Search Results (709)

This study proposes a hybrid analytical framework for detecting chondromalacia using vibroacoustic (VAG) signals from patients with knee osteoarthritis (OA) and healthy controls (HCs). The methodology combines nonlinear signal decomposition, feature extraction, and deep learning classification. Raw VAG signals, recorded with a custom multi-sensor system during open (OKC) and closed (CKC) kinetic chain knee flexion–extension, underwent preprocessing (denoising, segmentation, normalization). Ensemble Empirical Mode Decomposition (EEMD) was used to isolate Intrinsic Mode Functions (IMFs), and Detrended Fluctuation Analysis (DFA) computed local (α₁) and global (α₂) scaling exponents as well as breakpoint location. Frequency–energy features of IMFs were statistically assessed and selected via Neighborhood Component Analysis (NCA) for support vector machine (SVM) classification. Additionally, reconstructed α₁/α₂-based signals and raw signals were converted into continuous wavelet transform (CWT) scalograms, classified with convolutional neural networks (CNNs) at two resolutions. The SVM approach achieved the best performance in CKC conditions (accuracy 0.87, AUC 0.91). CNN classification on CWT scalograms also demonstrated robust OA/HC discrimination with acceptable computational times at higher resolutions. Results suggest that combining multiscale decomposition, nonlinear fluctuation analysis, and deep learning enables accurate, non-invasive detection of cartilage degeneration, with potential for early knee pathology diagnosis. Full article

Accurate and reliable credit risk classification is fundamental to the stability of financial systems and the efficient allocation of capital. However, with the rapid expansion of customer information in both volume and complexity, traditional rule-based or purely statistical approaches have become increasingly inadequate. Motivated by these challenges, this study introduces a domain-constrained stacking ensemble framework that systematically integrates business knowledge with advanced machine learning techniques. First, domain heuristics are embedded at multiple stages of the pipeline: threshold-based outlier removal improves data quality, target variable redefinition ensures consistency with industry practice, and feature discretization with monotonicity verification enhances interpretability. Then, each variable is transformed through Weight-of-Evidence (WOE) encoding and evaluated via Information Value (IV), which enables robust feature selection and effective dimensionality reduction. Next, on this transformed feature space, we train logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and a two-layer stacking ensemble. Finally, the ensemble aggregates cross-validated out-of-fold predictions from LR, RF and XGBoost as meta-features, which are fused by a meta-level logistic regression, thereby capturing both linear and nonlinear relationships while mitigating overfitting. Experimental results across two credit datasets demonstrate that the proposed framework achieves superior predictive performance compared with single models, highlighting its potential as a practical solution for credit risk assessment in real-world financial applications. Full article

Piezoelectric materials are fundamental elements in modern science and technology due to their unique ability to convert mechanical and electrical energy bidirectionally. They are widely employed in sensors, actuators, and energy-harvesting systems. In this work, we investigate the behavior of commercial lead zirconate titanate (PZT) sensors under frequency-mode excitation using a combined approach of impedance spectroscopy and optical interferometry. The impedance spectra reveal distinct resonance–antiresonance features that strongly depend on geometry, while interferometric measurements capture dynamic strain fields through fringe displacement analysis. The strongest deformation occurs near the first kilohertz resonance, directly correlated with the impedance phase, enabling the extraction of an effective piezoelectric constant (~40 pC/N). Moving beyond the linear regime, laser-induced excitation demonstrates optically driven activation of piezoelectric modes, with a frequency-dependent response and nonlinear scaling with optical power, characteristic of coupled pyroelectric–piezoelectric effects. These findings introduce a frequency-mode approach that combines impedance spectroscopy and optical interferometry to simultaneously probe electrical and mechanical responses in a single setup, enabling non-contact, frequency-selective sensing without surface modification or complex optical alignment. Although focused on macroscale ceramic PZTs, the non-contact measurement and activation strategies presented here offer scalable tools for informing the design and analysis of piezoelectric behavior in micro- and nanoscale systems. Such frequency-resolved, optical-access approaches are particularly valuable in the development of next-generation nanosensors, MEMS/NEMS devices, and optoelectronic interfaces where direct electrical probing is challenging or invasive. Full article

This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FC_ad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R²) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p < 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R² > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction. Full article

Effective management of municipal solid waste is crucial for achieving sustainable development and maintaining a healthy ecological environment. Municipal solid waste incineration (MSWI) processes are highly nonlinear and exhibit strong coupling characteristics, which makes long-term stable control challenging. Accurate prediction of the various toxic and harmful acidic gases that will be generated during this process is crucial for supporting optimization and control research. This study proposes a predictive model for acidic gases using Random Forest (RF) and Inverted Transformer (ITransformer). First, the RF algorithm is used to identify feature variables that strongly correlate with the target variables, thereby facilitating the shared feature selection process for multiple acidic gases. These selected features are then fed into a multi-output ITransformer model, which predicts the target variables and generates multiple evaluation metrics. Finally, the model’s hyperparameters are optimized based on these metrics and the threshold ranges of the acidic gases. The experimental results using real data from a specific incineration plant show that 13 features remain after the shared feature selection process. Compared to other models, the proposed approach uses the fewest shared features while reducing computational costs. Moreover, the R² values for NO_x, SO₂, and HCl are 0.9791, 0.9793, and 0.9838, respectively. Full article

