Paper Title Process Production Quality Prediction Algorithm Based on Mar-G LSTM Authors Yin Yanchao, Su Yifan (Corresponding Author), Tang Jun, Lin Wenqiang, Pu Haoran, Wang Linyu Affiliations 1. School of Mechanical and Electrical Engineering, Kunming University of Science and Technology 2. Yunnan Tobacco Industrial Co., Ltd. Funding National Key R&D Program of China (2023YFB3308401) National … Read more
Abstract: Predicting the parameters of Insulated Gate Bipolar Transistors (IGBTs) can effectively avoid economic losses and safety issues caused by their failures. By analyzing the parameters of IGBTs, a System on Chip (SoC) hardware system for IGBT parameter prediction based on LSTM networks is designed. This system uses an ARM processor as the main controller … Read more
ClickBlue Text| Follow“Electrical Engineering” Abstract:Short-term power load is highly random and volatile, making it difficult for traditional load forecasting methods to grasp the patterns of short-term load changes. To improve the accuracy of short-term power load forecasting, we propose a method that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory … Read more
Sea Temperature Forecast Model Based on PCA and LSTM Full text can be downloaded on PC at the following address: http://www.hyyb.org.cn/Magazine/Show.aspx?ID=3464 Reading Notes Authors: Li Jingshi1 2 Kuang Xiaodi1 2 Li Qiong3 He Enye1 2 Zhang Yubai3 Yuan Chengyi4 Zhang Yanlin5 Affiliations: 1. National Marine Environmental Forecasting Center, Beijing 100081; 2. Key Laboratory of Marine … Read more
In the field of maritime transportation, ensuring the safe navigation of vessels is crucial. To prevent maritime accidents, Professor Guo Jian’s team from the Information Engineering University of the People’s Liberation Army of China has developed a novel anomaly detection technology for ship trajectories based on artificial intelligence. This technology utilizes a model known as … Read more
Prediction of Protein-Ligand Binding Affinity Based on LSTM and Attention Mechanism Wang Wei1,2*, Wu Shiyu1, Liu Dong1,2, Liang Huiru1, Shi Jinling3, Zhou Yun1,2, Zhang Hongjun4, Wang Xianfang5 (1 Henan Normal University, School of Computer and Information Engineering, Xinxiang, Henan 453007; 2 Henan Province Education Artificial Intelligence and Personalized Learning Key Laboratory, Xinxiang, Henan 453007; 3 … Read more
2021 Issue 4 Public Sentiment Analysis of Meteorological Disasters Based on LSTM-BLS Jia Luo1 Lehao Wang2 Shanshan Tu1 Ge Song1 Ying Han2 1Hubei Provincial Public Meteorological Service Center, Wuhan, 430074 2Nanjing University of Information Science and Technology, School of Automation, Nanjing, 210044 Abstract:In recent years, Long Short-Term Memory (LSTM) networks have shown certain advantages in … Read more
Research on LSTM Water Level Prediction Model Based on Improved Attention Mechanism Ma Fei 1, Tu Zhenyu 1*, Zhu Songting 2, Xiang Xinyue 1, Sun Yifei 1, Fang Qiang 1 (1. School of Information Engineering, Nanchang Engineering College, Nanchang, Jiangxi, 330099; 2. Jiangxi Flood Control Information Center, Nanchang, Jiangxi, 330009) Abstract In order to further … Read more
♚ Author: Yishui Hancheng, CSDN Blog Expert, Research Directions: Machine Learning, Deep Learning, NLP, CV Blog: http://yishuihancheng.blog.csdn.net Traditional linear models struggle with multivariate or multi-input problems, whereas neural networks like LSTM excel at handling multiple variables, making them suitable for time series prediction tasks. In the following article, you will learn how to build an … Read more
Source: DeepHub IMBA This article is approximately 5500 words, recommended reading time is over 10 minutes. This article explores the hybrid modeling method that combines temporal features and static features in the field of quantitative trading. By integrating Stacked Sparse Denoising Autoencoder (SSDA) and Long Short-Term Memory based Autoencoder (LSTM-AE), we aim to build a … Read more