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Recently, deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection. However, extracting polarimetric and spatial features on the whole PolSAR image will result in high computational complexity. In addition, in the massive data ship detection task, the image to be detected contains a large number of invalid areas, such as land and seawater without ships. Therefore, using ship coarse detection methods to quickly locate the potential areas of ships, that is, ship potential area extraction, is an important prerequisite for PolSAR ship detection. Since existing unsupervised PolSAR ship detection methods based on pixel-level features often rely on fine sea–land segmentation pre-processing and have poor applicability to images with complex backgrounds, in order to solve the abovementioned issue, this paper proposes a PolSAR ship potential area extraction method based on the neighborhood semantic differences of an LDA bag-of-words topic model. Specifically, a polarimetric feature suitable for the scattering diversity condition is selected, and a polarimetric feature map is constructed; the superpixel segmentation method is used to generate the bag of words on the feature map, and latent high-level semantic features are extracted and classified with the improved LDA bag-of-words topic model method to obtain the PolSAR ship potential area extraction result, i.e., the PolSAR ship coarse detection result. The experimental results on the self-established PolSAR dataset validate the effectiveness and demonstrate the superiority of our method. [ABSTRACT FROM AUTHOR]

Deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection over the past few years. However, the backscattering of manmade targets, including ships, is sensitive to the relative geometry between target orientation and radar line of sight, which makes the diversity of polarimetric and spatial features of ships. The diversity of scattering leads to a relative increase in the scarcity of PolSAR-labeled samples, which are difficult to obtain. To solve the abovementioned issue and extract the polarimetric and spatial features of PolSAR images better, this paper proposes a few-shot PolSAR ship detection method based on the combination of constructed polarimetric input data selection and improved contrastive self-supervised learning (CSSL) pre-training. Specifically, eight polarimetric feature extraction methods are adopted to construct deep learning network input data with polarimetric features. The backbone is pre-trained with un-labeled PolSAR input data through an improved CSSL method without negative samples, which enhances the representation capability by the multi-scale feature fusion module (MFFM) and implements a regularization strategy by the mix-up auxiliary pathway (MUAP). The pre-trained backbone is applied to the downstream ship detection network; only a few labeled samples are used for fine-tuning and the construction method of polarimetric input data with the best detection effect is studied. The comparison and ablation experiment results on the self-established PolSAR ship detection dataset verify the superiority of the proposed method, especially in the case of few-shot learning. [ABSTRACT FROM AUTHOR]

The tunneling collapse is the main engineering disaster during tunnel construction by the drilling and blasting method. It has become a key issue to accurately evaluate the tunneling collapse risk. As for assessing the tunneling collapse risk and providing basic risk-controlling strategies, this research proposes a novel multi-source information fusion approach that combines the cloud model (CM), the support vector machine (SVM), and the evidence-based reasoning (ER). In the data processing phase, a variety of information sources are trained by different models to analyze the collapse risk value. The judgment from each model is then evaluated according to the model's performance which is characterized by the reliability and importance weight. The judgments from different models are fused via evidential reasoning to give the overall collapse risk level. The novel approach has been successfully applied in the case of the Jinzhupa tunnel of the Pu-Yan Highway (Fujian, China). The results indicate that the proposed multi-source information fusion method has an evaluation accuracy of 87.5%, while the single-source information method has an accuracy of less than 70%. In addition, the predicted deformation of the surrounding rocks using artificial intelligence is utilized as a source of information to derive an advanced risk assessment. As a result, the decision-makers have a longer response time before a disaster occurs. Furthermore, the fusion model has excellent performance even if the risk result of different models has high conflict. [ABSTRACT FROM AUTHOR]

