ID 原文 译文
52577 随着深度学习的发展，越来越多的热泵系统故障诊断方法引入深度学习技术并取得了较好的效果。 With the development of deep learning, more and more heat pump system fault diagnosis methods use deep learning technology and get good results.
52578 基于深度学习的故障诊断技术需要依赖大量带有标记的故障数据，而现实中这类数据获取较为困难，这限制了智能诊断技术的应用。 The fault diagnosis technology based on deep learning needs to rely on a large number of labeled fault data, but in reality, such data is very difficult to obtain, which limits the application of intelligent diagnosis technology.
52579 针对这一问题，本文提出利用生成对抗网络(GAN)学习故障数据的分布，并生成更多的标记数据，实现故障数据集的扩充。 Aiming at this issue, the generative adversarial network(GAN) is proposed to learn the distribution of fault data and generate more labeled data to achieve the augmentation of the fault data set.
52580 针对热泵系统运行数据结构复杂且不同故障间差异小给模型学习带来较大困难这一问题，本文提出利用热泵系统基准模型将运行数据转化为残差数据并作为训练数据，降低数据复杂度，增加差异性。 For the complex operation data structure of the heat pump systems and the small difference of data value between different faults bring great difficulty to model learning, this paper proposes to use the heat pump system benchmark model to convert the operation data into residual data and use it as training data to reduce data complexity and increase the difference of data value.
52581 利用MMD指标和1-NN指标对生成的数据进行分析，发现生成数据分布和真实数据接近，且利用残差数据训练的GAN模型质量更高。 Using the MMD and 1-NN indicator to analyze the generated data, it is found that the distribution of the generated data is close to the real data, and the GAN model trained with residual data is of higher quality.
52582 利用故障诊断方法对引入不同比例生成数据的模型训练结果进行分析，发现生成数据的引入可以提高数据量不足条件下的故障诊断精度。 Using the method of fault diagnosis to analyze the training results of models that use different amounts of generated data, it is found that the introduction of generated data can improve the accuracy of fault diagnosis under insufficient data conditions.
52583 实验结果表明，基于GAN的数据扩充方法可有效降低智能诊断对标记数据的依赖，是一种应用前景广阔的技术。 The experimental results prove that the GAN-based data augmentation method can effectively reduce the dependence of intelligent diagnosis on labeled data, and has broad application prospects.
52584 提出了一种基于偏差的纯追踪双舵轮停车自动导引车(AGV)的全向运动轨迹跟踪算法。 An omnidirectional motion path tracking method based on deviation-based pure pursuit for dual steering wheel parking automated guided vehicle(AGV) is proposed.
52585 本文对全向双舵轮停车AGV进行了运动模型建模，设计了适合双舵轮全向驱动的运动控制方式。 The motion model of the omnidirectional dual steering wheel parking AGV is modeled, and a motion control method is designed for the dual steering wheel omnidirectional driving.
52586 根据双舵轮停车AGV常规曲线运动与横移曲线运动的特点，提出了基于距离与角度偏差的纯追踪双舵轮停车机器人的全向运动轨迹跟踪。 According to the characteristics of the conventional curve motion and the lateral curve motion of dual steering wheel parking AGV, an omnidirectional motion path tracking of AGV using pure pursuit based on distance and angle deviation is proposed.