Search Results (458)

To address the challenges of unstable vibration signals, indistinct fault features, and difficulties in feature extraction during rolling bearing operation, this paper presents a novel fault diagnosis method based on the fusion of PSR-CRP and DenseNet. The Phase Space Reconstruction (PSR) method transforms one-dimensional bearing vibration data into a three-dimensional space. Euclidean distances between phase points are calculated and mapped into a Color Recurrence Plot (CRP) to represent the bearings’ operational state. This approach effectively reduces feature extraction ambiguity compared to RP, GAF, and MTF methods. Fault features are extracted and classified using DenseNet’s densely connected topology. Compared with CNN and ViT models, DenseNet improves diagnostic accuracy by reusing limited features across multiple dimensions. The training set accuracy was 99.82% and 99.90%, while the test set accuracy is 97.03% and 95.08% for the CWRU and JNU datasets under five-fold cross-validation; F1 scores were 0.9739 and 0.9537, respectively. This method achieves highly accurate diagnosis under conditions of non-smooth signals and inconspicuous fault characteristics and is applicable to fault diagnosis scenarios for precision components in aerospace, military systems, robotics, and related fields. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

In recent years, heavy metal contamination in agricultural products has become a growing concern in the field of food safety. Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. This study proposes an efficient and precise non-destructive detection method for Cu stress in oilseed rape, which is based on hyperspectral false-color image construction using principal component analysis (PCA). By comprehensively capturing the spectral representation of oilseed rape plants, both the one-dimensional (1D) spectral sequence and spatial image data were utilized for multi-class classification. The classification performance of models based on 1D spectral sequences was compared from two perspectives: first, between machine learning and deep learning methods (best accuracy: 93.49% vs. 96.69%); and second, between shallow and deep convolutional neural networks (CNNs) (best accuracy: 95.15% vs. 96.69%). For spatial image data, deep residual networks were employed to evaluate the effectiveness of visible-light and false-color images. The RegNet architecture was chosen for its flexible parameterization and proven effectiveness in extracting multi-scale features from hyperspectral false-color images. This flexibility enabled RegNetX-6.4GF to achieve optimal performance on the dataset constructed from three types of false-color images, with the model reaching a Macro-Precision, Macro-Recall, Macro-F₁, and Accuracy of 98.17%, 98.15%, 98.15%, and 98.15%, respectively. Furthermore, Grad-CAM visualizations revealed that latent physiological changes in plants under heavy metal stress guided feature learning within CNNs, and demonstrated the effectiveness of false-color image construction in extracting discriminative features. Overall, the proposed technique can be integrated into portable hyperspectral imaging devices, enabling real-time and non-destructive detection of heavy metal stress in modern agricultural practices. Full article

Mental health disparities among those who self-identify as gay men in Peru remain a pressing public health concern, yet predictive models for early identification remain limited. This research aims to (1) develop machine learning and deep learning models to predict mental health issues in those who self-identify as gay men, and (2) evaluate the influence of demographic, economic, health-related, behavioral and social factors using interpretability techniques to enhance understanding of the factors shaping mental health outcomes. A dataset of 2186 gay men from the First Virtual Survey for LGBTIQ+ People in Peru (2017) was analyzed, considering demographic, economic, health-related, behavioral, and social factors. Several classification models were developed and compared, including Logistic Regression, Artificial Neural Networks, Random Forest, Gradient Boosting Machines, eXtreme Gradient Boosting, and a One-dimensional Convolutional Neural Network (1D-CNN). Additionally, the Shapley values and Layer-wise Relevance Propagation (LRP) heatmaps methods were used to evaluate the influence of the studied variables on the prediction of mental health issues. The results revealed that the 1D-CNN model demonstrated the strongest performance, achieving the highest classification accuracy and discrimination capability. Explainability analyses underlined prior infectious diseases diagnosis, access to medical assistance, experiences of discrimination, age, and sexual identity expression as key predictors of mental health outcomes. These findings suggest that advanced predictive techniques can provide valuable insights for identifying at-risk individuals, informing targeted interventions, and improving access to mental health care. Future research should refine these models to enhance predictive accuracy, broaden applicability, and support the integration of artificial intelligence into public health strategies aimed at addressing the mental health needs of this population. Full article

