ID 原文 译文
23695 在合成数据集和真实数据集上的实验表明，所提算法扩展了传统可能性聚类性能，改进了聚类结果。 Experimental results on synthetic and real data sets show that the proposed method extends the traditional possibilistic clustering performance, and improves the clustering results.
23696 为了有效地保证智能电网中业务的隔离性和解决智能电网无线资源高效分配问题，该文借助网络虚拟化技术，提出一种面向电力无线虚拟专网的资源优化分配机制。 To guarantee the isolation of the smart grid business and optimize the allocation of wireless resources, an optimal resource allocation mechanism for electric power wireless virtual networks is proposed.
23697 首先，根据电力无线专网的特点，提出电力无线专网虚拟化系统模型，并通过建立网络模型抽象物理无线资源，实现资源共享； First, a virtualization system model according to the characteristics of electric power wireless network is proposed, and abstract physical wireless resource is built to realize resource sharing.
23698 然后，综合网络成本、利润、业务隔离性约束、回程容量约束、QoS 约束等因素，提出配用电接入网无线资源分配模型，且设计一种禁忌搜索的算法，在满足业务隔离和业务 QoS 要求的情况下，解决了配用电无线专网最优化虚拟资源分配。 Then, a wireless resource allocation model with several factors is designed, which are network cost, profit, service isolation constraint, backhaul bandwidth constraint, and QoS constraints. Finally, a tabu search algorithm based on these models is designed to allocate virtual resource to realize business isolation and QoS requirements.
23699 仿真实验表明，该文提出的基于禁忌搜索的电力无线虚拟专网资源优化分配机制，在满足业务 QoS 要求的情况下，降低了基站能耗，并提高了网络运行的经济效益。 The simulation results show that, the proposed network model and optimal resources allocation mechanism can support QoS requirements, reduce the energy consumption of base stations, as well as improve the economic benefits of the network.
23700 视频压缩感知在采集端资源受限的视频采集应用场景有重要研究意义。 Compressed Video Sensing (CVS) has great significance to the scenarios with a resource-deprived video acquisition side.
23701 重构算法是视频压缩感知的关键技术，基于多假设预测的“预测-残差重构”框架具有良好的重构性能。 Reconstruction algorithm is the key technique in compressed video sensing. The Multi-Hypothesis (MH) prediction-based "prediction-residual reconstruction" framework has good reconstruction performance.
23702 但现有的多假设预测算法大多在观测域提出，这种预测方法由于受到不重叠分块的限制，造成了预测帧的块效应，降低了重构质量。 However, most of the existing multi-hypothesis prediction algorithms are proposed in measurement domain, which cause block artifacts in the predicted frames and decrease reconstruction accuracy due to the restriction of non-overlapping block partitioning.
23703 针对此问题，该文将像素域多假设预测与观测域多假设预测相结合，提出两级多假设重构思想(2sMHR)，并设计了基于图像组(Gw_2sMHR)和基于帧(Fw_2sMHR)的两种实现方法。 To address this issue, this paper proposes a two-stage Multi-Hypothesis Reconstruction (2sMHR) idea by incorporating the measurement-domain MH prediction with pixel-domain MH prediction. Two implementation schemes, GOP-wise (Gw) and Frame-wise (Fw) scheme, are designed for the 2sMHR.
23704 仿真结果表明，所提 2sMHR 重构算法能有效减小块效应，相比于现有最好的多假设预测算法具有更低的时间复杂度和更高的视频重构质量。 Simulation results show that the proposed 2sMHR algorithm can effectively reduce block artifacts and obtain higher video reconstruction accuracy while requiring lower computational complexity than the state-of- the-art CVS prediction methods.