Search Results (2,779)

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

The revitalization of idle rural homesteads constitutes a key policy instrument for promoting rural revitalization and enhancing rural household income. However, existing studies predominantly adopt a single-actor or policy-instrument perspective and lack a systematic examination of the complex coupling relationships among multiple actors, resource endowments, and institutional environments. Adopting a rural operational perspective, this study explores how internal and external elements interact to reshape rural systems and identifies the configurational paths through which multiple actors’ behavioral logics drive efficient idle homestead revitalization. Drawing on symbiosis theory, we constructed a systemic analytical framework. A mixed-methods design combining fuzzy-set Qualitative Comparative Analysis and case studies of 35 typical villages in Zhejiang Province was employed. The findings reveal that (1) there exist four equifinal high-performance paths—market-driven, government-led, multi-actor co-governance, and endogenous development; (2) efficient revitalization does not rely on any single actor but depends on the dynamic alignment between actor behavioral logics, resource endowments, and the institutional environment; and (3) a robust policy environment can compensate for resource endowment deficiencies, whereas superior resource conditions allow for optimal efficiency, even with streamlined actor participation. Theoretically, this study extends symbiosis theory to the field of rural governance. Practically, it offers transferable governance solutions for Global South countries confronting rural decline and addressing rural poverty. Full article

This paper proposes a Cross-Representation Attention-Based Neural Network with fuzzy image enhancement for joint classification and segmentation of chest X-ray and kidney images. First, each input image is transformed into three complementary representations using histogram spread, fuzzy entropy, and fuzzy standard deviation-based enhancement. These representations emphasize different intensity distributions, informative regions, and local structural variations. A Cross-Representation Attention Fusion module then models multidirectional relationships among the enhanced representations and adaptively integrates their complementary features into a unified feature space. The fused features are processed by a shared encoder with task-specific classification and segmentation heads. The framework is evaluated for clinically relevant chest X-ray abnormalities, including pneumonia, pneumothorax, pleural effusion, and lung opacity, and for kidney-image classes comprising normal, tumor/renal cell carcinoma, and cystic renal mass cases. Experimental results show that the proposed method outperforms conventional and recent baseline models in both classification and segmentation. Ablation studies confirm that the fuzzy enhancement branches, cross-representation attention, and joint multi-task learning each contribute to the overall performance. Statistical and qualitative analyses further demonstrate the stability of the results and the model’s ability to localize relevant lesion regions. The proposed framework provides an effective and interpretable approach to unified medical image classification and segmentation while maintaining a reasonable balance between predictive performance and computational cost. Full article

We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization. Full article

Ancient timber structures are important carriers of valuable cultural heritage, and their structural safety directly determines whether historic buildings can remain in safe service over time. Cracks represent one of the most widespread and important forms of damage in ancient timber structures. They can directly lead to cross-sectional weakening of structural members, degradation of load-bearing capacity, and the gradual development of overall structural safety risks. To address the limitations of existing crack assessment methods, such as strong subjectivity in weight determination, insufficient accuracy in grade boundary discrimination, and inadequate coupling with mechanical performance, this study proposes a crack safety assessment method for ancient timber structures based on an improved entropy–fuzzy matter-element model. A multi-dimensional evaluation index system is established, incorporating crack geometric characteristics, structural load-bearing capacity, and service time effects. A mechanically driven load-carrying capacity degradation index is introduced to quantitatively characterize the influence mechanism of crack propagation on structural performance deterioration. The entropy weight method is employed to objectively determine the weights of each indicator, and an asymmetric closeness degree is introduced to improve the traditional fuzzy matter-element model, thereby enhancing the stability and accuracy of safety grade classification. A case study of the Bawang Academy, Shenyang Jianzhu University, is conducted. Crack parameters are obtained using image recognition and three-dimensional laser scanning techniques, and a comprehensive structural safety assessment is performed. The results indicate that the proposed method can accurately reflect the actual damage distribution and deterioration level of the structure, providing a reliable theoretical basis and technical support for crack safety evaluation and preventive conservation of ancient timber structures. Full article

