ID 原文 译文
15225 电路通过调节死区最小化处理模块与非重叠模块，抑制功率器件的大电流直通现象，有助于提升电源模块的效率； By adjusting dead zone minimization processing module and non-overlapping module, this circuit can suppress the high-current pass-through phenomenon of power devices, which is helpful to improve the efficiency of power module.
15226 利用氮化镓器件的高频特性，使电源系统的工作频率大幅提升。 Using the high frequency characteristic of gallium nitride device, the working frequency of power supply system is increased greatly.
15227 电源系统测试结果表明，1 MHz时输出波形上升沿、下降沿时间分别为10 ns和5 ns; The test results of the power system show that the time of the output waveform rising edge and falling edge is 10 ns and 5 ns respectively at 1 MHz.
15228 10 MHz时输出波形上升沿、下降沿时间分别为14 ns和8 ns。 At 10 MHz,the time of output waveform rising edge and falling edge is 14 ns and 8 ns respectively.
15229 系统可实现10 W左右的大功率输出。 The system can achieve about 10 W high power output.
15230 1 MHz和10 MHz工作频率下系统达到的效率分别为93.7%和83.5%。 The efficiency of the system at 1 MHz and 10 MHz is 93. 7 % and 83. 5 % respectively.
15231 特征工程可以自动地处理和生成那些判别性高的特征，而无需人为的操作。特征工程在机器学习中是不可避免的一环，也是至关重要的一环。 Feature engineering can automatically process and generate those highly discriminative features without human operation.Feature engineering is an inevitable and crucial part of machine learning.
15232 提出一种基于强化学习(RL)的方法，将特征工程作为一个马尔可夫决策过程(MDP)，在上限置信区间算法(UCT)的基础上提出一个近似的方法求解二分类数值数据的特征工程问题，来自动获得最佳的变换策略。 The article proposes a method based on reinforcement learning(RL), taking feature engineering as a Markov decision process(MDP), and proposes an approximate method based on the upper limit confidence interval algorithm( UCT) to solve the feature engineering of binary numerical data problem to automatically obtain the best transformation strategy.
15233 在5个公开的数据集上验证所提出方法的有效性，FScore平均提高了9.032%，同时与其他用有限元变换进行特征工程的方法进行比较。 The effectiveness of the proposed method is verified on five public data sets. The FScore of the five public data sets is improved by an average of 9. 032 %. It is also compared with other papers that use finite element transfor-mation for feature engineering.
15234 该方法确实可以得到判别性高的特征，提高模型的学习能力，得到更高的精度。 This method can indeed obtain highly discriminative features, improve the learning ability of the model, and obtain higher accuracy.