Search Results (6,962)

Accurate photovoltaic (PV) power output forecasting is critical for ensuring stable operation of modern power systems, yet it is constrained by high-dimensional redundancy in input weather data and the inherent heterogeneity of output scenarios. To address these challenges, this paper proposes a novel cascaded data-driven forecasting approach that enhances forecasting accuracy through systematically improving and optimizing the feature extraction, scenario clustering, and temporal modeling. Firstly, guided by weather data–PV power output correlations, the Deep Autoencoder (DAE) is enhanced by integrating Pearson Correlation Coefficient loss, reconstruction loss, and Kullback–Leibler divergence sparsity penalty into a multi-objective loss function to extract key weather factors. Secondly, the Fuzzy C-Means (FCM) algorithm is comprehensively refined through Mahalanobis distance-based sample similarity measurement, max–min dissimilarity principle for initial center selection, and Partition Entropy Index-driven optimal cluster determination to effectively cluster complex PV power output scenarios. Thirdly, a Long Short-Term Memory–Temporal Pattern Attention (LSTM–TPA) model is constructed. It utilizes the gating mechanism and TPA to capture time-dependent relationships between key weather factors and PV power output within each scenario, thereby heightening the sensitivity to key weather dynamics. Validation using actual data from distributed PV power plants demonstrates that: (1) The enhanced DAE eliminates redundant data while strengthening feature representation, thereby enabling extraction of key weather factors. (2) The enhanced FCM achieves marked improvements in both the Silhouette Coefficient and Calinski–Harabasz Index, consequently generating distinct typical output scenarios. (3) The constructed LSTM–TPA model adaptively adjusts the forecasting weights and obtains superior capability in capturing fine-grained temporal features. The proposed approach significantly outperforms conventional approaches (CNN–LSTM, ARIMA–LSTM), exhibiting the highest forecasting accuracy (97.986%), optimal evaluation metrics (such as Mean Absolute Error, etc.), and exceptional generalization capability. This novel cascaded data-driven model has achieved a comprehensive improvement in the accuracy and robustness of PV power output forecasting through step-by-step collaborative optimization. Full article

Multi-UAV cooperative missions demand millisecond-level coordination across three key resource dimensions—battery energy, wireless bandwidth, and onboard computing power—where traditional Min or linearly weighted schedulers struggle to balance safety with efficiency. We propose a prediction-enhanced two-stage T-norm–Choquet–OWA resource aggregator. First, an LSTM-EMA model forecasts resource trajectories 3 s ahead; next, a first-stage T-norm (min) pinpoints the bottleneck resource, and a second-stage Choquet–OWA, driven by an adaptive interaction measure $\varphi$, elastically compensates according to instantaneous power usage, achieving a “bottleneck-first, efficiency-recovery” coordination strategy. Theoretical analysis establishes monotonicity, tight bounds, bottleneck prioritization, and Lyapunov stability, with node-level complexity of only $O\left(1\right)$. In joint simulations involving 360 UAVs, the method holds the average round-trip time (RTT) at 55 ms, cutting latency by 5%, 10%, 15%, and 20% relative to Min, DRL-PPO, single-layer OWA, and WSM, respectively. Jitter remains within 11 ms, the packet-loss rate stays below 0.03%, and residual battery increases by about 12% over the best heuristic baseline. These results confirm the low-latency, high-stability benefits of the prediction-based peak-shaving plus two-stage fuzzy aggregation approach for large-scale UAV swarms. Full article

Ni-Cr alloys are some of the most important materials being utilized in the manufacturing industry. Their unique properties make them attractive for various applications, especially in the aerospace and automobile industries. Since machining these materials is challenging due to their properties, it is necessary to understand their machining processes and how to improve them. As a result, time and again, effort has been made to understand and model the machining of Ni-Cr alloys. In this action, different approaches, i.e., neural networks, fuzzy systems, simulations, etc., have been of great help. At the same time, efforts have been made to optimize the machining processes to find how to obtain the best outputs from the processes. Different methods, such as multi-criteria decision-making, meta-heuristic algorithms, desirability functions, etc., have been utilized in this respect. This work aims to prepare an exhaustive review of the methods used for modeling and optimization of the machining of Ni-Cr alloys. It considers five major machining operations and collects data on how these methods or algorithms have been used to improve the machining and to what extent. The use of newer advanced algorithms in manufacturing processes is on the rise, and this manuscript aims to record the methods used, their effectiveness, and their shortcomings. It also provides an insight into the methods and their compatibility. Suggestions for future work are also discussed at the end of this study. Full article

