Search Results (570)

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. Full article

Wikidata provides extensive coverage of tourism-related Points of Interest (POIs), yet its heterogeneous type system and uneven metadata limit its direct use in smart tourism applications. This paper presents an end-to-end pipeline that transforms Wikidata POIs into a compact and interpretable tourism-oriented representation supporting multi-category assignments. We collect POIs from six countries—Greece, Italy, Spain, Norway, Sweden, and Denmark—and construct a dataset that integrates core identifiers with textual descriptions, type information, heritage indicators, geographic coordinates, and Wikipedia sitelinks. We introduce an eight-category tourism taxonomy capturing key themes, including cultural venues, archaeological and historic sites, monuments, fortifications, religious sites, protected areas, natural features, and coastal or water locations. As a reproducible baseline, category likelihoods are estimated using sentence embeddings and similarity to category anchor descriptions, producing a probability vector for each POI. Building on this baseline, we propose a fuzzy inference layer that integrates embedding-based probabilities with structured Wikidata signals to generate interpretable membership degrees across categories and enable principled multi-category classification. This fusion is particularly valuable for smart tourism applications, as it supports robust faceted exploration and personalized recommendations (e.g., “historic + coastal”), while providing evidence-based explanations that enhance user trust and facilitate curator oversight when POI metadata is sparse or ambiguous. The resulting pipeline produces ranked POI catalogs by country and category, country-level tourism profiles, and diagnostic views for examining uncertain cases. The approach is fully reproducible and readily adaptable to other geographic regions or domain taxonomies. Full article

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces. Full article

The rapid growth of user-generated reviews on e-commerce platforms has created a significant decision-making challenge for both consumers and sellers, particularly in morphologically rich low-resource languages such as Turkish. This study proposes a unified artificial intelligence framework for Turkish e-commerce review intelligence by integrating transformer-based sentiment classification, instruction-tuned large language model summarization, and explainable fuzzy logic-based product evaluation within a single end-to-end architecture. A balanced dataset containing 183,333 Turkish reviews was constructed from Trendyol, Amazon Turkey, and Hepsiburada using LLM-assisted annotation and stratified downsampling. Experimental evaluations demonstrated that the fine-tuned BERTurk 128k model achieved a macro F1-score of 0.9243 on the held-out test set. To overcome the limitations of multilingual news-oriented summarization models on informal review text, the framework employed the Turkish instruction-tuned Kumru-2B model together with structured prompt engineering to generate sentiment-aware abstractive summaries. In addition, a Mamdani-type fuzzy inference system was designed to combine sentiment distribution, seller reliability, star ratings, and review volume into an interpretable product-level score. The complete pipeline was integrated into a FastAPI and React-based web platform capable of processing approximately 850 reviews in under 60 s. The findings demonstrate that domain-specific Turkish language models combined with explainable reasoning mechanisms can provide accurate, scalable, and human-interpretable decision support for large-scale e-commerce environments. Full article

Existing accounts of platform-mediated complementor growth rest on two limiting assumptions: that platform enablement constitutes a homogeneous environmental input and that firm growth is a unitary outcome. This double simplification obscures how distinct platform provisions generate qualitatively different forms of firm transformation. This study asks which combinations of mechanistically distinct platform enablement types and internal strategic response capabilities activate which forms of leapfrog growth among complementor firms operating under dual institutional governance. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on survey data from 374 complementor firms in China’s autonomous driving platform ecosystem. Five antecedent conditions are examined across two dimensions: platform enablement, comprising rule-based enablement (RE) and business platform enablement (BPE); and strategic response capabilities, comprising network linkage capability (NLC), organizational ambidexterity (OA), and policy responsiveness (PR). Three outcome variables capture three non-reducible leapfrog dimensions: technology-chain (TL), value-chain (VL), and institutional-chain (IL) transitions. A reverse-causality robustness check and a common-method-bias assessment corroborate the validity of findings. The analysis identifies equifinal configurational pathways with distinct dominant logics across the three chains. Technology-chain transitions are predominantly network-linkage-driven; value-chain transitions are policy-responsiveness-anchored; institutional-chain transitions exhibit genuine equifinality between network-linkage and policy-responsiveness pathways, both requiring dual-platform enablement as a universal structural precondition. No single enabling condition or capability suffices; leapfrog growth is irreducibly configurational and causally asymmetric. The study offers a dual-enablement, three-chain configurational framework for understanding platform-mediated firm growth under dual institutional governance. For complementor firms, findings support dimension-selective capability investment over uniform accumulation strategies. For platform orchestrators, differentiated governance design calibrated to specific complementor upgrading trajectories outperforms homogeneous resource provisioning. For policymakers, institutionalized consultative channels linking private platform governance with public regulatory processes are recommended to facilitate coordinated digital industrial transformation. Full article

