Search Results (1,432)

This study focuses on reducing harmonic distortion in residential electrical systems caused by the use of nonlinear household appliances. A combined prediction and control framework is proposed to estimate and reduce total harmonic distortion (THD) under different operating conditions. Measurements were collected from common domestic appliances, along with environmental factors such as temperature and humidity. An auto-optimized neighborhood fuzzy rough set (AO-NFRS) method is used to identify important input features. These features are then used in a physics-informed machine learning model to predict THD. Based on the predicted values, a Bayesian-optimized ANFIS controller is applied to decide the suitable filtering mode in real time. The results show that the proposed method improves prediction accuracy and reduces harmonic distortion compared to existing methods. It also provides stable filter switching under changing load conditions. The study demonstrates that combining measurement data, physical relationships, and adaptive control can improve power quality in residential systems. Full article

Conventional hole finishing requires separate drilling and top/bottom chamfering, and excessive insert pressure can leave scratch-type marks on the hole wall. This study develops a clip-spring-based tool that performs drilling and top/bottom chamfering in a single machining cycle, with the chamfer depth passively set by equilibrium between the hole-wall reaction and the restoring force of a replaceable clip spring. A dual-angle insert–clip-spring interface produces a non-monotonic force drop followed by a low-incremental-stiffness plateau, separating high-force burr engagement from lower-force hole passage. Four insert-geometry and spring-bottom-shape combinations were analyzed by nonlinear finite element analysis, and the two embossed-bottom cases were supported by compression tests. The dual-angle/embossed case showed a 72% Force Dip, which compression testing reproduced together with the low-force plateau, and one-step machining confirmed process feasibility. The measured hole-wall roughness decreased fourfold, from Ra 1.7 μm to 0.4 μm. These results demonstrate a passive geometric route to Force Dip generation and self-equilibrating depth setting under the tested condition. Full article

Vehicle vibration and noise are predominantly driven by road-surface excitation, making robust prediction of these phenomena based on pavement roughness a central challenge in automotive development. This paper presents a fully automated pipeline that begins with high-resolution laser-triangulation scans of actual test tracks to produce centerline elevation profiles. These profiles are processed and classified by a MATLAB routine using ISO 8608-based power-spectral-density analysis to extract the Gh₀ roughness coefficient. Concurrently, in-service acoustic and chassis-vibration data, collected at two representative speeds, are transformed into feature vectors comprising statistical PSD descriptors. A regression model then learns the mapping from these features to Gh₀, evaluating the feasibility of mapping vehicle-borne signatures to roughness metrics. Predicted Gh₀ values drive a profile-synthesis algorithm to generate two-dimensional height grids, which are exported as CRG files and imported into a multibody simulation software (MSC ADAMS) as well as driver-in-the-loop platforms. Simulation results closely reproduce the primary excitation characteristics of the physical tracks, demonstrating a preliminary proof-of-concept pipeline for virtual road surface generation. While the cross-validated regression model indicates limited generalization on the current small dataset ($R^2=-0.2783$), the end-to-end workflow establishes the baseline integration required for future data-driven NVH simulation. To extend applicability beyond a single test vehicle, a set of Vehicle Calibration Transforms is proposed to adapt power-spectral-density features from arbitrary vehicles into the calibrated feature domain. The complete workflow promises to streamline virtual NVH validation, reduce prototype testing, and support full NVH simulator engineering in future research. Full article

