Vision-based approaches are widely used in steel crack detection. After processing the images taken by the camera, the superficial defects can be detected. Due to the common limitation of the nature of photographic images, internal features of objects cannot be fully discovered. In order to overcome the drawbacks of vision-based methods, this work presents an approach for detecting cracks in infrared thermal imaging steel sheets using Convolutional Neural Networks (CNN). Firstly, a horizontal heat conduction method is researched to thermally excite the surface of the steel sheet, and a rolling electric heating device is developed to be a thermal excitation source. Secondly, we analyze the temperature difference between normal area and different crack depths, and study the influence of temperature change. Thirdly, 3000 infrared thermograms labeled for penetrating cracks, non-penetrating cracks, and surface scratches are fabricated into a databank. Then, the CNN is trained and validated on the data bank. Finally, improving the original Faster Region-based Convolutional Neural Network (Faster R-CNN) network, the feature maps of multiple levels in the feature extraction network are aggregated, and the anchor selection scheme of the Region Proposal Network (RPN) is adjusted from 9 to 25. The robustness of the improved Faster R-CNN is demonstrated by evaluating the detection results on the 125 images outside of the data bank, the accuracy and Mean Average Precision (mAP) are 95.54% and 92.41%, respectively, which outperform the original algorithm by increasing the accuracy of 3.18% and mAP of 1.88%.

Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecasting model, which could investigate the tendency in saturation line and issue early warnings. In order to fill the gap, this work presents a hybrid network - Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), namely CNN-LSTM network for predicting the tailings pond risk. Firstly, the nonliear data processing method was composed to impute the missing value with the numerical inversion (NI) method, which combines correlation analysis, sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new forecasting model was proposed to monitor the saturation line, which is the lifeline of the tailings pond and can directly reflect the stability of the tailings pond. The CNN was used to identify and learn the spatial structures in the time series, then followed by LSTM cells for detecting the long-term dependence. Finally, different experiments were conducted to evaluate the effectiveness of the model by comparing it with other state-of-the-art algorithms. The results showed that combing CNN with LSTM layers achieves the best score in mean absolute error (MAE), root-mean-square error (RMSE) and coefficient of determination (R ² ).

Landslide risk assessment is an important component of the landslide research field. For the problem of landslide assessment indicators, we utilize the TOPSIS-Entropy method to assess the risk situation of landslide occurrences, which is easy to obtain directly from sensor data. By using the TOPSIS-Entropy method in landslide datasets, the instability margins of landslide risk are obtained, reflecting the current instability probability of the landslide body. For the landslide prediction issue, deep neural networks are used to predict the corresponding landslide instability margins (LIMs). Attention mechanism-based (Attn) temporal convolutional networks (TCN) connected with recurrent neural network (RNN) models for landslide risk prediction are proposed, including TCN-Attn-RNN and RNN-Attn-TCN, which both use an encoder–decoder architecture. The encoder in the first model uses the temporal convolutional network (TCN), and the decoder uses a neural network with an RNN architecture, including long short-term memory (LSTM) networks, gated recurrent units (GRUs), and their derivative algorithms. In the second model, the encoder uses a neural network with an RNN architecture, and the decoder uses a TCN. Combining the TOPSIS-Entropy method with TCN-Attn-RNN and RNN-Attn-TCN, reliable prediction models of landslide risk are proposed. By building a landslide simulation platform, we obtained landslide data. Compared to their counterparts, the proposed prediction models of landslide risk instability margins have better predictive effects.

Aiming at the problems of electrical connector defect detection, such as low automation, low detection accuracy, slow detection speed, and poor robustness, an improved Yolo v3 algorithm was proposed in this paper to detect electrical connector defects. First, the K-means clustering algorithm is used to perform cluster analysis on the data set of this paper to obtain three kinds of candidate frames with aspect ratios, aiming at improving the detection accuracy for the defective objects in this paper; the 8-fold downsampled feature map outputted by the third residual block of the backbone network is upsampled 4 times, and the obtained feature map is merged with the 2-fold downsampled feature map outputted by the second residual block to obtain the fusion feature detection layer; the 6 DBL units passed by the target detection layer are changed to 2 DBL unit plus 2 residual units to improve feature reuse and acquisition; In addition, single-scale feature maps are chose for target detection in this paper instead of multi-scale prediction of the original network, which not only saves the calculation amount, but also avoids false detection to a certain extent.; A new detection method is proposed for relative rotation defects between the inner ring area and the outer ring area of the electrical connector. The qualitative and quantitative experimental results show that the improved Yolo v3 algorithm in this paper has better performance and speed for defect detection of various types of electrical connectors, with an accuracy rate of 93.5%, which is more accurate than Faster R-CNN. The original Yolo v3 is faster and basically meets the requirements of the industrial field for electrical connector testing.

The capture and prediction of rainfall-induced landslide warning signals is the premise for the implementation of landslide warning measures. An attention-fusion entropy weight method (En-Attn) for capturing warning features is proposed. An attention-based temporal convolutional neural network (ATCN) is used to predict the warning signals. Specifically, the sensor data are analyzed using Pearson correlation analysis after obtaining data from the sensors on rainfall, moisture content, displacement, and soil stress. The comprehensive evaluation score is obtained offline using multiple entropy weight methods. Then, the attention mechanism is used to weight and sum different entropy values to obtain the final landslide hazard degree (LHD). The LHD realizes the warning signal capture of the sensor data. The prediction process adopts a model built by ATCN and uses a sliding window for online dynamic prediction. The input is the landslide sensor data at the last moment, and the output is the LHD at the future moment. The effectiveness of the method is verified by two datasets obtained from the rainfall-induced landslide simulation experiment.

This paper presents the design principles and fabrication techniques for simultaneously forming non-coplanar resonant beams and crab-leg supporting beams of dual-axis bulk micromachined resonant accelerometers by masked-maskless combined anisotropic etching. Four resonant beams are located at the surface of a silicon substrate, whereas the gravity centre of a proof mass lies within the neutral plane of four crab-leg supporting beams on the same substrate. Compared with early reported mechanical structures, the simple structure not only eliminates the bending moments caused by in-plane acceleration, and thereby avoiding the rotation of the proof mass, but also providing sufficiently small rigidity to X and Y axes accelerations, potentially leading to a large sensitivity for measuring the in-plane acceleration.