Achieving sustainable development in the aviation industry increasingly relies on the synergistic operation of complex product innovation ecosystems. These ecosystems not only drive technological breakthroughs, but also serve as crucial enablers of resource efficiency, ecological resilience, and long-term industrial competitiveness. This study explores how specific configurations of synergistic factors within innovation ecosystems support sustainable innovation outcomes in the aviation sector. Drawing on the innovation ecosystem theory and principles of sustainable development, we employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine 15 representative aviation equipment R&D cases, including AVIC Tongfei and AVIC Xifei. The analysis centers on five key dimensions: core enterprise leadership, value chain collaboration, cross-organizational innovation, technology–market feedback loops, and institutional policy support. These dimensions interact to shape multiple synergy pathways that facilitate sustainable transformation. The results reveal that no single factor alone is sufficient to ensure high innovation sustainability. Instead, three distinct synergy configurations emerge: (1) core enterprise-led model, which reduces resource redundancy through optimized value chain governance; (2) the industry chain collaboration model, which enhances environmental performance via modular design and lifecycle management; and (3) cross-organization innovation collaboration model, which improves material reuse and infrastructure sharing through collaborative mechanisms. Together, these pathways form a reinforcing cycle of innovation–efficiency–sustainability, offering a practical framework for aligning technological advancement with ecological goals. This study deepens the understanding of how innovation ecosystem mechanisms contribute to sustainable development, particularly in high-integration industries. It offers actionable insights into achieving the Sustainable Development Goals (SDGs) through collaborative innovation and systemic resource optimization. Full article

This study addresses this gap by proposing a multilevel fuzzy evaluation model combined with an analytic hierarchy process (AHP) to quantify the greenness of furniture products across their entire lifecycle. Focusing on an office desk as a case study, we developed an indicator system encompassing environmental attributes, resource efficiency, energy consumption, economic costs, and quality performance. Weighting results revealed that environmental attributes (27.2%) and resource efficiency (27.2%) dominated the greenness evaluation, with material recycling rate (33.5%) and solid waste pollution (24.3%) as critical sub-indicators. The prototype achieved a moderate greenness score of 70.38/100, highlighting optimization potential in renewable material adoption (10% current rate) and modular design for disassembly. Mechanically recycled materials could reduce lifecycle emissions by 18–25% in key categories. The model demonstrates scalability for diverse furniture types and informs policy-making by prioritizing high-impact areas such as toxic material reduction and energy-efficient manufacturing, thus amplifying its global and interdisciplinary multiplier effects. Full article

Accurate prediction and management of the service life of buildings and structural components are crucial for ensuring durability and economic efficiency. This paper investigates both discrete- and continuous-time Markov chains as probabilistic models for representing deterioration processes of building structures. Transition probabilities, fundamental matrices, and absorption times are computed to quantify expected lifespans and degradation pathways. Numerical simulations illustrate how state probabilities evolve, inevitably converging toward structural failure in the absence of maintenance interventions. Additionally, this study explicitly addresses uncertainties inherent in lifecycle predictions through the application of fuzzy set theory. A fuzzy Markov chain model is formulated to represent imprecise deterioration states and transition probabilities, which validate the predictable yet uncertain progression of structural deterioration through graphical analyses and fuzzy simulations. The proposed methodology, including fuzzy modeling, provides building managers and engineers with a robust analytical framework to optimize maintenance scheduling, refurbishment planning, and resource allocation for sustainable lifecycle management under uncertainty. Full article

