Triangular Membership Functions Research Articles

A fire risk assessment method for offshore platforms: applications of 24Model with BN

ABSTRACT This paper integrates the 24Model with a BN and proposes a fire risk assessment method for offshore platforms. Triangular fuzzy membership function fuzzy set theory, coefficient of variation method and the combination of node state distance function and fuzzy inference are used to address the subjectivity and uncertainty due to insufficient objective data and conflicting opinions of multiple experts. Based on the proposed method, the human, organisational and technical factors of an offshore platform are comprehensively analysed from the perspective of the system. Based on diagnostic inference and sensitivity analysis, the main affecting factors and causal path of the risk scenario are determined. The results show that the proposed method could help in identifying the root causes of the failures and provide new insights into the probabilities and relationships among different factors.

Cyclic multi-hoist scheduling with fuzzy processing times in flexible manufacturing lines

This work addresses the problem of scheduling multiple hoists (robots), which arises on real automated electroplating lines. Several hoists operate on a shared track and should not collide with each other. The processing time for each operation is preset in interval form. The problem simultaneously takes into account two criteria: maximum processing quality and maximum line productivity. To do this, we estimate the dependence of the duration of operations on the level of their quality and, in accordance with these estimates, we represent the processing times in the form of fuzzy numbers with triangular membership functions. The novelty of this study is two-fold. First, we propose a new mathematical model based on the use of fuzzy numbers and fuzzy operations on them, which is designed to deal with complex galvanic lines served by multiple hoists. The second new feature is a new combinatorial method developed for scheduling multiple hoists. For this, we modify and refine the so-called “method of prohibited intervals”, which has been actively used in scheduling theory in recent decades to solve problems, but until now was known only for the case of a single hoist. Thus, the second novelty of this work is that it is actually the first attempt to use the powerful technique of fuzzy set theory to solve a practical problem of scheduling multiple hoists. Computational experiments on real and randomly generated instances show that the new algorithm can successfully solve real problems that arise in practice.

Analysis of Fuzzy Membership Function on Greenhouse Gas Emission Estimation by Triangular and Trapezoidal Membership Functions in Indian Smart Cities

Analysis of Fuzzy Membership Function on Greenhouse Gas Emission Estimation by Triangular and Trapezoidal Membership Functions in Indian Smart Cities

From Fuzzy to Mobile Fuzzy

From Fuzzy to Mobile Fuzzy

Implementation of adaptive neuro-fuzzy inference system in design and process optimization of latent heat storage system

This study focuses on implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an artificial neural network model to optimize latent heat thermal energy storage (LHTES) systems. Experimental data comprising seven input parameters (length, shell diameter, tube diameter, mass flow rate, difference in temperature, latent heat, and density) and two output parameters (charging and partial charging time) are collected and used to train the ANFIS model. Initially, the model is developed using a grid partition method, employing Triangular and Gbell membership functions and assigning diverse membership rules. This initial model demonstrated satisfactory accuracy based on quality metrics (R2, mean absolute, and root mean square error). Then, a subtractive clustering approach is applied to further enhance the model's accuracy, which groups different data clusters together. This enhanced model exhibited superior performance compared to the grid partition method, making it the optimal choice. In addition, an experimentally validated numerical model is developed using COMSOL Multiphysics and is utilized to crosscheck the predicted output of the ANFIS model. From a set of 100 combinations of storage capacity between 0.45 and 0.55 MJ, a fixed latent heat storage capacity of 0.456 MJ was identified by considering the combination with the least charging time based on the input parameters. Using this storage capacity, a minimum charging time of 223 min and a partial charging time of 180 min was determined. The numerical model also confirmed these optimal values.

