ID 原文 译文
2323 性能评估实验引入了多种噪声类型，实验结果表明 PCNMF 可有效提高说话人确认系统的鲁棒性，特别是在未知和非平稳噪声条件下其等错率相比基线系统(Multi-Condition)平均降低了 5.2% 。 Our experiment takes unknown and unstable noises into account, demonstrating that the proposed PCNMF achieves significant improvement of robustness under various noise conditions. Particularly, the equal error rate of PCNMF isreduced by an average of 5. 2% in comparison with the base-line (Multi-Condition system).
2324 结合目标雷达散射截面积的随机性，该文提出了一种针对多目标定位的稳健功率分配算法。 Taking into account the probabilistic uncertainty on the target radar cross section parameter, a robust powerallocation scheme is presented for multiple target localization.
2325 目的是在高概率满足多目标定位精度约束的前提下，尽可能的节省集中式多输入多输出(Multiple-Input Multiple-Output，MIMO)雷达的功率资源。 The aim of this scheme is to minimize the total power con-sumption of the colocated MIMO radar, while meeting a specified multi-target localization accuracy requirement with high probability.
2326 该文首先推导了各个目标定位误差的克拉美罗界(Cramer-Rao Lower Bound，CRLB)。 Firstly, the Cramer Rao Lower Bound (CRLB)is derived.
2327 然后以最小化MIMO 雷达发射功率为目标，在满足多目标定位 CRLB 不大于给定误差的联合概率超过某一置信水平的条件下建立了机会约束模型。 Then, the chance constrained model is built with the objective of minimizing the total transmit power of the colocated MIMO radar, while the joint CRLB outage probability isenforced to be greater than a specified probability.
2328 通过构建问题的库恩塔克条件，该文将机会约束问题等效变换为非线性方程求解问题，并解析地给出了最优解表达式。 By formulating the Karush-Kuhn-Tuckers conditions, we transform the re-sulting chance constrained problem into a nonlinear equation solving problem, and then obtain its optimal solution in an ana-lytical form.
2329 最后，仿真实验验证了算法的有效性和稳健性。 Finally, the effectiveness and robustness of the proposed algorithm are verified by the simulation results.
2330 峰值功率是影响数据中心能效的一个重要因素。 Peak power is a critical factor on the power consumption of a data center.
2331 本文提出一种功率感知数据库系统中连接算法的峰值功率估算方法，非运行时峰值功率的估算的挑战在于没有运行时的系统信息作为模型的输入。 This paper proposes a peakpower estimation method to predict the peak power to join operations in DBM S. The challenge of non-runtime peak power estimation lies in that there is no runtime system information to use for model construction.
2332 为克服估算困难，提出使用 CPU 密集度作为 CPU 功耗指示量，理论上分析了异步 I/O 连接算法在峰值功率发生阶段的特性， To overcome this is-sue, this paper uses CPU-boundedness as the proxy of CPU power consumption and analyzes the characteristics of the peak power occurring stage of join algorithms with async I /O in theory.