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Real-time target detection in traffic scenarios is the prerequisite of electronic monitoring, automatic driving, and other functions. In view of the low detection efficiency of existing target detection algorithms and the low accuracy of most light target detection algorithms, which are easy to obtain wrong or insufficient target detection, this paper adopts the improved YOLOv5 target detection algorithm for model training, and the pseudo-label strategy for training process optimization. Then, the KITTI traffic target dataset tags are merged into three categories for model training and testing. Through the experimental comparison, the improved YOLOv5 model in this paper achieves 92.5% mAP in all categories, which is 3% higher than the original YOLOv5 training model. Finally, the three categories of the trained models are deployed on the Jetson Nano embedded platform for inference testing, and TensorRT is used to accelerate inference. The average inference time per frame of image is 77 ms, which meets the goal of real-time detection. [ABSTRACT FROM AUTHOR]

Shield load is the main performance indicator of the shield, accurate load prediction is significant to ensure the safety and efficiency of the shield and the stability of the surrounding environment. Recognizing the limitations of the traditional prediction methods, this paper proposes a hybrid model (CBM), combining convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM) and attention mechanism, to predict the shield load accurately based on the high-dimensional feature and time series characteristic of the data. The proposed model not only can extract the high-dimensional features and time series characteristics of the data, but also can highlight the importance of high-dimensional features and important time node information. The experiment results show that compared with the existing models, the proposed model achieves a higher prediction performance, the prediction accuracy of the thrust and torque is 94.2% and 96.2%. [ABSTRACT FROM AUTHOR]

Since it is difficult to online measure the underflow concentration of the thickener in the thickening-dehydration process, a broad learning system(BLS) based soft sensor modeling method is proposed in this paper. The method has high precision and strong generalization capability. First, several pressure sensors are installed inside the thickener, and the historical dataset under normal operating conditions is established. Then, the soft sensor model is trained by employing the BLS method to online predict the underflow concentration of the thickener. Finally, the efficiency of the proposed method is verified by simulation experiments. Compared with other traditional machine learning methods, the BLS method has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Based on convolutional neural networks， this paper proposes innovative ideas for diagnosing intracranial tissue and organ diseases. The images of Parkinson’s disease patients were preprocessed by gray mapping， histogram equalization， improved wavelet denoising and image enhancement. A convolutional neural network is designed and built on the basis of VGG-Net network model. Regularization strategy is adopted to avoid over-fitting problem. Then the ten-fold cross-validation of the network model is carried out five times with MRI image set of patients. And feature visualization is performed by deconvolution network to dig out latent features of diseases. The result and objective evaluation show that the network can make a good diagnosis of MRI image for Parkinson’s disease. [ABSTRACT FROM AUTHOR]