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles

This study aimed to solve the problem that is the frequent switching between the acceleration and braking modes of the driverless ferry vehicle, affecting the comfort and stability of speed control. The driverless ferry vehicle encounters unknown obstacles on the road that affect the normal planning and tracking control of the ferry vehicle and finally lead to the problem that the driverless ferry vehicle cannot drive normally. First of all, in the longitudinal control, the fuzzy PID control algorithm was utilized to produce the fuzzy PID acceleration controller by taking into account the difference between the actual and expected speeds and choosing the triangular membership function. According to the relationship between the brake oil pressure and brake torque, the brake controller was designed. The acceleration/braking switching module with acceleration tolerance zone was added to the longitudinal controller, and the acceleration/braking mode-switching controller was designed. Secondly, in the lateral control, the tire cornering stiffness was analyzed, an MPC controller with a planning module was designed, and a lateral motion controller with an obstacle avoidance replanning function was proposed. Finally, according to the prediction time domain of different planning modules corresponding to different speeds, a coordinated control strategy of horizontal and longitudinal motion was proposed by using a real-time speed adjustment planning module to predict the time domain. Through the joint simulation analysis of MATLAB and CarSim, the results show that the driving stability of the ferry vehicle was significantly improved, and the longitudinal speed error of the ferry vehicle was reduced by 43.59%. The ferry’s avoidance of obstacles and tracking of reference trajectories were significantly improved, so that the tracking error can be reduced by 61.11%.

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

EXPERIMENTAL INVESTIGATION AND INTELLIGENT PREDICTION OF MECHANICAL STRENGTH OF PERVIOUS CONCRETE

This study determines the mechanical strength characteristics of pervious concrete using fuzzy logic. During construction, the process of determining the quality of concrete produced takes about 28 days before testing, training the fuzzy logic model through the research data gotten from the laboratory experiments in order to intelligently predict the mechanical strength of pervious concrete. Crushed granite was used in this experiment to ensure the pervious concrete has a little degree of fine aggregates and the maximum size of coarse aggregate used was 20 mm. Compressive, flexural and split tensile strength tests were carried out on pervious concrete of mixing ratios 1:4, 1:5 and 1:6 with each of the mixing ratios having varying water to cement ratios of 0.4, 0.5, 0.6 and 0.7. The developed fuzzy logic model was applied to predict the mechanical strengths of the concrete by using data obtained from experimental results, with input parameters of water content, crushing value, weight and density and the mechanical strengths as the output parameters employing the triangular membership function. The model was calibrated using the error measures of the root mean square error (RMSE), mean absolute percentage error (MAPE) and R squared were computed for the model. In this study, it was observed that higher water to cement ratio slightly reduces the mechanical strength of PC but increases the workability compared to increase in cement to granite ratio which notably increases the strength and reduced the workability, alternatively, higher water to cement ratio causes a notable decrease in permeability of the pervious concrete. The correlation between the experimental and predicted values was excellent. This shows that fuzzy logic can be used to predict the mechanical properties of pervious concrete, and fuzzy logic can also be used to appropriately deduce missing values in the mechanical properties of PC subject to further research

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Wearable sensor-based on exercise monitoring system for disabled the individuals using a multi-attribute fuzzy evaluation mode

Recently, there has been a lot of interest in using the wearable sensors for tracking the exercise progress because of the unbiased accuracy and precision they are provided throughout the continual monitoring. For those with physical impairments, the system’s non-intrusive, lightweight ways of the monitoring activity may ease their load and enhance the quality of their decision-making. As a different measuring unit measures the exercise activity levels recorded by the each wearable sensor, it is challenging to assess the monitoring system. Hence, this paper proposes a Hybridized Fuzzy Multi-Attribute for Exercise Monitoring System (HFMA-EMS) to address the uncertainty issues of the wearable sensors. The Triangular Fuzzy membership function is proposed to begin classifying the observed values. Pair-wise attribute comparison and evaluator weighting in a T-spherical uncertain linguistic set setting utilizing the Techniques for Ordering of Preferences by Similarities to Ideal Solutions (TOPSIS). In the suggested method, a utility function is used to assess the merits of a model in which attribute the weights are calculated, followed by an exercise in which the attributes are ordered employing the Measurements of the Alternative and Ranking Compromise Solutions model (MARCOS). The performance is performed to analyze the proposed method’s accuracy, precision, recall, f1-score, and correct and incorrect exercise assessment by an accelerometer, gyroscope, and magnetic field sensor unit. The application scenario of the HFMA-EMS can be used in the clinical applications, healthcare management, and sports injury detection.