Resilience is critical for construction projects to cope with diverse risks and uncertainties. Inter-organizational relationship governance has been widely recognized as an important means of strengthening project resilience. However, existing research has paid limited attention to how different governance mechanisms interact and combine to enhance resilience in construction projects. Drawing on a configurational perspective, this study examines how contractual, hierarchical, and network governance jointly contribute to construction project resilience. Based on survey data from 289 practitioners, fuzzy-set qualitative comparative analysis (fsQCA) is employed to identify the governance configurations associated with high project resilience. The results reveal three configurational pathways leading to high resilience: (1) relational–structural network governance coupled with contractual governance; (2) a combination of contractual, hierarchical, and network governance; (3) relational–cognitive network governance coupled with contractual governance. These findings offer important theoretical and practical implications for understanding the role of hybrid governance in the resilience of construction projects. Theoretically, this study extends resilience research by demonstrating that contractual, hierarchical, and network governance do not operate in isolation but jointly enhance project resilience through distinct configurations. Practically, these findings offer guidance for project stakeholders to optimize and integrate governance mechanisms, thereby improving their capacity to anticipate, respond to, and manage internal and external crises. Full article

This paper introduces a multi-feature coordinated adaptive equivalent consumption minimization strategy (MFCA-ECMS) using fuzzy logic control (FLC) to enhance hydrogen efficiency in fuel cell hybrid electric commercial vehicles (FCHECVs) and extend the lifespan of the fuel cell system (FCS), contributing to sustainable, low-carbon transport. First, a baseline ECMS model is established for the FCHECV, whilst the optimal equivalent factor (EF) is determined using a multi-island genetic algorithm (MIGA) based on representative driving cycles. Second, an adaptive EF framework is developed to overcome the inherent limitation of conventional ECMS—its reliance on a fixed EF—by dynamically integrating three operational features: variation in the battery’s state of charge (SOC), the rate of change in the FCS’s output power, and fluctuations in vehicle power demand. Third, feature-specific adaptive weights are assigned and updated in real time using a fuzzy inference system to regulate the EF online, incorporating multiple features. Simulations are conducted under different initial SOC levels ($90\%$ and $45\%$) across different driving cycles. The results demonstrate that the MFCA-ECMS consistently reduces hydrogen consumption (HC). Compared to the charge-depleting and charge-sustaining (CD-CS) strategy, it achieves HC reductions of $17.98\%$ on the stochastic driving cycle (Random-C) and $18.73\%$ on the urban dynamometer driving schedule (UDDS), outperforming both CD-CS and conventional ECMS in all tested scenarios. Furthermore, the MFCA-ECMS actively suppresses FCS power fluctuations. Regardless of the initial SOC, the proportion of power change rates within the reasonable range exceeds $97\%$, thereby contributing to extending the FCS lifespan. This reduces emissions and operating costs, enabling sustainable hydrogen-powered commercial vehicle deployment. Full article

The Poisson multi-Bernoulli mixture (PMBM) filter is an effective approach for multi-object tracking in complex scenarios. However, its performance deteriorates when surviving objects spawn, as the PMBM filter only classifies detected objects as either new-born or surviving, thereby ignoring information from the surviving objects and preventing timely identification of spawning events. To address this limitation, this paper proposes the Dynamic Hybrid Detection-Gamma Gaussian inverse Wishart Spawning Poisson multi-Bernoulli mixture (DHD-GGIW-SPMBM) filter, which models spawning objects independently using a Bernoulli process to enhance tracking accuracy. The probability generating functional is employed to derive the recursive prediction and update equations of the proposed filter, and its conjugacy after prediction and update is formally proven. Additionally, a dynamic hybrid detection method is introduced to evaluate the consistency between measurements and theoretical samples, enabling the detection of spawning events. The detection results guide an evidential Gaussian mixture model (EGMM) for fuzzy partitioning of the spawning process, reducing errors under closely spaced and high-clutter conditions. Simulation results demonstrate that, compared with existing spawning-capable filters, the proposed DHD-GGIW-SPMBM filter achieves superior tracking performance, faster identification of spawned objects, and robust operation in complex scenarios. Full article

