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[Display omitted] • In the case of a small number of known deposits and mineral occurrences, the prospecting prediction model based on the Siamese network can extract effective features without relying on prior knowledge to realize supervised prospecting prediction. • On the basis of sliding window data enhancement, this paper generates paired sample data sets by randomly combining deposit samples and non-deposit samples, which can further enhance the data and improve the generalization ability and prediction accuracy of the model to a certain extent. • Compared with the prediction results of the weight of evidence method and the convolutional neural network, the prediction results of the Siamese network are better, and 7 new gold prospecting areas have been discovered in Zhangjiakou, Hebei Province, China. Supervised neural networks constitute an important research area for the intelligent prediction of locations for prospecting mineral deposits. Accurate training of a supervised neural network requires many training samples, something that is often difficult to obtain. This paper reports the use of geological, geochemical, and geophysical data by a Siamese network to overcome the problem of insufficient training samples, and implements a supervised deep learning prospecting prediction method based on the Siamese network. Intelligent prediction for gold deposits prospecting is carried out for the Chongli mineral concentrate area in Zhangjiakou, Hebei Province, China, and compared with the weight of evidence method and convolutional neural network model. The results show that:(a) the performance of the Siamese network model is no less than that of the convolutional neural network (CNN) model and better than that of the weight of evidence (WOE) method; (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. This study suggests Siamese network model as an effective mineral prospectivity modeling tool. This method is also suitable for prospecting prediction using geoscience data in other areas. [ABSTRACT FROM AUTHOR]

• Current pose assessment systems ignore the similarity between comparative poses. • A new pose evaluation system that can provide comprehensive feedback was proposed. • The proposed pose matching method is feasible on the eight-section brocade dataset. • The proposed method is one-stage and it has been deployed in software for testing. The application of pose assessment on rehabilitation training has gradually received attention in recent years. However, current evaluation indicators of these methods are mostly based on the score or scoring function that defined by users, which is too subjective and hard to be used by patients directly. In this paper, we conceptualized a new idea for pose matching, namely pose-guided matching that aims at providing objective and accurate score, feedback and guidance (i.e. guided) to the patients when the pose is compared to the standard pose. More specifically, we proposed a pair-based Siamese Convolutional Neural Network (SCNN) abbreviated ST-AMCNN to realize the idea of pose-guided matching on the eight-section brocade dataset which is one of the most representative traditional rehabilitation exercises in China. We simplified the multi-stages pose matching by merging two standalone modules (i.e. alignment and matching module) into a one-stage task. Such that, only one loss function is required to tune, which reduces the computational complexity. On top of the Spatial Transformer Networks (STN) employed as an alignment module, we proposed a new Attention-based Multi-Scale Convolution (AMC) to match different posture parts (i.e. multi-scale). Furthermore, the proposed AMC can assign more weight to useful pose features as opposed to other irrelevant features e.g. background features for performance gain. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is adopted to visualize the matching result for the learner. Experimental results indicate that ST-AMCNN achieves a competitive performance than the state-of-the-art models and can provide accurate feedback for learners on rehabilitation training. Simultaneously, the proposed method is also deployed in client software for testing. [ABSTRACT FROM AUTHOR]

Accurate, fast, and reliable estimation of field rock strength is significant for the assessment of rock mass strength. This paper presents a novel method to continuously estimate the field strength parameters of rock by incorporating a deep convolutional neural network (DCNN) technique into the drilling process. A DCNN model is established based on the stochastic pooling method and softmax loss. The proposed CNN model framework includes : (1) collecting drilling performance parameters of intact rock, (2) establishing a strength parameter database via standard laboratory tests , (3) training the proposed CNN model using images created from the dataset of drilling performance parameters and rock strength parameters (unconfined compressive strength (UCS), cohesion, and internal friction angle), and (4) performing a new estimation to evaluate the reliability of the trained CNN model. The experimental results show that the predicted rock strength parameters are within the accepted error range of 10% compared to the results from the conventional standard test. The proposed CNN shows superior performance for UCS estimation of various rock types and achieves higher accuracy than the Mohr–Coulomb criterion. In particular, the proposed method can overcome major limitations of analytical models based on the force limit and energy equilibrium method. This method enables continuous and reliable field measurements of rock strength parameters at a speed several orders of magnitude faster than the standard test. This continuous and practical technique has been applied in an engineering problem in the Hanjiang-to-Weihe River Project of China and shows potential for field applications in rock engineering. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]