ID 原文 译文
16835 相比于ACE算法设计者给出的积分区分器，该新区分器的步数提高了4步。 Compared with the distinguishers given by ACE’s designer, thisnew result prominently increases 4 steps indeed.
16836 电阻点焊是多种因素交互作用的复杂过程。 Resistance spot welding is a complex process in which many factors interact.
16837 该过程的复杂性加上数据规模小和工艺不稳定问题使得难以建立精确的数学模型来对电阻点焊参数进行预测。 Given the small size ofdata sets available and the complex nature of unstable processes, it is difficult to establish an accuratemathematical model to predict the parameters of resistance spot welding.
16838 该文提出一种将贝叶斯极限梯度提升机(Bayes-XGBoost)与粒子群优化(PSO)算法结合的方法，对厚度为0.15 mm的镍片和0.4 mm的不锈钢电池正极帽选取合适的样本特征和样本组合； An optimal computing method forsolving this problem is presented. The method combines Bayes-XGBoost with the Particle Swarm Optimization(PSO) algorithm to select suitable features and to enable the optimal combinations of samples for 0.15 mmnickel sheets and for 0.4 mm stainless steel battery positive caps;
16839 利用极限梯度提升机(XGBoost)的非线性切分能力和防控过拟合机制对点焊工艺参数进行正向训练，并引入贝叶斯优化为梯度提升机选取最佳超参数； The non-linear slicing ability and anti-overfitting mechanism of eXtreme Gradient Boosting (XGBoost) are used to train forward spot weldingparameters; and Bayesian optimization is applied to the XGBoost's optimal parameter selection.
16840 利用粒子群优化算法的全局寻优能力，对可变目标值的工艺参数进行反向预测，从而得到最优工艺参数。 The method uses the global optimization feature of Particle Swarm Optimization (PSO) to predict the backward process parameters with variable target values such that the optimal process parameters are obtained.
16841 电阻点焊实验表明该方法比文中其他对比算法具有较强的综合性能，能够有效辅助点焊工艺。 Compared withother algorithms mentioned in this paper, this method offers more comprehensive performance and possessesbetter capabilities to effectively assist in the spot welding process, which are demonstrated by the resistancespot welding experiments performed.
16842 随着车联网(IoV)的迅猛发展，请求进行任务卸载的汽车终端用户也逐渐增长， With the rapid development of the Internet of Vehicles (IoV), the number of cars and usersrequesting tasks offloading is also increasing.
16843 而基于移动边缘计算(MEC)的通信网络能够有效地解决任务卸载在上行传输时延较高的挑战，但是该网络模型同时也面临着信道资源不足的问题。 The Mobile Edge Computing (MEC) can effectively solve the challenge of high offload transmission delays for task offloading in communication network, but there still is a problem that the channel resources are insufficient in the network model.
16844 该文引入的非正交多址(NOMA)技术相较于正交多址(OMA)能够在相同的信道资源条件下为更多的用户提供任务卸载，同时考虑到任务卸载过程中多方面的影响因子，提出了混合NOMA-MEC卸载策略。 Compared with traditional Orthogonal Multiple Access (OMA), the technology of Non-Orthogonal Multiple Access (NOMA) can servicemore users with task offload under the same channel resource conditions.