1DCNN

Short-Term Power Load Forecasting Based on CEEMDAN-LSTM-CNN Network

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Short-Term Power Load Forecasting Based on CEEMDAN-LSTM-CNN Network

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

Public Sentiment Analysis of Meteorological Disasters Based on LSTM-BLS

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Public Sentiment Analysis of Meteorological Disasters Based on LSTM-BLS

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

Deep Learning Heart Sound Classification Based on Log-Mel Spectrogram

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Deep Learning Heart Sound Classification Based on Log-Mel Spectrogram

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

Short-Term Power Load Forecasting Based on CNN-LSTM Network

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Short-Term Power Load Forecasting Based on CNN-LSTM Network

Click the blue text| Follow “Electrical Engineering” Abstract:Traditional neural networks have low accuracy in load forecasting with strong temporal correlation. To effectively improve the accuracy of short-term power load forecasting, a load forecasting method based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network is proposed. Five-dimensional load feature data … Read more

Human Activity Recognition Based on LSTM-CNN

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Human Activity Recognition Based on LSTM-CNN

Source: DeepHub IMBA This article is about 3400 words long and is recommended to read for more than 10 minutes. This article will guide you to recognize human activities using raw data generated by mobile sensors. Human Activity Recognition (HAR) is a method that uses Artificial Intelligence (AI) to recognize human activities from raw data … Read more

Innovative CNN-LSTM-Attention Model for High-Performance Predictions

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Innovative CNN-LSTM-Attention Model for High-Performance Predictions

Today, I would like to introduce a powerful deep learning model: CNN-LSTM-Attention! This model combines three different types of neural network architectures, fully exploiting the spatial and temporal information in the data. It not only captures the local features and long-term dependencies of the data but also automatically focuses on the most important parts of … Read more

Current Research Status and Development Trends of Intelligent Tool Wear Monitoring Methods

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Current Research Status and Development Trends of Intelligent Tool Wear Monitoring Methods

Editor’s Note The real-time monitoring of tool wear during machining is of significant importance for reducing equipment downtime and lowering costs caused by tool wear. Traditional tool wear monitoring methods based on signal processing and shallow learning models require manual extraction of lengthy features, which cannot achieve intelligent monitoring. To overcome this inherent limitation, deep … Read more

How Deep Learning Models Handle Variable Size Inputs

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How Deep Learning Models Handle Variable Size Inputs

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP graduate students, university professors, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more

Transformer, CNN, GNN, RNN: Understanding Attention Mechanisms

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Transformer, CNN, GNN, RNN: Understanding Attention Mechanisms

Follow the official account “ML_NLP“ Set as “Starred“, essential resources delivered first-hand! Looking back at the phrase from 2017, “Attention is all you need”, it truly was a prophetic statement. The Transformer model started with machine translation in natural language processing, gradually influencing the field (I was still using LSTM in my graduation thesis in … Read more

Can A Concise Architecture Be Efficient And Accurate? Tsinghua & Huawei Propose A New Residual Recurrent Super-Resolution Model: RRN!

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Can A Concise Architecture Be Efficient And Accurate? Tsinghua & Huawei Propose A New Residual Recurrent Super-Resolution Model: RRN!

Sharing a paper on video super-resolution titled Revisiting Temporal Modeling for Video Super-resolution, which is a BMVC 2020 paper. The results of this paper currently rank first on several datasets for video super-resolution, and the code has been open-sourced. Affiliations: Tsinghua University, New York University, Huawei Noah’s Ark Lab 1 Highlights This paper proposes a … Read more