Biodegradation performance of ball burnished magnesium alloy in stimulated body fluids for bioresorbable implant applications

Magnesium alloys are found to be potential bioresorbable materials. However, the application range of these alloys as bioresorbable materials has been limited due to rapid degradation in physiological fluids. The current study deals with the enhancement of degradation behavior of Mg Ze41A alloy in SBF solution by ball burnishing. Further, the influence of burnishing parameters on biodegradation rate has been investigated by Tafel plots, polarization resistance, electrochemical impedance spectroscopy and surface morphology analysis of degraded samples. Additionally, the biodegradation rate has been estimated by ANFIS modeling. The biodegradation rate of Mg Ze41A has been reduced from 1.106 mm/yr to 0.367 mm/yr with an improvement of 68.82% at optimum burnishing condition of burnishing force 50 N, burnishing speed 1400 RPM, burnishing feed 125 mm/min and 2 passes. ANOVA analysis has revealed that burnishing force and feed have contributed the most for the enhancement. The shift in degradation potential towards noble direction and reduction in degradation current density in potentiodynamic polarization curves and the highest charge transfer resistance in electrochemical impedance spectroscopy of the burnished samples are observed for burnished samples. The results reveal that ball burnishing process is a potential surface modification method in reducing the biodegradation rate of Mg Ze41A alloy. The ball burnishing has resulted in lowest biodegradation rate at a degradation current density of 5.743 μA/cm2 and degradation potential of −1.513 V. The ANFIS model developed with 81 fuzzy rules and triangular membership function estimated the biodegradation rate with an accuracy of 95.03%. The decrease in biodegradation rate of ball burnished magnesium substrate in SBF is essentially due to cumulative effect of compressive residual stress, microhardness, surface finish, work hardening and grain refinement.

Assessment of stability of earth dams based on construction pace by fuzzy inference logic

This study aims to evaluate the earth dam stability fuzzy model based on the pace of construction by way of influential parameters, including the dam construction time (embankment consolidation time) in the stability analysis of earth dams. A fuzzy inference system based on the Sugeno inference engine was developed, and 620 if-then rules in plane strain and axial symmetry (Plaxis) were used to train the fuzzy system. The input parameters included the dam height, crest width, dam slope, cohesion, friction angle, elasticity modulus, Poisson’s ratio, unsaturated unit weight, consolidation (construction) time and permeability coefficient, with the factor of safety output. To develop the fuzzy inference system, a triangular membership function was employed, calculating the influential parameters over five periods in Matrix laboratory (Matlab) software. The fuzzy model results were compared with the results of numerical Plaxis and fast Lagrangian analysis of continua models. High coefficients of determination (R2) suggest good agreement between the fuzzy and numerical models, and it can be assumed that the proposed fuzzy model is efficient and effective for the stability analysis, and hence assessment of factors of safety, of earth dams.

Investigation on the influence of a new trapezoidal-triangular membership function in IT2FLS with type-reductions for a manufacturing application

The performance of Interval type-2 fuzzy logic system (IT2FLS) can be affected by many factors including the type of reduction methodology followed and the kind of membership function applied. Further, a particular membership function is influenced by its construction, the type of optimisation and adaptiveness applied, and the learning scheme adopted. The available literature lags in providing detailed information about such factors affecting the performance of IT2FLS. In this work, an attempt has been made to comprehensively study the factors affecting the performance of IT2FLS by introducing a new trapezoidal-triangular membership function (TTMF). A real-time application of drilling operation has been considered as an example for predicting temperature of the job, which is considered as one of the key state variables to evaluate. A detailed comparison based on membership functions (MFs) such as triangular membership function (TrMF), trapezoidal membership function (TMF), the newly introduced trapezoidal-triangular membership function (TTMF), semi-elliptic membership function (SEMF), and Gaussian membership function (GMF) has been performed and presented. Further, the average error rate obtained with two “type-reduction” methods such as “Wu-Mendel” uncertainty bounds and Center of sets type reduction (COS TR) has also been discussed. This study provides information for selecting a particular MF and “type reduction” scheme for the implementation of IT2FLS. Also, concludes that MF having fewer parameters such as GMF and SEMF possess significant advantages in terms of computation complexity compared to others.

