ID 原文 译文
58818 实验结果表明,所设计的传感器湿度灵敏度约为69.6pm/%相对湿度,温度灵敏度约为15pm/℃。 Experimental results show that the designed sensor has a humidity sensitivity of 69. 6pm/% RH and a temperature sensitivity of 15pm/℃.
58819 该传感器对温湿度响应灵敏,是一种结构简单且紧凑的温湿度同时测量传感器,可被广泛应用。 The designed sensor is very sensitive to temperature and humidity, and has a good application prospect.
58820 针对传统自动文本摘要模型受循环神经网络长度限制而无法生成高质量的长文本摘要这一问题,提出了一种结合主题感知与通信代理的文本摘要模型。 To solve the problem that the traditional automatic text summary model cannot generate a high-quality long text summary due to the limitation of the length of the RNN (Recurrent Neural Network), a model of abstractive text summarization for topic-aware communicating agents has been proposed.
58821 首先,将编码器划分为相互之间存在通信的多个代理,以解决长短期记忆网络输入序列较长而不能联合先验信息生成摘要的问题; First, the problem that the LSTM (Long Short-Term Memory) input sequence is too long to generate the abstract with prior information has been solved by dividing the encoder into multiple collaborating agents.
58822 然后,使用联合注意力机制加入主题信息,提高生成摘要与源文本的相关性; Then for providing topic information and improving the correlation between the generated abstract and the source text, the joint attention mechanism has been added into our model.
58823 最后,使用带有强化学习的混合训练方法对模型进行训练,解决曝光偏差问题,直接对评价指标进行优化。 Finally, a hybrid training method with reinforcement learning has been employed in order to solve the problem of exposure bias, and optimize the evaluation index directly.
58824 实验结果表明,该模型不仅生成了主题突出的长文本摘要,并且得分比目前最先进的模型有一定提升。说明在主题信息的帮助下,该通信代理模型能够更好地生成长文本摘要。 The results show that our model not only generate long text summaries with prominent themes, but also has a higher score than the state-of-the-art models, which indicates that with the help of topic information, the model for communicating agents can be expected to generate long text summaries better.
58825 针对传统的空间锥体目标微动分类需人工构造、提取特征而缺乏通用性、智能性及在强噪声条件下分类性能差等问题,提出一种卷积神经网络和双向长短期记忆网络相结合的网络新模型。 To overcome the shortcomings of traditional micro-motion classification of spatial cone targets, such as manual construction, feature extraction, and lack of generality, intelligence and poor classification performance under strong noise, a new network model combining a convolutional neural network and a bidirectional long short-term memory network is proposed.
58826 该网络将残差网络、Inception网络及双向长短期记忆网络融合成一体化网络,通过增加网络的深度和宽度来挖掘更高维度的抽象特征以提升网络的分类准确率,而双向长短期记忆网络的推理能力能提高网络的容错性,时序分类的优势,以及残差网络跳跃式的旁路支线结构还能减少参数冗余,加快网络训练速度。 The network combines the residual network, inception network and bidirectional long short-term memory network into an integrated network. By increasing the depth and width of the network to mine the abstract features of higher dimensions, the classification accuracy of the network can be improved. The reasoning ability of the bidirectional long short-term memory network can improve the fault tolerance of the network, and the advantages of time series classification and the jumping bypass branch structure of the residual network can also reduce parameter redundancy and speed up network training.
58827 仿真结果表明,该网络模型不仅能实现更快速的智能分类,同时比ResNet-18、GoogLeNet模型的精度分别提高5%、4%,验证了该模型的有效性和泛化能力。 Simulation results show that the network model not only achieves faster intelligent classification, but also improves the accuracy of ResNet-18 and GoogLeNet models by 5% and 4% respectively, thus verifying the validity and generalization ability of the model.