In order to recognize various metal transfer modes, by the creation of apattern recognition system for metal transfer mode, five kinds of spectrum signal in gas metal arcwelding (MIG, MAG and CO_2) are collected and t...In order to recognize various metal transfer modes, by the creation of apattern recognition system for metal transfer mode, five kinds of spectrum signal in gas metal arcwelding (MIG, MAG and CO_2) are collected and taken as training samples. These samples arepretreated by computer. Several key characteristic parameters of the spectrum signal are creativelyextracted, and a corresponding recognition function and a minimum-distance-classifier areconstructed. The results show that using this method, the pattern recognition of several kinds ofmetal transfer mode for the metal gas arc welding can be done successfully. It has good accuracy andrecognition precision, basis for controlling the metal gas arc welding metal transferautomatically, and relative important parameters in welding process, such as the frequency ofdroplet transfer and the approximate diameter of each droplet, can also be obtained.展开更多
The complex relationship between environmental exposure and human health constitutes a major global challenge requiring innovative solutions.Artificial Intelligence(AI)and Machine Learning(ML)show exceptional strength...The complex relationship between environmental exposure and human health constitutes a major global challenge requiring innovative solutions.Artificial Intelligence(AI)and Machine Learning(ML)show exceptional strength for data analysis and pattern recognition.Applying these technologies to environmental health provides new insights to improve and advance environmental exposure assessment,health risk assessment,and related policy development.It is with great pleasure that we present this Special Issue of Environment&Health on Machine Learning and Artificial Intelligence for Environmental Health.This collection of research highlights the latest advancements and broad potential of ML and AI to empower our response to pressing and future environmental health issues.展开更多
基金This project is supported by National Natural Science Foundation of China (No.59990470).
文摘In order to recognize various metal transfer modes, by the creation of apattern recognition system for metal transfer mode, five kinds of spectrum signal in gas metal arcwelding (MIG, MAG and CO_2) are collected and taken as training samples. These samples arepretreated by computer. Several key characteristic parameters of the spectrum signal are creativelyextracted, and a corresponding recognition function and a minimum-distance-classifier areconstructed. The results show that using this method, the pattern recognition of several kinds ofmetal transfer mode for the metal gas arc welding can be done successfully. It has good accuracy andrecognition precision, basis for controlling the metal gas arc welding metal transferautomatically, and relative important parameters in welding process, such as the frequency ofdroplet transfer and the approximate diameter of each droplet, can also be obtained.
文摘The complex relationship between environmental exposure and human health constitutes a major global challenge requiring innovative solutions.Artificial Intelligence(AI)and Machine Learning(ML)show exceptional strength for data analysis and pattern recognition.Applying these technologies to environmental health provides new insights to improve and advance environmental exposure assessment,health risk assessment,and related policy development.It is with great pleasure that we present this Special Issue of Environment&Health on Machine Learning and Artificial Intelligence for Environmental Health.This collection of research highlights the latest advancements and broad potential of ML and AI to empower our response to pressing and future environmental health issues.