Computational study of a co-infection model of HIV/AIDS and hepatitis C virus models

Hepatitis C infection and HIV/AIDS contaminations are normal in certain areas of the world, and because of their geographic overlap, co-infection can’t be precluded as the two illnesses have a similar transmission course. This current work presents a co-infection model of HIV/AIDS and Hepatitis C virus with fuzzy parameters. The application of fuzzy theory aids in tackling the issues associated with measuring uncertainty in the mathematical depiction of diseases. The fuzzy reproduction number and fuzzy equilibrium points have been determined in this context, focusing on a model applicable to a specific group defined by a triangular membership function. Furthermore, for the model, a fuzzy non-standard finite difference (NSFD) technique has been developed, and its convergence is examined within a fuzzy framework. The suggested model is numerically validated, confirming the dependability of the devised NSFD technique, which successfully retains all of the key properties of a continuous dynamical system.

Solution of generalized fractional Jaulent–Miodek model with uncertain initial conditions

This paper analyses a coupled system of generalized coupled system of fractional Jaulent–Miodek equations, including uncertain initial conditions with fuzzy extension. In this regard, an extension of the homotopy with a generalized integral algorithm is adopted for a class of time-fractional fuzzy Jaulent–Miodek models by mixing the fuzzy q-homotopy analysis algorithm with a generalized integral transform and Caputo fractional derivative. The triangular fuzzy numbers (TFNs)are expressed in double parametric form using κ-cut and r-cut and utilized to explain the uncertainties arising in the initial conditions of highly nonlinear differential equations with generalized Hukuhara differentiability (gH-differentiability). The TFNs are controlled by the κ-cut and r-cut, and the variability of uncertainty is examined using a “triangular membership function” (TMF). The results are analyzed by finding the solutions for different spatial coordinate values of time with κ-cut and r-cut for both lower and upper bounds and validated through numerical and graphical representations in crisp cases. Finally, it can be seen that the uncertain probability density function rapidly decreases at the left and right edges when the fractional order is increased, and it is observed that the obtained solutions are more accurate than the existing results through the Hermite wavelet method in the literature.

An Enhanced Security in Medical Image Encryption Using Dynamic Chaotic Fuzzy Based Technique

As IoT and cloud computing have grown in popularity, medical images are now often transmitted between devices or accessed directly from the cloud. With this, the security is always a concern as these images are prone to many types of attack. We have proposed a proven method that is efficient in terms of security, time complexity, and integrity in order to be cloud-friendly so that it may be launched into the cloud and made accessible to users at any time. The goal of the work is to create a dynamic key that, depending on fuzzy values, alters the reproduction rate parameters with each repetition. By applying the last chaotic value created from the previous iteration, the fuzzy triangular membership function has been used in this manner to generate the reproduction rate parameter. The uniqueness and major benefit of the suggested strategy are that it can increase the security of the algorithm that makes use of a chaotic map and a static key. The method has been put forth when designing algorithms so that it should not only demonstrate security against different attacks but also provide efficiency towards computational complexity. The technique has been tested against a set of images and an existing algorithm using a variety of security metrics, including the correlation coefficient, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and entropy. It has been determined from the comparative analysis that the proposed approach can make the existing algorithm more secure.

