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Purpose: In this paper, a three-dimensional model of the disc brake pair considering the particle flows is firstly established, and the finite element simulation analysis of the brake pair friction performance is carried out to obtain the influence law of different braking parameters on the macroscopic friction coefficient and standard deviation. Methods: The macroscopic friction coefficient in the interface of brake pairs decreases from 0.4 to 0.152 with the increase of particle size, from 0.4 to 0.131 with the increase of particle density, and from 0.028 to 0.385 with the increase of rough peak radius of brake block. It was also found that the macroscopic friction coefficient gradually increased with the increase of the ratio of the rough peak radius of the brake block to the particle size, and the friction coefficient changed most drastically at its size ratio of about 1.5. By analyzing the change mechanism of the brake pair friction performance, it is believed that it is related to the embedding and deformation of the third body particles, and the simulation results are verified by bench tests. Results: Finally, a macroscopic friction coefficient model considering the third body particles and interface parameters were established, and the steady-state macroscopic friction coefficients under different braking parameters were calculated, and the R-square values of the models were above 0.9. Conclusion: This study can provide a reference for the braking pair friction algorithm of intelligent vehicles driving in sandy and dusty road conditions. [ABSTRACT FROM AUTHOR]

Electronic medical record named entity recognition can extract important clinical information from unstructured text, which is helpful for clinical diagnosis and medical decision-making. However, due to the particularity of the medical field, it is difficult for researchers to obtain sufficient labeled electronic medical records. Models trained using traditional supervised learning methods with insufficient data are not promising. To solve this problem, this paper proposes two weakly supervised learning methods, sampling-based active learning and parameter-based transfer learning, to achieve better performance. In sampling-based active learning, two uncertainty sampling strategies, least confidence sampling and entropy sampling, are used to select data from unlabeled dataset for retraining. In parameter-based transfer learning, the parameters of word representation layer and encoding layer in the source domain are initialized to the corresponding layer of the target domain, and the objective is to learn generalized linguistic knowledge from the source domain. Finally, we use a voting mechanism to ensemble these individual models to get better prediction results. Experiment on the CCKS2017 official test set shows that our system for MER achieves 0.8972 F1 score and gets better performance than the supervised methods, which obtains 0.8921 F1 score and proves the effectiveness of our approaches. The experimental results show that the weakly supervised learning methods proposed in this paper achieve the satisfactory performance as the supervised methods under comparable conditions. [ABSTRACT FROM AUTHOR]

Combining the advantages of cryptography and digital watermarking, commutative encryption and watermarking (CEW) addresses the limitations of traditional information security technologies by simultaneously ensuring security and confirming copyright ownership. Existing CEW algorithms for vector geographic data cannot simultaneously meet the requirements of lossless and applicability to all types of vector geographic data. This investigation proposes a lossless CEW algorithm for all types vector geographic data. In the encryption scheme, all coordinate points are stored in a one-dimensional set for permutation encryption. This procedure is applicable to all types of vector geographic data. Then, the original coordinates are replaced with the encrypted coordinates according to the original spatial structure. Since encryption preserves the size of coordinate values, they can be gridded after normalization to ensure compatibility between encryption and watermarking. Subsequently, a characteristic matrix is generated by conducting singular value decomposition on the coordinate values within the grid. Finally, the XOR operation is executed between the encrypted watermark information and this matrix to complete the construction of the zero watermark. Experiments demonstrate that the encryption scheme can yield favorable encryption outcomes with just one scrambling, and the efficiency is greatly improved. The watermarking scheme is robust against most attacks on vector geographic data. [ABSTRACT FROM AUTHOR]