The moisture content of tobacco, as an important characteristic which should be kept at a desired level to maintain consistent product quality in drying process, is difficult to perform the direct measurement and anomaly detection due to its large delay in actual process. Therefore, an intelligent real-time detection method is an urgent and challenging task in ensuring the product quality. This paper proposes a time-domain raw data conversion method along with a novel deep learning architecture called multi-hierarchical convolutional neural network (MHCNN) for moisture prediction, in which the proposed architecture automatically learns multi-hierarchical features from transformed image-like data and simultaneously performs online prediction. Experiments are conducted on the real production data from the cigarette factory and the presented model performs well on overall testing dataset. Specifically, the MAE, RMSE and R² of normal production batch can reach to 0.0131, 0.0244, and 0.9721 respectively, which are far superior to the estimation of experience and other alternatives. It demonstrates that the proposed online prediction strategy can simultaneously perform multi-hierarchical feature extraction and moisture online prediction with high precise to eliminate the detection delay for process optimization and control.

As a critical component of rotating machinery, rolling bearings are essential for the safe and efficient operation of machinery. Sudden faults of rolling bearings can lead to unscheduled downtime and substantial economic costs. Therefore, diagnosing and identifying the equipment status is essential for ensuring the operation and decreasing the additional maintenance costs of the machines. However, extracting the features from the early bearing fault signals is challenging under background noise interference. With the purpose of solving the above problem, we propose an integrated rolling bearing fault diagnosis model based on the improved grey wolf optimized variational modal decomposition (IGVMD) and an improved 1DCNN with a parametric rectified linear unit (PReLU). Firstly, an improved grey wolf optimizer (IGWO) with the fitness function, the minimum envelope entropy, is designed for adaptively searching the optimal parameter values of the VMD model. The performance of the basic grey wolf optimizer (GWO) algorithm by introducing three improvement strategies, the non-linear convergence factor adjustment strategy, the grey wolf adaptive position update strategy, and the Levy flight strategy in the IGWO algorithm, is improved. Then, an improved 1DCNN model with the PReLU activation function is proposed, which extracts the bearing fault features, and a grid search to optimize the model parameters of the 1DCNN is introduced. Finally, the effectiveness of the proposed model is demonstrated well by employing two experimental datasets. The preliminary comparative results of the average identification accuracy in the proposed method in two datasets are 99.98% and 99.50%, respectively, suggesting that this proposed method has a relatively higher recognition accuracy and application values.

Reconstructing high-resolution flow fields remains a significant challenge in fluid mechanics due to the limitations of measurement techniques. In recent years, physics-informed neural networks (PINNs) have been proposed to reconstruct flow fields by embedding physical constraints. However, accurate solutions are difficult to obtain when faced with complex fluid properties or high noise levels. Therefore, a physics-informed convolutional network based on feature fusion (FFPICN) is proposed to reconstruct high-resolution flow fields from sparse and noisy velocity data. The framework mainly consists of a feature extraction module and a physical information module. Unlike traditional PINNs that mainly rely on fully connected networks to extract features, the new feature extraction module comprises a parallel convolutional layer, a feature fusion layer, and a convolutional fusion module to extract and fuse shallow and deep fluid features. The spatial feature extraction ability of convolutional neural networks is used to enhance the ability to capture small-scale fluid structures. The physical information module embeds the constraints of the transformed Navier–Stokes equations into deep features to improve the accuracy of flow field reconstruction. Furthermore, a preprocessing technique is designed to linearly transform the data and the Navier–Stokes equations concurrently. Finally, wake and turbulence data are investigated at different levels of sparsity and noise. Experimental results show that FFPICN can reconstruct high-resolution flow fields even at 1% sparsity or 20% noise level. The relative L2 error of the reconstructed flow field by FFPICN is reduced by about 30% compared to traditional PINNs and their variants.

Excessive time complexity has severely restricted the application of support vector machine (SVM) in large-scale multi-label classification. Thus, this paper proposes an efficient multi-label SVM classification algorithm by combining approximate extreme points method and divide-and-conquer strategy (AEDC-MLSVM). The AEDC-MLSVM classification algorithm firstly uses the approximate extreme points method to obtain the representative set from the multi-label training data set. While persisting almost all the useful information of multi-label training set, representative set can effectively reduce the scale of multi-label training set. Secondly, to acquire an efficient multi-label SVM classification model, the SVM based on the improved divide-and-conquer strategy is trained on the representative set, which will further improve the training speed and classification performance. The improvement is reflected in two aspects. (1) The improved divide-and-conquer strategy is applied to divide the representative set into subsets and this can ensure that each representative subset contains a certain number of positive and negative instances. This will avoid singular problems and overcome computation load imbalance problem. (2) The different error cost (DEC) method is applied to overcome the label imbalance problem. Effective experiments have proved that the training and testing speed of AEDC-MLSVM classification algorithm can be accelerated substantially while ensuring the classification performance.

A newly designed autonomous instrument was evaluated for long-term ocean turbulence measurements in the South China Sea from October 19, 2013, to February 10, 2014 at 21°09.90′N, 117°42.03′E. A shear velocity time series was collected continuously for 115 days at a single depth of 250 m using two orthogonal shear probes (PNS06). The mechanical design of wings is developed for an observation platform to achieve characteristics of stability and flexibility of current direction tracking. The depth and pitch in comparison with ambient current speed are analyzed, and the results indicate that the platform has satisfied stability requirements for turbulence measurement. The comparison between the instrument heading and current direction shows that the instrument flexibly responds to variations of ambient currents. Throughout the observation period, 62% of the data sets is of significant correlation with correlation coefficients above 0.5, and 4% is of strong correlation. The shear velocity spectra correspond well to the theoretical Nasmyth spectrum, and the magnitude of dissipation rates estimated from shear is approximately Ο(10⁻⁸) W kg⁻¹. The moored instrument is functional as designed and provides a reliable observation platform for extended turbulent measurements.