Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems.

In recent years, vortex spinning has garnered significant attention owing to its high efficiency and superior yarn quality. However, the drafting process involves multiple interrelated parameters, and different combinations of parameters can considerably influence subsequent spinning performance. To address this, the present study introduces a novel hybrid optimization algorithm to enhance spinning quality by rationalizing the coordination of drafting parameters. First, orthogonal experiments were conducted with the draft ratio and roller center distance as variables, using the mean grayscale value and grayscale standard deviation of the post-experiment silver images as multi-objective functions to evaluate drafting effectiveness. Subsequently, a regression model between drafting parameters and drafting outcomes was constructed using the Particle Swarm Optimization–Backpropagation Neural Network (PSO-BP) algorithm, followed by multi-objective optimization via the Non-dominated Sorting Genetic Algorithm II (NSGA-II) genetic algorithm to obtain a Pareto-optimal solution set. Finally, the entropy-weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to comprehensively evaluate the Pareto-optimal set and determine the optimal combination of process parameters. The results demonstrate that, under the optimal parameter combination, the deviation between the measured quality indicators of the drafted sliver and the predicted values remains within 6%, confirming the effectiveness of the proposed model as a viable approach for optimizing drafting parameter configurations.

Motor starter calibration requires long-distance rotation of a setting cam to locate current graduation points, generating substantial non-value-added mechanical travel time on production lines. This paper proposes a cam pre-adjustment angle prediction method that integrates a phenomenological gray-box bimetallic model with a hierarchical combinatorial algorithm framework. A generalized lumped-parameter model incorporating heat dissipation correction and mechanical gap compensation is constructed to describe the electrothermal–mechanical coupling of the bimetallic strip. An improved fuzzy C-means (IFCM) algorithm addresses the cold-start problem for new material batches, and an adaptive particle swarm optimization (APSO) algorithm performs online parameter identification. To handle the process asymmetry arising from the unidirectional cam rotation mechanism, an optimized gray wolf optimizer with one-sided error control (GWO-OSE) based on an asymmetric loss function is employed to inversely determine the optimal pre-adjustment angle while actively suppressing over-prediction. Validation on 1200 production line samples across three material batches demonstrates an over-prediction rate of only 2.8%, a mean absolute angle prediction error of 23.9°, a reduction in single-product calibration time of approximately 12 s, and an improvement in overall production line efficiency of 24.5%. This efficiency gain results from the process-level redesign facilitated by the pre-adjustment strategy rather than from minimizing absolute prediction error, and the proposed method provides an engineering-applicable optimization strategy for reducing non-value-added calibration time in motor starter production lines.

Conventional overload verification of bimetallic protection elements requires long-duration current loading and considerable time and energy consumption. This study proposes a rapid verification method combining a first-order thermodynamic equivalence model with image-based deflection measurement. Local linear interpolation between adjacent measured frames was used to determine the energization time corresponding to a prescribed target deflection. A current-dependent empirical correction factor was introduced to compensate for systematic deviations caused by simplified lumped thermal parameters and test-specific thermal boundary conditions. The model was identified using one target-deflection dataset and evaluated at two additional target deflections without parameter re-identification. Repeated tests were also conducted to assess cycle-to-cycle stability. The results show that the corrected model substantially reduced the temperature-rise deviation, maintained good prediction consistency across different target deflections, and exhibited low cycle-to-cycle variability. The method provides a physics-informed semi-empirical framework for rapid evaluation of bimetallic-strip overload characteristics. Further re-identification and assembled-product validation are required when the specimen configuration or thermal boundary conditions change.

Intradialytic hypotension (IDH) is a common complication of hemodialysis and is associated with treatment interruption, reduced patient tolerance, cardiovascular events, and hospitalization. Existing machine learning studies often use only limited dynamic dialysis information and provide insufficient feature interpretation. This study proposes an IDH risk prediction method based on multi-domain dynamic features and an improved Sparrow Search Algorithm (ISSA). After temporal alignment, outlier handling, and missing-value imputation, a candidate feature set was constructed from 12 baseline variables and 48 time-domain, frequency-domain, and time–frequency-domain features extracted from six continuous dialysis parameters. ISSA was then used to select a 13-dimensional feature subset comprising eight baseline and five dynamic features. Several machine learning models were evaluated before and after feature selection. Using a public hemodialysis monitoring dataset from a tertiary medical center, XGBoost achieved the best overall performance. Adding the five selected dynamic features increased ROC-AUC from 0.8355 to 0.8410 (ΔAUC = 0.0055, 95% CI: 0.0010–0.0100, p = 0.020), indicating a statistically significant incremental contribution. TreeSHAP analysis identified the spectral centroid of dialysis duration, conductivity, dialysate temperature, dialysis duration, and pre-dialysis body weight as important predictors. The proposed framework supports compact and interpretable early IDH risk prediction.