Search Results (91)

Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into robotic decision-making. First, classical conditioning and olfactorily triggered spatial memory experiments demonstrated that bumblebees could form robust odor memories and that training frequency is positively correlated with both proboscis extension response retention and spatial directional preference. Based on these biological findings, a bio-inspired navigation framework, termed Bio-Nav, is constructed by integrating a Partially Observable Markov Decision Process, a Hidden Markov Model, short-term memory, long-term directional reference memory, fuzzy inference, and value iteration. High-fidelity two-dimensional turbulent simulations show that the proposed algorithm substantially outperforms moth-inspired search, Infotaxis, and standard POMDP-based navigation. In 100 Monte Carlo trials, Bio-Nav achieved a success rate of 96.0%, an average of 20.3 search steps, an average path length of 155.1 cm, and a path-to-straight-line distance ratio of 1.6. Even under strong turbulence, the success rate remained above 91%. These results indicate that memory–perception coupling, inspired by bumblebee navigation, provides an effective and robust strategy for odor source localization in complex turbulent environments, offering a generalizable principle for bio-inspired robotic search under uncertainty. Full article

This work presents the design of an attitude control experiment for onboard OPS-SAT-1 satellite execution, conceived with inherent extensibility to future mission architectures. OPS-SATs are ESA nanosatellite mission series designed as an in-orbit testbed for validating novel software and control techniques under real space conditions, OPS-SAT-1 being the first mission. Equipped with an advanced payload computer, OPS-SAT-1 enabled experimentation with innovative mission operations, including real-time attitude control strategies. Two attitude control algorithms, a modified Proportional–Integral–Derivative (mPID) and a fuzzy logic controller, were designed and implemented for the OPS-SAT-1. The design methodology applied to these controllers consisted of (i) modelling the space environment and satellite characteristics, (ii) assessing actuator feasibility, (iii) determining the operational ranges for attitude error and angular velocity, (iv) parametrizing controllers within these ranges, (v) fine-tuning controllers using multi-objective genetic optimization, and (vi) robustness analysis using the Monte Carlo method. Despite the technical issues related to communication with the OPS-SAT-1 hardware, which prevented the execution of the experiment in orbit, this work presents the simulation results that were obtained. These results indicate that fuzzy logic controllers may outperform PID controllers in terms of the accumulated error, settling time and steady-state error, whereas power efficiency appears to be less robust than in the PID. This suggest that a large uncertainty in the model could lead the PID to become more efficient. Near the nominal scenario, the fuzzy controller achieves superior error–cost trade-offs, enabling precise attitude stabilization with lower energy consumption. These findings suggest the potential advantages of modern control approaches compared to classical methods, which will be further assessed through future in-orbit experiments. Full article

In the field of modern power systems, power supply reliability has become a core indicator for measuring distribution network performance. It serves not only as a fundamental criterion for judging the continuous power supply capacity of distribution networks but also as a key benchmark for evaluating their power quality. Considering the current status of reliability assessment for distribution network power supply, this study conducts an in-depth analysis of a series of key indicators, namely outage duration, outage frequency, the number of affected customers, power supply reliability rate, and the proportion of affected customers. Through a detailed deconstruction of these indicators, an evaluation model for distribution network power supply reliability is established. In the process of model construction, this study innovatively combines the hierarchical recursive weighting method with the entropy measurement weight determination method to accurately define the weights of each evaluation dimension. On this basis, a fuzzy membership quantification strategy is introduced to precisely determine the classification level of distribution networks, and Monte Carlo simulation combined with triangular fuzzy number is used to carry out uncertainty modeling on the reliability score, realizing the transformation from deterministic evaluation to probabilistic evaluation. This strategy is developed to transform qualitative issues into quantitative analysis, effectively clarify the fuzzy and complex interrelationships among multiple influencing factors, and thereby realize a comprehensive evaluation of power supply reliability for distribution networks. To verify the effectiveness and practicality of the proposed method, a distribution network in a specific region is selected as the research object. The aforementioned model and method are applied to assess its power supply reliability, and the precise classification of distribution network levels in this region is successfully realized. This combined model significantly improves the accuracy of evaluation while ensuring the scientific rigor and fairness of the evaluation process. It provides an innovative and practical method for the field of distribution network power supply reliability assessment, and offers substantive reference and support for relevant decision-making and practical operations. Full article

