ID 原文 译文
16875 仿真结果表明，PCOSA算法能找出用户所处MEC节点下与用户卸载表现最相近的k个用户形成匿名集，有效保护了所有用户隐私。 Simulation results validate that the PCOSA can find out k users with the closest offloading performance to form anonymous sets, which protects effectively the privacy of all users.
16876 机票动态定价旨在构建机票售价策略以最大化航班座位收益。 The core of the dynamic flight pricing is to yield a pricing strategy with maximum seat revenue.
16877 现有机票定价算法都建立在提前预测各票价等级的需求量基础之上，会因票价等级需求量的预测偏差而降低模型性能。 The state-of-the-art flight pricing approaches are built on forecasting the fare demand. They suffer low profit due to the inaccurate prediction.
16878 为此，提出基于策略学习的机票动态定价算法，其核心是不再预测各票价等级的需求量，而是将机票动态定价问题建模为离线强化学习问题。 To tackle the above issue, an approach for dynamic flight pricing based on strategy learning is proposed.
16879 通过设计定价策略评估和策略更新的方式，从历史购票数据上学习具有最大期望收益的机票动态定价策略。 That approach resorts to reinforcement learning to output pricing strategy with the highest expected return.
16880 同时设计了与现行定价策略和需求量预测方法的对比方法及评价指标。 That strategy is learned by iteratively policy evaluation and policy improvement.
16881 在两趟航班的多组定价结果表明：相比于现行机票销售策略，策略学习算法在座位收益上的提升率分别为30.94%和39.96%，且比基于需求量预测方法提升了6.04%和3.36%。 The rate of profit improvement on the two flights is empirically 30.94% and 39.96% over the existing pricing strategy, while that rate is 6.04% and 3.36% over the demand forecasting algorithm.
16882 该文提出了基于超像素级卷积神经网络(sp-CNN)的多聚焦图像融合算法。 This paper proposes a multi-focus image fusion algorithm based on super pixel-level ConvolutionalNeural Network (sp-CNN).
16883 该方法首先对源图像进行多尺度超像素分割，将获取的超像素输入sp-CNN，并对输出的初始分类映射图进行连通域操作得到初始决策图； In this method, multi-scale super pixel segmentation is firstly applied to the sourceimage to obtain the super pixels.
16884 然后根据多幅初始决策图的异同获得不确定区域，并利用空间频率对其再分类，得到阶段决策图； Secondly, the sp-CNN is proposed to acquire the initial decision maps.Thirdly, according to the similarities and differences of the multiple initial decision maps, the uncertain regionis reclassified by spatial frequency to obtain the phase decision map.