SA-LSTM This project tries to implementSA-LSTMproposed inDescribing Videos by Exploiting Temporal Structure[1],ICCV 2015. Environment Ubuntu 16.04 CUDA 9.0 cuDNN 7.3.1 Nvidia Geforce GTX Titan Xp 12GB Requirements Java 8 Python 2.7.12 PyTorch 1.0 ...
本申请公开了基于Sa‑LSTM的水面漂浮物短时漂移轨迹预测方法及系统,方法包括:获取水面漂浮物体轨迹数据集并进行数据预处理,得到水面漂浮物体多维因子融合数据集,水面漂浮物体多维因子融合数据集的多维因子包括纬向速度影响因子和径向速度影响因子;根据预设LSTM神经元选择规则,对LSTM单元进行堆叠并引入空间注意力模块,构建Sa...
The online SA-LSTM updater comprehensively integrates spatial鈥搕emporal context during tracking, producing dynamic template features with enhanced representations of target appearance. Extensive experiments across multiple benchmark datasets, including GOT-10K, LaSOT, TrackingNet, OTB-100, UAV123, and NFS...
基于WT-SA-LSTM的降水量预测 认领 Precipitation Prediction Based on WT-SA-LSTM 在线阅读 下载PDF 引用 收藏 分享 摘要 监测和预测降水量对于农业、水资源管理、气象灾害预警等方面都至关重要。本文利用1991~2020年江西省九江市10个地面气象观测站月降水量实测数据,建立SARIMA、随机森林、LSTM降水量预测模型。结果...
In this paper, we propose a Hybrid-Mode Siamese tracker featuring an online SA-LSTM updater. Distinct learning operators are tailored to exploit characteristics at different depth levels of the backbone, integrating convolution and transformers to form a Hybrid-Mode backbone. This backbone efficiently ...
Using ground-measured global horizontal irradiance dataset, the proposed SA-Bi-LSTM-Bi-GRU model achieves exceptional forecasting performance. It significantly reduces MAE by 64.40–90.34 W/m2, RMSE by 16.15–57.30% W/m2, MAPE by 41.38–69.78%, NMRSE by 8.29–21.65% W/m2 compared to ...
The KVMD-KTCN-LSTM-SA model effectively reduces runoff fluctuation and combines the advantages of multiple models and achieves satisfactory runoff prediction results. During the testing period, the proposed model achieves NSE of 0.978 and R2 of 0.982 at Wuluwati station, NSE of 0.975 and R2 of...
LSTM的计算公式: 参考Pytorch 循环神经网络: 三. LSTM的变体 peephole LSTM:在计算遗忘门、输入门、输出门时要考虑cell的状态。 耦合遗忘门和输入门:遗忘率和输入率总和为1。 GRU GRU对LSTM做了两个大改动: 将输入门、遗忘门、输出门变为两个门:更新门ztzt(Update Gate)和重置门$r_t$(Reset Gate)。
针对生物质锅炉燃烧过程的动态特性,提出一种改进的长短期记忆-自注意力机制全卷积神经网络(LSTM-SAFCN)模型用于预测NO_(x)排放浓度.首先利用完全自适应噪声集合经验模态分解法(CEEMDAN)对数据进行预处理,消除数据噪声对NO_(x)排放浓度预测的影响;其次融合自注意力机制与长短时记忆-全卷积神经网络(LSTM-FCN)进行特征...
计及相似日的LSTM光伏出力预测模型研究 为了提高光伏电站输出功率的预测精度,该文构建基于灰色关联度分析法(GRA)和长短期记忆神经网络(LSTM)的光伏发电组合预测模型.在运用GRA方法确定影响光伏出力的主要气... 王涛,王旭,许野,... - 《太阳能学报》 被引量: 0发表: 2023年 基于相似日选取和PCA-LSTM的光伏出力...