Search Results (7,088)

This paper focuses on the Pinghu Formation in the K region of the Xihu Depression, conducting a systematic study on the channel types, migration patterns, and the coupling mechanisms of tectonics, paleogeomorphology, and tidal dynamics in the tidal-controlled and river-controlled composite delta system of the region. By integrating core, well logging, and 3D seismic data, and addressing the challenges of channel identification under the influence of coal seams, methods such as PCA, K-means clustering, and fuzzy c-means clustering were employed for multi-attribute fusion analysis. An indicator system for channel identification and type classification was established, revealing the sedimentary characteristics of tidal-modified delta channels and their planar distribution and migration evolution process. The results of the study indicate that: (1) The early stage of the Pinghu Formation developed a tidal-controlled delta, with channels in network, linear, and dendritic shapes, where individual channels were small and fragmented; in the later stage, it transformed into a river-controlled delta, with sandbodies more concentrated; (2) In areas with weak tectonic constraints, the control of geomorphic boundaries became more prominent, and the barrier islands’ shielding effect on tides led to river-controlled migration of the channels, with limited tidal channels and tidal-modified sandbodies developed only in local areas; (3) The planar distribution and evolution of channels in the study area showed significant differences at different times due to the influences of geomorphology and tectonics. The findings of this paper provide new insights into the sedimentary evolution of tidal-modified delta channels. Full article

Ferroresonance is a nonlinear phenomenon in power systems capable of producing irregular oscillations and severe overvoltages that threaten transformers, voltage transformers, cables, and associated equipment. This paper presents a structured comprehensive review of ferroresonance detection and mitigation techniques reported up to 2025, with particular emphasis on artificial intelligence (AI)-based approaches published during the last five years. A systematic literature search was conducted across IEEE Xplore, Scopus, Web of Science, and Google Scholar using predefined ferroresonance- and AI-related keywords. Eligible studies were screened using explicit inclusion criteria requiring demonstrated ferroresonance relevance. Numerical modeling approaches, electromagnetic transient tools, ferroresonance modes, and mitigation strategies are synthesized, followed by a critical evaluation of machine learning, deep learning, fuzzy logic, evolutionary algorithms, and hybrid intelligent frameworks. Particular emphasis is placed on signal preprocessing, data representation, real-time protection constraints, and cross-topology robustness. The review identifies key research gaps, including the scarcity of benchmark datasets, limited validation under realistic network variability, and the absence of standardized evaluation methodologies. While this work is presented as a structured comprehensive review, PRISMA-inspired screening principles were applied to enhance transparency and reproducibility. Current evidence indicates that hybrid approaches combining physics-informed preprocessing—particularly wavelet-based feature extraction—with lightweight neural classifiers offer the most practical pathway for relay-grade ferroresonance protection in modern smart grids. Full article

This paper systematically investigates the dynamical properties of a class of max-type fuzzy difference equation. The study first establishes the existence and uniqueness of the solution sequence under given initial conditions with positive fuzzy numbers. Subsequently, by applying the cut-set theory, the fuzzy equation is transformed into a system coupled by two ordinary difference equations. Through a combination of case analysis and mathematical induction, the study rigorously demonstrates that the solutions of this system exhibit global periodicity with a period of 4, while also deriving the exact closed-form expressions of the periodic solutions. Based on the periodic solutions obtained from the ordinary difference system, the research successfully reveals the periodic characteristics of the solutions to the original fuzzy difference equation and rigorously analyzes their boundedness and persistence. Finally, numerical simulations conducted with Matlab 2016 provide robust data support for the theoretical conclusions and the effectiveness of the methodology. Full article

The paper proposes a completely new geometric interpretation of intuitionistic fuzzy sets for confidence-aware decision making. Instead of directly using ordered pairs of membership and non-membership degrees, we reinterpret the intuitionistic fuzzy triangle as a structured coordinate framework that simultaneously represents the evaluation outcomes and the evaluators’ expertise in the following manner: experts’ confidence levels are modelled with line segments parallel to the hypotenuse, while evaluation scores are represented by line segments radiating from the origin of the coordinate system toward the hypotenuse. Their intersections form a finite lattice of points whose total number depends on the chosen confidence and assessment scales. The proposed construction preserves the semantic foundations of intuitionistic fuzziness: points closer to the origin reflect higher uncertainty in the evaluator’s confidence, while points onto the hypotenuse represent determinate judgments (with varying degrees of positivity or negativity) based on the complete evaluator’s confidence. The geometric distances between intersections provide a formal explanation of varying discriminative power: assessments from highly confident reviewers are more distinguishable than those from less confident ones. In addition, a colour-based visualization further supports the intuitive interpretation of confidence-weighted evaluations. The proposed framework offers an alternative yet fully consistent way to model expertise-dependent decision processes within the intuitionistic fuzzy setting, bridging geometric insight and practical evaluation scenarios via a structured system of parallels and meridians. Full article

