Search Results (628)

This work presents a data-driven approach for diagnosing sensor faults and leaks in hydraulic pipelines using neuro-fuzzy Zonotopic Kalman Filters (ZKF). The approach involves two key steps: first, identifying the nonlinear pipeline system using an adaptive neuro-fuzzy inference system (ANFIS), resulting in a set of Takagi–Sugeno fuzzy models derived from pressure and flow data, and second, implementing a neuro-fuzzy ZKF bench to detect pipeline leaks and sensor faults with adaptive thresholds. The learning phase of the neuro-fuzzy systems considers only fault-free data. Fault isolation is achieved by comparing zonotopic sets and evaluating a fault signature matrix. The method accounts for parametric uncertainty and measurement noise, ensuring robustness. Experimental validation on a hydraulic pipeline demonstrated high precision (up to 99.24%), recall (up to 99.20%), and low false positive rates (as low as 0.76%) across various fault scenarios and operational points. Full article

This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance the prediction of mechanical properties in 3D-printed components. Despite AM’s versatility in producing complex geometries, achieving consistent mechanical performance remains challenging due to various process parameters and the anisotropic behavior of printed parts. The proposed approach combines the learning capabilities of neural networks with the decision-making strengths of fuzzy logic, enabling the ANFIS to refine printing parameters to improve part quality. Experimental data collected from AM processes are used to train the ANFIS model, allowing it to predict outputs such as stress, strain, and Young’s modulus under various printing parameters values. The predictive performance of the model was assessed with the root mean square error (RMSE) and coefficient of determination (R²) as evaluation metrics. The study initially examined the impact of key parameters on model performance and subsequently compared two fuzzy partitioning techniques—grid partitioning and subtractive clustering—to identify the most effective configuration. The experimental results and analysis demonstrated that ANFIS could dynamically adjust key printing parameters, leading to significant improvements in the prediction accuracy of stress, strain, and Young’s modulus, showcasing its potential to address the inherent complexities of additive manufacturing processes. Full article

Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller. Full article

With the in-depth implementation of China’s “Belt and Road” strategic policy, member countries along the Belt and Road have gained enormous economic benefits. Thus, it is important to accurately grasp the factors that affect carbon emissions and coordinate the relationship between economic development and environmental protection, which can impact the living environment of people worldwide. In this study, the researchers gathered data from the World Bank database, identified key indicators significantly impacting carbon emissions, employed the Pearson correlation coefficient and random forest model to perform dimensionality reduction on these indicators, and subsequently assessed the refined data using a panel regression model to examine the correlation and significance of these indicators and carbon emissions across various country types. To ensure the stability of the results, three prediction models were selected for coupling analysis: the adaptive neuro-fuzzy inference system (ANFIS) from the field of machine learning, the autoregressive integrated moving average (ARIMA) model, and the exponential smoothing method prediction model (ES) from the field of time series prediction. These models were used to assess carbon emissions from 54 countries along the Belt and Road from 2021 to 2030, and a coupling formula was defined to integrate the prediction results. The findings demonstrated that the integrated prediction amalgamates the forecasting traits of the three approaches, manifesting remarkable stability. The error analysis also indicated that the short-term prediction results are satisfactory. This has substantial practical implications for China in terms of fine-tuning its foreign policy, considering the entire situation and planning accordingly, and advancing energy conservation and emission reduction worldwide. Full article

The performance of solar photovoltaic systems is impacted by dust accumulation, raising maintenance concerns and discouraging wider adoption to accelerate decarbonization pathways. This research investigates the influence of environmental dynamics on dust accumulation based on several locations, considering weather conditions, seasonality, and angular installation variations, over a three-month period. Low-iron glass coupons were employed to collect on-site soiling from four different locations: agricultural, residential, industrial, and desert. The samples collected were characterized using scanning electron microscopy (SEM) for morphology, X-ray diffraction (XRD) for mineralogy, energy-dispersive X-ray spectroscopy (EDX) for elemental analysis, spectrophotometry for optical properties, and I–V tracing for efficiency analysis. The data were processed using ANFIS techniques to extract the maximum power point (MPP) and reduce the power losses. The results showed significant differences in the dust properties across the sites, influenced by the topography, weather conditions, and human activity. The measurements revealed a decrease in transmittance of up to 17.98%, resulting in power losses of up to 22.66% after three months. The findings highlight the necessity for tailored maintenance strategies to mitigate the impact of human activities and site-specific factors on performance. This could be employed in developing predictive models providing valuable insights for sustaining solar energy systems. Full article