Decision-making in cyber defense exercises (CDX) is shaped by technical, emotional, motivational, and collaborative human factors under uncertainty and time pressure. This study proposes a human-centered Adaptive Neuro-Fuzzy Inference System (ANFIS) framework to model and predict Counterfactual Decision Reflection (CDR) outcomes in CDX environments. Two complementary datasets representing technical, emotional, motivational, and teamwork-related dimensions were collected from the international Lithuanian Armed Forces cyber defense exercise Amber Mist 2024 and analyzed using Spearman correlation, 3D regression surface modeling, fuzzy rule extraction, and ANFIS prediction to investigate the relationship between human factors and CDR. The results demonstrated that teamwork, communication, and collaboration have a stronger influence on decision stability than isolated technical competencies. Baseline ANFIS evaluation indicated that triangular membership functions provided the best generalization, while generalized bell functions achieved the lowest training errors. To improve model robustness, multiple synthetic data augmentation methods were evaluated. The augmented ANFIS models substantially improved predictive performance, reducing testing error values significantly. The findings confirm that synthetic-data-enhanced neuro-fuzzy modeling provides an effective and interpretable framework for analyzing human-centered cybersecurity decision-making processes in cyber defense exercises. Full article

Driven by the rising demand for point-of-care testing (POCT) in aging societies, accurate temperature regulation of reaction solutions has become a core technical bottleneck for miniaturized chemiluminescence immunoassay systems, since conventional indirect control strategies inevitably produce systematic deviations. To tackle this challenge, we present an integrated solution that couples multiphysics simulation, data-driven temperature estimation modeling, and embedded hardware design. We constructed a COMSOL heat transfer model to analyze the thermal performance of the microfluidic chip. Meanwhile, a grey wolf optimization (GWO) enhanced long short-term memory (LSTM) network was developed to infer the unmeasured actual reaction solution temperature based on accessible parameters, including heating voltage, ambient temperature and substrate temperature. The obtained temperature estimation was then fed back to a fuzzy PID controller for closed-loop regulation. Experimental results demonstrated that the GWO-LSTM model limited the estimation error within 0.3 °C, and the steady-state temperature control accuracy reached ±0.2 °C or higher under fluctuating ambient conditions and diverse initial states. For cardiac troponin I (cTnI) detection, the proposed system shortened the incubation duration and reduced the coefficient of variation from 10.77% to 2.69%. This work addresses the key bottleneck restricting precise temperature control in microfluidic chemiluminescence analyzers, which provides robust technical support for the development of next-generation high-performance POCT instruments. Full article

Although two-view Takagi-Sugeno-Kang (TSK) fuzzy classifiers have shown promising performance, most existing methods rely on static fusion and lack dynamic boundary modeling. Moreover, complex coupling architectures may obscure rule-level semantics and reduce interpretability. This study proposes a residual-directed two-view deep TSK fuzzy classifier (R-TSKFC), which establishes a hierarchical two-view collaborative learning framework to overcome the limitations of static fusion and enable adaptive modeling of shared decision boundaries. A two-view training strategy is proposed to implicitly enforce semantic consistency between views and suppress view-specific drift for the parameter space. In the feature space, a residual-directed mechanism explicitly conveys boundary-relevant information between views, allowing the proposed model to focus on under-learned regions and enhance discriminative capability. Extensive experiments on seven UCI benchmark datasets and seven real-world medical datasets demonstrate that R-TSKFC achieves competitive classification performance, with consistent improvements over representative baselines. The model also exhibits competitive performance in terms of classification accuracy, generalization ability, and computational efficiency, while preserving interpretability by relying on original input features. Moreover, the residual-directed mechanism provides an auxiliary perspective for understanding the decision process at each network layer, without compromising the transparency of the learned fuzzy rules. Full article

The increasing demand for clean, reliable, and sustainable energy has intensified the need for advanced control strategies in modern wind energy conversion systems. Although conventional backstepping control (BC) offers strong stability and robustness, its performance may deteriorate under parameter uncertainties and dynamic operating conditions, leading to power fluctuations and reduced energy quality. To overcome these challenges, this study proposes an intelligent fuzzy fractional-order BC (FFOBC) strategy for multi-machine wind energy systems. By integrating fuzzy logic with fractional-order calculus into the classical BC framework, the proposed approach enhances adaptability, dynamic response, and robustness against system disturbances and nonlinearities. The controller is implemented at the machine-side inverter and validated in MATLAB/Simulink under varying wind and load conditions. Comparative results demonstrate that the proposed FFOBC significantly outperforms conventional sliding mode control in terms of overshoot reduction, steady-state accuracy, response smoothness, and total harmonic distortion minimization. Furthermore, the proposed strategy improves energy conversion efficiency, reduces mechanical and electrical stress, and ensures stable power injection into the grid. These findings highlight the potential of the proposed intelligent control framework to support sustainable, resilient, and high-quality wind energy integration in future smart power systems. Full article

