ID	原文	译文
53617	通过优化小区内的切换门限，可以最小化系统总资源开销。	The optimized intra-cell handover threshold can minimize the total system re- source consumption for the given real-time traffic requrements.
53618	通过优化小区间的切换门限，能够保证用户实时业务需求的同时均衡网络负载。	The designed inter-cell handover threshold can improve the QoS of mobile users and balance the network load.
53619	通过预测用户未来大尺度信道信息，设计主动的双门限切换策略，可以有效降低切换延迟。	By predicting the large-scale channel information of users in the future, designing an active dual-threshold handover strategy can effectively reduce the handover delay.
53620	仿真结果表明，与现有的切换策略相比，所提算法不仅提高了资源利用率，还降低了用户掉线概率与切换次数。	The simulation results show that compared with existing handover methods, the proposed proactive handover strategy can effectively reduce the outage probability, reduce the number of user handovers, improve user experience, and reduce system resource consumption.
53621	针对目前瓦斯浓度预测与瓦斯安全状态分类方法中主观性较强、超参数难以选取、解释性差、无法有效地利用样本之间时序信息等问题，本文提出了基于高斯过程混合模型的瓦斯浓度预测与安全状态分类方法。	In order to solve the problems of subjectivity, difficulty in selecting hyper-parameters, poor interpretability, and ineffective use of temporal information among samples on the existing methods of gas concentration prediction and gas safety state classification, this paper proposes a novel gas concentration prediction and safety state classification method based on the mixture of Gaussian processes.
53622	高斯过程是机器学习领域中解决非线性回归问题的典型方法，能够有效地利用数据之间的相关性，常用于时间序列的建模与预测。	In fact, the Gaussian process model is a classic method to solve nonlinear regression problems in machine learning. It can effectively get the correlations between temporal data and thus is often used in time se- ries modeling and prediction.
53623	然而，单个高斯过程存在着一定的局限性，难以对非平稳、多模态的数据进行有效地建模和回归分析。	However, a single Gaussian process has certain limitations, and cannot model the data gener- ated from a non-stationary source.
53624	在高斯过程的基础上引入其混合模型，则可增强模型的表达能力，能够对有复杂结构的数据进行建模。	The Mixture of Gaussian processes ( MGP) can enhance the model capacity, and fit the data with a complex structure.
53625	我们将瓦斯安全状态根据风险由高至低分成红橙黄蓝四个等级，在每个风险等级上瓦斯浓度数据采用单个高斯过程进行建模。	We try to divide the gas safety status into four levels according to the risk from high to low, namely, red, orange, yellow, and blue. Since the gas concentration data in each risk level are generated by their specific time sequence characteristics, they can be modeled by a single Gaussian process.
53626	由于一般瓦斯浓度数据包含着各个风险等级的数据，高斯过程混合模型则可用于对整体数据进行建模和回归分析。	Because the general gas concentration data come from four risk levels, the MGP can be used to model the whole data.