ID 原文 译文
40806 本文采用二阶结构并借助Matlab SDToolbox设计一种使用开关电容电路结构实现的二阶16bit高精度Sigma-Delta调制器。 In this paper, a second-order 16 bit high-precision sigma delta modulator with switched capacitor circuit structure is designed by using the second-order structure and MATLAB SDtoolbox.
40807 通过对调制器的输出结果进行验证证明其具有16bit精度，从而降低了14bit以上调制器的阶数。 The output results of the modulator are verified to have the accuracy of 16 bit, thus reducing the order of the modulator above 14 bit.
40808 通过对实际晶体管级电路的仿真，验证了该方法的有效性。 The effectiveness of the proposed method is verified by the simulation of a practical transistor level circuit.
40809 为了提升电感电流断续模式(discontinuousconductionmode， DCM)下开关电源转换器的瞬态响应性能，提出了一种基于电荷平衡原理和平均电流的差分外推控制方法。 In order to improve the transient response of the switching converter indiscontinueous conducting mode(DCM), a differential extrapolation control method is proposed based on the charge balance principleusing Boost convertor as an example.
40810 以Boost转换器为例，该方法利用采样的输出电压进行差分外推(differential extrapolation， DE)，预估负载对电荷的影响，进而通过平衡算法得到电流控制量。 This method uses the variation of sampled output voltage to carry on an extension, which indicates the load influence on the output capacitor charge, and then obtains the current control quantity by the balance algorithm.
40811 相较传统的使用电荷平衡原理的电流控制方法(charge balance principle based current control， CBPC)，差分外推控制方法缩短了负载估计的延迟，减小了负载估计误差，为DCM转换器的控制设计提供了有效参考。 Compared with the traditional charge balance principle based current control(CBPC), this method reduces control delay and the transient instability caused by the approximate of load information.The method also providesreferencefor thedesign of DCM controllers.
40812 通过Boost样机实验对比了CBPC控制与DE控制在输入、负载和参考电压发生扰动时的响应波形，结果表明，本文提出的DE控制对扰动的响应更加优异。 The response waveforms of CBPC control and DE control are comparedby Boost prototype, when input, load and reference voltage are disturbed.Among these experiments, the response is better when using DE control proposed in this paper.
40813 为了解决由非技术性损失所造成的用户用电异常问题，本文提出了一种基于双向长短时记忆神经网络(Bi-LSTM)的用户异常用电行为检测方法。 To solve the problem of abnormal user power consumption caused by non-technical losses, a method for detecting abnormal power consumption behavior based on a bidirectional long-term short-term memory neural network(Bi-LSTM) is proposed.
40814 该方法首先采用插值法处理用电缺失数据，并通过分位数归一化平滑用电异常行为值的不对称分布性。 In this method, firstly, the missing data is processed by interpolation method, and the asymmetric distribution of abnormal electricity consumption behavior value is smoothed by quantile normalization.
40815 然后，结合LSTM神经网络单元和双向网络构建Bi-LSTM模型，用于获取用户异常用电行为中的隐含关系。 Then, the Bi-LSTM model is constructed by combining the LSTM neural network unit and bidirectional network, which is used to obtain the implicit relationship of abnormal electricity consumption behavior of users.