Search Results (3,047)

Higher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in Türkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO₂e (1.19 tCO₂e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions. Full article

Anti-lock braking systems (ABSs) play a vital role in vehicle safety by preventing wheel lockup and maintaining stability during braking. However, their performance is strongly affected by variations in tire–road friction, which often limits the effectiveness of conventional controllers. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in MATLAB/Simulink. Two input variables (slip error and slip rate) and one output variable (brake pressure adjustment) were defined, with triangular and trapezoidal membership functions and 15 linguistic rules forming the control strategy. Simulation results under diverse road conditions—including dry asphalt, concrete, wet asphalt, snow, and ice—demonstrate substantial performance gains. On high- and medium-friction surfaces, stopping distance and stopping time were reduced by more than 30–40%, while improvements of up to 25% were observed on wet surfaces. Even on snow and ice, the system maintained consistent, albeit modest, benefits. Importantly, the proposed FLC–ABS was benchmarked against two recent studies: one reporting that an FLC reduced stopping distance to 258 m in 15 s compared with 272 m in 15.6 s using PID, and another where PID outperformed an FLC, achieving 130.21 m in 9.67 s against 280.03 m in 16.76 s. In contrast, our system achieved a stopping distance of only 24.41 m in 7.87 s, representing over a 90% improvement relative to both studies. These results confirm that the proposed FLC–ABS not only demonstrates clear numerical superiority but also underscores the importance of rigorous modeling and systematic controller design, offering a robust and effective solution for improving braking efficiency and vehicle safety across diverse road conditions. Full article

This paper presents a structured strategy for integrating the Sustainable Development Goals (SDGs) into university courses by linking them to competencies and learning outcomes. The proposed methodology, based on fuzzy logic, evaluates the degree of alignment between teaching activities and selected SDGs through matrices that connect competencies with assessment activities and expected learning outcomes, improving the gap regarding the inclusion of the SDGs and their articulation in terms of competencies. The approach was applied to two subjects from the Master’s Degree in Renewable Energy and Energy Efficiency at the Distance University of Madrid: “Electricity Market” and “Wind Energy”. In both cases, the learning outcomes were redesigned, and the activities were adjusted to ensure meaningful incorporation of sustainability principles into the curriculum. The method enables quantification of each activity’s contribution to the SDGs and supports a critical review of curriculum design to ensure coherent integration. The results indicate that project-based activities show the highest alignment with the SDGs, particularly with Goals 7, and 12, which achieve an average rating of 0.7 (high). The developed tool provides a practical and replicable solution for sustainability-oriented curriculum planning and can be adapted to other disciplines and educational programs. Full article

This study focuses on improving the control system of vehicle suspension, which is critical for optimizing driving dynamics and enhancing passenger comfort. Traditional passive suspension systems are limited in their ability to effectively mitigate road-induced vibrations, often resulting in compromised ride quality and vehicle handling. To overcome these limitations, this work explores the application of active suspension control strategies aimed at improving both comfort and performance. Type-I and Type-II Fuzzy Logic Control (FLC) methods were designed and implemented to enhance vehicle stability and ride quality. The Harris Hawks Optimization (HHO) algorithm was employed to optimize the membership function parameters of both fuzzy control types. The system was tested under two distinct road disturbance inputs to evaluate performance. The designed control methods were evaluated in simulations where results demonstrated that the proposed active control approaches significantly outperformed the passive suspension system in terms of vibration reduction. Specifically, the Type-II FLC achieved a 54.7% reduction in vehicle body displacement and a 76.8% reduction in acceleration for the first road input, while improvements of 75.2% and 72.8% were recorded, respectively, for the second input. Performance was assessed using percentage-based metrics and Root Mean Square Error (RMSE) criteria. Numerical and graphical analyses of suspension deflection and tire deformation further confirm that the proposed control strategies substantially enhance both ride comfort and vehicle handling. Full article

