ID 原文 译文
40816 最后，采用交叉熵确定最优参数，从而识别出异常用电用户行为。 Finally, the cross entropy is used to determine the optimal parameters, thereby detecting the abnormal power consumer.
40817 实验表明，该方法的识别性能显著优于其他模型，并且结合国家电网的实际数据验证了该方法的准确性和稳定性。 The experiments are conducted based on real energy consumption data, and the results show that the detection performance of this proposed method outperforms other methods in terms of accuracy and stability.
40818 针对无线传感器网络能耗不均衡、网络生存期短的问题，提出一种基于改进灰狼优化的分簇路由算法。 In order to solve the problems of uneven energy consumption and short network lifetime in wireless sensor networks, a clustering routing algorithm based on improved grey wolf optimization is proposed.
40819 在簇构建阶段，首先采用自组织神经网络映射(SOM)对网络节点聚类分簇，然后在簇内使用改进的灰狼优化算法选择最优簇头; In the cluster construction phase, the self-organizing map network(SOM) clustering algorithm is used to cluster the network nodes, and then the improved grey wolf optimization algorithm is used to select the optimal cluster head in each cluster.
40820 簇间路由阶段，综合考虑节点的剩余能量和地理位置，为簇首选择合理的下一跳; In the inter-cluster routing stage, a reasonable next hop is selected for the cluster head by comprehensively considering the residual energy and geographical location of the nodes.
40821 簇内通信阶段，引入轮询控制机制，进一步降低网络能耗。 In the intra-cluster communication phase, polling control mechanism is introduced to further reduce network energy consumption.
40822 仿真结果表明:在不同规模的场景下，所提算法均能够均衡网络能耗、延长网络生存期、提高网络吞吐量。 Simulation results show that the proposed algorithm can balance the network energy consumption, prolong the network lifetime and improve the network throughput in different scale scenarios.
40823 在机器学习不平衡分类方法研究中，由于多数类与少数类样本数量之间存在较大差异，导致分类器易出现判定准确率低的问题。 In the research of imbalance classification methods of machine learning, the classifier is prone to the problem of low judgment accuracydue to the large difference of the number between the majority class and the minority class.
40824 以SMOTE为代表的一类过采样方法是处理该问题的一种有效手段。 A class of oversampling methods represented by SMOTE are effective to deal with this problem.
40825 该类方法在选定的线段中随机生成少数类新点来重新平衡数据集，但存在忽略少数类样本在超维空间中分布多样性的缺陷。 These types of methods randomly generate the minority new points in the selected line segment to rebalance the data set, but there is the defect of ignoring the diversity of minority samples in the super-dimensional space.