Search Results (366)

Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce based on multi-source imaging. The approach integrates terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI) to achieve rapid and non-invasive nitrogen detection. Spectral imaging data of lettuce samples under different nitrogen gradients (20–150%) were simultaneously acquired using a THz-TDS system (0.2–1.2 THz) and a NIR-HSI system (1000–1600 nm), with image segmentation applied to remove background interference. During data processing, Savitzky–Golay smoothing, MSC (for THz data), and SNV (for NIR data) were employed for combined preprocessing, and sample partitioning was performed using the SPXY algorithm. Subsequently, SCARS/iPLS/IRIV algorithms were applied for THz feature selection, while RF/SPA/ICO methods were used for NIR feature screening, followed by nitrogen content prediction modeling with LS-SVM and KELM. Furthermore, small-sample learning was utilized to fuse crop feature information from the two modalities, providing a more comprehensive and effective detection strategy. The results demonstrated that the THz-based model with SCARS-selected power spectrum features and an RBF-kernel LS-SVM achieved the best predictive performance (R² = 0.96, RMSE = 0.20), while the NIR-based model with ICO features and an RBF-kernel LS-SVM achieved the highest accuracy (R² = 0.967, RMSE = 0.193). The fusion model, combining SCARS and ICO features, exhibited the best overall performance, with training accuracy of 96.25% and prediction accuracy of 95.94%. This dual-spectral technique leverages the complementary responses of nitrogen in molecular vibrations (THz) and organic chemical bonds (NIR), significantly enhancing model performance. To the best of our knowledge, this is the first study to realize the synergistic application of THz and NIR spectroscopy in nitrogen detection of facility-grown lettuce, providing a high-precision, non-destructive solution for rapid crop nutrition diagnosis. Full article

This paper presents a novel support vector machine (SVM) classification approach that simultaneously accounts for both overall and extreme misclassification errors via a dual-norm regularization strategy. Traditional SVMs minimize the $L_1$-norm of slack variables to control global misclassification, while least squares SVM (LSSVM) minimizes the sum of squared errors. In contrast, our method preserves the classical $L_1$-norm penalty to maintain overall classification fidelity and incorporates an additional $L_{\infty }$-norm term to penalize the largest slack variable, thereby constraining the worst-case margin violation. This composite objective yields a more robust and generalizable classifier, particularly effective when occasional large deviations disproportionately affect decision boundaries. The resulting optimization problem minimizes a regularized objective combining the model norm, the sum of slack variables, and the maximum slack variable, with two hyperparameters, $C_1$ and $C_2$, balancing global error against extremal robustness. By formulating the problem under convex constraints, the optimization remains tractable and guarantees a globally optimal solution. Experimental evaluations on benchmark datasets demonstrate that the proposed method achieves comparable or superior classification accuracy while reducing the impact of outliers and maintaining a sparse model structure. These results underscore the advantage of jointly enforcing $L_1$ and $L_{\infty }$ penalties, providing an effective mechanism to balance average performance with worst-case error sensitivity in support vector classification. Full article

To address the challenges of complex harmonic characteristics, multi-source coupling, and strong time variability in aggregated loads downstream of high-voltage substations, this paper proposes an Adaptive Multi-Task Gaussian Process Regression (AMT-GPR) method for harmonic modeling. First, field measurements from the medium-voltage side of a 500 kV substation are denoised and analyzed using Fourier transform to reveal the dynamic patterns and interdependencies of harmonic current magnitudes. Then, a multi-task GPR framework is constructed, incorporating task correlation modeling and adaptive kernel functions to capture inter-task coupling and differences in feature scales. Finally, a probabilistic harmonic model is developed based on multiple sets of measured data, and the modeling performance of AMT-GPR is compared with single-task GPR, conventional MT-GPR, and mainstream machine learning approaches including RBF, LS-SVM, and LSTM. Simulation results demonstrate that traditional harmonic modeling methods are insufficient to capture the dynamic behavior and uncertainty of aggregated loads and AMT-GPR maintains strong robustness under small-sample conditions, significantly reduces prediction errors, and yields narrower uncertainty intervals, outperforming the baseline models. These findings validate the effectiveness of the proposed method in modeling harmonics of aggregated loads in high-voltage substations and provide theoretical support for subsequent harmonic assessment and mitigation strategies. Full article

