ID 原文 译文
45266 随着用户对流量需求的指数级增长，运营商密集地部署微蜂窝来满足用户的服务质量， With the exponentially increasing of users’ demand for mobile data traffic, massive small cells have been deployed to satisfy the users’ quality of service (QoS) by operators.
45267 然而这会引发巨大的能耗。 However, a significant energy would be consumed caused by dense deployment.
45268 基于此，在满足用户服务质量（QoS, quality of service）的条件下，提出一种密集的异构蜂窝网络的部署策略。 To this end, a dense heterogeneous cellular network deployment strategy was proposed with Qos guarantee to decline system energy consumption.
45269 首先，采用密集 Macro-Femto 偏置蜂窝网络建立模型，利用随机几何理论分析信干燥比（SINR）覆盖率和平均用户速率这 2 种 QoS 指标。 Firstly, a dense Macro-Femto biased cellular network was used to build the network model, the two QoS indicators of SINR coverage and user average rate was analyzed by stochastic geometry theory.
45270 然后，在满足 QoS 约束条件下，以平均区域功耗（ASPC, average spatial power consumption）作为优化目标，运用经典的最优化理论得到 Femto 的最优部署密度和发射功率。 Then, under the condition of QoS constraint, average spatial power consumption (ASPC) was taken haste optimization goal, optimal deploy density and transmission power of Femto were achieved by using optimization theory.
45271 最后，通过仿真实验证明，与传统单独考虑基站密度的策略相比，提出的部署策略可以在保证 QoS 的同时，获得更高的能效。 Finally, the experimental results show that, comparing with the traditional strategy which only considering base station density, the proposed strategy has advantages in terms of energy efficiency while QoS guarantee.
45272 针对远程监督的基本假设过强容易引入噪声数据的问题，提出了一种可以对远程监督自动生成的训练数据去噪的人物实体关系抽取模型。 Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data, a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was proposed.
45273 在训练数据生成阶段，通过多示例学习的思想和基于 TF-IDF 的关系指示词发现的方法对远程监督产生的数据进行去噪处理，使训练数据达到人工标注质量。 For training data generation, the data produced by distant supervision would be filtered by multiple instance learning and the method of TF-IDF-based relation keyword detecting, which tried to make the training data has the manual annotation quality.
45274 在模型分类器中，提出采用词法特征和句法特征相结合的多因子特征作为关系特征向量用于分类器的学习。 Furthermore, the model combined lexical and syntactic features to extract the effective relation feature vector from two angles of words and semantics for classifier.
45275 在大规模真实数据集上的实验结果表明，所提模型结果优于同类型的关系抽取方法。 The experiment results on large scale real-world datasets show that the proposed model outperforms other relation extraction methods which based on distant supervision.