This study investigates the influence of six metallic and non-metallic elements (Fe, Cr, Pb, Cu, Al, Si) on the quality of engine oil under normal, cautious, and critical conditions. To achieve this, the research employs the Design of Experiments (DoE) approach, specifically the Box–Behnken Design (BBD) method, for designing experiments. The electrical properties of 70 engine oil samples prepared under varying conditions were analyzed. Machine learning models, including RBF, ANFIS, MLP, GPR, and SVM, were utilized to predict the concentrations of the six pollutants in the lubricant oil samples based on their electrical characteristics. The models’ performance was assessed using RMSE and R² indicators during train, test, and All stages. The results revealed that the Radial Basis Function (RBF) model exhibited the best overall performance (RMSE = 0.01, R² = 0.99). The study proceeds with optimizing RBF model parameters, such as hidden size (best = 17), spread (best = 0.4 or higher), and training algorithm (best = trainlm), to estimate each pollutant individually. The generalizability of the model was assessed by reducing the training data percentage and increasing the testing data percentage. The results demonstrated the model’s proper performance for all pollutants in various training sizes (RMSE = 0.01, R² = 0.99). However, as the training data ratio reduced to 60:40 and 50:50, the model’s performance in estimating Cu deteriorated, resulting in increased RMSE values (10.76 or 11.85) and decreased R² values (0.89 or 0.87) across the All step. This academic research hopes to contribute to the field of applied studies, considering the inherent complexities of lubricants and the challenges in measuring small-scale electrical properties. Full article

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks’ learning capabilities with fuzzy logic’s rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems’ performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment. Full article

Uninterruptible Power Supplies (UPSs) protect electronic equipment by delivering consistent power. Among the core components of a UPS is the inverter, which converts stored DC energy from batteries into AC power. This work focuses on a cascaded multilevel inverter topology for its ability to reduce voltage Total Harmonic Distortion (THD), which is essential for maintaining UPS efficiency and power quality. Using an ANFIS (Adaptive Neuro-Fuzzy Inference System) model, enhanced with the Particle Swarm Optimization (PSO) algorithm, the switching angles were optimized to minimize THD. This work focused on an online UPS with a seven-level inverter structure powered by three LifePo4 S17 batteries, with critical load levels (100%, 95%, 50%, 15%, and 5%) represented in 35 experimental cases. The experimental design allowed the ANFIS-PSO model to adapt to varying voltages, achieving robust THD reduction. The results demonstrated that this combination of ANFIS and PSO provided effective angle optimization, with a low standard deviation of 0.06 between the training and simulated %THD, highlighting the process’s accuracy. The analysis showed that, in most cases, the simulated THD values closely aligned with, or even improved upon, the calculated values, with discrepancies not exceeding 0.2%. These findings support the ANFIS-PSO model’s potential in enhancing power electronics applications, particularly in critical sectors like renewable energy and power transmission, where THD minimization is crucial. Full article

