Abstract Wheel measurement and positioning technology is an important technology in vehicle production, and is of great importance for the safety of vehicles. At present, visual measurement and other methods are widely used in automotive production and inspection, but these methods are limited to the measurement of regular-sized vehicles. There is no effective solution to the problem of wheel alignment for large special-purpose vehicles with multiple tires. In order to solve the wheel positioning problem of large-size special-purpose vehicles, this paper designs a vision measurement system for wheel parameters in large scenes by combining vision sensors with linear motion guides to extend the vision measurement range and complete the global calibration of multiple-vision sensors with the help of laser trackers and self-researched stereo targets. This paper proposes an Elementary–Advance global calibration algorithm to improve the accuracy of coordinate system conversion during global calibration. This paper establishes a correction model for the measurement errors that exist in the initial global calibration process, and iterates the objective function to optimize the coordinate system conversion parameters between multiple vision sensors to improve the measurement accuracy of the system. The experimental results show that the goodness of fit and the accuracy of fit for the feature cylinder are 98.31% and 99.03% based on the global calibration method of this paper. The standard deviation of measurements for the standard ruler of the Inva alloy is less than 0.391 mm in the large dimensional range of 6050 mm × 3500 mm × 800 mm. Finally, by comparing the measurement results with the TrukCam four-wheel alignment instrument, the feasibility of the visual measurement system designed in this paper for large scenarios of wheel parameters is verified, and the measurement accuracy requirements for four-wheel alignment of special-purpose vehicles are met.
Fast dot-ELISA (FD-ELISA) was established on the basis of dot-ELISA for the diagnosis of schistosomiasis japonica. 96 sera speimens from patients with schistosomiasis, 88 (91. 7%) were positive by this method: while with 20 sera specimens from patients with clonorchiasis sinensis and 30 sera specimens from healthy indivduals, only 1 specimen was positive respectively. The result showed that the specificty and sensitivity of FD-ELISA were similar to those of dot-ELISA, but this method had the advantage of being fast (taking-less than half hour), simple and saving reagents. It showed to be very suitable for field application.
Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on information propagation schemes. However, existing propagation-based methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality user and item representations. In this paper, we propose a new model, called Hierarchy-Aware Knowledge Gated Network (HAKG), to tackle the aforementioned problems. Technically, we model users and items (that are captured by a user-item graph), as well as entities and relations (that are captured in a KG) in hyperbolic space, and design a hyperbolic aggregation scheme to gather relational contexts over KG. Meanwhile, we introduce a novel angle constraint to preserve characteristics of items in the embedding space. Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns. Experimental results on three benchmark datasets show that, HAKG achieves significant improvement over the state-of-the-art methods like CKAN, Hyper-Know, and KGIN. Further analyses on the learned hyperbolic embeddings confirm that HAKG offers meaningful insights into the hierarchies of data.
Recently, deep learning has attracted more and more attention in phase unwrapping of fringe projection three-dimensional (3D) measurement, with the aim to improve the performance leveraging the powerful Convolutional Neural Network (CNN) models. In this paper, for the first time (to the best of our knowledge), we introduce the Transformer into the phase unwrapping which is different from CNN and propose Hformer model dedicated to phase unwrapping via fringe order prediction. The proposed model has a hybrid CNN-Transformer architecture that is mainly composed of backbone, encoder and decoder to take advantage of both CNN and Transformer. Encoder and decoder with cross attention are designed for the fringe order prediction. Experimental results show that the proposed Hformer model achieves better performance in fringe order prediction compared with the CNN models such as U-Net and DCNN. Moreover, ablation study on Hformer is made to verify the improved feature pyramid networks (FPN) and testing strategy with flipping in the predicted fringe order. Our work opens an alternative way to deep learning based phase unwrapping methods, which are dominated by CNN in fringe projection 3D measurement.
In close-range photogrammetry, it is difficult to meet the measurement requirements of large scenes in actual engineering due to the limited capacity of coded targets. To expand the capacity of the coded target, we propose a binary step-response serial-coded target (BSSCT). The BSSCT introduces periodic binary wave information as an additional feature in the dot-dispersing coded target. Also, a robust recognition algorithm for the BSSCT is developed, the P2-Invariant, and the step period is used for decoding. The capacity of the coded target reaches 7 magnitudes without increasing the auxiliary points. Under different lighting conditions and viewing angles, the measurement experiments show that the BSSCT outperforms other state-of-the-art coded targets. Our BSSCT is a promising standard method for large field system calibration and object measurement.
The Haihe River Basin (HRB), located in northern China with a drainage area of 318,200 km, is one of the most developed regions in China. With rapid population growth and economic development, the combined problems of water shortage and groundwater overpumping significantly constrain the sustainable development in this area. In order to strengthen the unified management of groundwater and surface water, we developed hydrologic modeling of surface water and groundwater interaction by coupling SWAT (for surface water simulation) and MODFLOW (for groundwater simulation). The newly developed modeling framework reasonably captured the spatiotemporal variability of the Research Article British Journal of Environment & Climate Change, 3(3): 421-443, 2013 422 hydrological processes of the surface water and groundwater in the study area. The modeling results showed a good agreement with the measurements of surface water and groundwater during 1996-2006. Results of model evaluation indicated that the developed model could be a promising tool in watershed management planning under the context of global climate change and the “South-North Water Transfer Project”. In the HRB, climate change has significant effects on surface hydrology as indicated by the predicted increases on actual evapotranspiration and precipitation during 2041-2050 relative to those during 1991-2000. Changes of groundwater storage were mainly contributed by water diversion which would reduce the requirement of water pumping from groundwater especially for domestic and industrial uses. By the middle of the 21 century, increased water supply by projected precipitation and water diversion would result in annual increases of 3.9~9.9 billion m for river discharge and 1.7~2.9 billion m for groundwater storage as annual averages.
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