Cold vaccine delivery is often known as a high-cost logistic process, which forces many pharmaceutical manufacturers, particularly small- and medium-sized enterprises (SMEs), to subcontract logistics operations of vaccines to third-party logistics (3PL). It is clear that maintaining the traceability and trackability of vaccines in this dynamic collaborative environment is fundamental for guaranteeing the safety of product. However, the lack of a unified vaccine logistics platform holds back comprehensive supervision and traceability, posing significant challenges to the development of useful cold chain logistics systems. To address these challenges, in this study we propose a blockchain-enabled platform for the evaluation and selection of 3PL providers in vaccine supply chains. We leveraged consortium blockchain technology to guarantee data integrity, transparency, and decentralization, facilitating trust among four main players of vaccine supply chain. We utilized smart contracts as a main part of this platform, which are responsible for automating key operational processes, including 3PL evaluation, contract execution, and monitoring. In this respect, the Fuzzy Analytic Hierarchy Process (FAHP) engine is integrated into the proposed platform to enable a data-driven, multi-criteria decision-making framework for selecting the most suitable 3PL providers. We evaluated the proposed platform through case study and gas consumption analysis; the results of the case study validate high operational accuracy (93.21%), precision (90.23%), recall (94.50%), and an F1-score of 92.32% for the platform, which offers a robust solution to enhance accountability, reliability, and decision-making in vaccine distribution networks. Full article

The reliable operation of transmission lines is essential for grid stability. Growing electricity demand pushes existing lines to full capacity, while new construction is constrained by resources and the environment. Dynamic capacity increase technology addresses this by boosting transmission capacity without physical upgrades, with the identification of weak points along the line being central to its application. This study integrates correlation analysis and the Analytic Hierarchy Process to develop an evaluation method for transmission line segments, with a supporting software implementation also developed. A system of characteristic quantities was first established using operation and maintenance guidelines combined with correlation analysis. The Analytic Hierarchy Process was applied to score features and derive weights after consistency validation. Preprocessed line data were then weighted to calculate segment weakness levels, and fuzzy comprehensive evaluation was used for both qualitative and quantitative condition analysis. The model was validated through a case study, and its software implementation streamlines and enhances the assessment process. Full article

Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) struggle to balance end effector pose accuracy and robustness in apple picking. They are also prone to target loss and control singularities. A progressive Hybrid Automatic Switching Visual Servoing (HAVS) method is proposed and applied to an apple-picking robotic system. HAVS integrates PBVS and IBVS to coordinate control of the manipulator end effector pose. A depth-based switching function is designed. When target depth is below an optimal threshold, the controller switches to PBVS for precise final positioning. This reduces target loss and control singularities. An adaptive proportional-derivative (PD) controller with fuzzy gain scheduling updates the control gains online to enhance responsiveness and stability. The hardware consists of a six-axis manipulator, a depth camera, and a mobile base. You Only Look Once version 5 (YOLOv5) performs apple detection and generates control commands. Indoors, success rate was 96%, which was 4 and 10 percentage points higher than PBVS only and IBVS only. Average picking time was 12.5 s, 0.3 s, and 1.1 s shorter. Outdoors, success rate was 87.5%, average time was 13.2 s, and damage rate was 4.2%. This method provides a reference implementation for visual servo control in agricultural picking robots. Full article