Ovarian cancer’s high mortality is primarily due to late-stage diagnosis, underscoring the critical need for improved early detection tools. This study develops and validates explainable artificial intelligence (XAI) models to discriminate malignant from benign ovarian masses using readily available demographic and laboratory data. A dataset of 309 patients (140 malignant, 169 benign) with 47 clinical parameters was analyzed. The Boruta algorithm selected 19 significant features, including tumor markers (CA125, HE4, CEA, CA19-9, AFP), hematological indices, liver function tests, and electrolytes. Five ensemble machine learning algorithms were optimized and evaluated using repeated stratified 5-fold cross-validation. The Gradient Boosting model achieved the highest performance with 88.99% (±3.2%) accuracy, 0.934 AUC-ROC, and 0.782 Matthews correlation coefficient. SHAP analysis identified HE4, CEA, globulin, CA125, and age as the most globally important features. Unlike black-box approaches, our XAI framework provides clinically interpretable decision pathways through LIME and SHAP visualizations, revealing how feature values push predictions toward malignancy or benignity. Partial dependence plots illustrated non-linear risk relationships, such as a sharp increase in malignancy probability with CA125 > 35 U/mL. This explainable approach demonstrates that ensemble models can achieve high diagnostic accuracy using routine lab data alone, performing comparably to established clinical indices while ensuring transparency and clinical plausibility. The integration of state-of-the-art XAI techniques highlights established biomarkers and reveals potential novel contributors like inflammatory and hepatic indices, offering a pragmatic, scalable triage tool to augment existing diagnostic pathways, particularly in resource-constrained settings. Full article

Due to the interference of artifacts and the nonlinearity of electroencephalogram (EEG) signals, the extraction of representational features has become a challenge in EEG emotion recognition. In this work, we reduce the dimensionality of phase space trajectories by introducing local linear embedding (LLE), which projects the trajectories onto a 2-D plane while preserving their local topological structure, and innovatively construct 16 topological features from different perspectives to quantitatively describe the nonlinear dynamic patterns induced by emotions on a multi-scale level. By using independent feature evaluation, we select core features with significant discrimination and combine the activation patterns of brain topography with model gain ranking to optimize the electrode channels. Validation of the SEED and HIED datasets resulted in subject-dependent average accuracies of 90.33% for normal-hearing subjects (3-Class) and 77.17% for hearing-impaired subjects (4-Class), and we also used differential entropy (DE) features to explore the potential of integrating topological features. By quantifying topological features, the 6-Class task achieved an average accuracy of 77.5% in distinguishing emotions across different subject groups. Full article

The global integration of digital technologies into energy systems constitutes a critical pathway for achieving sustainable and intelligent energy governance. This study evaluates the effectiveness of the energy digital transformation policies across eighteen major economies through a comprehensive four-dimensional framework, which encompasses policy objectives, intensity, instruments, and stakeholder engagement. Through the application of the entropy-weighted TOPSIS method, our comparative analysis identifies a distinct hierarchy in national policy performance. The first tier, including the United Kingdom, the United States, South Korea, Australia, China, and Germany, demonstrates high coherence, enforceable mechanisms, and multi-actor coordination. The second tier, comprising Saudi Arabia, France, Turkey, Russia, Canada, and India, exhibits partial alignment with notable strengths in selected dimensions yet significant gaps in enforceability or stakeholder integration. The third tier, featuring Italy, Brazil, Argentina, Mexico, Japan, and Indonesia, is characterized by fragmented approaches and aspirational goals lacking implementation specificity. Stakeholder inclusiveness emerges as the most influential dimension, accounting for 38.3% of total weighting and substantially accounting for variations in efficacy. Moreover, nonlinear threshold effects are identified, indicating that subcritical performance in any dimension leads to disproportionate declines in overall outcomes. These findings underscore that synergistic policy design, which entails balancing objectives, governance capacity, instruments, and actors, is indispensable for effective energy digitalization. Full article

