Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive in...In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.展开更多
As an emerging groupⅢ–Ⅵsemiconductor two-dimensional(2D)material,gallium selenide(GaSe)has attracted much attention due to its excellent optical and electrical properties.In this work,high-quality epitaxial growth ...As an emerging groupⅢ–Ⅵsemiconductor two-dimensional(2D)material,gallium selenide(GaSe)has attracted much attention due to its excellent optical and electrical properties.In this work,high-quality epitaxial growth of few-layer GaSe nanoflakes with different thickness is achieved via chemical vapor deposition(CVD)method.Due to the non-centrosymmetric structure,the grown GaSe nanoflakes exhibits excellent second harmonic generation(SHG).In addition,the constructed GaSe nanoflake-based photodetector exhibits stable and fast response under visible light excitation,with a rise time of 6 ms and decay time of 10 ms.These achievements clearly demonstrate the possibility of using GaSe nanoflake in the applications of nonlinear optics and(opto)-electronics.展开更多
Metal organic frameworks(MOFs)with their large surface area and numerous active sites have attracted significant research attention.Recently,the application of MOFs for the catalytic degradation of organic pollutants ...Metal organic frameworks(MOFs)with their large surface area and numerous active sites have attracted significant research attention.Recently,the application of MOFs for the catalytic degradation of organic pollutants has provided effective solutions to address diverse environmental problems.In this review,the latest progress in MOF-based removal and degradation of organic pollutants is summarized according to the different roles of MOFs in the removal reaction systems,such as physical adsorbents,enzyme-immobilization carriers,nanozymes,catalysts for photocatalysis,photo-Fenton and sulfate radical based advanced oxidation processes(SR-AOPs).Finally,the opportunities and challenges of developing advanced MOFs for the removal of organic pollutants are discussed and anticipated.展开更多
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
文摘In recent years,learning-based low-light image enhancement methods have shown excellent performance,but the heuristic design adopted by most methods requires high engineering skills for developers,causing expensive inference costs that are unfriendly to the hardware platform.To handle this issue,we propose to automatically discover an efficient architecture,called progressive attentive Retinex network(PAR-Net).We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation.A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction.To endow the searched architecture with the hardware-aware property,we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework.Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods.A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.51902227 and 11574241)the Open Project of State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology,China(Grant No.P2020-021).
文摘As an emerging groupⅢ–Ⅵsemiconductor two-dimensional(2D)material,gallium selenide(GaSe)has attracted much attention due to its excellent optical and electrical properties.In this work,high-quality epitaxial growth of few-layer GaSe nanoflakes with different thickness is achieved via chemical vapor deposition(CVD)method.Due to the non-centrosymmetric structure,the grown GaSe nanoflakes exhibits excellent second harmonic generation(SHG).In addition,the constructed GaSe nanoflake-based photodetector exhibits stable and fast response under visible light excitation,with a rise time of 6 ms and decay time of 10 ms.These achievements clearly demonstrate the possibility of using GaSe nanoflake in the applications of nonlinear optics and(opto)-electronics.
基金supported by the National Key Research and Development Program of China(No.2020YFC1606801).
文摘Metal organic frameworks(MOFs)with their large surface area and numerous active sites have attracted significant research attention.Recently,the application of MOFs for the catalytic degradation of organic pollutants has provided effective solutions to address diverse environmental problems.In this review,the latest progress in MOF-based removal and degradation of organic pollutants is summarized according to the different roles of MOFs in the removal reaction systems,such as physical adsorbents,enzyme-immobilization carriers,nanozymes,catalysts for photocatalysis,photo-Fenton and sulfate radical based advanced oxidation processes(SR-AOPs).Finally,the opportunities and challenges of developing advanced MOFs for the removal of organic pollutants are discussed and anticipated.