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article

Accurate carbon footprint accounting is essential for advancing carbon management and optimising energy systems. However, existing uncertainty analyses are often difficult to reproduce and interpret because the underlying evidence is scattered across multiple sources, thereby making it hard to attribute uncertainty to specific drivers. To address this gap, we propose a transparent uncertainty quantification framework that (i) distills multi-source evidence into auditable rules, (ii) propagates ambiguity through fuzzy aggregation, and (iii) maps interval outputs to probabilistic representations for scenario-based Monte Carlo simulation. In the case study, the proposed method yields more stable carbon footprint estimates than conventional approaches, with lower variability across repeated simulations, and it enables clearer uncertainty attribution by separating random variation from structural uncertainty. Overall, this paper provides a traceable and interpretable method for high-precision uncertainty quantification in carbon footprint assessment. Full article

This study develops a carbon emissions reduction strategy for solar-powered coffee machines in Taiwanese convenience stores, aiming to strike a balance between profitability and decarbonization. An integrated framework of the fuzzy nonlinear multi-objective programming (FNMOP) model, Sobol sensitivity analysis, and Monte Carlo simulation was applied to quantify uncertainties in electricity supply, consumer demand, and investment costs. Solar-powered machines reduce annual CO₂ emissions by 172–215 kg per store. Allocating 0.49–0.61% of coffee profits as subsidies shortens payback to [6.5, 9.375] years. Monte Carlo simulation confirms robustness with a 95% confidence interval of [5.8, 11.2] years, while urban stores achieve payback 18–25% faster. Sobol analysis identifies annual savings and net profit margins as key drivers. The framework demonstrates scalability and international applicability, providing empirical evidence for policymakers and retailers to accelerate the adoption of renewable energy in consumer-facing operations. Its methodological integration and consumer-side focus offer a replicable model for convenience store chains in high-density retail markets worldwide, with potential multiplier effects across sectors and supply chains. Full article

Modern smart cities face increasing pressure to invest in sustainable and reliable energy systems while navigating uncertainties arising from fluctuating market conditions, evolving technology landscapes, and diverse expert opinions. Traditional multi-criteria decision-making (MCDM) approaches often fail to fully represent these uncertainties as they typically rely on crisp inputs, lack temporal memory, and do not explicitly account for stochastic variability. To address these limitations, this study introduces a novel Stochastic Fractional Fuzzy Tensor (SFFT)-based Group Decision-Making framework. The proposed approach integrates three dimensions of uncertainty within a unified mathematical structure: fuzzy representation of subjective expert assessments, fractional temporal operators (Caputo derivative, $\alpha =0.85$) to model the influence of historical evaluations, and stochastic diffusion terms ($\sigma =0.05$) to capture real-world volatility. A complete decision algorithm is developed and applied to a realistic smart city renewable energy selection problem involving six alternatives and six criteria evaluated by three experts. The SFFT-based evaluation identified Geothermal Energy as the optimal choice with a score of 0.798, followed by Offshore Wind (0.722) and Waste-to-Hydrogen (0.713). Comparative evaluation against benchmark MCDM methods—TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), and WSM (Weighted Sum Model)—demonstrates that the SFFT approach yields more robust and stable rankings, particularly under uncertainty and model perturbations. Extensive sensitivity analysis confirms high resilience of the top-ranked alternative, with Geothermal retaining the first position in 82.4% of 5000 Monte Carlo simulations under simultaneous variations in weights, memory parameter ($\alpha \in [0.25,0.95]$), and noise intensity ($\sigma \in [0.01,0.10]$). This research provides a realistic, mathematically grounded, and decision-maker-friendly tool for strategic planning in uncertain, dynamic urban environments, with strong potential for deployment in wider engineering, management, and policy applications. Full article

