ID 原文 译文
19185 定义VSN的状态集、行为集、状态转移概率，考虑传感网能量受限以及完成VSNR的时间，给出奖赏函数的表达式，并使用免模型强化学习算法求解特定状态下的行为，从而最大化网络资源提供者的长期收益。 Then, difining the state, action, and transition probability of the VSN, the expected reward is given by considering the energy and time to complete theVSNR, and the model-free reinforcement learning approach is used to maximize the long-term reward of the network resource provider.
19186 数值结果表明，该文的资源分配策略能有效提高传感网资源提供者的收益。 The numerical results show that the resource allocation strategy of this paper can effectively improve the revenue of the sensor network resource providers.
19187 面对多变的网络环境，现有的网络服务质量(QoS)映射中流聚集方法缺乏灵活性。 Facing changeable network environment, current Quality of Service (QoS)-aware flow aggregation scheme is lack of flexibility.
19188 针对现有聚集方法的缺陷，该文提出一种动态聚集方法。 A dynamic flow aggregation method to overcome present problems is proposed.
19189 使用增强粗糙K均值算法(ERKM)，按照网络流的QoS属性将网络流进行合理聚集，并且在网络处于高负载状况时，通过隶属度弹性聚集网络流，从而适应网络的变化，使得网络流聚集具有灵活性。 An Enhanced Rough K-Means (ERKM) algorithm is used to aggregate network flows properly. Importantly, it is able to adjust degree of membership to face ever-changing internet environment to make algorithm more flexible.
19190 最后进行了网络流聚集实验和调度实验。 Internet scheduler experiment is carried out and a comparison is made with existing methods.
19191 实验表明，相比于现有的方法，该方法能够更加弹性地应对不同网络状态，并且更好地保障网络流的QoS指标。 Experimental results suggest that proposed method has advantages not only on flexibility of aggregation, but also on assurance of QoS of Internet flows.
19192 此外，还进一步验证了该文方法在不同网络环境下的QoS类聚集的一致性。 In addition, the consistency of QoS allocation under differentnetwork environment is investigated.
19193 针对机动平台大斜视(HS) SAR存在的方位相位系数空变特性，该文提出一种基于多普勒重采样的改进谱分析(SPECAN)成像方法。 A modified SPECtral ANalysis (SPECAN) algorithm based on Doppler resampling is proposed to deal with the azimuth Space-Variant (SV) phase coefficients of the High Squint (HS) SAR data acquired from maneuvering platform.
19194 首先，对于恒加速度HS SAR，给出了一种正交坐标斜距模型，可以处理传统方法中距离走动校正(RWC)引起的坐标旋转，解决了斜距模型与信号之间的失配问题。 Firstly, for HS SAR with constant acceleration, an orthogonal coordinate slant range model is presented, which can handle the coordinate rotation caused by the traditional method of Range WalkCorrection (RWC), and solve the mismatch between the range model and the signal after RWC.