The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models a...The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.展开更多
针对岩石薄片图像超分辨率重建过程中因纹理复杂导致现有重建方法效果不理想的问题,提出面向岩石薄片图像的超分辨率网络模型(super-resolution denoising diffusion probability model of rock slice,rsDDPMSR).针对传统上采样方法往...针对岩石薄片图像超分辨率重建过程中因纹理复杂导致现有重建方法效果不理想的问题,提出面向岩石薄片图像的超分辨率网络模型(super-resolution denoising diffusion probability model of rock slice,rsDDPMSR).针对传统上采样方法往往会导致伪影和低分辨率图像先验信息利用不充分的问题提出分层特征增强网络(layered feature enhancement network,LFE-Net),利用双通路网络对平稳小波变换分解后的高频与低频分量进行分层特征增强.为引导扩散模型的生成方向并提供丰富先验信息,将经过LFE-Net增强后的低分辨率特征与目标高分辨率加噪图像特征通道拼接作为扩散模型的条件输入.在U-Net的基础上设计了双编码器多尺度噪声预测网络(ACA-U-Net)有效处理岩石薄片多尺度信息并在跳跃连接中引入时间感知的自适应交叉注意力机制适配扩散模型不同去噪阶段的特征分布变化增强模型对关键区域的关注程度,有效提升图像重建细节.实验结果表明,rsDDPMSR在2×、4×、8×放大倍数下,峰值信噪比(PSNR)和结构相似度(SSIM)相比于CAMixerSR、SDFlow、IDM和SR3等主流重建方法具有更优的重建效果.展开更多
文摘The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.
文摘针对岩石薄片图像超分辨率重建过程中因纹理复杂导致现有重建方法效果不理想的问题,提出面向岩石薄片图像的超分辨率网络模型(super-resolution denoising diffusion probability model of rock slice,rsDDPMSR).针对传统上采样方法往往会导致伪影和低分辨率图像先验信息利用不充分的问题提出分层特征增强网络(layered feature enhancement network,LFE-Net),利用双通路网络对平稳小波变换分解后的高频与低频分量进行分层特征增强.为引导扩散模型的生成方向并提供丰富先验信息,将经过LFE-Net增强后的低分辨率特征与目标高分辨率加噪图像特征通道拼接作为扩散模型的条件输入.在U-Net的基础上设计了双编码器多尺度噪声预测网络(ACA-U-Net)有效处理岩石薄片多尺度信息并在跳跃连接中引入时间感知的自适应交叉注意力机制适配扩散模型不同去噪阶段的特征分布变化增强模型对关键区域的关注程度,有效提升图像重建细节.实验结果表明,rsDDPMSR在2×、4×、8×放大倍数下,峰值信噪比(PSNR)和结构相似度(SSIM)相比于CAMixerSR、SDFlow、IDM和SR3等主流重建方法具有更优的重建效果.