Water quality evaluation is a crucial component of water source management and pollution prevention, essential for achieving regional water safety and sustainable development. The spatial distribution and trends of major water pollutants in Wan’an Reservoir were analyzed. Subsequently, a fuzzy membership model was employed to develop a comprehensive water quality evaluation method. This approach assessed spatial variations in water quality across the upper, middle, and lower reaches of the reservoir, identifying key factors influencing water quality. The results indicate that water quality in Wan’an Reservoir, primarily characterized by total nitrogen, was poor. Notably, 50% of the sampling points in the main stream were identified as highly polluted, with the highest exceedance rate observed in the middle reaches of the tributaries. Sampling points classified as Class I were predominantly located in the upper reaches, where water quality benefitted from clean incoming water and minimal disturbance. In contrast, the lower reaches experienced more severe pollution due to the cumulative effects of domestic sewage, industrial wastewater, and agricultural runoff. These findings are crucial for developing effective water environmental protection strategies and promoting the sustainable utilization and protection of water resources. Full article

Recently, Transformers have made significant progress in the joint classification task of HSI and LiDAR due to their efficient modeling of long-range dependencies and adaptive feature learning mechanisms. However, existing methods face two key challenges: first, the feature extraction stage does not explicitly model category ambiguity; second, the feature fusion stage lacks a dynamic perception mechanism for inter-modal differences and uncertainties. To this end, this paper proposes a Deep Fuzzy Fusion Network (DFNet) for the joint classification of hyperspectral and LiDAR data. DFNet adopts a dual-branch architecture, integrating CNN and Transformer structures, respectively, to extract multi-scale spatial–spectral features from hyperspectral and LiDAR data. To enhance the model’s discriminative robustness in ambiguous regions, both branches incorporate fuzzy learning modules that model class uncertainty through learnable Gaussian membership functions. In the modality fusion stage, a Fuzzy-Enhanced Cross-Modal Fusion (FECF) module is designed, which combines membership-aware attention mechanisms with fuzzy inference operators to achieve dynamic adjustment of modality feature weights and efficient integration of complementary information. DFNet, through a hierarchical design, realizes uncertainty representation within and fusion control between modalities. The proposed DFNet is evaluated on three public datasets, and the extensive experimental results indicate that the proposed DFNet considerably outperforms other state-of-the-art methods. Full article

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy analytic hierarchy process (GD-FAHP) was proposed and integrated with the Entropy Weight Method (EWM) to assess the hazard and vulnerability of land subsidence using multiple evaluation factors, thereby deriving the spatial distribution characteristics of subsidence risk in Wuhan. Results indicated the following: (1) Maximum subsidence rates reached −49 mm/a, with the most severe deformation localized in Hongshan District, exhibiting a cumulative displacement of −135 mm. Comparative validation between InSAR results and leveling was conducted, demonstrating the reliability of InSAR monitoring. (2) Areas with frequent urban construction largely coincided with subsidence locations. In addition, the analysis indicated that rainfall and hydrogeological conditions were also correlated with land subsidence. (3) The proposed risk assessment model effectively identified high-risk areas concentrated in central urban zones, particularly the Hongshan and Wuchang Districts. This research establishes a methodological framework for urban hazard mitigation and provides actionable insights for subsidence risk reduction strategies. Full article

Understanding psychological change requires a quantitative framework capable of capturing the complex and dynamic relationships among personal constructs. Personal Construct Psychology emphasizes the hierarchical reorganization of bipolar constructs, yet existing qualitative methods inadequately model the reciprocal and graded influences involved in such change. This paper introduces the Presence–Balance (PB) space, a centrality measure for constructs represented within Fuzzy Cognitive Maps (FCMs). FCMs model cognitive systems as directed, weighted graphs, allowing for nuanced analysis of construct interactions. The PB space operationalizes two orthogonal dimensions: Presence, representing the overall connectivity and activation of a construct, and Implication Balance, quantifying the directional asymmetry between influences exerted and received. By formalizing Hinkle’s hierarchical theory within a rigorous mathematical framework, the PB space enables precise identification of constructs that drive or resist transformation. This dual-dimensional model provides a structured method for analyzing personal construct systems, supporting both theoretical exploration and clinically relevant interpretations in the study of psychological change. Full article

