The technological innovation of thin-film lithium niobate(TFLN)is supplanting the traditional lithium niobate industry and generating a vast array of ultra-compact and low-loss optical waveguide devices,providing an u...The technological innovation of thin-film lithium niobate(TFLN)is supplanting the traditional lithium niobate industry and generating a vast array of ultra-compact and low-loss optical waveguide devices,providing an unprecedented prospect for chip-scale integrated optics.Because of its unique strong quadratic nonlinearity,TFLN is widely used to create new coherent light,which significantly promotes all-optical signal processes,especially in terms of speed.Herein,we review recent advances in TFLN,review the thorough optimization strategies of all-optical devices with unique characteristics based on TFLN,and discuss the challenges and perspectives of the developed nonlinear devices.展开更多
Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,whi...Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.展开更多
基金supported by the National Key R&D Program of China(No.2021YFB2800700)the National Natural Science Foundation of China(Nos.62275047,61875241,and 22102023)+1 种基金the Fellowship of China Postdoctoral Science Foundation(Nos.2021M700768 and 2022M710672)the Natural Science Foundation of Jiangsu Province(No.BK20220816).
文摘The technological innovation of thin-film lithium niobate(TFLN)is supplanting the traditional lithium niobate industry and generating a vast array of ultra-compact and low-loss optical waveguide devices,providing an unprecedented prospect for chip-scale integrated optics.Because of its unique strong quadratic nonlinearity,TFLN is widely used to create new coherent light,which significantly promotes all-optical signal processes,especially in terms of speed.Herein,we review recent advances in TFLN,review the thorough optimization strategies of all-optical devices with unique characteristics based on TFLN,and discuss the challenges and perspectives of the developed nonlinear devices.
基金This work was supported by the Science and Technology Project of China Southern Power Grid Corporation(ZBKJXM20180157)the National Natural Science Foundation of China(Grant Nos.61772456,61761136020).
文摘Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.
