Search Results (132)

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems. Full article

This study proposes a Fuzzy-Based Control System (FBCS) for a Bulk Service Queueing Model with Vacation, designed to optimize service performance by dynamically adjusting system parameters. The queueing model is categorized into three service levels: (A) High Bulk Service, where a large number of arrivals are processed simultaneously; (B) Medium Single Service, where individual packets are handled at a moderate rate; and (C) Low Vacation, where the server takes minimal breaks to maintain efficiency. The Mamdani Inference System (MIS) is implemented to regulate key parameters, such as service rate, bulk size, and vacation duration, based on input variables including queue length, arrival rate, and server utilization. The Mamdani-based fuzzy control mechanism utilizes rule-based reasoning to ensure adaptive decision-making, effectively balancing system performance under varying conditions. By integrating bulk service with a controlled vacation policy, the model achieves an optimal trade-off between processing efficiency and resource utilization. This study examines the effects of fuzzy-based control on key performance metrics, including queue stability, waiting time, and system utilization. The results indicate that the proposed approach enhances operational efficiency and service continuity compared to traditional queueing models. Full article

Background: Abdominal aortic aneurysm (AAA) is a chronic inflammatory disease characterized by the proteolytic breakdown of the extracellular matrix. A clinical biomarker is needed for risk stratification and prognosis. Methods: In this single-center, 5-year observational study, 452 patients were enrolled: 343 with AAA (≥3 cm), and 109 controls (<3 cm). Plasma levels of six inflammatory proteins (human epididymis protein 4 (HE4), matrix metalloproteinase (MMP) 1 and 3, cathepsin S, chitinase 3 like-1, cathepsin S, and B-cell activating factor (BAFF)) were quantified at baseline. Patients were followed for a total of 5 years (60 months), and major adverse cardiovascular events (MACEs, defined as the composite of myocardial infarction, cerebrovascular attack, and cardiovascular-related death) were recorded. A Cox proportional hazard model was created using biomarker levels, age, sex, hypertension, hypercholesterolemia, diabetes mellitus, smoking status, and coronary artery disease to determine whether the baseline levels of these proteins were associated with MACEs over 5 years. Results: HE4, MMP-3, BAFF, and cathepsin S levels were significantly elevated in AAA patients compared to controls (all p < 0.05). HE4/WFDC2, MMP-3, and Chitinase 3-like 1 were significantly linearly associated with AAA diameter at baseline. With every normalized unit increase in HE4/WFDC2, MMP-3, and Chitinase 3-like 1, there was an increase in abdominal aortic diameter by 0.154 (95% CI: 0.032–0.276, p = 0.013), 0.186 (95% CI: 0.064–0.309, p = 0.003), and 0.231 (0.110–0.353, p < 0.001) centimeters, respectively. Among patients with AAA, elevated HE4 was associated with higher risk of MACEs (adjusted HR 1.249; 95% CI: 1.057–1.476; p = 0.009). Patients with high baseline HE4 (≥9.338 ng/mL) had significantly lower freedom from MACEs at 5 years (76.7% vs. 84.8%, p = 0.022). Conclusions: HE4 may be a potential prognostic biomarker that can be used to risk stratify patients with AAA to better personalize treatment strategies to reduce adverse events. Full article

Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena. Full article

Parameterization of SWMM subcatchments is labor-intensive and a major source of model uncertainty. This study presents the Rapid Catchment Generator (RCG), a fuzzy logic framework that derives hydraulic width, average slope, and impervious fraction from three easily accessible descriptors—area, landform type, and land cover type—and inserts them directly into SWMM input files. A sensitivity analysis of 116,640 synthetic simulations confirmed that width, slope, and imperviousness are the dominant controls on runoff and infiltration. Their relationships are encoded in triangular membership functions covering nine geomorphic classes and twelve imperviousness classes, linked through expert-calibrated Mamdani rules. Validation on a calibrated 37-subcatchment, 10-hectare urban basin in Wrocław, Poland, showed Mean Absolute Percentage Errors of 15.9–16.0% for total runoff, 19% for infiltration, and 29–37% for peak flow, while preserving hydrograph shape. RCG thus reduces model setup time and provides a transparent, reproducible starting point for rapid scenario screening and subsequent fine-scale calibration. Full article

The performance degradation of photovoltaic (PV) systems, comprising solar panels and DC-DC converters, is often caused by various anomalies related to manufacturing defects, operational conditions, or environmental factors. These faults significantly reduce energy output, preventing the system from reaching its nominal power and expected production levels. Given the demonstrated impact of such faults on PV system efficiency, an effective diagnostic method is essential for proactive maintenance and optimal performance. This paper presents a fault detection algorithm based on a Mamdani-type fuzzy logic approach. The proposed method utilizes three key inputs—panel current, panel voltage, and converter voltage—to assess system health. By computing the distortion ratios of these electrical parameters and processing them through a fuzzy logic controller, the algorithm accurately identifies fault conditions. Simulation results validate the effectiveness of this approach, demonstrating its capability to detect and classify 12 distinct faults in both the PV array and the DC-DC converter. The study highlights the potential of fuzzy logic-based diagnostics in enhancing the reliability and maintenance of photovoltaic systems. Full article

