sensor nodes. A modified back propagation method is used to train the neural network. Proposed model is tested using network simulator NS2 and result shows accuracy up to 95%.

A novel neuro-fuzzy-based localisation system for WSN using node proximity

Localisation is a challenging issue in wireless sensor network. This paper describes a neural network and fuzzy logic-based approach for localisation in wireless sensor network (WSN). The received signal strength indicators (RSSIs) of some anchor nodes are used as basic parameter to estimate the location of sensor nodes. Using fuzzy logic the RSSI values of anchor nodes are categorised into some predefined regions and RSSI patterns are generated using fuzzy inference rules. These patterns are then used as input to a trained neural network (NN) for estimating a proximity factor of sensor nodes which in turn is used to calculate their positions. The RSSI patterns are used to find out the weighted position of the anchor nodes which when divided by the proximity factor gives the estimated position of the sensor nodes. A modified back propagation method is used to train the neural network. Proposed model is tested using network simulator NS2 and result shows accuracy up to 95%.

A quasi-qualitative analysis of flipped classroom implementation in an engineering course: from theory to practice

Students have different learning approaches and acquire knowledge in different ways owing to their varied perceptions and grasping abilities. Treating students equally, following the traditional teacher-cantered approach, may not be effective and calls for innovations in knowledge dissemination. This article takes a student-centric approach to implement a ‘flipped classroom’ model in an engineering course for constructivism (i.e. experience-based learning), and students’ personalized learning. The performance of the flipped classroom approach is compared with the traditional lecturing approach using four lenses: students’ grades, students’ reflection using three surveys at different stages during the semester, teacher’s observation, and peer observation. In most of the existing approaches, analysis of the qualitative survey data is performed manually without computer assistance, and therefore these are criticized for being primeval and instinctive. In this research, a novel approach, based on fuzzy logic, is devised to evaluate the qualitative survey responses from the students. It is shown in this research, that the linguistic survey responses can be summarized in an index more meaningfully using a fuzzy logic-based approach. The results further showed that the flipped classroom model helped students to improve their grades and enhance engagements with their teacher and their peers. Increased interaction, during interactive class activities, was instrumental in evolving students’ problem-solving and critical analysis skills towards better learning experiences. Furthermore, students’ survey results, analyzed using the novel fuzzy indexing and a non-parametric statistical test showed that the intervention in the form of flipped classes was effective and found to be statistically significant. The findings of this study may help other academics, endeavoring for experience-based, and students’ personalized learning.

A fuzzy model for selecting travel services to produce personalized offers to Internet users

With the spread of information technologies in the field of tourism and the emergence of a specific sector of e-tourism, an increasingly urgent problem is not the traditional search for relevant information on the Internet, but the search for personally oriented, personalized information adapted to the individual needs of specific users. The main issue in e-tourism is to determine the most suitable tourist resources from the many available for use by a particular user. Recommender systems in e-tourism provide the mechanism of satisfying customized users’ requirements by supplying them with adequate information about travel resources and services. The implementation of tourist goals can be carried out by a variety of different combinations of travel services. The optimal selection of services is a complex multi-alternative task due to the lack of data about the quality of services and the complexity of the description of user preferences. The paper considers the possibility of using fuzzy logic to the development of the e-tourism support system. Based on user profile data, web services that provide information about travel services, and ratings of services by other users, the system evaluates various alternatives of service’s composition and selects the recommended objects (hotels, flights, restaurants, car rental, museums, etc.). The service-oriented paradigm has been proposed to model the composition of tourism services, and fuzzy logic is used to evaluate the quality and compliance of tourism services with user requests and preferences. The proposed fuzzy logic-based approach analyzes the quality of travel services for personal recommendations to the tourist. The proposed model can provide decision support to users to select the most appropriate travel services and to plan a trip.

Identification of Chaboche\u2013Lemaitre combined isotropic\u2013kinematic hardening model parameters assisted by the fuzzy logic analysis

A very good knowledge of material properties is required in the analysis of severe plastic deformation problems in which the classical material processing methods are accelerated by the application of the additional cyclic load. A general fuzzy logic-based approach is proposed for the analysis of experimental and numerical data in this paper. As an application of the fuzzy analysis, the calibration of Chaboche–Lemaitre model hardening parameters of PA6 aluminum is considered here. The experimental data obtained in a symmetrical strain-controlled cyclic tension–compression test were used to estimate the material’s hardening parameters. The numerically generated curves were compared to the experimental ones. For better fitting of numerical and experimental results, the optimization approach using the least-square method was applied. Unfortunately, commonly accepted calibration methods can provide various sets of hardening parameters. In order to choose the most reliable set, the fuzzy analysis was used. Primarily selected values of hardening parameters were assumed to be fuzzy input parameters. The error of the hysteresis loop approximation for each set was used to compute its membership function. The discrete value of this error was obtained in the defuzzification step. The correct selections of hardening parameters were verified in ratcheting and mean stress relaxation tests. The application of the fuzzy analysis has improved the convergence between experimental and numerical stress–strain curves. The fuzzy logic allows analyzing the variation of elastic–plastic material response when some imprecisions or uncertainties of input parameters are taken into consideration.