The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Rec...The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)efforts.However,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems.This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity inference.Unlike traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for analysis.It emphasizes the low-frequency components by calculating their energy spectral density values.Subsequently,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational costs.Notably,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone architecture.The computational feasibility and data sensitivity of the proposed scheme are thoroughly examined.Impressively,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,respectively.Concurrently,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.展开更多
This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch si...This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.展开更多
The increasing demand for hydrogen energy to address environmental issues and achieve carbon neutrality has elevated interest in green hydrogen production,which does not rely on fossil fuels.Among various hydrogen pro...The increasing demand for hydrogen energy to address environmental issues and achieve carbon neutrality has elevated interest in green hydrogen production,which does not rely on fossil fuels.Among various hydrogen production technologies,anion exchange membrane water electrolyzer(AEMWE)has emerged as a next-generation technology known for its high hydrogen production efficiency and its ability to use non-metal catalysts.However,this technology faces significant challenges,particularly in terms of the membrane durability and low ionic conductivity.To address these challenges,research efforts have focused on developing membranes with a new backbone structure and anion exchange groups to enhance durability and ionic conductivity.Notably,the super-acid-catalyzed condensation(SACC)synthesis method stands out due to its user convenience,the ability to create high molecular weight(MW)polymers,and the use of oxygen-tolerant organic catalysts.Although the synthesis of anion exchange membranes(AEMs)using the SACC method began in 2015,and despite growing interest in this synthesis approach,there remains a scarcity of review papers focusing on AEMs synthesized using the SACC method.The review covers the basics of SACC synthesis,presents various polymers synthesized using this method,and summarizes the development of these polymers,particularly their building blocks including aryl,ketone,and anion exchange groups.We systematically describe the effects of changes in the molecular structure of each polymer component,conducted by various research groups,on the mechanical properties,conductivity,and operational stability of the membrane.This review will provide insights into the development of AEMs with superior performance and operational stability suitable for water electrolysis applications.展开更多
The traditional calculation method of frequency-domain Green function mainly utilizes series or asymptotic expansion to carry out numerical approximation, however, this method requires very careful zoning, thus the co...The traditional calculation method of frequency-domain Green function mainly utilizes series or asymptotic expansion to carry out numerical approximation, however, this method requires very careful zoning, thus the computing process is complex with many cycles, which has greatly affected the computing efficiency. To improve the computing efficiency, this paper introduces Gaussian integral to the numerical calculation of the frequency-domain Green function and its partial derivatives. It then compares the calculation result with that in existing references. The comparison results demonstrate that, on the basis of its sufficient accuracy, the method has greatly simplified the computing process, reduced the zoning and improved the computing efficiency.展开更多
基金supported by National Natural Science Foundation of China(Nos.61902158 and 62202210).
文摘The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)efforts.However,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems.This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity inference.Unlike traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for analysis.It emphasizes the low-frequency components by calculating their energy spectral density values.Subsequently,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational costs.Notably,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone architecture.The computational feasibility and data sensitivity of the proposed scheme are thoroughly examined.Impressively,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,respectively.Concurrently,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.
基金supported by the National Key R&D Program(No.2022YFA1602201)。
文摘This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.
基金supported by the KRISS(Korea Research Institute of Standards and Science)MPI Lab.program。
文摘The increasing demand for hydrogen energy to address environmental issues and achieve carbon neutrality has elevated interest in green hydrogen production,which does not rely on fossil fuels.Among various hydrogen production technologies,anion exchange membrane water electrolyzer(AEMWE)has emerged as a next-generation technology known for its high hydrogen production efficiency and its ability to use non-metal catalysts.However,this technology faces significant challenges,particularly in terms of the membrane durability and low ionic conductivity.To address these challenges,research efforts have focused on developing membranes with a new backbone structure and anion exchange groups to enhance durability and ionic conductivity.Notably,the super-acid-catalyzed condensation(SACC)synthesis method stands out due to its user convenience,the ability to create high molecular weight(MW)polymers,and the use of oxygen-tolerant organic catalysts.Although the synthesis of anion exchange membranes(AEMs)using the SACC method began in 2015,and despite growing interest in this synthesis approach,there remains a scarcity of review papers focusing on AEMs synthesized using the SACC method.The review covers the basics of SACC synthesis,presents various polymers synthesized using this method,and summarizes the development of these polymers,particularly their building blocks including aryl,ketone,and anion exchange groups.We systematically describe the effects of changes in the molecular structure of each polymer component,conducted by various research groups,on the mechanical properties,conductivity,and operational stability of the membrane.This review will provide insights into the development of AEMs with superior performance and operational stability suitable for water electrolysis applications.
基金Supported by the National Natural Science Foundation of China under Grant No.50779007the National Science Foundation for Young Scientists of China under Grant No.50809018+2 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20070217074the Defence Advance Research Program of Science and Technology of Ship Industry under Grant No.07J1.1.6Harbin Engineering University Foundation under Grant No.HEUFT07069
文摘The traditional calculation method of frequency-domain Green function mainly utilizes series or asymptotic expansion to carry out numerical approximation, however, this method requires very careful zoning, thus the computing process is complex with many cycles, which has greatly affected the computing efficiency. To improve the computing efficiency, this paper introduces Gaussian integral to the numerical calculation of the frequency-domain Green function and its partial derivatives. It then compares the calculation result with that in existing references. The comparison results demonstrate that, on the basis of its sufficient accuracy, the method has greatly simplified the computing process, reduced the zoning and improved the computing efficiency.