With the continuous advancement of intelligent, integrated, and sophisticated modern marine equipment, bearing fault diagnosis faces increasingly severe technical challenges. Compared with traditional industrial environments, marine propulsion systems are characterized by multi-bearing coupled vibrations and complex operating conditions. To address these characteristics, this paper proposes a fault diagnosis method that combines a least squares support vector machine (LSSVM) with multi-domain feature extraction based on an improved hippopotamus optimization algorithm (LCM-HO). This method directly extracts time, spectral, and time-frequency domain features from the raw signal, effectively avoiding complex preprocessing and enhancing its potential for field engineering applications. Experimental verification using the Paderborn bearing dataset and a self-built marine bearing test bench demonstrates that the LCM-HO-LSSVM method achieves diagnostic accuracy rates of 99.11% and 98.00%, respectively, demonstrating significant performance improvements. This research provides a reliable, efficient, and robust technical solution for bearing fault diagnosis in complex marine environments. Full article

Rolling bearings, as essential parts of rotating machinery, face significant challenges in fault diagnosis due to limited fault samples and high noise interference, both of which reduce the effectiveness of traditional methods. To tackle this, this study proposes a fault diagnosis approach that combines Digital Twin (DT) and deep learning. First, actual bearing vibration data were collected using an experimental platform. After denoising the data, a high-fidelity digital twin system was built by integrating the bearing dynamics model with a Generative Adversarial Network (GAN), thereby effectively increasing the fault data. Next, the Wavelet Synchro-Extracting Transform (WSET) is used for high-resolution time-frequency analysis, and convolutional neural networks (CNNs) are employed to extract deep fault features adaptively. The fully connected layer of the CNN is then combined with a Least Squares Support Vector Machine (LSSVM), with key parameters optimized through an Improved Pelican Optimization Algorithm (IPOA) to improve classification accuracy significantly. Experimental results based on both simulated and publicly available datasets show that the proposed model has excellent generalizability and operational flexibility, surpassing existing deep learning-based diagnostic methods in complex industrial settings. Full article

Current methods of detecting bogie gearbox faults mainly depend on manual judgment, which leads to inaccurate fault identification. In this study, a fault diagnosis model is proposed based on a pied kingfisher optimizer-improved complete ensemble empirical mode decomposition with adaptive noise (PKO-ICEEMDAN), improved multi-scale weighted permutation entropy (IMWPE), and a starfish optimization algorithm optimizing a least-squares support vector machine (SFOA-LSSVM). Firstly, the acceleration signals of a bogie gearbox under six different working conditions were extracted through experiments. Secondly, the acceleration signals were decomposed by ICEEMDAN optimized by PKO to obtain the intrinsic mode function (IMF). Thirdly, IMFs with rich fault information were selected to reconstruct the signals according to the double screening criteria of both the correlation coefficient and variance contribution rate, and the IMWPE of the reconstructed signals was extracted. Finally, IMWPE as a feature vector was input into LSSVM optimized by the SFOA for fault diagnosis and compared with various models. The results show that the average accuracy of the training data of the proposed model was 99.13%, and the standard deviation was 0.09, while the average accuracy of the testing data was 99.44%, and the standard deviation was 0.12. Thus, the effectiveness of the proposed fault diagnosis model for the bogie gearbox was verified. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

Machine learning-guided predictive models are attractive in rock modeling for different scholars to obtain continuous profiles of rock compressive strength in rock engineering. The major objectives of the study are to assess the implications of machine learning (ML)-based connectionist models to obtain the unconfined compressive strength (UCS) of rock, to perform parametric sensitivity analysis on petrophysical parameters, and to develop an improved correlation for UCS prediction. The least-squares support vector machine (LSSVM) is applied to develop data-driven models for the prediction of UCS. Additionally, the random forest (RF) algorithm is applied to verify the effectiveness of predictive models. A database containing well-logging data is processed and utilized to construct connectionist models to obtain UCS. For the efficacy of predictive models, statistical performance indicators such as the coefficient of determination (CC), average percentage relative error, and maximum average percentage error are utilized in the study. It is revealed that the RF- and LSSVM-based models for predicting UCS perform excellently with high precision. Considering the parametric sensitivity analysis in the predictive models for UCS, the formation compressional wave velocity and formation gamma-ray are the most strongly contributing predictor variables rather than other input variables such as the modulus of elasticity, acoustic shear wave velocity, and rock bulk density. The improved correlation for predicting UCS shows high precision, achieving a CC of 96% and root mean squared error of 0.54 MPa. This systematic research workflow is significant and can be utilized for connectionist robust model development and variable selections in the petroleum and mining fields, such as predicting reservoir properties, the drilling rate of penetration, sanding potentiality of hydrocarbon reservoir rocks, and for the practical implications of boring and geotechnical engineering projects. Full article