A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for fast and robust DC bus voltage regulation with a Deep Q-Learning (DQL) agent that learns optimal high-level policies for charging, discharging, and load management. This dual-layer design leverages the real-time precision of SMC and the adaptive decision-making capability of DQL to achieve dynamic power sharing and balanced state-of-charge levels across storage units, thereby reducing asymmetric wear. Simulation results under variable operating scenarios showed that the proposed method significantly improvedvoltage stability, loweredthe occurrence of deep battery discharges, and decreased load shedding compared to conventional fuzzy-logic-based energymanagement, highlighting its effectiveness and resilience in the presence of renewable generation variability and fluctuating load demands. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

This paper presents a fuzzy logic control strategy for synchronising the vertical lifting and positioning of a multi-actuator hydraulic system designed for a 360-ton movable platform. The primary focus is on achieving precise actuator movement coordination under uneven loading conditions without using external reference systems or high-cost sensors. A mathematical model and a simulation environment were developed in MATLAB/Simulink with Fuzzy Logic Toolbox. Four fuzzy controller variants were evaluated regarding positioning accuracy, robustness, and compliance with dynamic constraints. The results demonstrate the effectiveness of the proposed control method, particularly when using Gaussian membership functions and PROD–PROBOR fuzzy operators. The system achieved sub-millimetre synchronisation accuracy even under 20% load imbalance. This work contributes to developing decentralised, sensor-light control strategies for large-scale hydraulic systems and offers a validated foundation for future experimental implementation in the PANDA particle detector project. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

Higher requirements have been placed on fault detection for continuously operating machines in modern factories. Manual inspection faces challenges related to timeliness, leading to the emergence of autonomous diagnostic robots. To overcome the safety limitations of existing diagnostic robots in factory environments, a hierarchical fuzzy logic-based navigation and obstacle avoidance algorithm is proposed in this study. The algorithm is constructed based on zero-order Takagi–Sugeno type fuzzy control, comprising subfunctions for navigation, static obstacle avoidance, and dynamic obstacle avoidance. Coordinated navigation and equipment protection are achieved by jointly considering the information of the fault point and surrounding equipment. The concept of a dynamic safety boundary is introduced, wherein the normalized breached level is used to replace the traditional distance-based input. In the inference process for dynamic obstacle avoidance, the relative speed direction is additionally considered. A Mamdani-type fuzzy inference system is employed to infer the necessity of obstacle avoidance and determine the priority target for avoidance, thereby enabling multi-objective planning. Simulation results demonstrate that the proposed algorithm can guide the diagnostic robot to within 30 cm of the fault point while ensuring collision avoidance with both equipment and obstacles, enhancing the completeness and safety of the fault point searching process. Full article

Accurate docking of Autonomous Guided Vehicles (AGVs) is a critical requirement for efficient automated production systems in Industry 4.0, particularly for collaborative tasks with robotic arms that have a limited working range. This paper introduces a cost-effective software-upgrade solution to enhance the precision of the final docking phase without requiring new hardware. Our approach is based on a two-stage strategy: first, a switch from a global dead reckoning system to a local navigation scheme, is triggered near the docking station; second, a dedicated Takagi-Sugeno Fuzzy Logic Controller (FLC), guides the AGV to its final position with high accuracy. The core novelty of our FLC is its implementation as a gain-scheduling lookup table (LUT), which synthesizes critical state variables—heading error and distance error—from limited proximity sensor data, to robustly handle positional uncertainty and environmental variations. This method directly addresses the inadequacies of traditional odometry, whose cumulative errors become unacceptable at the critical docking point. For experimental validation, we assume the global navigation delivers the AGV to a general switching point, near the assembly station with an unknown true pose. We detail the design of the fuzzy controller and present experimental results that demonstrate a significant improvement, achieving repeatable docking accuracy within industrially acceptable tolerances. Full article

