• The results indicate the proposed algorithm is effective.

    最后的数值实验也表明了算法的有效性。

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  • The proposed algorithm improves the scheduling success ratio very much.

    这种方法极大地提高了算法的调度成功率。

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  • The proposed algorithm will ease users? Load to solve practical problem.

    该算法的提出,大大减轻了用户解决具体应用问题的负担。

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  • The single harmonic can be detected by extending the proposed algorithm.

    若进行扩展,该算法还能应用于单次谐波的检测。

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  • As a result, the proposed algorithm has global and superlinear convergence.

    作为结果,算法具有全局和超线性收敛性。

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  • The proposed algorithm comprises of spatial and temporal segmentation modules.

    该算法由空域分割和时域分割组成。

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  • Experimental results show that the proposed algorithm solves footstep slip effectively.

    实验结果表明此算法有效地解决了脚步滑动现象。

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  • The experimental results show that the proposed algorithm has good robustness and invisibility.

    实验结果表明,本算法具有较好的鲁棒性和不可见性。

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  • Experiment results on large image database demonstrate the effectiveness of the proposed algorithm.

    在海量图像数据库上的实验结果说明了该算法的有效性。

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  • The proposed algorithm not only has lithe overhead but also ADAPTS to the changes of channel state.

    本文所提出的算法不仅具有较小的控制协议开销,而且对信道状态的变化具有自适应能力。

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  • Base on image correlation, the proposed algorithm correct transmissive error with full search method.

    该方法以图像相关性作为判别依据,使用全搜索的方法来恢复数据。

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  • Experimental results show that the proposed algorithm noticeably improves the performance of tracking.

    实验结果表明,该方法明显提高了跟踪性能。

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  • Experimental results show that the proposed algorithm can achieve better results with strong robustness.

    实验结果表明,该方法能取得较好的结果,并具有较强的鲁棒性。

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  • The proposed algorithm has a faster convergence speed, for it USES the information of former iterations.

    该算法充分利用了迭代过程中的前几次迭代信息,从而使算法收敛速度更快。

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  • Monte Carlo simulation results show the efficiency and high location precision of the proposed algorithm.

    通过蒙特卡罗模拟,证明了该算法的有效性和定位精度较高。

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  • The proposed algorithm can extract the watermark without the help from the original digital audio signal.

    该算法提取水印信息时不需要原始数字音频信号。

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  • Furthermore, by using an error bound condition, we establish linear convergence of the proposed algorithm.

    在误差界假设条件下,我们得到了改进算法的线性收敛速度。

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  • It is shown that the proposed algorithm has the virtues of strong anti-jamming capability and high precise.

    实验表明,该自动提取算法具有一定的抗干扰能力和较好的检测精度。

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  • Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.

    最后模拟试验结果说明所提出的算法可以有效地解决网格中任务死锁问题。

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  • The experimental results show that the proposed algorithm can improve machining efficiency and surface quality.

    试验结果表明,该算法可有效地提高自由曲面的加工效率和表面加工质量。

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  • Experimental result show that the proposed algorithm is competent and has well prospects to this set of problems.

    实验结果揭示了所提算法是解决大规模模式识别问题的一个有效且具有良好前景的方法。

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  • The network structure and handover process of the proposed algorithm are described, and its performance is analyzed.

    文中给出了算法实现的网络结构及其切换过程,并且对其性能进行了分析。

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  • The proposed algorithm incorporates the thoughts of heuristic algorithm, randomized algorithm and local optimization.

    这种算法结合了启发式算法和随机化算法以及局部寻优的思想。

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  • The experimental results on synthetic datasets show that the proposed algorithm has low error rate and high efficiency.

    模拟数据集上的实验结果表明,该算法具有较低的误差率和较高的效率。

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  • Compared with the original algorithm, the proposed algorithm has advantages of low complexity and lower decoding delay.

    与原算法相比,简化算法具有低复杂度、译码延时少的优点。

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  • The experiment results revealed the proposed algorithm is computationally efficient while keeping better image quality.

    实验结果表明,算法在降低计算复杂度的同时,保持了较好的图像质量。

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  • Experimental results show that the proposed algorithm can track automatically and correct the excursion during tracking.

    实验结果表明,本方法可以实现对目标的自动跟踪,同时有效修正了对红外目标的跟踪偏移。

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  • Simulated experiments show that the proposed algorithm can obtain a better video quality than the other search algorithms.

    大量模拟实验表明:该算法能获得更好的视频质量。

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  • The proposed algorithm is then compared with the commonly used DR algorithm, and we show that our algorithm is very effective.

    最后,将提出的这种自适应DR算法与普通DR算法进行了比较研究。

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  • Experiments with UCI datasets show that the performance of the proposed algorithm is improved with high correct recognition rate.

    在UCI数据库上进行了实验,结果表明所提出的算法具有较高的识别率。

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