ID 原文 译文
58588 测试结果表明,当工作电压为15 V时,该放大器芯片在88~98 GHz范围内,典型小信号增益为20 dB,连续波状态下饱和输出功率大于250 mW。 Moreover, the saturated output power is more than 250 mW at the continuous-wave mode.
58589 在98 GHz下,芯片实现最大输出功率为405 mW,功率增益为13 dB,功率附加效率为14.4%。 At 98 GHz, a peak output power of 405 mW has been achieved with an associated power gain of 13 dB and a power-added-efficiency of 14. 4%.
58590 因此,该GaN功率放大器芯片输出相应的最大功率密度达到3.4 W/mm。 Thus, this GaN MMIC delivers a corresponding peak power density of 3. 4 W/mm at the W band.
58591 为解决在冲击干扰下信号的恢复问题,提出了一种利用lp范数约束的优化算法。 This work addresses the signal recovery problem in the presence of impulsive disturbance utilizing lp-norm optimization.
58592 因为lp(0 因此利用交替方向乘子法来有效解决该优化问题。 In doing so, the resultant optimization is difficult to solve, especially when 0 1, because it is nonconvex. In this work, the alternating direction method for multipliers steps is developed to efficiently obtain the solution from this optimization.
58593 文中分别利用迭代重加权最小二乘法和内点法求得了优化问题中对应优化变量的迭代方程, 并将该算法用于图像增强。 In each step of the alternating direction method for multipliers, the corresponding solutions are respectively obtained by utilizing the iteratively reweighted least squares and interior-point approach.
58594 数值仿真结果说明了相比于lp-ADM算法,加权lp范数约束优化算法有更好的恢复性能。 Numerical studies including an application of image enhancement demonstrate the superior performance of the proposed weighted estimation algorithms compared to the lp-ADM approach.
58595 针对现有检测方法对算法生成的恶意域名检测效率不高,尤其对几种难检测的恶意域名类型检测率低的问题,提出了一种改进的基于卷积神经网络的恶意域名检测算法。 Aiming at the problem that the existing detection methods are not efficient in detecting the malicious domain name generated by the algorithm, especially the detection rate of several types of malicious domain names that are difficult to detect is low, an improved algorithm for detection of the malicious domain name based on the convolutional neural network is proposed.
58596 该算法在现有的卷积神经网络模型的基础上,增加了提取更深层字符级特征的卷积分支,从而同时提取恶意域名的浅层和深层字符级特征并融合; Based on the existing convolutional neural network model, this algorithm adds convolutional branches to extract deeper character-level features, so that both shallow and deep character-level features of malicious domain names could be extracted and fused simultaneously.
58597 引入一种聚焦损失函数以解决样本难易程度和数量的双重不平衡导致检测率低的问题,可提高对难样本的检测准确率。 A focal loss function is introduced as a loss function to solve the problem of sample imbalance caused by difficulty and quantity, which is used to improve the detection accuracy of hard-to-detect samples.