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Focusing on the low accuracy and timeliness of traditional fault diagnosis methods for rolling bearings which combine massive amounts of data, a fault diagnosis method for rolling bearings based on Gramian angular field (GAF) coding technology and an improved ResNet50 model is proposed. Using the Graham angle field technology to recode the one-dimensional vibration signal into a two-dimensional feature image, using the two-dimensional feature image as the input for the model, combined with the advantages of the ResNet algorithm in image feature extraction and classification recognition, we realized automatic feature extraction and fault diagnosis, and, finally, achieved the classification of different fault types. In order to verify the effectiveness of the method, the rolling bearing data of Casey Reserve University are selected for verification, and compared with other commonly used intelligent algorithms, the results show that the proposed method has a higher classification accuracy and better timeliness than other intelligent algorithms. [ABSTRACT FROM AUTHOR]

Sleep, as the basis for regular body functioning, can affect human health. Poor sleep conditions can lead to various physical ailments, such as poor immunity, memory loss, slow cognitive development, and cardiovascular diseases. Along the increasing stress in society comes with a growing surge in conditions associated with sleep disorders. Studies have shown that sleep stages are essential for the body's memory, immune system, and brain functioning. Therefore, automatic sleep stage classification is of great medical practice importance as a basis for monitoring sleep conditions. Although previous research into the classification of sleep stages has been promising, several challenges remain to be addressed: (1) The EEG signal is a non-smooth signal with harrowing feature extraction and high requirements for model accuracy. (2) Some existing network models suffer from overfitting and gradient descent. (3) Correlation between long time sequences is challenging to capture. This paper proposes NAMRTNet, a deep model architecture based on the original single-channel EEG signal to address these challenges. The model uses a modified ResNet network to extract features from sub-epochs of individual epochs, a lightweight attention mechanism normalization-based attention module (NAM) to suppress insignificant features, and a temporal convolutional network (TCN) network to capture dependencies between features of long time series. The recognition rate of 20-fold cross-validation with the NAMRTNet model for Fpz-cz channel data in the public sleep dataset Sleep-EDF was 86.2%. The experimental results demonstrate the network's superiority in this paper, surpassing some state-of-the-art techniques in different evaluation metrics. Furthermore, the total time to train the network was 5.1 h, which was much less than the training time of other models. [ABSTRACT FROM AUTHOR]

Currently, malware shows an explosive growth trend. Demand for classifying malware is also increasing. The problem is the low accuracy of both malware detection and classification. From the static features of malicious families, a new deep learning method of TCN-BiGRU was proposed in this study, which combined temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU). First, we extracted the features of malware assembly code sequences and byte code sequences. Second, we shortened the opcode sequences by TCN to explore the features in the data and then used the BiGRU network to capture the opcode sequences in both directions to achieve deep extraction of the features of the opcode sequences. Finally, the fully connected and softmax layers were used to output predictions of the deep features. Multiple comparisons and ablation experiments demonstrated that the accuracy of malware detection and classification were effectively improved by our method. Our overall performance was 99.72% for samples comprising nine different classes, and our overall performance was 96.54% for samples comprising two different classes. [ABSTRACT FROM AUTHOR]

In the industrial domain, estimating the pose of texture-less shiny parts is challenging but worthwhile. In this study, it is impractical to utilize texture information to obtain the pose because the features are likely to be affected by the surrounding objects. In addition, the colors of the metal parts are similar, making object segmentation challenging. This study proposes dividing the entire process into three steps: object detection, feature extraction, and pose estimation. We use the Mask-RCNN to detect objects and HRNet to extract the corresponding features. For metal parts of different shapes, different keypoints were chosen accordingly. Conventional contour-based methods are inapplicable to parts containing planar surfaces because the objects occlude each other in clustered environments. In this case, we used dense discrete points along the edges as semantic keypoints for metal parts containing planar elements. We chose skeleton points as semantic keypoints for parts containing cylindrical components. Subsequently, we combined the localization of semantic keypoints and the corresponding CAD model information to estimate the 6D pose of an individual object in sight. The implementation of deep learning approaches requires massive training datasets and intensive labeling. Thus, we propose a method to generate training datasets and automatically label them. Experiments show that the algorithm based on synthetic data performs well in a natural environment, despite not utilizing real scenario images for training. [ABSTRACT FROM AUTHOR]