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

In this paper, an emission- and reliability-aware stochastic optimization model is proposed for the economic planning of electric bus parking lots (EBPLs) with photovoltaic (PV) and wind-turbine (WT) resources in an 85-bus radial distribution network. The model simultaneously minimizes operating, emission, and energy-loss costs while increasing system reliability, measured by energy not supplied (ENS), and uses a fuzzy decision-making approach to determine the final solution. To address optimization challenges, a new multi-objective entropy-guided Sinh–Cosh Optimizer (MO-ESCHO) is proposed to efficiently mitigate premature convergence and produce a well-distributed Pareto front. Also, a hybrid forecasting architecture that combines MO-ESCHO and artificial neural networks (ANN) is proposed for accurate prediction of PV and WT power and network loading. The framework is tested across five cases, progressively incorporating EBPL, demand response (DR), forecast information, and stochastic simulation of uncertainties using a new hybrid Unscented Transformation–Cubature Quadrature Rule (UT-CQR) method. Comparative analyses against conventional methods confirm superior performance in achieving better objective values and ensuring computational efficiency. The outcomes indicate that the combination of EBPL with RES reduces operating costs by 5.23%, emission costs by 27.39%, and ENS by 11.48% compared with the base case with RES alone. Moreover, incorporating the stochastic model increases operating costs by 6.03%, emission costs by 5.05%, and ENS by 7.94% over the deterministic forecast case, reflecting the added complexity of uncertainty. The main contributions lie in coupling EBPLs and RES under uncertainty and proposing UT-CQR, which exhibits robust system performance with reduced variance and lower computational effort compared with Monte Carlo and cloud-model approaches. Full article

This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders. The system also includes an ROS2-based software stack enabling autonomous navigation via the NAV2 framework and Adaptive Monte Carlo Localization (AMCL). For real-time object detection, a lightweight YOLO11n model is developed and implemented on a Raspberry Pi 4 to enable the robot to identify common household items. The robot’s motion control is achieved by a fuzzy logic-enhanced PID controller that dynamically modifies gain values based on navigation conditions. Remote supervision, task management, and real-time status monitoring are provided by a user-friendly Flutter-based mobile application. Simulations and real-world experiments demonstrate the robustness, modularity, and responsiveness of the robot in dynamic environments. This robot achieves a 3 cm localization error and a 95% task execution success rate. Full article

The paper manufacturing industry faces significant challenges in coordinating production and distribution decisions under uncertain market conditions. This research presents an integrated production–distribution planning model for paper manufacturing that addresses demand uncertainty through fuzzy set theory. The model considers multiple paper grades, production facilities, warehouses, and customer zones while minimizing total supply chain costs. A hybrid intelligent algorithm combining genetic algorithms with fuzzy simulation is developed to solve the complex optimization problem. The approach handles fuzzy demand parameters using credibility theory and employs Monte Carlo simulation for fuzzy variable evaluation. Computational experiments demonstrate the effectiveness of the proposed methodology, achieving cost reductions of 12–18% compared to traditional deterministic approaches. The results indicate that the fuzzy–genetic algorithm approach provides robust solutions that perform well under various uncertainty scenarios, making it suitable for practical implementation in paper manufacturing supply chains. Full article

Heading is a crucial navigation parameter for high-precision flight guidance. Since the heading changes rapidly while unmanned aerial vehicles (UAVs) track great ellipse routes in polar regions, it is necessary to implement special guidance algorithms. This article presents a high-precision polar flight guidance algorithm for fixed-wing UAVs along great ellipse routes based on heading prediction. Specifically, a globally applicable definition of polar grid frame was proposed. On this basis, a novel flight guidance algorithm based on heading prediction was developed. Therein, the calculation method for grid azimuth on great ellipse routes based on the WGS-84 ellipse model was derived in detail, realizing accurate heading estimation and prediction. Subsequently, the predicted grid heading was utilized to tackle the difficulty of heading changes, enabling the UAV to predict and adjust its heading in advance. Moreover, an adaptive predicted lead-time adjustment strategy based on fuzzy decision-making was introduced to improve the prediction accuracy under challenging situations, and an enhanced particle swarm optimization algorithm was employed to determine the hyperparameters in fuzzy rules. To verify the effectiveness of the proposed algorithm, extensive simulations were operated using the Monte Carlo method, and the proposed algorithm demonstrated 3–4 times higher guidance accuracy compared to conventional algorithms. Full article