Accurate prediction of coal demand is essential for optimizing energy resources in long-term power system planning. This paper examines the coal demand in North China from 2007 to 2022 using econometric methods to identify key influencing factors as input variables. Then, the Sparrow Search Algorithm (SSA) is used to optimize the key parameters of the Least Squares Support Vector Machine (LSSVM) algorithm to enhance the prediction accuracy of coal demand. Case studies are conducted on actual data in North China, and the results show that the proposed hybrid SSA and LSSVM method outperforms traditional approaches in small-sample, multivariable forecasting, making it suitable for predictions in long-term power system planning. Full article

The highly stochastic nature of rainfall presents significant challenges for the accurate prediction of its time series. To enhance the prediction performance of non-stationary rainfall time series, this study proposes a hybrid deep learning forecasting framework—VMD-IWMA-BiLSTM—that integrates Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Bidirectional Long Short-Term Memory network (BiLSTM). Firstly, VMD is employed to decompose the original rainfall series into multiple modes, extracting Intrinsic Mode Functions (IMFs) with more stable frequency characteristics. Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. Finally, experimental validation is conducted using daily rainfall data from 2020 to 2024 at the Xinzheng National Meteorological Observatory. The results demonstrate that the proposed framework outperforms traditional models such as LSTM, ARIMA, SVM, and LSSVM in terms of prediction accuracy. This research provides new insights and effective technical pathways for improving rainfall time series prediction accuracy and addressing the challenges posed by high randomness. Full article

With the development of the shipbuilding industry, nonparametric prediction has become the mainstream method for predicting ship maneuvering motion. However, the lack of transparency and interpretability make the output process of the prediction results challenging to track and understand. An interpretable deep learning framework based on the NbeatsX model is presented for nonparametric ship maneuvering motion prediction. Its three-tier fully connected architecture incorporates trend, seasonal, and exogenous constraints to decompose motion data, enhancing temporal and contextual learning while rendering the prediction process transparent. On the KVLCC2 zig-zag maneuver dataset, NbeatsX achieves NRMSEs of 0.01872, 0.01234, and 0.01661 for surge speed, sway speed, and yaw rate, with SMAPEs of 9.21%, 6.40%, and 7.66% and R² values all above 0.995, yielding a more than 20% average error reduction compared with LS-SVM, LSTM, and LSTM–Attention and reducing total training time by about 15%. This method unifies high-fidelity forecasting with transparent decision tracing. It is an effective aid for ship maneuvering, offering more credible support for maritime navigation and safety decision-making, and it has substantial practical application potential. Full article

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

The displacement of a concrete gravity dam is a direct manifestation of its deformation. It provides an intuitive reflection of the dam’s overall operational behavior and serves as a key indicator of the dam’s safe operating condition. In this paper, we propose a factor set that considers the hysteresis effects of temperature on displacement and ranks the importance of the features to select the optimal factor sets at different measurement points by the ReliefF method. Then, we realize the simultaneous prediction of the displacements at multiple measurement points by the multi-input multi-output least-squares support vector machine with particle swarm optimization (MIMO-PSO-LSSVM). The case study demonstrates that this method effectively enhances the accuracy and efficiency of gravity dam displacement prediction, thereby providing a novel reference for dam safety monitoring and health service diagnosis. Full article

Reliable fault diagnosis in aero-engine bearing systems is essential for maintaining process stability and safety. However, acquiring fault samples in aerospace applications is costly and difficult, resulting in severely limited data for model training. Traditional methods often perform poorly under such constraints, lacking the ability to extract discriminative features or effectively correlate observed signal changes with underlying process faults. To address this challenge, this study presents a process-oriented framework—WSET-CNN-OOA-LSSVM—designed for effective fault recognition in small-sample scenarios. The framework begins with Wavelet Synchroextracting Transform (WSET), enhancing time–frequency resolution and capturing energy-concentrated fault signatures that reflect degradation along the process timeline. A tailored CNN with asymmetric pooling and progressive dropout preserves temporal dynamics while preventing overfitting. To compensate for limited labels, confidence-based pseudo-labeling is employed, guided by Mahalanobis distance and adaptive thresholds to ensure reliability. Classification is finalized using an Osprey Optimization Algorithm (OOA)-enhanced Least Squares SVM, which adapts decision boundaries to reflect subtle process state transitions. Validated on both test bench and real aero-engine data, the framework achieves 93.4% accuracy with only five fault samples per class and 100% in full-scale scenarios, outperforming eight existing methods. Therefore, the experimental results confirm that the proposed framework can effectively overcome the data scarcity challenge in aerospace bearing fault diagnosis, demonstrating its practical viability for few-shot learning applications in industrial condition monitoring. Full article