Battery locomotives used in underground coal mining operations require continuous maintenance, and failures occurring during these operations pose significant occupational safety and health (OSH) risks. Traditional Risk Assessment Methods (TRAMs), particularly the Risk Matrix Method (RMM), often fail to capture the uncertainty and subjectivity inherent in complex mining environments. This study develops a fuzzy logic-based risk assessment framework to improve the evaluation of accident risks associated with maintenance and repair activities in battery locomotive workshops of an underground coal mine in Turkey. Two fuzzy inference models (FL-Basic and FL-Advanced) based on expert knowledge and linguistic variables were designed using Mamdani-type inference with centroid defuzzification. The mathematical formulation of the fuzzy inference and defuzzification steps is presented explicitly, and a six-step algorithm formalises the proposed framework. The rule base of FL-Advanced systematically upweights the severity dimension relative to RMM through reassignment of 16 of the 25 consequent categories. The outputs of these models were compared with RMM to analyse their effectiveness in identifying critical hazards. Application results from Karadon Hard Coal Company show that the proposed FL-Advanced model significantly reduces ambiguity, prioritises high-severity risks more realistically, and provides a more consistent decision-making structure for OSH specialists. The study highlights the advantages of fuzzy logic for modelling uncertain, incomplete, and human-dependent data in hazardous underground mining conditions. Full article

Train fires in railway bridge–tunnel connection sections (BTCSs) create severe evacuation challenges because tunnel–bridge spatial transitions interact with heat, smoke, visibility loss, and constrained rescue conditions. Existing evacuation management methods remain limited in coupling quantitative risk assessment with adaptive route guidance under evolving fire hazards. To address this issue, this paper proposes a large language model (LLM)-enhanced intelligent decision-support system (IDSS) framework for quantitative risk assessment and dynamic evacuation routing in BTCS fire scenarios. First, a multi-dimensional risk assessment model is established using the analytic hierarchy process and fuzzy comprehensive evaluation to quantify post-stop evacuation risk from the perspectives of evacuation organization, structural damage, and line recovery. Second, a dynamic topology-based routing method is developed to prune fire-threatened nodes and identify safer evacuation paths under evolving hazard conditions. The risk assessment model and routing algorithm are further embedded as callable tools into an LLM-enhanced evacuation IDSS under a perception–reasoning–recommendation architecture, in which an LLM orchestrates tool invocation, situational reasoning, and recommendation generation, thereby enabling autonomous risk interpretation, dynamic route replanning, and cross-regional collaborative decision support. The proposed framework is validated through a representative real-world railway engineering case. The results show that the IDSS-recommended routes achieved higher comprehensive safety scores (80.44 and 79.56) than routes involving fire-affected areas did (77.00 and 77.88). Workflow analysis further indicates that the proposed IDSS reduces the manual route-derivation workload by integrating risk assessment, topology pruning, and route allocation into structured, human-reviewable evacuation recommendations. Expert evaluations further confirm the rationality and compliance of the outputs, with review scores ranging from 1.76 to 1.92 out of 2.00. Overall, the proposed framework offers a feasible decision-support approach for intelligent evacuation management in complex railway fire emergencies. Full article

In deep foundation pit risk assessment, expert ratings are often aggregated without preserving the dispersion of individual opinions, yet such dispersion directly reflects the reliability of the assessment. To address this shortcoming, this study integrates dual probabilistic linguistic term sets (DPLTS), entropy theory, and the best–worst method (BWM). In this DPLTS framework, the membership set L(p) encodes the central tendency of expert ratings (the assessed risk level), while the non-membership set U(q) encodes the dispersion of ratings, serving as a proxy for expert disagreement—a source of uncertainty that is as critical as the risk level itself for decision-making. The least common multiple expansion method standardizes information length. Secondary indicator weights are determined using fuzzy entropy and cross-entropy, while primary indicator weights are derived via BWM, forming a combined subjective-objective weighting model. Hierarchical aggregation yields the overall risk expectation value. A case study assesses the project as Level III (moderate) risk, with a low variance of 0.0503 indicating strong expert consensus. The risk expectation varies by less than 4% under different entropy measures, confirming robustness. Comparative analysis with fuzzy comprehensive evaluation and CRITIC–Grey system methods shows consistent results, with all three identifying excavation and support as key risk indicators. The proposed method provides not only a reliable risk level but also a quantitative measure of expert agreement, offering enhanced support for targeted risk management. Full article