To address the limitations in time–frequency feature representation of shearer arm gear faults and the issues of parameter redundancy and low training efficiency in standard convolutional neural networks (CNNs), this study proposes a diagnostic method based on an improved S-transform and a Depthwise Separable Convolutional Neural Network (DSCNN). First, the improved S-transform is employed to perform time–frequency analysis on the vibration signals, converting the original one-dimensional signals into two-dimensional time–frequency images to fully preserve the fault characteristics of the gear. Then, a neural network model combining standard convolution and depthwise separable convolution is constructed for fault identification. The experimental dataset includes five gear conditions: tooth deficiency, tooth breakage, tooth wear, tooth crack, and normal. The performance of various frequency-domain and time-frequency methods—Wavelet Transform, Fourier Transform, S-transform, and Gramian Angular Field (GAF)—is compared using the same network model. Furthermore, Grad-CAM is applied to visualize the responses of key convolutional layers, highlighting the regions of interest related to gear fault features. Finally, four typical CNN architectures are analyzed and compared: Deep Convolutional Neural Network (DCNN), InceptionV3, Residual Network (ResNet), and Pyramid Convolutional Neural Network (PCNN). Experimental results demonstrate that frequency–domain representations consistently outperform raw time-domain signals in fault diagnosis tasks. Grad-CAM effectively verifies the model’s accurate focus on critical fault features. Moreover, the proposed method achieves high classification accuracy while reducing both training time and the number of model parameters. Full article

The sharp decline in streamflow prediction accuracy with increasing lead times remains a persistent challenge for effective water resources management and flood mitigation. In this study, we developed a coupled deep learning model for daily streamflow prediction in the Hanjiang River Basin, China. The proposed model integrates self-attention (SA), a one-dimensional convolutional neural network (1D-CNN), and bidirectional long short-term memory (BiLSTM). The model’s effectiveness was assessed during flood events, and its predictive uncertainty was quantified using kernel density estimation (KDE). The results demonstrate that the proposed model consistently outperforms baseline models across all lead times. It achieved Nash-Sutcliffe Efficiency (NSE) scores of 0.92, 0.86, and 0.79 for 1-, 3-, and 5-days, respectively, showing particular strength at these extended lead time predictions. During major flood events, the model demonstrated an enhanced capacity to capture peak magnitudes and timings. It achieved the highest NSE values of 0.924, 0.862, and 0.797 for the 1-, 3-, and 5-day forecasting horizons, respectively, thereby showcasing the strengths of integrating CNN and SA mechanisms for recognizing local hydrological patterns. Furthermore, KDE-based uncertainty analysis identified a high prediction interval coverage in different forecast periods and a relatively narrow prediction interval width, indicating the strong robustness of the proposed model. Overall, the proposed SA-CNN-BiLSTM model demonstrates significantly improved accuracy, especially for extended lead times and flood events, and provides robust uncertainty quantification, thereby offering a more reliable tool for reservoir operation and flood risk management. Full article

We introduce a hybrid deep learning model for recognizing hand gestures from electromyography (EMG) signals in subacute stroke patients: the one-dimensional convolutional long short-term memory neural network (CNN-LSTM). The proposed network was trained, tested, and cross-validated on seven hand gesture movements, collected via EMG from 25 patients exhibiting clinical features of paresis. EMG data from these patients were collected twice post-stroke, at least one week apart, and divided into datasets A and B to assess performance over time while balancing subject-specific content and minimizing training bias. Dataset A had a median post-stroke time of 16.0 ± 8.6 days, while dataset B had a median of 19.2 ± 13.7 days. In classification tests based on the number of gesture classes (ranging from two to seven), the hybrid model achieved accuracies ranging from 85.66% to 82.27% in dataset A and from 88.36% to 81.69% in dataset B. To address the limitations of deep learning with small datasets, we developed a novel bilateral data fusion approach that incorporates EMG signals from the non-paretic limb during training. This approach significantly enhanced model performance across both datasets, as evidenced by improvements in sensitivity, specificity, accuracy, and F1-score metrics. The most substantial gains were observed in the three-gesture subset, where classification accuracy increased from 73.01% to 78.42% in dataset A, and from 77.95% to 85.69% in dataset B. In conclusion, although these results may be slightly lower than those of traditional supervised learning algorithms, the combination of bilateral data fusion and the absence of feature engineering offers a novel perspective for neurorehabilitation, where every data segment is critically significant. Full article