The fast growth of electric vehicles (EVs) and renewable energy motivates reliable charging infrastructure with balanced energy management and good power quality. However, conventional converter controllers like proportional and integral (PI) and fuzzy logic controllers (FLCs) exhibit slow dynamic response, poor adaptability to varying solar conditions, unbalanced energy management, low power quality, and higher total harmonic distortion (THD). To overcome these limitations, this work proposes an adaptive neuro-fuzzy inference system (ANFIS) controller for balanced energy management and improved power quality in EV charging stations. The ANFIS controller is a combination of a fuzzy inference system (FIS) and a neural network (NN). The FIS provides the best maximum power point tracking and robust control during changing solar PV conditions. The NN optimally controls the flow of power between the solar PV system, energy storage battery (ESB), EV, and utility grid. The entire system is simulated in MATLAB/Simulink. It consists of a PV system with a capacity of 2 kW, an ESB with a capacity of 10 kWh and an EV battery with a capacity of 4 kWh, which are linked by bidirectional DC/DC converters. A 30 kVA bidirectional inverter, along with an LCL filter, is connected between the 500 V DC bus and 440 V utility grid, allowing for both directions. The results validate the effectiveness of the proposed ANFIS controller in terms of DC bus voltage stability, faster dynamic response, enhanced renewable energy utilization, improved efficiency to 98.86%, reduced voltage and current THD to 4.65% and 2.15% respectively, reduced utility grid stress, and enhanced energy management compared to conventional PI and FLCs. Full article

Municipal solid waste incineration (MSWI) is a critical technology for advancing urban sustainability, contributing to improved environmental quality, optimized energy structures, and the circular economy. However, the realization of these sustainability benefits is contingent upon the stable, efficient, and low-emission operation of the incineration process. This operational stability is directly governed by several key variables, such as furnace temperature, main steam flow rate, flue gas oxygen content, and burnout point temperature. The inherent complexity of controlling these interconnected variables necessitates the development of an accurate multi-variable prediction model to ensure both energy recovery efficiency and environmental compliance, which are core pillars of sustainable waste management. Existing studies have often addressed these key controlled variables in isolation, lacking a unified modeling framework. Furthermore, they have not adequately considered how dimensional differences among these variables impact the performance evaluation of predictive models, a critical oversight for ensuring holistic process sustainability. To address these gaps and support the intelligent operation of sustainable waste-to-energy systems, this study proposes a novel multi-controlled variable modeling method based on shared features and an improved fuzzy neural network. Our integrated approach begins by calculating the Pearson correlation coefficient between each manipulated variable and each controlled variable—selected based on expert knowledge—to assess the distinguishability of operating conditions within the current dataset. Subsequently, a correlation threshold, informed by expert knowledge, is applied to identify shared features that influence multiple controlled variables simultaneously. Finally, we enhance the fuzzy neural network by redefining its evaluation criterion to accommodate variable dimensional differences, leading to the development of a robust multi-controlled variable prediction model. This model is designed to provide a more comprehensive and accurate basis for process control, directly contributing to improved energy efficiency and reduced environmental impact. The effectiveness of our proposed model is validated using operational data from an actual MSWI plant, demonstrating its potential to support more sustainable and economically viable waste-to-energy operations. Full article

Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations. Full article

To address the chattering phenomenon and sensitivity to load disturbances in conventional sliding mode observers (SMO) for sensorless permanent magnet synchronous motor (PMSM) control, this paper proposes a robust sensorless control strategy integrating a fuzzy adaptive SMO with an improved sliding mode speed controller. In the observer design, a continuous hyperbolic tangent function, $tanh \left(ax\right)$, replaces the traditional sign function, while a fuzzy logic controller adaptively tunes the convergence factor a to enhance estimation accuracy and suppress high-frequency chattering. Simultaneously, an adaptive quadrature phase-locked loop (AQPLL) is incorporated to achieve adaptive matching across various operating conditions by updating parameters online, which effectively reduces phase delay and improves the dynamic performance of rotor position and speed estimation. Furthermore, a non-singular terminal sliding mode control (NTSMC) strategy is employed in the outer speed loop with a proposed segmented terminal reaching law. This law ensures rapid response in large-error regions and mitigates steady-state oscillations in small-error regions, thereby strengthening system robustness against load disturbances. The stability of the proposed system is rigorously verified via Lyapunov stability analysis. Simulation and experimental results demonstrate that the proposed approach significantly reduces speed and position estimation errors under varying speeds and sudden load changes compared to the conventional SMO-PI method, while effectively suppressing system chattering to confirm its engineering feasibility. Full article

Humanitarian logistics faces challenges such as conflicting objectives, severe uncertainty, temporal dynamics, and the need for interpretable decisions. This research presents an integrated decision-making framework that simultaneously considers fuzzy uncertainty, system dynamics, and adaptive decision logic. Operational uncertainties are modeled using triangular fuzzy numbers and a dynamic representation of the system allows for continuous updating of decisions over time. Computational results based on simulated data show that the proposed framework is capable of generating stable, diverse, and interpretable solutions. An improvement in the average quality of the Pareto front of more than 5% and a reduction in the distance from the reference front of about 30% are observed compared to non-adaptive approaches. Also, stability and dynamic behavior analyses show that the decisions are robust to changing environmental conditions and parameters and have high adaptability. These features make the proposed framework a reliable tool for decision support in relief operations. Full article