This study presents a radiation-aware path planning framework for mobile robots operating in hazardous environments containing radiation sources, shielding structures, and moving obstacles. The proposed method integrates A*-based global planning, chaotic particle swarm optimization (CPSO)-based route refinement, B-Spline trajectory smoothing, and exposure-dependent speed adaptation within a unified dynamic planning architecture. The framework represents the radiation field as a physically parameterized dose-rate map in mSv/h by combining inverse-square source decay with line-of-sight material attenuation through shielding materials. Route generation is therefore evaluated in terms of cumulative absorbed dose, path length, mission time, trajectory roughness, computational cost, success rate, and dynamic obstacle interaction. The proposed method is compared with Pure A*, Informed RRT*, A*-PSO, A*-CPSO, and a risk-aware A*+DWA baseline under identical seed sets and computational budgets. In static scenarios, the proposed method reduced cumulative absorbed dose by approximately 33.3%, 47.2%, and 21.4% compared with Pure A* under low-, medium-, and high-risk conditions, respectively. In dynamic scenarios, the corresponding dose reductions were approximately 32.5%, 41.4%, and 40.1%. Additional ablation, sensitivity, statistical significance, and latency analyses were conducted to isolate the contribution of each component and evaluate computational feasibility. The results show that the proposed framework provides a balanced trade-off between absorbed dose reduction, trajectory feasibility, mission time, and online replanning performance under the tested simulation conditions. Full article

Commutators of fractional integral operators play an important role in harmonic analysis due to their close connections with function regularity and partial differential equations. In this paper, we study higher-order commutators of fractional integral operators with rough variable kernels. Compared with first-order commutators, the higher-order setting involves more complicated interactions between the oscillation of the underlying BMO function and the fractional integral structure, which requires new ideas and techniques. We establish boundedness properties for these higher-order commutators under sharp conditions on the angular integrability of the variable kernels. Our results extend the existing boundedness theory of first-order commutators to higher-order cases and demonstrate the applicability of the developed techniques to a broader class of fractional integral operators with rough variable kernels. Full article

Contact-angle prediction from literature data is vulnerable to target leakage when solid surface-free-energy descriptors are reconstructed using the liquid, which is later treated as the target. We developed a target-masked workflow that removes the target liquid before fitting nonnegative Owens-Wendt-Rabel-Kaelble components by nonnegative least squares and uses that physical prediction to anchor residual learning. A row-level revision audit re-extracted or excluded mismatched legacy sources before all models were retrained. Development used nested source-group cross-validation; the fixed cross-source external confirmation set was excluded from selection. The revised residual model achieved mean absolute errors of 15.5 degrees in nested validation and 13.2 degrees on that confirmation set. Surface-cluster bootstrap supported improvement over physics, whereas source-cluster uncertainty remained substantial. Diagnostics showed source dependence, sparse roughness, and limited strict unseen-liquid support. The method is therefore positioned as an auditable, risk-aware and reproducible materials-screening tool for surfaces with at least two non-target probes, with explicit out-of-distribution risk and refusal conditions rather than universal transfer claims. Full article

Incoloy 800H (Fe–Ni–Cr) is an iron-based superalloy that is difficult to machine because of its rapid work-hardening behaviour. Although Taguchi-based grey relational analysis has previously been applied to machining optimisation problems, studies employing an integrated grey-fuzzy framework for the dry turning of Incoloy 800H remain limited. Therefore, the present investigation aims to develop and validate a grey-fuzzy optimisation approach for determining the optimal dry turning parameters of Incoloy 800H by simultaneously minimising machining forces, surface roughness, and specific cutting pressure. Cutting speed (35, 45, and 55 m/min), feed rate (0.02, 0.04, and 0.06 mm/rev), and depth of cut (0.5, 0.75, and 1 mm) were selected as input factors, whereas feed force, thrust force, cutting force, surface roughness, and specific cutting pressure were considered output responses. Experiments were conducted using a Taguchi L27 orthogonal array (OA). The proposed methodology integrates grey relational analysis (GRA) with fuzzy logic (FL) to obtain a grey-fuzzy reasoning grade (GFRG) for multi-response optimisation. Analysis of variance (ANOVA) was employed to identify the most influential machining parameter. The results demonstrated that the grey-fuzzy approach provided a more discriminative optimisation index than conventional grey relational analysis by reducing uncertainty in multi-response decision-making. The confirmation experiments revealed an increase in GFRG from 0.550 to 0.900, corresponding to a relative improvement of 63.64% at the optimal parameter setting. The proposed methodology demonstrates that integrating grey relational analysis with fuzzy inference provides a reliable and statistically supported approach for multi-response optimisation of dry turning parameters for Incoloy 800H. Full article