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy. Full article

Precise extrinsic calibration is a fundamental prerequisite for data fusion in multi-LiDAR systems. However, conventional methods are often encumbered by dependencies on initial estimates, auxiliary sensors, or manual feature selection, which renders them complex, time-consuming, and limited in adaptability across diverse environments. To address these limitations, this paper proposes a novel, high-precision extrinsic calibration method for multi-LiDAR systems with a narrow Field of View (FoV), achieved through the synergistic use of circular and planar features. Our approach commences with the automatic segmentation of the calibration target’s point cloud using an improved VoxelNet. Subsequently, a denoising step, combining RANSAC and a Gaussian Mean Intensity Filter (GMIF), is applied to ensure high-quality feature extraction. From the refined point cloud, planar and circular features are robustly extracted via Principal Component Analysis (PCA) and least-squares fitting, respectively. Finally, the extrinsic parameters are optimized by minimizing a nonlinear objective function formulated with joint constraints from both geometric features. Simulation results validate the high precision of our method, with rotational and translational errors contained within 0.08° and 0.8 cm. Furthermore, real-world experiments confirm its effectiveness and superiority, outperforming conventional point-cloud registration techniques. Full article

This paper presents an optimized hybrid deep learning model for power load forecasting—QR-FMD-CNN-BiGRU-Attention—that integrates similar day selection, load decomposition, and deep learning to address the nonlinearity and volatility of power load data. Firstly, the original data are classified using Gaussian Mixture Clustering optimized by ICPO (ICPO-GMM), and similar day samples consistent with the predicted day category are selected. Secondly, the load data are decomposed into multi-scale components (IMFs) using feature mode decomposition optimized by ICPO (ICPO-FMD). Then, with the IMFs as targets, the quantile interval forecasting is trained using the CNN-BiGRU-Attention model optimized by ICPO. Subsequently, the forecasting model is applied to the features of the predicted day to generate interval forecasting results. Finally, the model’s performance is validated through comparative evaluation metrics, sensitivity analysis, and interpretability analysis. The experimental results show that compared with the comparative algorithm presented in this paper, the improved model has improved RMSE by at least 39.84%, MAE by 26.12%, MAPE by 45.28%, PICP and MPIW indicators by at least 3.80% and 2.27%, indicating that the model not only outperforms the comparative model in accuracy, but also exhibits stronger adaptability and robustness in complex load fluctuation scenarios. Full article

Rotating machinery under variable-speed conditions suffers from amplitude modulation (AM) effects induced by speed fluctuations, complicating accurate fault detection. To address this issue, an amplitude normalization method based on support vector regression (SVR) is proposed to estimate and remove the AM effects. The method employs a correlation-based feature selection strategy to construct feature vectors strongly associated with rotational speed, thereby enabling the accurate quantification of speed-induced AM effects. The robust nonlinear fitting capability of SVR is then utilized to model and remove these effects, enhancing fault signal clarity. The proposed method is validated through two case studies and compared with advanced amplitude normalization techniques, demonstrating its superior accuracy, robustness, and reliability. Experimental results demonstrate that the proposed method accurately estimates and eliminates speed-induced AM, significantly improving fault diagnosis accuracy by up to 34.7%. Full article

In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power forecasting method for distributed photovoltaics that can identify seasonal characteristics matching weather types, enabling a deeper analysis of complex meteorological changes. First, historical power data is decomposed seasonally using the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Next, each component is reconstructed based on a characteristic similarity approach, and a two-stage feature selection process is applied to identify the most relevant features for reconstruction, addressing the issue of nonlinear variable selection. A CNN-LSTM-KAN model with multi-dimensional spatial representation is then proposed to model different weather types obtained by the K-shape clustering method, enabling the segmentation of weather processes. Finally, the proposed method is applied to a case study of distributed PV users in a certain province for short-term power prediction. The results indicate that, compared to traditional methods, the average RMSE decreases by 8.93%, the average MAE decreases by 4.82%, and the R² increases by 9.17%, demonstrating the effectiveness of the proposed method. Full article

Precise wind power forecasting has several benefits, such as optimized peak regulation in power systems, enhanced safety analysis, and improved energy efficiency. Considering the substantial influence of meteorological data, such as wind speed and temperature, on wind power generation, and to minimize the impact of fluctuations and complexity of wind power data on the forecast results, this paper proposes a combined wind power forecasting method. This approach is based on the long short-term memory network (LSTM) model, using the maximal information coefficient (MIC) method to select numerical weather prediction (NWP) and combining the efficiency of complete EEMD with the adaptive noise (CEEMDAN) method for nonlinear signal decomposition. Results indicate that the accuracy of the forecast results is supported by NWP. Moreover, wind power data are decomposed by the CEEMDAN algorithm and converted into relatively regular sub-sequences with small fluctuations. The MIC algorithm effectively reduces the redundant information in NWP data, and the LSTM algorithm addresses the uncertainty of wind power data. Finally, the wind power of multiple wind farms is forecasted. Comparison of the forecast results of different methods revealed that the NWP-CEEMDAN-LSTM method proposed in this paper, which considers feature extraction using MIC, effectively tracks power fluctuations and improves forecast performance, thereby reducing the forecast error of wind power. Full article

A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials. Full article