To address the inefficiencies of traditional numerical simulations and the high cost of experimental validation in the aerodynamic–stealth integrated design of S-shaped inlets for aero-engines, this study proposes a novel parameter prediction model based on a fuzzy C-means (FCM) clustering and nonlinear dynamic adaptive particle swarm optimization-enhanced radial basis function neural network (NDAPSO-RBFNN). The FCM algorithm is applied to reduce the feature dimensionality of aerodynamic parameters and determine the optimal hidden layer structure of the RBF network using clustering validity indices. Meanwhile, the NDAPSO algorithm introduces a three-stage adaptive inertia weight mechanism to balance global exploration and local exploitation effectively. Simulation results demonstrate that the proposed model significantly improves training efficiency and generalization capability. Specifically, the model achieves a root mean square error (RMSE) of $3.81\times {10}^{-8}$ on the training set and $8.26\times {10}^{-8}$ on the test set, demonstrating robust predictive accuracy. Furthermore, $98.3\%$ of the predicted values fall within the $y=x\pm 3\beta$ confidence interval ($\beta =1.2\times {10}^{-7}$). Compared with traditional PSO-RBF models, the number of iterations of NDAPSO-RBF network is lower, the single prediction time of NDAPSO-RBF network is shorter, and the number of calls to the standard deviation of the NDAPSO-RBF network is lower. These results indicate that the proposed model not only provides a reliable and efficient surrogate modeling method for complex inlet flow fields but also offers a promising approach for real-time multi-objective aerodynamic–stealth optimization in aerospace applications. Full article

The lubrication state of the feed system of a CNC machine tool will affect its positioning accuracy, repetition accuracy, and minimum movement amount. Insufficient or excessive lubrication will affect the accuracy. The primary objective of this study is to resolve issues related to the lubrication condition of the feed system, aiming to enhance its operational stability and accuracy. In this study, a measurement system based on images of Newton’s rings was developed. The relationship between the pattern of Newton’s rings and the oil film thickness was established based on the theoretical principle of Newton’s rings. Furthermore, fuzzy logic theory was applied to predict the oil film thickness. In the oil film thickness prediction model based on the radius of Newton’s rings, the average error is 6.5%. When the average feed rate increases by 2 m/min, the oil film thickness value decreases by 43%. Finally, the prediction model is compared with the results of an actual verification experiment. The trends in oil supply timing are consistent between the predicted and experimental results, and the relative error values are less than 10%. Therefore, this study solves the problem of insufficient or excessive oil supply in the feed system guideway, increasing the accuracy of CNC machine tools and contributing to green energy technology. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

The study explores the scenarios and approach to the design of the software for managing notifications about the fuzzily planned activities. Though many such scenarios can be solved by using traditional time and activity management tools such as organizers, diaries, planners, or schedulers, practical situations often arise when people tend to avoid overmanagement for real-life situations, when the plans might be flexible, and the planned activities might depend on location, contextual, and time information, which may not necessarily be well known or configured in advance. In this contribution, we describe examples of such situations and define the concept of soft planning. Following the principles of the human-driven design paradigm, we conducted a small-scale survey to gather insights into user preferences and identify drawbacks of existing digitalized activity planning and decision-making tools, often based on configurable notification management software. The findings reveal that while notifications are useful, users often encounter issues such as information overload, lack of contextual awareness, and disruptions caused by the notifications arriving at inconvenient or even inappropriate times. Full article

Environmental perception is crucial for achieving autonomous driving of auxiliary haulage vehicles in underground coal mines. The complex underground environment and working conditions, such as dust pollution, uneven lighting, and sensor data abnormalities, pose challenges to multimodal fusion perception. These challenges include: (1) the lack of a reasonable and effective method for evaluating the reliability of different modality data; (2) the absence of in-depth fusion methods for different modality data that can handle sensor failures; and (3) the lack of a multimodal dataset for underground coal mines to support model training. To address these issues, this paper proposes a coal mine underground BEV multiscale-enhanced fusion perception model based on dynamic weight adjustment. First, camera and LiDAR modality data are uniformly mapped into BEV space to achieve multimodal feature alignment. Then, a Mixture of Experts-Fuzzy Logic Inference Module (MoE-FLIM) is designed to infer weights for different modality data based on BEV feature dimensions. Next, a Pyramid Multiscale Feature Enhancement and Fusion Module (PMS-FFEM) is introduced to ensure the model’s perception performance in the event of sensor data abnormalities. Lastly, a multimodal dataset for underground coal mines is constructed to provide support for model training and testing in real-world scenarios. Experimental results show that the proposed method demonstrates good accuracy and stability in object-detection tasks in coal mine underground environments, maintaining high detection performance, especially in typical complex scenes such as low light and dust fog. Full article