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment. Full article

In this paper, we propose a new class of intuitionistic fuzzy b-metric spaces in the sense of Romaguera and investigate their fixed-point properties. Within this framework, we define and analyze Hicks-type contraction mappings. In addition, the concept of K-stationary intuitionistic fuzzy b-metrics is introduced and examined through illustrative examples. Our findings generalize classical results in fuzzy b-metric spaces and extend fixed-point theorems to the intuitionistic fuzzy setting. This study enriches fixed-point theory in intuitionistic fuzzy environments and provides a basis for further theoretical investigations and applications. Full article

As Agricultural Heritage Systems (AHS) shift from recognition toward dynamic conservation and revitalization, understanding how visitors perceive heritage values is essential for improving interpretation and management. Guided by landsenses ecology, this study provides one of the first comparative assessments of visitor perceptions across different types of Tea Agricultural Heritage Systems (TAHS), using three representative cases in Fujian, China. A visitor-oriented framework integrating physical, psychological, and cultural perceptions was developed, and 600 questionnaire responses were analyzed through entropy-weighted fuzzy comprehensive evaluation. The results show that visitors generally perceived the three TAHS positively, but perception levels differed significantly across dimensions and heritage types (p < 0.01). Psychological perceptions, especially sense of safety, sense of space, and sense of belonging, were more readily formed, whereas deeper cultural perceptions, such as understanding of heritage cultural content and community cultural connections, remained weaker. These findings reveal a hierarchical pattern in which immediate sensory and psychological experiences precede deeper cultural cognition. Practically, the study suggests that TAHS conservation should move beyond resource protection by translating heritage values into identifiable, contextualized, and participatory visitor experiences through interpretation systems, community-based participation, and experiential presentation. Full article

Direct Torque Control (DTC) for induction motors (IMs) is an advanced method derived from Field-Oriented Control (FOC). In DTC, a voltage source inverter (VSI) is employed to directly regulate the stator flux linkage and electromagnetic torque through space vector modulation (VSM), where the optimal switching vector is selected for the VSI. Similarly to FOC, the stator flux and electromagnetic torque are independently controlled to deliver enhanced dynamic performance. However, DTC still suffers from certain drawbacks, such as slow transient response, limited dynamic performance, and high ripples in torque and flux. In this paper, an improved DTC method is proposed for a three-phase squirrel-cage induction motor. Specifically, a Type-2 fuzzy logic controller is employed to regulate both the stator flux and electromagnetic torque (T2FLC). The proposed method (FLCDTC) combines a three-level VSI with dual-band hysteresis (DBHW) switching to generate the gating signals for the insulated gate bipolar transistors (IGBTs). This approach effectively reduces the total harmonic distortion (THD) in torque and stator current, lowers the common-mode voltage (CMV), and enhances the overall motor performance. Simulation results under random noise distribution demonstrate the robustness of the proposed controller, even at low operating speeds. Finally, the effectiveness of the algorithm is validated in real-time through hardware-in-the-loop (HIL) implementation. Full article

This study evaluates the environmental performance of 34 single-engine light utility helicopters across five operational phases: ground idle departure, ground idle arrival, takeoff, approach, and landing-takeoff (LTO). A hybrid multi-criteria decision-making (MCDM) framework integrating interval type-2 fuzzy sets with the Analytic Hierarchy Process (AHP), VIKOR, and TOPSIS was applied to ensure robust and reliable assessment. Six criteria: shaft horsepower (SHP), fuel flow, hydrocarbon (HC), carbon monoxide (CO), particulate matter (PM), and nitrogen oxides (NOx) were considered to capture both engine performance and environmental impact, with relative importance determined through AHP. VIKOR generated a compromise ranking, while TOPSIS validated the results. The analysis revealed that the HUGHES 500 (DDA250-C18, A34), HUGHES 501 (DDA250-C20B, A29), and BELL 206B-1 (DDA250-C20, A32) engines achieved the best environmental performance due to low fuel consumption and reduced emissions across NOx, PM, HC, and CO. In contrast, engines such as K-1200 (T53 17A-1, A1) and BELL UH-1H (T53 L13, A2) performed the poorest, with high fuel flow and elevated emissions. Sensitivity analysis showed minimal changes in rankings when the NOx weight was varied, confirming the robustness of the framework. These results highlight that emissions and fuel efficiency are more critical than engine power in determining environmental sustainability. Full article

