Unsupervised learning of depth estimation, camera motion prediction and dynamic object localization from video

Estimating scene depth, predicting camera motion and localizing dynamic objects from monocular videos are fundamental but challenging research topics in computer vision. Deep learning has demonstrated an amazing performance for these tasks recently. This article presents a novel unsupervised deep learning framework for scene depth estimation, camera motion prediction and dynamic object localization from videos. Consecutive stereo image pairs are used to train the system while only monocular images are needed for inference. The supervisory signals for the training stage come from various forms of image synthesis. Due to the use of consecutive stereo video, both spatial and temporal photometric errors are used to synthesize the images. Furthermore, to relieve the impacts of occlusions, adaptive left-right consistency and forward-backward consistency losses are added to the objective function. Experimental results on the KITTI and Cityscapes datasets demonstrate that our method is more effective in depth estimation, camera motion prediction and dynamic object localization compared to previous models

Numerical Modeling of Mineralizing Processes During the Formation of the Yangzhuang Kiruna-Type Iron Deposit, Middle and Lower Yangtze River Metallogenic Belt, China: Implications for the Genesis and Longevity of Kiruna-Type Iron Oxide-Apatite Systems

The Yangzhuang iron deposit is a Kiruna-type iron oxide-apatite (IOA) deposit within the Ningwu mining district of the Middle and Lower Yangtze River Metallogenic Belt (MLYRMB), China. This study applies a numerical modeling approach to identify the key processes associated with the formation of the deposit that cannot be easily identified using traditional analytical approaches, including the duration of the mineralizing process and the genesis of iron orebodies within intrusions associated with the deposit. This approach highlights the practical value of numerical modeling in quantitatively analyzing mineralizing processes during the formation of mineral deposits and assesses how these methods can be used in future geological research. Our numerical model links heat transfer, pressure, fluid flow, chemical reactions, and the movement of ore-forming material. Results show that temperature anomaly and structure (occurrence of the contact of intrusion and the Triassic Xujiashan group) are two key factors controlling the formation of the Yangzhuang deposit. This modeling also indicates that the formation of the Yangzhuang deposit only took some 8000 years, a reaction that is likely to be controlled by temperature and diffusion rates within the system. The dynamic changes of temperature and the distribution of mineralization also indicate that the orebodies located inside the intrusions most likely formed after magma ascent rather than representing blocks of existing mineralization that descended into the magma as a result of stoping or other similar processes. All these data form the basis for future research into the forming processes of Kiruna-type IOA systems as well as magmatic–hydrothermal systems more broadly, including providing useful insights for future exploration for these systems. The simulation approach used in this study has several limitations, such as oversimplified chemical reactions, uncertainty of pre-metallogenic conditions and limitation of 2D model. Future development into both theories and methods will definitely improve the practical significance of numerical simulation of ore-forming processes and provide quantitative results for more geological issues

Unsupervised framework for depth estimation and camera motion prediction from video

Depth estimation from monocular video plays a crucial role in scene perception. The significant drawback of supervised learning models is the need for vast amounts of manually labeled data (ground truth) for training. To overcome this limitation, unsupervised learning strategies without the requirement for ground truth have achieved extensive attention from researchers in the past few years. This paper presents a novel unsupervised framework for estimating single-view depth and predicting camera motion jointly. Stereo image sequences are used to train the model while monocular images are required for inference. The presented framework is composed of two CNNs (depth CNN and pose CNN) which are trained concurrently and tested independently. The objective function is constructed on the basis of the epipolar geometry constraints between stereo image sequences. To improve the accuracy of the model, a left-right consistency loss is added to the objective function. The use of stereo image sequences enables us to utilize both spatial information between stereo images and temporal photometric warp error from image sequences. Experimental results on the KITTI and Cityscapes datasets show that our model not only outperforms prior unsupervised approaches but also achieving better results comparable with several supervised methods. Moreover, we also train our model on the Euroc dataset which is captured in an indoor environment. Experiments in indoor and outdoor scenes are conducted to test the generalization capability of the model