Understanding erosion–corrosion mechanisms in selective laser-melted (SLM) Ti-6Al-4V is essential for optimizing component durability in demanding sectors such as oil and gas, hydropower, and offshore engineering, where slurry-induced degradation prevails. Nevertheless, it is challenging to experimentally evaluate slurry erosion–corrosion over a wide range of SLM processing parameters and various slurry erosion–corrosion operating conditions. The adaptive neuro-fuzzy inference system (ANFIS) offers a robust computational approach for modeling complex systems with independent variables, making it well suited for this investigation. This study aims to assess the efficacy of ANFIS in predicting the mass loss of as-built SLM-processed Ti-6Al-4V under slurry erosion–corrosion conditions, with a focus on the synergistic effects of impact angle and erodent mass in both saline and pure water environments, validated against empirical data. The quantitative analysis reveals that erodent mass is the dominant factor influencing mass loss, followed by impact angles. Notably, the combined effect of erodent mass and impact angles in saline environments (e.g., sea water) exacerbates material loss by approximately 16% compared to pure water, highlighting the critical role of electrochemical corrosion in synergy with mechanical erosion. The results demonstrate that the ANFIS model accurately simulates the degradation behavior of SLM-processed Ti-6Al-4V subjected to water–silica slurry impacts within the experimental parameter space; however, predictive generalization beyond these conditions should be interpreted carefully due to validation constraint. Full article

High-performance speed regulation of interior permanent magnet synchronous motor (IPMSM) drives in electric vehicle (EV) applications becomes particularly challenging in the field-weakening region, where voltage constraints, parameter variations, and nonlinear aerodynamic loads significantly affect the closed-loop stability. To address these challenges, this paper proposes a stability-aware adaptive fractional-order speed control framework for EV traction systems. The framework integrates a fractional-order PI (FOPI) core to provide iso-damping robustness, a bounded fuzzy gain-scheduling mechanism for real-time adaptation, and an offline multi-objective optimization layer for systematic parameter tuning. A Lyapunov-based qualitative analysis is provided to justify closed-loop ultimate boundedness under adaptive gain modulation and field-weakening constraints. The fuzzy scheduler is explicitly structured to regulate the error energy dissipation rate by modulating the proportional and integral gains while preserving the gain boundedness. The controller parameters are optimized using a diversity-driven fractional-order multi-objective PSO algorithm to balance the tracking accuracy and control effort. The proposed framework was validated using a high-fidelity MATLAB/Simulink–CarSim 2023 co-simulation platform under the aggressive US06 driving cycle. The results demonstrated a zero-overshoot transient response, robustness against a 2.5× inertia mismatch, and sustained performance under flux-linkage and inductance variations in deep field-weakening operation. Compared with conventional PI-based strategies, the proposed approach reduced the speed RMSE by 82%, lowered the current THD from 18.5% to 3.2%, and reduced the cumulative DC-link current-squared index by 6.7%. These results validate the practical robustness and computational feasibility of the proposed stability-aware framework for EV traction control. Full article

Accurate image similarity assessment is a key problem in computer vision, particularly in segmentation and classification problems. Classical Hausdorff and Fréchet metrics provide pointwise distance values and do not allow similarity to be evaluated in the form of intervals, which limits their applicability in problems where uncertainty plays a significant role. In this study, a combined approach to computing distances between images based on fuzzy Fréchet and Hausdorff metrics is developed. Two theorems are proved demonstrating that, for convex polygonal contours, the fuzzy Hausdorff distance coincides with the fuzzy Fréchet distance. This result makes it possible to replace the computation of the fuzzy Hausdorff metric with the simpler fuzzy discrete Fréchet metric. A method and algorithms for determining the fuzzy discrete Fréchet distance and a combined distance between convex polygons are proposed; their computational complexity is evaluated, and an application example is provided. The results show that the combined fuzzy metric reduces computation time by at least a factor of two compared to the direct computation of the fuzzy Hausdorff metric, while preserving similarity assessment accuracy. The proposed approach can be applied to shape analysis, segmentation evaluation, and similarity modeling in image classification systems. Future research directions include extending the method to non-convex polygons and arbitrary geometric objects. Full article