An Experimental Private Small Hydropower Plant Investments Selection Classification System

Investment selection problems and models are crucial for humans, communities, and states. Private small hydroelectric power/ hydropower plant investments (PSHPPIs) selection problem is a unique one in those problems and models. Many available scientific methods can be implemented on this research topic. One methods group is type-2 Mamdani’s type fuzzy inference systems (FISs). This study presents an experimental one-node general type-2 4-zslices Mamdani’s type FIS model for PSHPPIs selection. There are 3 input variables (total estimated electricity generation: M1, total estimated cost: M2, change in the average runoff: M3), 1 output variable (O1: selected/unselected, selection classification of PSHPPIs), and 49 rules. M1 is designed in 5 triangular and trapezoidal general type-2 4-zslices fuzzy membership functions (MFs), M2 in 5 triangular and trapezoidal general type-2 4-zslices fuzzy MFs, M3 in 5 Gaussian type-2 4-zslices fuzzy MFs, and O1 in 2 Gaussian type-2 4-zslices fuzzy MFs. All modeling details are finalized by a human-expert decision process. JuzzyOnline V2.0, a browser-based platform of Juzzy (a Java-based library: Juzzy toolkit), is used for all FIS modeling and calculation activities. Five PSHPPI options assumed to be in their very early investment stages in Turkiye are evaluated in an experimental application. It is clear that an improved, tuned, and fine-tuned version of this model will help private investors to take more convenient/satisfying actions/decisions in a very short evaluation period in their PSHPPIs decision processes. An automatic investment selection classification system can be built in an appropriate development period after experiencing many methods and models. This study is one of the open-science humanoid robot, robot and platform development activities entitled Global Power Robots and Platforms: GPRP/GPRAP (Global Power Plants Developers: GP2D/G2PD/GPPD, Global Power Plants Engineers: GP2E/G2PE/ GPPE, Global Power Plants Owners: GP2O/G2PO/GPOO, Global Power Prediction Systems: GP2S/G2PS/GPPS), Global Profile Analyses Systems, and Global Social Network Analyses Systems.

Fuzzy-IoT smart irrigation system for precision scheduling and monitoring

Adopting and developing sustainable precision irrigation systems allows for improving productivity, reducing water and energy losses in agricultural fields.This paper deals with the development and real-time implementation of a smart precision irrigation system (SPIS). The proposed system combines two irrigation approaches, processed by a feedback fuzzy logic (FL) controller, and long-range data transmission and monitoring via the LoRa protocol. The combined approaches not only enhance overall performance but also address several limitations commonly associated with existing approaches. The proposed system uses the collected data from the sensors unit to provide the optimal irrigation decisionand communicates it with the operator (farmer) via a developed IoT platform. Data from soil moisture, ambient temperature, solar irradiance, and rainfall sensors are processed through the FL controller to adjust the irrigation times. The inputs and outputs are fuzzified by trapezoidal and triangular membership functions. Mamdani's fuzzy inference is used to control the system through a set of linguistic control rules for a more accurate decision for each scenario. The field experimentation of the developed SPIS in the eastern region of Morocco shows high efficiency and reliability by saving water and energy compared to traditional irrigation control. The proposed system, with remote data monitoring, promotes better management of irrigation, water, and energy consumption, and it is suitable to be mounted with any type of irrigation installation in the field, without extensive modifications.

Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets

Hot work poses great hazards to industrial operation site. Although regulatory and guidance exist, accidents caused by hot work continue to occur. Risk assessment has proved to be an effective method for risk control. However, current studies paid less attention to the evolution process of industrial hot work accidents and the quantitative risk assessment seems to be insufficient. In this context, we aimed to perform comprehensive risk evolution analysis and quantitative risk assessment of hot work using adaptive bow tie (ABT) and Petri nets (PNs). The ABT model is improved to analyze deep causes, consequences, and their relationships. PNs are employed to conduct quantitative risk assessment. To address uncertainties, the triangular membership function and improved regional center method are proposed to calculate the confidences of all places and transitions in the PNs. The probabilities are finally obtained by data fitting method. The study found that the most likely consequence of hot work accident is near miss. The results also indicate that hot work accident prevention measures should emphasize on the isolation of workplace, combustible gases test, removal of combustible substances, job organization, and job training. Besides, emergency devices maintenance and emergency drilling are critical to prevent accidents from expanding.