The tunneling collapse is the main engineering hazard in the construction of the drilling-and-blasting method. The accurate assessment of the tunneling collapse risk has become a key issue in tunnel construction. As for assessing the tunneling collapse risk and providing basic risk controlling strategies, this research proposes a novel multi-source information fusion approach that combines Bayesian network (BN), cloud model (CM), support vector machine (SVM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation technique. Those methods (CM, BN, SVM) are used to analyze multi-source information (i.e. statistical data, physical sensors, and expert judgment provided by humans) respectively and construct basic probability assignments (BPAs) of input factors under different risk states. Then, these BPAs will be merged at the decision level to achieve an overall risk evaluation, using an improved D–S evidence theory. The MC technology is proposed to simulate the uncertainty and randomness of data. The novel approach has been successfully applied in the case of the Jinzhupa tunnel of the Pu-Yan Highway (Fujian, China). The results indicate that the developed new multi-source information fusion method is feasible for (a) Fusing multi-source information effectively from different models with a high-risk assessment accuracy of 98.1%; (b) Performing strong robustness to bias, which can achieve acceptable risk assessment accuracy even under a 20% bias; and (c) Exhibiting a more outstanding risk assessment performance (97.9% accuracy) than the single-information model (78.8% accuracy) under a high bias (20%). Since the proposed reliable risk analysis method can efficiently integrate multi-source information with conflicts, uncertainties, and bias, it provides an in-depth analysis of the tunnel collapse and the most critical risk factors, and then appropriate remedial measures can be taken at an early stage. [ABSTRACT FROM AUTHOR]

On the western plateau of China, ventilation problems brought on by low atmospheric pressure must be overcome. And CO migration after blasting in high-altitude tunnel by inclined shaft has become a significant scientific issue. In this study, the Computational Fluid Dynamics (CFD) method was used to analyze the flow field characteristics at the junction of the inclined shaft and tunnel. In addition, the effects of different fan opening modes and different initial CO concentration distributions on the ventilation were discussed. The simulation results showed that the main difference in the ventilation wind field was reflected in the position of the vortex region due to the different fan opening modes. Meanwhile, various initial CO concentration distributions showed different migration when there was no air volume difference between the left and right tunnels. Eliminating vortex zones and fully using high velocity airflow could improve relative ventilation efficiency by at least 18%. CO would accumulate in the opposite direction of the tunnel if only one of the fans was turned on. Therefore, a two-stage ventilation scheme was proposed, and the energy consumption was reduced by at least 33%. This research can provide guidance on high-altitude tunnel construction with multiple working faces to improve ventilation efficiency and reduce energy consumption. [ABSTRACT FROM AUTHOR]

Collapse is one of the most dangerous aspects of drilling–blasting construction in highway tunnels. To accurately control tunnel-collapse risk, a multistate dynamic Bayesian network (DBN) evaluation method for highway tunnel collapse based on parameter learning was proposed. First, by analyzing the risk mechanism of tunnel construction, the initial BN model was established based on the causal relationship between risk factors and construction risk in hydrogeological conditions, construction technology, and construction management. Next, the construction process was discretized into finite time slices. In consideration of the fuzzy uncertainty of nodes, node polymorphism was introduced to construct a multistate DBN. Then, 50 typical tunnel-collapse cases were taken as sample data, and the conditional probability distribution of initial BN was derived using parameter learning based on the expectation-maximization (EM) algorithm. Using DBN reasoning and sensitivity analysis, the dynamic risk probability and the dominant factors of tunnel collapse were predicted. Finally, the DBN model was fed back with the measured cumulative values and velocity of the crown settlement, which updated the dynamic risk probability assessment results. In analyzing the collapse probability of Jinzhupa tunnel passing through the angular unconformity contact zone as an example, the results demonstrated that dynamic risk assessment results combined with monitoring data could better reflect the reality of construction contingencies, providing real-time risk management guidance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel combined machining approach. The approach is focused on combining short electric arc machining and electrochemical machining (SEAM-ECM). It aims to improve the surface integrity of TC4 titanium alloy by adding compressed air and electrolyte into the SEAM. This approach can change the material removal mechanism of the conventional SEAM and improve the gap flow field distribution and discharge state using the dual fluid properties of electrolyte and air mixed medium. Flow field simulation demonstrates the effects of gas addition on the state of the gap flow field and the electrical conductivity of the mixed medium. The experiments compare the effects of the presence or absence of air and the electrical conductivity of the solution on the machining performance. The results show that SEAM-ECM with electrolyte and air reduces the relative electrode wear rate (REWR) while maintaining a good material removal rate (MRR). In addition, SEAM-ECM utilizes the electrolytic effect to weaken the recast layer compared to SEAM with deionized water and air. The addition of high-speed air reduces defects such as melt drops, particles, and holes. It performs with higher precision and finish than ECM alone, and the overall surface integrity is significantly improved. [ABSTRACT FROM AUTHOR]