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Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions.

In recent years, significant advances have been gained in visual detection, and an abundance of outstanding models have been proposed. However, state-of-the-art object detection networks have some inefficiencies in detecting small targets. They commonly fail to run on portable devices or embedded systems due to their high complexity. In this workpaper, a real-time object detection model, termed as Tiny Fast You Only Look Once (TF-YOLO), is developed to implement in an embedded system. Firstly, the k-means++ algorithm is applied to cluster the dataset, which contributes to more excellent priori boxes of the targets. Secondly, inspired by the multi-scale prediction idea in the Feature Pyramid Networks (FPN) algorithm, the framework in YOLOv3 is effectively improved and optimized, by three scales to detect the earlier extracted features. In this way, the modified network is sensitive for small targets. Experimental results demonstrate that the proposed TF-YOLO method is a smaller, faster and more efficient network model increasing the performance of end-to-end training and real-time object detection for a variety of devices.

Knowledge distillation serves as a prevalent model compression strategy within scene text detection, enabling the transfer of learned representations from a high-capacity teacher architecture to a streamlined student counterpart. Building upon this concept, deep mutual learning alleviates dependence on the teacher model through interactive learning among student models. However, existing deep mutual learning networks inadequately address the complex redundant backgrounds and text feature distributions in scene text images, failing to effectively balance the trade-off between model performance and lightweight design. To address this issue, this paper proposes an improved self-mutual learning framework based on deep mutual learning. By employing a design that incorporates parallel multi-detection heads and interactive learning, the proposed approach simplifies the model training process while significantly improving detection accuracy. Specifically, the framework introduces a pruning mechanism that enables different detection heads to capture input features with varying degrees of sparsity. This not only reduces interference from redundant backgrounds but also leads to a more lightweight implementation. Moreover, varying feature sparsity among detection heads promotes more diverse knowledge exchange throughout mutual learning. This substantially boosts the distilled model’s resilience in intricate text environments. Comprehensive evaluations show that our approach achieves superior F-measure scores compared to leading knowledge distillation methods.

To date, different types brain signals have been employed to develop BCI systems. Because of its convenience and low cost, EEG signal has become the main medium in BCI systems. [...]the method should have a rigorous mathematical basis and can be easily improved theoretically. [...]the method has been widely used in clinical practice, and some mature and practical technical solutions have been formed. [...]as an important tool for feature extraction, wavelet transform also needs to be combined with artificial intelligence to achieve intelligent analysis results (Cao et al., 2019).

Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation. However, the useful weak features are usually corrupted by strong background noise, thus increasing the difficulty of the feature extraction. Thereby, a novel denoising method based on the tunable Q-factor wavelet transform (TQWT) using neighboring coefficients is proposed in this article. The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms, which can tune Q-factor according to the oscillatory behavior of the signal. Meanwhile, neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques. Because of having the combined advantages of the two methods, the presented denoising method is more practical and effective than other methods. The proposed method is applied to a simulated signal, a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case. The processing results demonstrate that the proposed method can successfully identify the fault features, showing that this method is more effective than the conventional wavelet thresholding denoising methods, term-by-term TQWT denoising schemes and spectral kurtosis.

To ensure the operational reliability of machinery, rolling bearings exposed to complex and poor conditions should be monitored in real-time. Traditional bearing fault diagnosis methods are always dependent on signal analysis and feature extraction, which are complex and time-consuming. Deep learning method exhibits a good ability in extracting the fault feature, while it is limited to noise pollution and insufficient sample data during the training procedure. In this study, a new sparse enhancement neural network based on generalized minimax-concave penalty and convolutional neural network is proposed to capture fault features automatically. To this end, the generalized minimax-concave penalty is first employed to expand the dataset by pollution data denoise and sparse enhancement of the insufficient samples. Second, the amplified dataset is employed to train the fault classification. By employing the datasets of drive end and fan end derived from the Case Western Reserve University (CWRU), a good prediction accuracy can be found in fault diagnosis for rolling bearings.

Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed “robust sum of template and pixel-wise learners” (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.

With the rapid development of sensor technology, the anomaly detection of multi-source time series data becomes more and more important. Traditional anomaly detection methods deal with the temporal and spatial information in the data independently, and fail to make full use of the potential of spatio-temporal information. To address this issue, this paper proposes a novel integration method that combines sensor embeddings and temporal representation networks, effectively exploiting spatio-temporal dynamics. In addition, the graph neural network is introduced to skillfully simulate the complexity of multi-source heterogeneous data. By applying a dual loss function—consisting of a reconstruction loss and a prediction loss—we further improve the accuracy of anomaly detection. This strategy not only promotes the ability to learn normal behavior patterns from historical data, but also significantly improves the predictive ability of the model, making anomaly detection more accurate. Experimental results on four multi-source sensor datasets show that our proposed method performs better than the existing models. In addition, our approach enhances the ability to interpret anomaly detection by analyzing the sensors associated with the detected anomalies.

Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects.

Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better.