0 Introduction The traction and braking forces of rail vehicles come from the adhesion between the wheel and rail, and the adhesion coefficient is influenced by various factors such as speed, axle load, and the condition of the wheel-rail surface. These factors are affected by the natural environment, leading to complex and variable adhesion characteristics. … Read more
Recommended Reading Time: 8min~13min Reason for Recommendation: This is a summary and reflection after watching Professor Li Hongyi’s deep learning videos from National Taiwan University. After finishing the introduction of the first part, particularly the introduction to RNN and especially LSTM, I felt enlightened. 1 0. Starting with RNN Recurrent Neural Network (RNN) is a … Read more
Importance of Neural Networks in Mathematical Competitions In mathematical competitions, neural networks have gradually become a powerful assistant for participants due to their strong data processing and pattern recognition capabilities. Especially when dealing with complex, nonlinear, high-dimensional data, neural networks perform exceptionally well. They not only help us solve basic problems such as classification and … Read more
Author: Shivam Bansal Translated by: Shen Libin Proofread by: Ding Nanya This article is approximately 2300 words, and is recommended to be read in 8 minutes. This article will detail the text classification problem and implement this process in Python. Introduction Text classification is a common natural language processing task in business problems, with the … Read more
Wireless communication plays an important role in our daily lives. The latest 5G network communication aims to provide faster, more reliable, and more efficient data transmission speeds and services than the existing 4G networks. Meanwhile, the rapid development of artificial intelligence in recent years has provided new methods for solving problems in traditional communication technologies, … Read more
Follow our public account to discover the beauty of CV technology Spatial-temporal prediction learning is a field with a wide range of application scenarios, such as weather forecasting, traffic flow prediction, precipitation prediction, autonomous driving, and human motion prediction. When it comes to spatial-temporal prediction, we must mention the classic model ConvLSTM and the most … Read more
Compiled by | Jiang Changzhi Today, I would like to introduce an article published in Nature Methods by a team from ETH Zurich and Friedrich Schiller University Jena. The article proposes a new method for generating small molecule structures from mass spectrometry based on an encoder-decoder neural network: MSNovelist. It first uses SIRIUS and CSI: … Read more
In chemical plants, various process equipment are usually connected by pipelines. Therefore, pipelines are known as the “veins” of the chemical plant, serving the role of transporting various process media. The damage to pipelines during operation is often caused by corrosion. According to statistics, the losses caused by metal corrosion in China exceed 40 billion … Read more
Figure1. Deep Learning Mind Map Introduction The concept of deep learning can be traced back to the field of cybernetics between 1940 and 1960. It later developed into connectionism during the 1980s and 1990s, with the third wave of development beginning in 2006 with the expansion of artificial neural networks, evolving into the highly popular … Read more
This article first summarizes the development history of large model technology, the iterative path of autonomous driving models, and the role of large models in the autonomous driving industry. Next, it details the basic definition, fundamental functions, and key technologies of large models, especially the Transformer attention mechanism and the pre-training-fine-tuning paradigm. The article also … Read more