A central open question in automotive intrusion detection is not merely whether relational representations of Controller Area Network (CAN) traffic improve performance, but which aspects of CAN traffic structure transfer robustly across attacks and which do not transfer across vehicle platforms, and why. To investigate this question systematically, we develop a lightweight intrusion-detection framework combining statistical traffic descriptors, structural identifier transition features, and graph topology representations extracted from sliding windows of CAN frames. Because CAN is a broadcast-only bus with no request–response mechanism, each ECU independently transmits its identifiers at fixed periodic rates; accordingly, the structural and graph-based features capture the temporal scheduling regularity of identifier broadcasts, not directed inter-ECU communication dependencies. Stress-testing the framework under cross-attack and cross-dataset transfer reveals a clear four-level hierarchy: (1) statistical features collapse under cross-attack transfer (ROC-AUC as low as 0.009), failing to generalise beyond the attack type seen during training; (2) structural transition features are the most robust form of representation, maintaining high cross-attack performance (ROC-AUC > 0.999) across all evaluated scenarios within the same vehicle platform; (3) graph topology features are scenario-dependent, achieving high robustness in DoS-trained scenarios but producing sub-random results in Fuzzy-trained scenarios, exposing a sensitivity to injection density profiles; and (4) the hybrid combination provides the strongest overall operational package, consistently across four classifiers. Cross-dataset transfer to the ROAD dataset reveals the precise boundary conditions of transferability: structural representations transfer only when an attack perturbs identifier transition regularity (correlated signal attacks, ROC-AUC = 0.81–0.83), while attacks that affect only payload semantics (speedometer) or exploit identifier–space novelty (fuzzing) lie outside the detection scope of transition-based features, regardless of the vehicle platform. A vehicle-specific calibration experiment further shows that the correlated-attack generalization gap can be closed with as little as 10% of target-vehicle normal traffic, whereas speedometer attacks remain structurally invisible by design. A key contribution of this work is therefore a transparent approach for identifying when relational CAN representations transfer and when they do not—a finding that is more scientifically valuable than a uniformly optimistic performance claim and which provides concrete guidance for practitioners designing cross-platform automotive IDS. Full article

The normal operational characteristics of power cables are a crucial foundation for developing their protection devices. To analyze the current and voltage characteristics of parallel cable lines, the parameter matrix of the phase-aligned parallel cable lines is first established based on Carson’s formula. The metal sheath is treated as a general line, considering its self-inductance and mutual inductance with the core loop, and its sheath circulating current is calculated. Then, the relationship between line voltage and current is established, and the effect of the sheath circulating current is equivalently incorporated into the line’s phase impedance matrix. A π-type equivalent circuit of the cable line is established, from which the operational parameters of the phase-aligned parallel cables are calculated. Indicators measuring the operational characteristics of phase-aligned parallel operation—sheath circulating current, imbalance, carrying capacity, and voltage deviation—are introduced, and the optimal arrangement is determined using the analytic hierarchy process. This study integrates Carson’s formula for impedance modeling and fuzzy AHP for multi-criteria optimization, addressing gaps in single-indicator approaches. The proposed method identifies the three-phase vertical layout as optimal, improving ampacity by 10% and reducing sheath circulating current by 28%, offering direct guidance for 500 kV cable projects. Full article

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article

The research aimed to create a hybrid model for assessing the carbon footprint of pilots’ education at a flight school, taking into account the level of implementation of green infrastructure by the educational institution, while excluding indirect emissions from the model. The study implemented an approach that combines fuzzy set theory with expert evaluation methods, utilizing membership functions and convolution mechanisms to incorporate subjective expert assessments into formalized numerical measures. The research was focused on two research questions: Does the proposed hybrid model allow for a practical assessment of a pilot’s carbon footprint during his training? Does the hybrid model provide the ability to automatically determine the level of carbon footprint of an aviation educational institution and generate substantiated recommendations for the strategic management of sustainable development of the educational process? The resulting model enables a quantitative assessment of individual CO₂ emissions during pilot training and provides collective insights into the overall carbon footprint, accounting for the green infrastructure’s level of implementation. The hybrid model was tested and validated using real data from the Technical University of Košice (Slovakia) within the “PILOT” study program (2022–2025). The experimental calculations are based on the Viper SD4, a homogeneous aircraft type. The model is designed to account for multiple aircraft types through weighted aggregation, a feature that can be used in future institutional implementations. These recommendations are practical for managers and specialists at aviation educational institutions, environmental analysts, curriculum developers, and policymakers focused on sustainable development. At the current stage, the model primarily captures direct training-related and institution-level operational emissions, while indirect emissions were included only to a limited extent because of insufficiently available and non-systematically recorded data. Therefore, the proposed framework should be interpreted as an operational decision-support model rather than a full greenhouse gas inventory covering all indirect emission sources. Full article

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article