ID 原文 译文
39416 在UNSW-NB15数据集上进行了实验，通过调整时间步长进行分析， Then, LSTM' s memory function and the powerful learning ability of sequence data are used to classify learning depth. Finally, the experiments are carried out with the UNSW-NB15 data set, which is analyzed by adjusting the time step.
39417 实验结果表明，该模型具有检测准确率高、误报率低的优点。 The experimental results show that the model has higher detection accuracy and lower false alarm rate.
39418 针对气象领域中仿真云图生成问题，提出一种基于深度生成对抗网络的仿真卫星云图生成方法。 To create the synthetic satellite cloud data in the domain of Meteorology, a method based on Generative Adversarial Networks(GAN) is proposed.
39419 利用深度学习的非线性映射能力和对栅格数据的信息提取能力，选取合适的数值模式产品要素作为输入，建立深度生成对抗模型提取同时次、同区域数值模式产品和卫星云图产品的对应有效信息，再利用提取的信息将数值模式产品重构为卫星云图产品。 Depending on ability of the nonlinear mapping and the information extraction of raster data with the deep learning network, a deep generative adversarial network model is proposed to extract the corresponding information between the numerical weather prediction(NWP) products and the satellite cloud data, and then the appropriate elements of the NWP product are chosen as the input to synthesize the corresponding satellite cloud data.
39420 基于欧洲中期天气预报中心数值模式再分析场产品和风云4A气象卫星产品的实验表明，所提方法可以有效的将数值模式产品重构为卫星云图仿真产品。 The experiments are conducted on the re-analysis products of the European Centre for Medium-Range Weather Forecasts(ECMWF) and FY-4 A satellite cloud date.The results show that the proposed method is effective to create synthetic satellite cloud data by using the NWP products.
39421 发票自动识别可有效提高财务工作效率。 Automatic identification of invoices can effectively improve financial efficiency.
39422 为避免低分辨率的发票图像影响自动识别的准确性，提出了一种用于对发票图像进行超分辨率处理的ESRGAN(EncoderSuper-resolutionGenerative Adversarial Network)网络。 But low-resolution invoice image reduces the accuracy of automatic identification, an ESRGAN(Encoder Super-resolution Generative Adversarial Network) network for super-resolution processing of invoice images is proposed. The ESRGAN network is based on a conditional generative adversarial network.
39423 ESRGAN网络是基于带条件的生成式对抗网络，设计了辅助编码器，引导网络生成更加真实的超分辨率图像。 An auxiliary encoder is designed to guide the network to generate a more realistic super-resolution image.
39424 基于实际发票图像，将ESRGAN网络与常规图像处理、SRCNN(Super-resolutionConvolutionalNeuralNetworks)网络和SRGAN(Super-resolutionGenerative Adversarial Network)网络进行对比实验，并通过峰值信噪比(Peak Signal to Noise Ratio， PSNR)和结构相似性(Structural Similarity， SSIM)评价指标进行模型评价。 Based on the actual invoice image, the ESRGAN network and the conventional image processing, SRCNN(Super-resolution Convolutional Neural Networks) network and SRGAN(Super-resolution Generative Adversarial Network) network. The model is evaluated through two evaluation indicators of peak signal-to-noise ratio(PSNR) and structural similarity(SSIM).
39425 实验结果表明基于ESRGAN超分辨率处理的图像在视觉效果和评价指标上均具有良好的效果。 The experimental results show that the images processed based on ESRGAN super-resolution are better on visual effects and evaluation indicators.