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article

This scoping review investigates the use of fuzzy logic in serious games. Articles were searched in nine databases: ACM Digital Library, IEEE Xplore, IOPscience, MDPI, PubMed, ScienceDirect, Springer, Wiley, and Web of Science. The search retrieved 494 articles published between January 2020 and February 2025, of which 28 met the inclusion criteria. Specifically, four research questions were addressed, focusing on the taxonomy of serious games that use fuzzy logic, the characteristics of game design, the purpose and implementation of the fuzzy logic system within the game, and the experiments conducted in the studies. Results reported that 80% of the studies focused on educational serious games, while 20% addressed health applications. Mouse, keyboard, and smartphone touch screen were the most widely used interaction methods. The adventure genre was the most widely implemented in the studies (35.71%). Fuzzy logic was mainly used for adjusting game difficulty, followed by providing tailored feedback in the game. Mamdani inference was the most widely used inference method in the studies. Although 79% of the studies involved human participants in their experiments, 57% did not perform any statistical analysis of their results. Full article

This paper investigates the effectiveness and limitations of the traditional Fine-Kinney method for occupational risk assessment, emphasizing its shortcomings in addressing complex and dynamic work environments. To overcome these challenges, two advanced methodologies, Fine-Kinney10 (FK10) and Fuzzy Fine-Kinney10 (FFK10), are introduced. The FK10 employs a symmetric scaling system (1–10) for probability, frequency, and severity indicators, providing a more balanced quantification of risks. Meanwhile, FFK10 incorporates fuzzy logic to handle uncertainty and subjectivity in risk assessment, significantly enhancing the sensitivity and accuracy of risk evaluation. These methodologies were applied to a linemen workshop in an energy production and distribution company, analyzing various types of accidents such as falls from heights, exposure to electric currents, slips on surfaces, and more. The applications highlighted the practical benefits of these methods in effectively assessing and mitigating risks. A significant finding includes the identification of risks related to falls from heights of <2.5 m (SH1) and road traffic accidents (SH6), where all three methods yielded different verbal outcomes. Compared to the traditional Fine-Kinney method, FK10 and FFK10 demonstrated superior ability in distinguishing risk levels and guiding targeted safety measures. The study underscores that FK10 and FFK10 represent significant advancements in occupational risk management, offering robust frameworks adaptable across various industries. Full article

Lean construction is considered a new methodology for minimizing the causes of waste that hinder the achievement of green building (GB) goals. The main aim of this study is to develop a lean model using fuzzy logic technique to mitigate causes of waste effect in GB projects and to determine the most appropriate lean tools affecting these causes. The inputs of this model include GB waste and four lean tools, comprising Quality Function Deployment (QFD), Last Planner System (LPS), Value Stream Mapping (VSM), and 5S, while the outputs include four improvement level indices based on the lean tools. The model uses various logical rules to achieve several relations among the inputs and outputs, and it is applied and verified using data related to several causes of waste categorized under five groups. The strongest correlation is found between VSM and 5S indices, while an adverse relationship is observed between QFD and 5S indices. The results indicate that a cause of waste that refers to poor assessment of site conditions is considered the most substantial one due to its high improvement level indices across all lean tools. The most significant waste group is related to GB stakeholders, which contains 38% of key causes of waste. The improvement using QFD increases by 10% compared to VSM and 28.20% compared to 5S. QFD and LPS are measured as the most suitable lean tools to mitigate the causes of waste effects due to their high impact and high improvement level indices. Full article

One of the limiting factors in the implementation of water resource management is the absence of tools that help water programs evaluate processes and progress. This is because, until now, the indicators that have been developed have not addressed specific local characteristics and issues. Therefore, in this research, a set of indicators has been proposed, with the purpose of developing a management index for urban public water supply, which will consider the Drinking Water and Sewer System of León (SAPAL), in the Mexican state of Guanajuato, as case study. This index will be useful to measure progress toward sustainable development, monitor the impact of public policies, and foster citizen participation. In order to propose a methodology that aligns with the changing environments, where proper decision-making is key to the current water management requirements, the combination of the Analytic Hierarchy Process (AHP) and Fuzzy Logic (FL) methodologies will be helpful for proper decision-making. All this will foster a paradigm shift towards appropriate water management actions that allow for the conditions and availability of human and natural resources, which the municipality has control of, for a long-term improvement that guarantees the well-being of the population. Full article