The demand for energy consumption promotes to find more coal in deep underground up to 1 000 m and brings more serious situation of water disaster. As one of the major methods for water disaster control, hydrogeochemi...The demand for energy consumption promotes to find more coal in deep underground up to 1 000 m and brings more serious situation of water disaster. As one of the major methods for water disaster control, hydrogeochemistry attracts a series of studies related to water source discrimination. In this paper, a simple method for constructing the water source discrimination model based on major ions and multivariate statistical analysis was reported using the following procedures： （1） collection of data and interpretation, （2） analysis of controlling factors based on the chemical composition of groundwater, （3） ＂pure＂ sample chosen, and （4） discrimination model establishment. After the processes, two functions and a diagram were established for three aquifers （the Quaternary, Coal bearing, and Taiyuan Fm.） from the Renlou Coal Mine in northern Anhui Province, China. The method can be applied in almost all coal mines and can be used for evaluating the contribution ratios if the water is collected from a mixing source.展开更多
We introduce a novel stretchable photodetector with enhanced multi-light source detection,capable of discriminating light sources using artificial intelligence(AI).These features highlight the application potential of...We introduce a novel stretchable photodetector with enhanced multi-light source detection,capable of discriminating light sources using artificial intelligence(AI).These features highlight the application potential of deep learning enhanced photodetectors in applications that require accurate for visual light communication(VLC).Experimental results showcased its excellent potential in real-world traffic system.This photodetector,fabricated using a composite structure of silver nanowires(AgNWs)/zinc sulfide(ZnS)-polyurethane acrylate(PUA)/AgNWs,maintained stable performance under 25%tensile strain and 2 mm bending radius.It shows high sensitivity at both 448 and 505 nm wavelengths,detecting light sources under mechanical deformations,different wavelengths and frequencies.By integrating a one-dimensional convolutional neural network(1D-CNN)model,we classified the light source power level with 96.52%accuracy even the light of two wavelengths is mixed.The model’s performance remains consistent across flat,bent,and stretched states,setting a precedent for flexible electronics combined with AI in dynamic environments.展开更多
基金Supported by the National Natural Science Foundation of China （41173016）
文摘The demand for energy consumption promotes to find more coal in deep underground up to 1 000 m and brings more serious situation of water disaster. As one of the major methods for water disaster control, hydrogeochemistry attracts a series of studies related to water source discrimination. In this paper, a simple method for constructing the water source discrimination model based on major ions and multivariate statistical analysis was reported using the following procedures： （1） collection of data and interpretation, （2） analysis of controlling factors based on the chemical composition of groundwater, （3） ＂pure＂ sample chosen, and （4） discrimination model establishment. After the processes, two functions and a diagram were established for three aquifers （the Quaternary, Coal bearing, and Taiyuan Fm.） from the Renlou Coal Mine in northern Anhui Province, China. The method can be applied in almost all coal mines and can be used for evaluating the contribution ratios if the water is collected from a mixing source.
基金supported by National Research Foundation of Korea(NRF)grants(Number RS-2023-00247545)funded by the Korean government(MSIP)funded and conducted under the Competency Development Program for Industry Specialists of the Korean Ministry of Trade,Industry and Energy(MOTIE),operated by Korea Institute for Advancement of Technology(KIAT)(No.P0023704,SemiconductorTrack Graduate School(SKKU)).
文摘We introduce a novel stretchable photodetector with enhanced multi-light source detection,capable of discriminating light sources using artificial intelligence(AI).These features highlight the application potential of deep learning enhanced photodetectors in applications that require accurate for visual light communication(VLC).Experimental results showcased its excellent potential in real-world traffic system.This photodetector,fabricated using a composite structure of silver nanowires(AgNWs)/zinc sulfide(ZnS)-polyurethane acrylate(PUA)/AgNWs,maintained stable performance under 25%tensile strain and 2 mm bending radius.It shows high sensitivity at both 448 and 505 nm wavelengths,detecting light sources under mechanical deformations,different wavelengths and frequencies.By integrating a one-dimensional convolutional neural network(1D-CNN)model,we classified the light source power level with 96.52%accuracy even the light of two wavelengths is mixed.The model’s performance remains consistent across flat,bent,and stretched states,setting a precedent for flexible electronics combined with AI in dynamic environments.