The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic monitoring signals, leveraging a hybrid framework that combines the feature learning capacity of a one-dimensional convolutional neural network (1D-CNN) with the classification robustness of a support vector machine (SVM). The proposed method effectively distinguishes various pipeline-related events—such as minor leakage, theft attempts, and human movement—by automatically extracting their vibration patterns. Notably, it addresses the common shortcomings of softmax-based classifiers in small-sample scenarios. When tested on a real-world dataset collected via the DAS3000 system from Hangzhou Optosensing Co., Ltd., the model achieved a high classification accuracy of 99.92% across six event types, with an average inference latency of just 0.819 milliseconds per signal. These results demonstrate its strong potential for rapid anomaly detection in pipeline systems. Beyond technical performance, the method offers three practical benefits: it integrates well with current monitoring infrastructures, significantly reduces manual inspection workloads, and provides early warnings for potential pipeline threats. Overall, this work lays the groundwork for a scalable, machine learning-enhanced solution aimed at ensuring the operational safety of critical energy assets. Full article

This paper presents a learning-assisted approach for state estimation of quadruped robots using observations of proprioceptive sensors, including multiple inertial measurement units (IMUs). Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing the dynamics of the body and legs, in addition to joint encoders. The extended Kalman filter (KF) is employed to fuse sensor data to estimate the robot’s states in the world frame and enhance the convergence of the extended KF (EKF). To circumvent the requirements for the measurements from the motion capture (mocap) system or other vision systems, the right-invariant EKF (RI-EKF) is extended to employ the foot IMU measurements for enhanced state estimation, and a learning-based approach is presented to estimate the vision system measurements for the EKF. One-dimensional convolutional neural networks (CNN) are leveraged to estimate required measurements using only the available proprioception data. Experiments on real data from a quadruped robot demonstrate that proprioception can be sufficient for state estimation. The proposed learning-assisted approach, which does not rely on data from vision systems, achieves competitive accuracy compared to EKF using mocap measurements and lower estimation errors than RI-EKF using multi-IMU measurements. Full article

This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals obtained under different load mechanical conditions. This preprocessing step enhances fault-related features, enabling improved learning while maintaining the simplicity of a lightweight CNN. The model achieved classification accuracies above 99.16% across all folds in five-fold cross-validation and demonstrated the ability to detect faults involving as few as three short-circuited turns. Comparative experiments with the Multi-Scale 1D-ResNet demonstrate that the proposed method achieves similar or superior performance while significantly reducing training time. These results highlight the model’s suitability for real-time fault detection in embedded and resource-constrained industrial environments. Full article

Aiming at addressing the problem of insufficient fusion of multi-source heterogeneous features in short-term power load forecasting, this paper proposes a three-channel LSTM-CNN hybrid forecasting model. This method extracts the temporal characteristics of time, weather, and historical loads through independent LSTM channels and realizes cross-modal spatial correlation mining by using a Convolutional Neural Network (CNN). The time channel takes hour, week, and holiday codes as input to capture the daily/weekly cycle patterns. The meteorological channel integrates real-time data such as temperature and humidity and models the nonlinear delay effect between them and the load. The historical load channel sequence of the past 24 h is analyzed to interpret the internal trend and fluctuation characteristics. The output of the three channels is concatenated and then input into a one-dimensional convolutional layer. Cross-modal cooperative features are extracted through local perception. Finally, the 24 h load prediction value is output through the fully connected layer. The experimental results show that the prediction model based on the three-channel LSTM-CNN has a better prediction effect compared with the existing models, and its average absolute percentage error on the two datasets is reduced to 1.367% and 0.974%, respectively. The research results provide an expandable framework for multi-source time series data modeling, supporting the precise dispatching of smart grids and optimal energy allocation. Full article

To address the limitations of traditional methods in adapting to complex operating conditions, this paper proposes a fault diagnosis approach combining multi-scale empirical mode decomposition (MS-EMD) and a one-dimensional convolutional neural network (1D CNN) integrated with a bidirectional gated recurrent unit (BiGRU). The method incorporates multi-scale down-sampling to generate signals at different time scales, utilizes EMD to extract multi-frequency features, and selects key intrinsic mode functions (IMFs) based on frequency energy entropy, significantly enhancing the stability and representational capability of signal decomposition. The 1D CNN-BiGRU module ensures efficient integration of local feature extraction and sequence modeling. Initially, down-sampling is applied to produce signals at various time scales, followed by EMD to decompose these signals and obtain comprehensive IMFs. Key IMFs are then selected using frequency energy entropy, and signals are reconstructed to highlight critical features, effectively eliminating redundant components and noise. Next, the multi-scale reconstructed signals are fed into the 1D CNN, which automatically extracts local signal features to strengthen feature representation. A multi-channel design further improves the ability to capture multi-scale information. Finally, the extracted features are input into the BiGRU, which leverages its sequence modeling capabilities to learn and classify fault patterns. Experimental results show that this method achieves an average fault diagnosis accuracy of 99.58% for gearboxes under noisy conditions, demonstrating a significant improvement over traditional methods. This validates its robustness and efficiency in complex environments. By integrating multi-scale signal decomposition and fusion, adaptively selecting critical features, and utilizing deep learning for feature modeling, this method significantly enhances the fault diagnosis capability of vibration signals from industrial robot gearboxes, offering a new approach for achieving high-precision intelligent diagnostics. Full article