Changes in Drought Characteristics in the Yellow River Basin during the Carbon-Neutral Period under Low-Emission Scenarios

Droughts have a severe impact on the environment and social economy, and predicting their future changes is challenging due to significant uncertainties in climate change and human activities. Many countries have pledged to achieve carbon neutrality to limit global warming; however, few studies have focused on drought changes during the carbon-neutral period. Here, we analyzed the variations in drought characteristics across the Yellow River Basin (YRB) during the carbon-neutral period under two low-emission scenarios from 7 CMIP6 model outputs. The results show that the temperature and precipitation will increase significantly during the 2015–2100 period under both SSP1-1.9 and SSP1-2.6 scenarios. Compared to the historical period (1979–2014), the hydrological drought frequency is projected to decrease by 15.5% (13.0–18.1%), while drought severity is expected to increase by 14.4% (13.2–15.7%) during the carbon-neutral period. Meteorological droughts exhibit a similar changing trend, although the results vary between different regions. In general, more severe hydrological droughts may occur in the southern YRB in the carbon-neutral period under low-emission scenarios. This study has implications for future drought mitigation within the Yellow River Basin

Influence of the Cross-Sectional Shape and Corner Radius on the Compressive Behaviour of Concrete Columns Confined by FRP and Stirrups

Axial compression tests were carried out on 72 FRP (fiber reinforced polymer)–stirrup composite-confined concrete columns. Stirrups ensure the residual bearing capacity and ductility after the FRP fractures. To reduce the effect of stress concentration at the corners of the confined square-section concrete columns and improve the restraint effect, an FRP–stirrup composite-confined concrete structure with rounded corners is proposed. Different corner radii of the stirrup and outer FRP were designed, and the corner radius of the stirrup was adjusted accurately to meet the designed corner radius of the outer FRP. The cross-section of the specimens gradually changed from square to circular as the corner radius increased. The influence of the cross-sectional shape and corner radius on the compressive behaviour of FRP–stirrup composite-confined concrete was analysed. An increase in the corner radius can cause the strain distribution of the FRP to be more uniform and strengthen the restraint effect. The larger the corner radius of the specimen, the better the improvement of mechanical properties. The strength of the circular section specimen was greatly improved. In addition, the test parameters also included the FRP layers, FRP types and stirrup spacing. With the same corner radius, increasing the number of FRP layers or densifying the stirrup spacing effectively improved the mechanical properties of the specimens. Finally, a database of FRP–stirrup composite-confined concrete column test results with different corner radii was established. The general calculation models were proposed, respectively, for the peak points, ultimate points and stress–strain models that are applicable to FRP-, stirrup- and FRP–stirrup-confined concrete columns with different cross-sectional shapes under axial compression

Compressive Behavior of Bamboo Sheet Twining Tube-Confined Concrete Columns

This study experimentally investigated various axial compressive parameters of a new type of confined concrete, which is termed bamboo sheet twining tube-confined concrete (BSTCC). This new composite structure was composed of an outer bamboo composite tube (BCT) jacket and a concrete core. Under axial compression, the parameters of thirty-six specimens include concrete strength (i.e., C30 and C50) and BCT thickness (i.e., 6, 12, 18, 24, and 30 layers). The mechanical properties of the BSTCC specimens from the perspective of the failure mode, stress-strain relationship, effect of BCT thickness and dilation behavior were analyzed. The results showed that, in compression, with an increase in BCT thickness in the range of 18-layers of bamboo sheets, the strength increased remarkably. When the strength of the concrete core was high, the confinement effect of the BCT was reduced. In addition, the BCT thickness relieved the dilation of the BSTCC specimens. Finally, the experimental results were compared with predictions obtained from 7 existing FRP-confined concrete models. All the predictions had good agreement with the test results, which further confirmed that the models developed for FRP-confined concrete can provide an acceptable approximation of the ultimate strength of the BSTCC specimens