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English grammar error correction, as a subtask in the field of natural language processing, can provide second language learners with services such as automatic correction of grammatical errors and article touch-up. Based on the current mainstream neural machine grammar error correction methods, this paper proposes an English grammar error correction model based on the Transformer model incorporating the replication mechanism (C-Transformer). Combining this model with the pinyin detection algorithm and the feedback filtering algorithm, and expanding the training data by creating pseudo-parallel sentence pairs, an automatic English grammar error correction system is successfully designed. Compared with the traditional CAMB grammar error correction model, the accuracy, recall and metrics of this paper’s model are improved by 16.68%, 20.38% and 17.33%, respectively. Moreover, in the English composition correction experiments for Chinese students, the average precision rate, recall rate and F1 value of this paper’s model for various types of grammatical error correction reached 84.70%, 71.85% and 77.75%, respectively, proving the effectiveness and superiority of this paper’s model. In addition, using the designed English grammar automatic error correction system to conduct teaching experiments, students’ knowledge of various grammar questions, especially in writing, was significantly improved, indicating that the designed system has a better application effect, which is of great significance for improving the effectiveness of university English teaching.

Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to verify the feasibility of these data expansion mechanisms. Finally, some issues for future research are discussed.

Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. This paper proposes an imbalanced fault diagnosis of rotatory machinery that combines time-frequency feature oversampling (TFFO) with a convolutional neural network (CNN). First, the sliding segmentation sampling method is employed to primarily increase the number of fault samples in the form of one-dimensional signals. Immediately after, the signals are converted into two-dimensional time-frequency feature maps by continuous wavelet transform (CWT). Subsequently, the minority samples are expanded again using the synthetic minority oversampling technique (SMOTE) to realize TFFO. After such two-fold data expansion, a balanced data set is obtained and imported to an improved 2dCNN based on the LeNet-5 to implement fault diagnosis. In order to verify the proposed method, two experiments involving single and compound faults are conducted on locomotive wheel-set bearings and a gearbox, resulting in several datasets with different imbalanced degrees and various signal-to-noise ratios. The results demonstrate the advantages of the proposed method in terms of classification accuracy and stability as well as noise robustness in imbalanced fault diagnosis, and the fault classification accuracy is over 97%.

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system’s normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset’s optimization to demonstrate the expanded dataset’s dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks.

The resulting fibre orientation when pressing Sheet Moulding Compound (SMC) is essentially dependent on the position and geometry of the resin mat in the mould. It is desirable to adapt the fibre orientation as best as possible to best withstand the applied load. The relationship between the position and geometry of the resin mat and the deflection of the final part under a specific load can be mapped using a numerical simulation; nevertheless, an optimisation is not feasible based on the simulation in an economically reasonable time frame due to the required computing time. For this reason, a metamodel based on an ensemble of artificial neural networks (ANN) was created on the basis of the simulation data, and an optimisation based on these ANN was set up. However, the investigations show that the minimal deflections found on the basis of the metamodel through optimisation have large deviations compared to the simulation. This leads to the hypothesis that the applied optimisation finds weak points of the metamodel instead of actual minima. From this hypothesis, an extension approach of the data set on which the metamodel is based by the weak points found is derived and implemented. This is done in an iterative process. The results show that the goal of increasing the prediction quality of the optimisation approach is achieved by the developed approach. The root mean square error (RMSE) could be reduced from the original deflection of 25 to 7 mm, which corresponds to a reduction of 72%. It is shown that the presented approach is successful in increasing prediction quality; nevertheless, further measures are needed to reach decent prediction quality.

Rolling bearing is one of the components with the high fault rate for rotating machinery. Big data-based deep learning is a hot topic in the field of bearing fault diagnosis. However, it is difficult to obtain the big actual data, which leads to a low accuracy of bearing fault diagnosis. WGAN-based data expansion approach is discussed in this paper. Firstly, the vibration signal is converted into the gray texture image by LBP to build the original data set. The small original data set is used to generate the new big data set by WGAN with GP. In order to verify its effectiveness, MMD is used for the expansion evaluation, and then the effect of the newly generated data on the original data expansion in different proportions is verified by CNN. The test results show that WGAN-GP data expansion approach can generate the high-quality samples, and CNN-based classification accuracy increases from 92.5% to 97.5% before and after the data expansion.

This study introduces a transfer learning-based approach for signal-level expansion of unseen acoustic signal data, aiming to address the scarcity of acoustic signal data in a specific domain. By establishing connections and sharing knowledge between the source and target domains, the method successfully mitigates cross-domain disparities, overcoming challenges posed by unavailable data in the target domain, thereby elevating the quality and precision of data expansion. Diverging from conventional methods that predominantly emphasize feature-level expansion, the proposed approach accentuates the preservation of data signal integrity and effectively achieves the expansion of unseen class samples within the target domain.The effectiveness of this method has been validated across four different types of signal datasets. In the bearing dataset, the expansion of unseen data achieved accuracies of 99% at the signal level and 95% at the spectral level. These experimental results not only demonstrate the method’s advantages in augmenting both seen and unseen data but also highlight its effectiveness and application potential in achieving comprehensive expansion of target signals.

Many existing intelligent recognition technologies require huge datasets for model learning. However, it is not easy to collect rectal cancer images, so the performance is usually low with limited training samples. In addition, traditional rectal cancer staging is time-consuming, error-prone, and susceptible to physicians’ subjective awareness as well as professional expertise. To settle these deficiencies, we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3. First, a novel deep learning model (RectalNet) is constructed based on residual learning, which combines the squeeze-excitation with the asymptotic output layer and new cross-convolution layer links in the residual block group. Furthermore, a two-stage data augmentation is designed to increase the number of images and reduce deep learning’s dependence on the volume of data. The experiment results demonstrate that the proposed method is superior to many existing ones, with an overall accuracy of 0.8583. Oppositely, other traditional techniques, such as VGG16, DenseNet121, EL, and DERNet, have an average accuracy of 0.6981, 0.7032, 0.7500, and 0.7685, respectively.

Discourse relation classification is a fundamental task for discourse analysis, which is essential for understanding the structure and connection of texts. Implicit discourse relation classification aims to determine the relationship between adjacent sentences and is very challenging because it lacks explicit discourse connectives as linguistic cues and sufficient annotated training data. In this paper, we propose a discriminative instance selection method to construct synthetic implicit discourse relation data from easy-to-collect explicit discourse relations. An expanded instance consists of an argument pair and its sense label. We introduce the argument pair type classification task, which aims to distinguish between implicit and explicit argument pairs and select the explicit argument pairs that are most similar to natural implicit argument pairs for data expansion. We also propose a simple label-smoothing technique to assign robust sense labels for the selected argument pairs. We evaluate our method on PDTB 2.0 and PDTB 3.0. The results show that our method can consistently improve the performance of the baseline model, and achieve competitive results with the state-of-the-art models.

INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper. OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy. METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis. RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.