To address the issues of poor differentiation capability for high-order signals and low average recognition rates in existing communication modulation recognition techniques, this paper first performs denoising using an entropy-based dynamic Singular Value Decomposition (SVD) method and proposes a three-channel convolutional gated recurrent units (GRU) model combined with an improved SE attention mechanism for automatic modulation recognition.The model denoises in-phase/quadrature (I/Q) signals using the SVD method to enhance signal quality. By combining one-dimensional (1D) convolutional and two-dimensional (2D) convolutional, it employs a three-channel approach to extract spatial features and capture local correlations. GRU is utilized to capture temporal sequence features so as to enhance the perception of dynamic changes. Additionally, an improved SE block is introduced to optimize feature representation, adaptively adjust channel weights, and improve classification performance. Experiments on the RadioML2016.10a dataset show that the model has a maximum classification recognition rate of 92.54%. Compared with traditional CNN, ResNet, CLDNN, GRU2, DAE, and LSTM2, the average recognition accuracy is improved by 5.41% to 8.93%. At the same time, the model significantly enhances the differentiation capability between 16QAM and 64QAM, reducing the average confusion probability by 27.70% to 39.40%. Full article

Background: Modern aquaculture is increasingly adopting industrialized recirculating aquaculture systems, in which the stable operation of its circulating water pump is essential. Yet, given the complex working conditions, this pump is prone to malfunctioning, so its timely fault prediction and accurate diagnosis are imperative. Traditional fault detection methods rely on manual feature extraction, limiting their ability to identify complex faults, and deep learning methods suffer from unstable recognition accuracy. To address these issues, a three-class fault detection method for water pumps based on a convolutional neural network, transformer, and bidirectional gated recurrent unit (CNN-transformer-BiGRU) is proposed here. Methods: It first uses the continuous wavelet transform to convert one-dimensional vibration signals into time–frequency images for input into a CNN to extract the time-domain and frequency-domain features. Next, the transformer enhances the model’s hierarchical learning ability. Finally, the BiGRU captures the forward/backward feature information in the signal sequence. Results: The experimental results show that this method’s accuracy in fault detection is 91.43%, significantly outperforming traditional machine learning models. Using it improved the accuracy, precision, and recall by 1.86%, 1.97%, and 1.86%, respectively, relative to the convolutional neural network and long short-term memory (CNN-LSTM) model. Conclusions: Hence, the proposed model has superior performance indicators. Applying it to aquaculture systems can effectively ensure their stable operation. Full article

To improve the robustness of intrusion detection systems constructed using deep learning models, a method based on an auxiliary adversarial training WGAN (AuxAtWGAN) is proposed from the defender’s perspective. First, one-dimensional traffic data are downscaled and processed into two-dimensional image data via a stacked autoencoder (SAE), and mixed adversarial samples are generated using the fast gradient sign method (FGSM), Projected Gradient Descent (PGD) and Carlini and Wagner (C&W) adversarial attacks. Second, the improved WGAN with an integrated perceptual network module is trained with mixed training samples composed of mixed adversarial samples and normal samples. Finally, the adversary-trained AuxAtWGAN model is attached to the original model for adversary sample detection, and the detected adversary samples are removed and input into the original model to improve the robustness of the original model. The average attack success rate of the original convolutional neural network (CNN) model against multiple adversarial samples is 75.17%, and after using AuxAtWGAN, the average attack success rate of the adversarial attacks decreases to 27.56%; moreover, the detection accuracy of the original CNN model against normal samples is still 93.57%. The experiment proves that AuxAtWGAN improves the robustness of the original model. In addition, validation experiments are conducted by attaching the AuxAtWGAN model to the Long Short-Term Memory Network (LSTM) and Residual Network34 (ResNet) models, which prove that the proposed method has high generalization performance. Full article