This study addresses the influence of process conditions on the dimensional accuracy, geometric deviations, and surface quality of PLA parts manufactured using high-dynamics material extrusion (MEX) technology. The aim was to identify the dominant sources of variability and to assess within-condition manufacturing consistency and inter-machine consistency. The investigation considered two 3D printers, nine build locations on the working platform, two printing strategies (layer-by-layer and model-by-model), and model face orientation. Additionally, an exploratory comparison of aligned and random seam configurations and an analysis of local temperature variations within the build chamber were performed. Regular dodecahedron geometries were manufactured using a Bambu Lab P1S system and processed under identical high-quality printing parameters. Dimensional measurements were performed using a Linear 100 universal length measuring machine, while full-field geometric deviations were acquired using a GOM Scan 1 structured-light 3D scanner. Surface roughness (Ra) was measured with a MarSurf XR 20 profilometer. Part orientation is the dominant source of dimensional variability, representing the largest relative contribution to linear deviation in the mixed-effects model (ΔR² = 0.776), while local temperature variations near the printing zone were associated with location-dependent dimensional deviations. Within the supplementary temperature dataset, the regression model including temperature and printers explained 66% of the variability in mean linear dimension. This association provides indirect evidence of a thermal contribution but does not establish direct causality. The layer-by-layer strategy provided better dimensional stability than the model-by-model approach. In the exploratory seam comparison, seam configuration did not explain the orientation-dependent LD pattern. Surface roughness variability was primarily geometry-driven (ΔR² = 0.852). Variability between independent manufacturing series and specimens for linear deviation and Ra was low after accounting for the investigated factors, indicating consistent process performance under constant settings; however, the present design did not allow measurement repeatability and reproducibility to be separated. In conclusion, dimensional accuracy in high-dynamics MEX is strongly associated with part orientation, while thermal variations may represent an additional contributing factor; however, the observed correlation between thermal conditions and dimensional variability does not establish direct causality. Full article

Driving safety at tunnel portals on mountain expressways is affected by multiple factors, including driver behavior, traffic conditions, tunnel environment, and meteorological conditions. The combined effects of these factors introduce uncertainty and fuzziness into the assessment process. To address the limitations of the existing studies, which often focus on single risk factors or rely on subjective weighting methods, this study proposes an improved rough-set-based fuzzy comprehensive evaluation method incorporating the principle of minimum relative entropy. By integrating accident causation analysis, relevant standards and literature, and field investigations, a driving safety assessment indicator system for tunnel portals on mountain expressways is constructed. Attribute importance information derived from algebraic rough set theory and conditional information entropy rough set theory is first integrated. The principle of minimum relative entropy is then applied to determine the comprehensive indicator weights. Continuous and discrete indicators are then processed using membership functions and driving safety probability analysis, respectively, to establish a fuzzy comprehensive evaluation model. The results show that environmental factors and driver factors have substantial effects on driving safety at tunnel portals, with pavement icing, distracted driving, friction coefficient, and snowfall identified as the main influencing indicators. Validation using 15 tunnel portal cases on mountain expressways in Shanxi Province shows an agreement rate of 86.67% between the safety grades obtained using the proposed model and the actual accident grades. The findings provide a quantitative basis for safety risk identification, grade determination, and the operational management and control of tunnel portals on mountain expressways. Full article