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events. Full article

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models. Full article

This paper introduces a novel real-time ANFIS controller, specifically designed for thermal synchronous generators, to mitigate the risks associated with cyber-physical attacks on power systems. The controller integrates the dynamic model of the turbine’s thermomechanical components, such as the boiler and heat transfer processes, within the synchronous generator. In contrast to previous studies, this model is designed for practical implementation and addresses often-overlooked areas, including the interaction between electrical and thermomechanical components, real-time control responses to cyber-physical attacks, and the incorporation of economic considerations alongside technical performance. This study takes a comprehensive approach to filling these gaps. Under normal conditions, the proposed controller significantly improves the management of industrial turbines and governors, optimizing existing control systems with a particular focus on minimizing generation costs. However, its primary innovation is its ability to respond dynamically to local and inter-area power oscillations triggered by cyber-physical attacks. In such events, the controller efficiently manages the turbines and governors of synchronous generators, ensuring the stability and reliability of power systems. This approach introduces a cutting-edge thermo-electrical control strategy that integrates both electrical and thermomechanical dynamics of thermal synchronous generators. The novelty lies in its real-time control capability to counteract the effects of cyber-physical attacks, as well as its simultaneous consideration of economic optimization and technical performance for power system stability. Unlike traditional methods, this work offers an adaptive control system using ANFIS (Adaptive NeuroFuzzy Inference System), ensuring robust performance under dynamic conditions, including interarea oscillations and voltage deviations. To validate its effectiveness, the controller undergoes extensive simulation testing in MATLAB/Simulink, with performance comparisons against previous state-of-the-art methods. Benchmarking is also conducted using IEEE standard test systems, including the IEEE 9-bus and IEEE 39-bus networks, to highlight its superiority in protecting power systems. Full article

For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R²). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TN_inf. The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Q_w, pH, SS, TP, NH₃-N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH₃-N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TN_inf) in full-scale wastewater treatment plants. Full article

The importance of establishing a proper method for calculating newly studied structural elements and primarily translating the results of experimental and numerical analyses into practical standards poses a significant challenge for structural researchers. In this study, which focuses on the importance of previously studied connections for the precast industry as a case study, two approaches are proposed for calculating the capacity of elements connected by rectangular steel tubes. The first approach involves a step-by-step calculation for analyzing the forces and capacities of different steel tube or concrete section parts under bending and shear, aiming to establish a standard calculation approach. Despite its complexity, the standard calculation approach has proven its accuracy by successfully solving examples with features similar to those of the experimental tests describing the process. The second approach relies on a look-up table generated from experimental data, developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for interpreting the data. ANFIS not only facilitates the evaluation of the capacity of non-experimentally tested elements but also resembles the calculation process. Evaluating ANFIS’s performance concerning the original results underscores its remarkable capacity to analyze experimental data. With a maximum calculation error of only 13% when compared to the experimental tests, ANFIS demonstrates considerable accuracy and user-friendliness. Following the initial internal force evaluations, the proposed standard calculation method requires eight specific control inputs, and comparing these inputs with experimental tests further confirms the effectiveness and safety of this approach for connection design. Full article

The concept of fair value, defined by the valuation of assets and liabilities at their current market worth, remains central to the International Financial Reporting Standards (IFRS) and has persisted despite critiques intensified by the 2008 financial crisis. This valuation method continues to be prevalent under both IFRS and the US Generally Accepted Accounting Principles (GAAP). The adoption of IFRS has notably enhanced the role of accounting in information analysis, vital for owners who prioritize both secure accounting practices and reliable data for strategic management decisions. Real estate, a significant business asset, has long been a focal point in accounting discussions, prompting extensive research into the applicability and effectiveness of various accounting standards. These investigations assess the adaptability of standards based on property type, utility, and valuation techniques. However, the challenge of accurately determining the fair value of real estate remains unresolved, signifying its importance not only in the corporate manufacturing realm but also among development companies striving to manage property values efficiently. This study addresses the challenge of accurately determining the fair market value of real estate in Kazakhstan, leveraging a multi-methodological approach that encompasses statistical models, regression analysis, data visualization, neural networks, and particularly, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The integration of these diverse methodologies not only enhances the robustness of real estate valuation but also introduces new insights into effective asset management. The findings suggest that ANFIS provides superior precision in real estate pricing, demonstrating its potential as a valuable tool for strategic management and investment decision-making. Full article

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO₂ emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles. Full article