This study presents a comparative analysis of two advanced classification techniques applied to Landsat 8 and Sentinel-2 imagery. The first technique is based on the combined use of Tversky’s fuzzy similarity and Mamdani-type fuzzy inference, specifically designed to handle transition zones—areas characterized by gradual shifts in land cover, such as from vegetation to suburban environments. The second approach is based on the Random Forest algorithm. After performing the ranking of spectral, textural, and geometric features using the fuzzy approach, a fuzzy system based on Tversky’s fuzzy similarity was developed. This system enables a more adaptive and nuanced classification of different land cover classes, including water bodies, forests, and cultivated areas. The results indicate that the proposed fuzzy approach slightly outperforms the Random Forest method in handling mixed land cover regions and reducing classification uncertainties, achieving overall accuracies of 98.5% for Sentinel-2 and 96.7% for Landsat 8. Full article

Soil temperature is a critical environmental factor that affects plant development, physiological processes, and overall productivity. This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. Soil temperature data were collected from Tokat, Türkiye, between 2016 and 2024 at depths of 5, 10, 20, 50, and 100 cm. The dataset was split into training (2016–2021) and testing (2022–2024) periods. Performance was evaluated using the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R²). The ANFIS achieved the best prediction accuracy (MAE = 1.46 °C, RMSE = 1.89 °C, R² = 0.95), followed by RF, XGB, MLP, KNN, SVR, MLR, and MFIS. This study underscores the potential of integrating machine learning and fuzzy logic techniques for more accurate soil temperature modeling, contributing to precision agriculture and better resource management. Full article

This study proposes an enhanced Sugeno–Takagi interval type-2 fuzzy logic system (SIT2FLS) to find the best values for two important parameters in Bee Colony Optimization (BCO). The aim of this study was to develop a stable controller for a mobile robot utilizing BCO in the fuzzy controller and to determine the best membership functions (MFs) in a type-1 fuzzy logic system (T1FLS) for control. Another objective was to use an SIT2FLS to find the best $\alpha$ and $\beta$ parameters for BCO to enhance the robot trajectory, which was evaluated through an analysis of the mean squared errors. Three types of perturbations were analyzed and simulated. The performance of the SIT2FLS-FBCO was evaluated and compared to that of the T1FLS-FBCO. In addition, a comparative study was performed to demonstrate that the improved BCO works well when there are disturbances affecting the controller. Finally, it was compared with the Mamdani approach, and an FBCO with an interval type-3 FLS was also developed. Full article

Background: Peripheral arterial disease (PAD) is a chronic atherosclerotic disease characterized by atheromatous plaque buildup within arteries of the lower limbs. It can lead to claudication, skin ulcerations, and, in severe cases, chronic limb-threatening ischemia, requiring amputation. There are several plasma protein biomarkers that have been suggested as prognostic markers for adverse events, including major adverse cardiovascular and limb events. However, the clinical benefit and ability to clinically adapt these biomarkers remains uncertain due to inconsistent findings possibly related to heterogenous study designs and differences in methodology. Objectives: This review aims to evaluate the current literature on the prognostic value of plasma protein biomarkers for PAD, their predictive ability for PAD-related adverse outcomes, and their potential roles in guiding PAD management. Methods: To address these challenges, we conducted a systematic review of MEDLINE, Embase, and Cochrane CENTRAL libraries of the current literature (2010–2024). Results: We found 55 studies that evaluated the prognostic value of 44 distinct plasma proteins across various pathophysiological processes. These included markers of immunity and inflammation, markers of metabolism, cardiac biomarkers, markers of kidney function, growth factors and hormones, markers of coagulation and platelet function, extracellular matrix and tissue remodeling proteins, and transport proteins. This review summarizes the existing evidence for prognostic protein plasma biomarkers for PAD and their association with adverse events related to PAD. Conclusions: With this review, we hope to provide a comprehensive list of the prognostic markers and their value as prognostic biomarkers to guide clinical decision making in these patients. Full article

Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces a hybrid machine learning approach to predict key effluent parameters from an advanced biological wastewater treatment plant and assesses the reuse potential of treated wastewater for irrigation. Three artificial intelligence (AI) models, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic-Mamdani (FLM), were applied to three years of daily inlet and outlet water quality data. Fuzzy Logic was employed to predict the usability potential of treated wastewater, with ANFIS categorizing quality parameters and ANN-based high-performance models (low MSE, 74–99% R²) applied in the fuzzy inference system. The qualitative reuse potential of treated wastewater for agricultural irrigation ranged from 69% to 72% based on the best-performing model. It was estimated that treated wastewater could irrigate approximately 35% of a 20,000-hectare agricultural area. By integrating machine learning models, this research enhances the accuracy and interpretability of wastewater quality predictions, providing a reliable framework for sustainable water resource management. The findings support the optimization of wastewater treatment processes and highlight AI’s role in advancing water reuse strategies in agriculture, ultimately contributing to improved irrigation efficiency and environmental conservation. Full article

During the execution of road infrastructure projects, project managers face significant challenges, including financial, technical, regulatory, and operational risks. More than 90% of infrastructure projects have incurred costs exceeding initial estimates, impacting both completion timelines and the operational efficiency of road infrastructure. Effectively assessing and managing these risks is crucial for improving project outcomes and ensuring the sustainability of infrastructure investments. To address these challenges, this study developed a hybrid model for risk assessment and management in road infrastructure projects. The model quantifies risks across seven key categories: Design, External, Resource, Employer, Contractor, Engineer, and Project, based on three primary input factors: Environment coefficient, Contractual coefficient, and Design coefficient. Initially, various machine learning models, including linear regression, Random Forest, Gradient Boosting, Stacking Models, and neural networks, were applied to assess risk predictions. However, due to the specific nature of the dataset, these models did not achieve satisfactory predictive accuracy. As a result, fuzzy logic systems (Mamdani and Sugeno) were employed, demonstrating superior performance in modeling risk occurrence probabilities. Comparative analysis between these two fuzzy logic approaches revealed that the Sugeno model provided the most accurate predictions. The findings highlight the benefits of applying fuzzy logic for risk assessment in complex infrastructure projects, providing a structured framework for enhancing decision-making processes. This study provides a structured methodology for accurately predicting risks and enhancing project safety, efficiency, and long-term sustainability. Full article

Clean water is vital for a sustainable environment, human wellness, and welfare, supporting life and contributing to a healthier environment. Fuzzy-logic-based techniques are quite effective at dealing with uncertainty about environmental issues. This study proposes two methodologies for assessing water quality based on Mamdani and Sugeno fuzzy systems, focusing on water’s physiochemical attributes, as these provide essential indicators of water’s chemical composition and potential health impacts. The goal is to evaluate water quality using a single numerical value which indicates total water quality at a specific location and time. This study utilizes data from the Acea Group and employs the Mamdani fuzzy inference system combined with various defuzzification techniques as well as the Sugeno fuzzy system with the weighted average defuzzification technique. The suggested model comprises three fuzzy middle models along with one ultimate fuzzy model. Each model has three input variables and 27 fuzzy rules, using a dataset of nine key factors to rate water quality for drinking purposes. This methodology is a suitable and alternative tool for effective water-management plans. Results show a final water quality score of 85.4% with Mamdani (centroid defuzzification) and 83.5% with Sugeno (weighted average defuzzification), indicating excellent drinking water quality in Tivoli, Italy. Water quality evaluation is vital for sustainability, ensuring clean resources, protecting biodiversity, and promoting long-term environmental health. Intermediate model evaluations for the Mamdani approach with centroid defuzzification showed amounts of 72.4%, 83.4%, and 92.5% for the first, second, and third fuzzy models, respectively. For the Sugeno method, the corresponding amounts were 76.2%, 83.5%, and 92.5%. These results show the precision of both fuzzy systems in capturing nuanced water quality variations. This study aims to develop fuzzy logic methodologies for evaluating drinking water quality using a single numerical index, ensuring a comprehensive and scalable tool for water management. Full article

In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving position, navigation, and timing (PNT) services. However, multipath (MP) and non-line-of-sight (NLOS) receptions remain the prominent vulnerability for the GNSS in harsh environments. The aim of this research is to investigate the impact of MP and NLOS receptions on GNSS performance and then propose a Received Signal Quality Monitoring (RSQM) algorithm. The RSQM algorithm works in two ways. Initially, it performs a signal quality test based on a fuzzy inference system. The input parameters are carrier-to-noise ratio (CNR), Normalized Range Residuals (NRR), and Code–Carrier Divergence (CCD), and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements while considering the satellite geometry constraints (i.e., $DOP\le 2$). For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configurations (GPS, BeiDou, GLONASS, and Galileo) has been carried out in various operating environments. This RSQM-based GNSS receiver has the capability to identify signal quality and perform adaptive navigation accordingly to improve navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4 m to 2.3 m on average in the complex urban environment. Combining the RSQM algorithm with the GNSS has great potential for the future industrial revolution (Industry 5.0), making things automatic and sustainable like autonomous vehicle operation. Full article