Rough set methodology has gained broad acceptance as a potent mathematical apparatus for feature selection within data mining and machine learning. Yet, classical rough sets hinge on equivalence relations to partition the universe, thereby demanding strict reflexivity, symmetry, and transitivity conditions that are arduous to satisfy in realistic settings. Although fuzzy rough sets have been explored to mitigate this rigidity, the entropy-based uncertainty measures employed in fuzzy approximation spaces remain acutely sensitive to data quality and noise corruption, potentially inducing severe bias in feature evaluation. Moreover, the literature currently lacks noise-tolerant uncertainty measures capable of accommodating a controlled fraction of classification errors while safeguarding the discriminative strength of feature subsets. Inspired by these gaps, this study develops a feature selection framework grounded in variable precision fuzzy entropy within the fuzzy rough set context. To this end, fuzzy decision is adopted to portray the membership degree of samples relative to decision classes, thereby enabling more precise detection and elimination of redundant attributes during approximation. An uncertainty quantifier termed fuzzy relational entropy is then introduced to appraise the distinguishing power of fuzzy similarity relations generated by attribute subsets. Leveraging fuzzy decision, a portfolio of uncertainty measure variants, specifically the variable precision joint discriminant index, the variable precision conditional discriminant index, and the variable precision mutual discriminant index, is developed to counteract noisy data effects. These variable precision discriminant indexes sanction a regulated error proportion and afford a measure of noise resistance. Finally, knowledge reduction for fuzzy decision systems is attacked from the angle of discriminative capability preservation, and a heuristic feature selection algorithm is crafted around the variable precision conditional discriminant index. Evaluation on twelve public UCI datasets reveals that the proposed algorithm effectively prunes redundant features and delivers competitive results against three representative alternatives: classical rough set, neighbourhood-based discriminant index, and fuzzy rough set feature selection. Additionally, it sustains stable classification performance across an extensive sweep of the variable precision parameter. Full article

This study evaluates noise resilience in supervised classification using shortest-length and coarsest-granularity reducts and constructs under a common evaluation framework. We analyze original datasets and training sets distorted by random attribute-value noise across 20 real-world datasets and four supervised classifiers. Experimental results, validated with the Wilcoxon signed-rank test, indicate no statistically significant difference in classification accuracy between reducts and constructs across the evaluated noise levels. These findings suggest that, for the considered random-noise model, the choice between shortest-length and coarsest-granularity subsets has limited impact on supervised classification accuracy. Full article

In Fusion Deposition Modeling-based manufacturing, process parameters affect the mechanical and physical properties of the print. Considering these properties, accurately predicting print quality is essential. This is where machine learning (ML) models for three-dimensional (3D) print quality prediction come to the forefront. In this study, a dataset was prepared under strict operational measurement standards—utilizing the Interquartile Range (IQR) method for data sanitization—encompassing 10 material types, 2 printer types, and 4 printing parameters. Five hundred different sample combinations were prepared and printed in sets of three according to ISO 527-2 Type 4 standard dimensions. Tensile, hardness, and surface roughness tests were applied to the prepared samples. Using this validated dataset, a Hybrid Multi-Material Quality–Ensemble System–Stacking–Gradient Boosting Regressor (HMQ-ES-Stack-GBR) architecture is proposed as a diagnostic framework for multi-output quality prediction. Particularly in terms of quality outputs such as tensile strength, hardness, and surface roughness, while also providing a quantitative analysis of the effect of material type on print quality. Furthermore, a multi-objective optimization pipeline integrating three distinct meta-heuristic algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—was coupled with the framework to systematically derive material-specific optimal processing parameter configurations. Furthermore, the study shows that open-system printers exhibit higher prediction errors than closed-system printers. Reflecting system-induced variability rather than full hardware independence. Although the study is limited to internal validation within the current experimental design and includes material imbalance across filament groups, the findings suggest that the proposed framework provides a promising diagnostic decision-support tool for pre-print quality estimation within the studied dataset. By accurately reflecting rather than physically overcoming manufacturing variability, it supports decision-making processes through pre-print quality estimation, thereby enabling proactive interventions that reduce raw material, time, and energy losses. Full article

Rule-based classifiers are often preferred over other types of learners due to the transparent mode in which decisions are made. Each decision rule includes in its premise conditions on attributes. When they are satisfied, the conclusion part of the rule comes into play and leads to assigning an object to a specific class. Following the classification process is relatively straightforward but can become more complex when the cardinality of rule set is high. Furthermore, when rules are induced from continuous data, the conditions listed belong to this domain as well, which makes them less general. This paper presents an illustrative example for the exploratory research methodology where the sets of rules are induced in the continuous input domain, but next, they are transformed by discretisation procedures, which results in a simplified representation of the data and knowledge patterns learnt. In addition, the rule sets are also filtered based on rankings obtained for variants of the transformed data. The processing results in reduced decision algorithms with categorical conditions. This simplification is advantageous in and of itself, but the experiments carried out on datasets in the stylometric domain show that it can also lead to enhanced performance of rule-based classifiers. Full article

