Error correction of semantic mathematical expressions based on bayesian algorithm.

The semantic information of mathematical expressions plays an important role in information retrieval and similarity calculation. However, a large number of presentational expressions in the presentation MathML format contained in electronic scientific documents do not reflect semantic information. It is a shortcut to extract semantic information using the rule mapping method to convert presentational expressions in presentation MathML format into semantic expressions in the content MathML format. However, the conversion result is prone to semantic errors because the expressions in the two formats do not have exact correspondences in grammatical structures and markups. In this study, a Bayesian error correction algorithm is proposed to correct the semantic errors in the conversion results of mathematical expressions based on the rule mapping method. In this study, the expressions in presentation MathML and content MathML in the NTCIR data set are used as the training set to optimize the parameters of the Bayesian model. The expressions in presentation MathML in the documents collected by the laboratory from the CNKI website are used as the test set to test the error correction results. The experimental results show that the average $ {F_1} $ value is 0.239 with the rule mapping method, and the average $ {F_1} $ value is 0.881 with the Bayesian error correction method, with the average error correction rate is 0.853.

Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.

A Pavement Crack Detection Method Based on Multiscale Attention and HFS.

To solve the problem of low detection accuracy due to the loss of detailed information when extracting pavement crack features in traditional U-shaped networks, a pavement crack detection method based on multiscale attention and hesitant fuzzy set (HFS) is proposed. First, the encoding-decoding structure is used to construct a pavement crack segmentation network, ResNeXt50 is used to extract features in the encoding stage, and a multiscale feature fusion module (MFF) is designed to obtain multiscale context information. Second, in the decoding stage, a high-efficiency dual attention module (EDA) is used to enhance the ability of capturing details of the cracks while suppressing background noise. Finally, the membership degree of the crack is calculated based on the advantages of the HFS in multiattribute decision-making to obtain the similarity of the crack, and the binary image after segmentation is judged by the hesitation fuzzy measure. The experiment was conducted on the public road crack dataset Crack500. In terms of segmentation performance, the evaluation indexes Intersection over Union (IoU), Precision, and Dice coefficients of the proposed network reached 55.56%, 74.26%, and 67.43%, respectively; in terms of classification performance, for transversal and longitudinal cracks, the classification accuracy was 84% ± 0.5%, while the block and the alligator were both 78% ± 0.5%. The experimental results prove that the crack details detected by the proposed method are more abundant, and the image detection effect of complex topological structures and small cracks are better.

The Simple Mix Design Method and Confined Behavior Analysis for Recycled Aggregate Concrete.

For the environment protection and sustainable development in building construction, waste concrete can be processed into recycled aggregate to mix the recycled aggregate concrete (RAC). However, the existing mix design methods of RAC were complex, and the mechanical properties of RAC were more weakened than ordinary concrete. This paper presents a simple mix design method for RAC, including orthogonal test and single-factor test. Then, in order to study the behavior of confined RAC, this paper presents a comprehensive experimental study on the RAC filled in steel tube (RCFST) specimens and the RAC filled in GFRP tube (RCFST) specimens. The results show that the proposed mix design method can mix different stable strength grades of RAC promptly and efficiently. In addition, the steel tube and GFRP tube can provide a well confining effect on core RAC to improve the mechanical behavior of column. Moreover, the properties of core RAC in steel tube are the same as the common passive confined concrete, and the properties of core RAC in the GFRP tube are the same as the common active confined concrete. The study results can provide reference for other kinds of RAC mixtures as well as be a foundation for theoretical studies on confined RAC.

[Application of computer-aided design in preoperative planning in total hip replacement].

OBJECTIVE: To investigate the advantages of using the preoperative computer-aided design system (CAD) in total hip replacement (THR). METHODS: From March 2002 to September 2005, 182 patients who underwent primary THR were screened and divided into 2 groups randomly. CAD and traditional preoperative templating were used in preoperative planning respectively. RESULTS: In group using CAD, the migration of the center of acetabulum was smaller, and the discrepancy of the limb length and the abductor force lever arm were fewer. All the differences above were significantly different. CONCLUSIONS: CAD helps remove much of the guesswork during surgery, and the implant can be more precise fitting to the patient. And equal limb lengths and balanced abductor force can be restored more accurately.