ID 原文 译文
25695 然后训练 BiLSTM-CＲF 深度学习模型并预测所有未曾标注的地址信息的序列标注; Then the BiLSTM-CＲF deep learning model is trained and all unlabeled annotation sequence is predicted．
25696 最后再次利用 KNN-CA 优化可求解 EBM 的序列标注，由此构建适用于中文地理命名实体(Chi-nese Geospatial Named Entities，CGSNE)识 别 及 相 关 研 究 的 序 列 标 注 语 料 库。 Eventually, the annotation sequence of solvable EBM is optimized via KNN-CA again so as to construct a sequence annotatied corpus dataset which is suitable for the identification of Chinese Geospatial Named Entities (CGSNE) and related researches．
25697 实 验 表 明，标 注 数 据 的 F1 值 达 到了 95. 35% 。 The experiment demonstrates that F1 score of labeled data reaches 95. 35% ．
25698 多机器人路径规划是机器人领域的一个热点问题，相比于单机器人路径规划，其算法难度和复杂度都有所增加，在规划时需要兼顾多机避障、相互协作等难点问题。 Multi-robot path planning is one of the most attractive issues in the field of multiple robots. Compared with the algorithm for the single-robot path planning, the problem of multi-robot path planning, which takes obstacle avoidance and cooperation into account simultaneously, is more difficult and complex．
25699 本文提出一种改进快速扩展随机树的多机器人编队路径规划算法，用于解决多机器人在复杂环境下的编队路径规划问题。 Hence, a novel improved rapidly-exploring random tree algorithm for multi-robot formation path planning in this paper is proposed to address these obstacle avoidance and cooperation problems．
25700 针对多机器人在编队规划中的位置约束问题，定义机器人之间的领航-跟随结构，并对机器人队形建模。 The constraint condition of positional relationship is defined by modeling the multi-robot formation shape．
25701 针对规划过程中编队朝向变化问题，建立搜索树扩展方向与队形方向之间的联系，通过调整队形方向改变规划时的编队朝向。 Besides, the heading of formation is adjusted with the direction the exploring tree expands．
25702 针对具有质点模型和非完整约束动力学模型两种不同模型的多机器人系统，分别进行了仿真实验。 Additionally, simulations are conducted for two different models of robots: the particle model and the non-holonomic constraint dynamic model.
25703 仿真结果表明该算法在处理多机器人编队路径规划问题时可以取得良好的效果。 Simulation results are provided to demonstrate the effectiveness of the proposed algorithm．
25704 已有的支持在线 /离线加密和外包解密的基于属性加密的方案可实现细粒度访问控制和数据保密性，但无法实现同一属性之间的层次关系的表达和数据防篡改，并且终端需要在离线加密之前确定用户的访问结构，每次加密都需重新生成中间密文。 Existing attribute-based encryption schemes which support online/offline outsourcing encryption and de-cryption can realize fine-grained access control and data confidentiality, but it cannot achieve the expression of hierarchical relationships between the same attributes and prevent data from being tampered. Besides, the terminal needs to determine the user's access structure before offline encryption, and the intermediate ciphertext needs to be regenerated every time．