ID 原文 译文
1233 特别是，针对以上四种剩余型蕴涵算子，得到了直觉模糊推理 a － 反向三 I 算法的一些关于鲁棒性的结论。 in particular, for four kinds of residual implications, some results concerning robustness of intuitionis-tic fuzzy inference a － reverse triple I methods are obtained.
1234 为改善以绝缘栅双极型晶体管(Insulated Gate Bipolar Transistor，IGBT)作为开关器件的单相全桥逆变器的效率，提出了一种节能型单相全桥零电流开关谐振极逆变器，在每个桥臂上分别并联 1 组辅助电路。 In order to improve the efficiency of single-phase full-bridge inverter with insulated gate bipolar transistor(IGBT)as switching devices, a energy-saving single-phase full-bridge zero-current switching resonant pole inverter is pro-posed. A set of auxiliary circuit is connected with parallel on each bridge arm.
1235 在工作过程中，主开关和辅助开关都能完成零电流软切换，可消除 IGBT 拖尾电流造成的关断损耗。 In the working process, both the main switchesand the auxiliary switches can achieve zero-current soft-switching , which can eliminate the turn-off loss caused by the tailcurrent of IGBT.
1236 分析了电路工作过程，在 2kW样机上的实验结果表明开关器件实现了零电流软切换。 The working process of the circuit is analyzed. The experimental results on the 2kW prototype show that theswitching devices achieve zero-current soft-switching.
1237 因此，该拓扑结构可实现以 IGBT 作为开关器件的单相全桥逆变器的节能运行。 Therefore, the topology can realize the energy-saving operation of thesingle-phase full-bridge inverter with IGBT as the switch devices.
1238 本文提出一种新的基于张量表示的域适配迁移学习中的特征表示方法，即融合联合域对齐和适配正则化的基于张量表示的迁移学习特征表示方法。 A novel feature representation based on tensor and domain adaption for transfer learning is proposed, which combines joint domain alignment and adaptation regularization.
1239 当源域和目标域差异很大时，仅将源域对齐潜在共享空间，会造成数据扭曲过大。 When the difference between the source domain and the tar-get domain is very large, only aligning the source domain to the potential shared subspace will result in too much data distor-tion.
1240 为缓解此问题，本文方法提出联合域对齐，即源域和目标域同时对齐共享子空间。 To alleviate this problem, this paper proposes joint domain alignment, which aligns the source domain and the target do-main to the potential shared subspaces simultaneously.
1241 并且本文方法将适配正则化引入张量表示空间求解。 Furthermore, the adaption regularization is introduced into the sub-space learning based on tensor.
1242 本文适配正则化包括动态分布对齐和图适配，以缩小域间分布差异和保留样本间流行一致性。 In the proposed method, adaptation regularization includes dynamic distribution alignment andgraph adaptation to reduce the distribution differences among different domains and preserve the manifold consistency.