High-precision flood identification from high-resolution remote sensing images using deep learning network models is challenging. Severe cloud interference, limited receptive fields, insufficient boundary refinement and spatial detail preservation, and difficulty in accurately distinguishing water bodies from ground object shadows constrain the extraction method. Therefore, this study proposes an automatic flood information extraction method that integrates an improved Fully Convolutional Network classification and recognition model (FCN-RAM) with a rough tolerance set. First, a tolerance rough set algorithm was employed for sample data preprocessing. Subsequently, a Residual Attention Module (RAM) was introduced to optimize the U-Net architecture, dynamically adjusting the response intensity of deep features in both the channel and spatial dimensions to construct a deep learning-based FCN-RAM. Finally, comparative analyses were conducted on three high-resolution remote sensing datasets with different resolutions: Global surface water detection in Large-size very-High-resolution satellite imagery (GLH-Water), Gaofen Image Dataset (GID), and Earth Surface Water Dataset (ESWD). The results demonstrated that FCN-RAM consistently and substantially outperformed the baseline U-Net across all three datasets, achieving F1-score improvements of 10.64% (GLH-Water), 9.71% (GID), and 10.64% (ESWD), with corresponding overall accuracy gains of 9.97%, 11.15%, and 10.22%, respectively. Notably, the Intersection-over-Union (IoU) scores were elevated by 17.59% (GLH-Water), 15.66% (GID), and 13.63% (ESWD). The method also surpassed state-of-the-art models including ResNet and Water-SCNet, attaining peak overall accuracies of 98.61% (GLH-Water) and 97.37% (GID). Notably, while the proposed framework exhibits remarkable generalization across the evaluated multi-resolution benchmarks, its current validation is primarily confined to static water body delineation tasks. The model’s transferability to highly heterogeneous geographical regions with scarce training samples, as well as its extendability toward dynamic time-series flood evolution modeling, warrants further systematic investigation. The proposed method significantly improves the accuracy of waterbody information extraction, meets the requirements for high-precision information extraction from high-resolution imagery, and provides technical support for intelligent flood information extraction using high-resolution remote sensing. Full article

The considerable expense associated with metal additive manufacturing (AM), partly attributed to the high costs of raw materials, forms a significant obstacle hindering the widespread adoption and scaling of this technology. In response to this challenge, this study endeavors to explore and optimize the laser powder bed fusion (LPBF) process parameters for Ti-6Al-4V powders with offsize (45–106 µm) and wide (15–106 µm) particle size distribution (PSD) which are more cost-effective. The outcomes will be compared to those of the same alloy with a standard 15–53 µm PSD. The primary focus of this investigation revolves around two key objectives: firstly, establishing correlations between the laser powder bed fusion process parameters and the resulting density, hardness, and roughness. This objective is achieved by investigating the impact of process parameters within the context of the contour–skin–core method. Secondly, the porosity, microstructure, elemental composition, and dimensional fidelity of several components made from the offsize and wide powders were investigated, utilizing the optimized process parameters for density. To this end, an efficient multi-step experimental design and optimization process was adopted. The findings resulted in the identification of correlations between the significant process parameters and the studied responses, enabling the achievement of 98.7% density and 40.6 HRC hardness for offsize powder and 98.7% density and 40 HRC hardness for wide powder. A separate set of optimized process parameters for larger geometries produced densities exceeding 99.9% in both as-built and HIP conditions across all three powders. Additionally, the results confirmed the higher sensitivity of the roughness to powder size, with the optimized values fluctuating between 9.5 µm and 15.7 µm. Comprehensive microstructural investigation reveals no significant differences in phase evolution or grain structure resulting from the use of offsize or wide powders. This study confirms the viability of utilizing powders containing a higher portion of large particles to mitigate the costs associated with LPBF processes. Full article