In the 2024 issue 7 of “Automobile Engineering”, the research results from the School of Mechanical and Automotive Engineering at South China University of Technology were published: “A Quantitative Diagnosis Method for Power Battery Faults Based on 1D CNN-LSTM Considering Cell Abnormalities”. The paper combines three types of features: the vehicle’s motion state, the drive … Read more
Peng Cheng 1，2，Jiang Jinyuan 1，Li Fengjuan 1 （1.Hunan University of Technology, School of Computer Science, Zhuzhou, Hunan 412007； 2.Central South University, School of Automation, Changsha, Hunan 410083） Abstract: This paper addresses the difficulty of hyperparameter configuration in fault diagnosis using deep learning methods. To effectively optimize the set of hyperparameters, we propose an optimization algorithm … 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
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
Source: DeepHub IMBA This article is about 1300 words long, and it is recommended to read it in 5 minutes. This paper treats heart sound signals as speech signal processing and achieves good results. This is a very interesting paper that proposes two heart rate sound classification models based on the log-mel spectrogram of heart … Read more
6 Tuesday July 2021 Testing “Gold” Room Time series refers to a sequence formed by arranging the values of the same statistical indicator in chronological order. Its essence is the trend of one or more random variables changing over time. The core of time series prediction methods is to mine such patterns from the data[1].Time … Read more
Click the blue WeChat name below the title to quickly follow Abstract Objective This study compares the prediction effects of unidimensional and multidimensional input models of Long Short-Term Memory (LSTM) neural networks and Back Propagation (BP) neural networks in the field of dynamic blood sugar.Method This study collected blood sugar values from 18 type 2 … Read more
This article is an excellent piece from the KX Forum, author ID: Flying Fish Oil 1 Introduction In recent years, the rapid development of natural language processing has introduced a series of related algorithms and models. For example, RNN (Recurrent Neural Network) for processing sequential data, LSTM (Long Short-Term Memory Network), GRU (Gated Recurrent Unit), … Read more
Author Information Liuyishi1, Guan Xuefeng1,2, Wu Huayi1,2, Cao Jun1, Zhang Na1 (1. Key Laboratory of Surveying and Mapping Remote Sensing Information Engineering, Wuhan University, Wuhan, Hubei 430079; 2. Collaborative Innovation Center of Geospatial Information Technology, Wuhan, Hubei 430079) “ AbstractA short-term traffic speed prediction model based on spatio-temporal correlation weighting using Long Short-Term Memory (LSTM) … Read more
First, I recommend a Jupyter environment, which is provided by Google called colab (https://colab.research.google.com/), where you can use free GPUs. The first time you use it, you need to download the relevant Python libraries in the experimental environment. !pip install torch!pip install torchtext!python -m spacy download en Our preliminary idea is to first input a … Read more