Reinforcement corrosion significantly reduces the load-bearing capacity, ductility, and energy dissipation of reinforced concrete (RC) structures, thereby increasing their seismic failure risk. To enhance the seismic performance of in-service RC structures, this study employs an FRP mesh–engineered cementitious composite (ECC) retrofitting method and develops a multi-objective optimization decision-making framework. A finite element model incorporating reinforcing steel corrosion, concrete deterioration, and bond–slip effects is first established and validated against experimental results. Based on this model, a six-story RC frame is selected as a case study, and eight alternative FRP mesh–ECC retrofitting schemes are designed. Five core indicators are quantified, namely annual collapse probability, expected annual loss, capital expenditure, carbon emissions, and downtime. The results indicate that FRP mesh–ECC retrofitting can significantly improve the seismic performance of corroded RC structures. The overall uniform retrofitting scheme (SCS-2) achieves the most significant improvements in seismic safety and economic performance, but they are associated with highest capital expenditure and carbon emission. Story-differentiated schemes (SCS-3 to SCS-6) provide a trade-off between performance enhancement and cost–emission control. While partial component-focused schemes (SCS-7 and SCS-8) cut cost and carbon but do not lower seismic downtime. Furthermore, the improved fuzzy-TOPSIS method with interval weights and Monte Carlo simulation indicates that the balanced scheme SCS-1 delivers the most robust performance across five dimensions, with a best probability close to 90%. The results confirm the potential of FRP mesh–ECC retrofitting at both component and structural levels and provide a practical reference for selecting seismic retrofitting strategies for existing RC structures. Full article

Arid regions in Central Asia face persistent and increasing water scarcity, with groundwater serving as the primary source for drinking water, irrigation, and industry. The effective exploration and management of groundwater resources are critical, but are constrained by limited monitoring infrastructure and complex hydrogeological settings. This study investigates the Akbakay aquifer, a representative area within Central Asia with challenging hydrogeological conditions, to delineate potential zones for fresh groundwater exploration. A multi-criteria decision analysis was conducted by integrating the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), supported by remote sensing datasets. To address the subjectivity of weight assignment, the AHP results were further validated using Monte Carlo simulations and fuzzy logic aggregation (Fuzzy Gamma). The integrated approach revealed stable high-suitability groundwater zones that consistently stand out across deterministic, probabilistic, and fuzzy assessments, thereby improving the reliability of the groundwater potential mapping. The findings demonstrate the applicability of combined AHP–GIS methods enhanced with uncertainty analysis for sustainable groundwater resource management in data-scarce arid regions of Central Asia. Full article

The quality of groundwater, a crucial freshwater resource in cold regions, directly affects human health. This study used groundwater quality monitoring data collected in the central Songnen Plain in 2014 and 2022 as a case study. The improved DRASTICL model was used to assess the vulnerability index, while water quality indicators were selected using a random forest algorithm and combined with the entropy-weighted groundwater quality index (E-GQI) approach to realize water quality assessment. Furthermore, self-organizing maps (SOM) were used for pollutant source analysis. Finally, the study identified the synergistic migration mechanism of NH₄⁺ and Cl⁻, as well as the activation trend of As in reducing environments. The uncertainty inherent to health risk assessment was considered by developing a kernel density estimation–trapezoidal fuzzy number–Monte Carlo simulation (KDE-TFN-MCSS) model that reduced the distribution mis-specification risks and high-risk misjudgment rates associated with conventional assessment methods. The results indicated that: (1) The water chemistry type in the study area was predominantly HCO₃⁻–Ca²⁺ with moderately to weakly alkaline water, and the primary and nitrogen pollution indicators were elevated, with the average NH₄⁺ concentration significantly increasing from 0.06 mg/L in 2014 to 1.26 mg/L in 2022, exceeding the Class III limit of 1.0 mg/L. (2) The groundwater quality in the central Songnen Plain was poor in 2014, comprising predominantly Classes IV and V; by 2022, it comprised mostly Classes I–IV following a banded distribution, but declined in some central and northern areas. (3) The results of the SOM analysis revealed that the principal hardness component shifted from Ca²⁺ in 2014 to Ca²⁺–Mg²⁺ synergy in 2022. Local high values of As and NH₄⁺ were determined to reflect geogenic origin and diffuse agricultural pollution, whereas the Cl⁻ distribution reflected the influence of de-icing agents and urbanization. (4) Through drinking water exposure, a deterministic evaluation conducted using the conventional four-step method indicated that the non-carcinogenic risk (HI) in the central and eastern areas significantly exceeded the threshold (HI > 1) in 2014, with the high-HI area expanding westward to the central and western regions in 2022; local areas in the north also exhibited carcinogenic risk (CR) values exceeding the threshold (CR > 0.0001). The results of a probabilistic evaluation conducted using the proposed simulation model indicated that, except for children’s CR in 2022, both HI and CR exceeded acceptable thresholds with 95% probability. Therefore, the proposed assessment method can provide a basis for improved groundwater pollution zoning and control decisions in cold regions. Full article