Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficul...Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.展开更多
Rural transformation can improve poverty reduction,living standards,and health outcomes in developing countries.However,impacts associated with rural transformation vary by region,household,and individual trait(includ...Rural transformation can improve poverty reduction,living standards,and health outcomes in developing countries.However,impacts associated with rural transformation vary by region,household,and individual trait(including gender).While research on rural transformation has been increasing over the last decade,there has been no comprehensive review conducted on the relationships between gender and rural transformation.Here,we conduct a systematic literature review to investigate the impacts of rural transformation on gender and the influence of gender inclusiveness on rural transformation.We reviewed 82 studies from 1960-2021 that explore the relationships between rural transformation and gender.We then developed a framework that captures incidences and flow directions between indicators.Results show that most studies examined the impacts of rural transformation on women and between gender indicators.Few investigated the role of women and the influence of gender inclusiveness on rural transformation.Overall,studies showed that rural transformation typically leads to positive outcomes for women regarding employment,income,and empowerment.However,negative impacts on women’s control over income,stability of new income sources,and access to healthy food are also common.Tailoring future development policies and programs to explicitly account for gender inclusiveness can lead to more successful rural transformation.展开更多
The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic sys...The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR(XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.展开更多
基金supported by the National Natural Science Foundation of China (42274144,42304122,and 41974155)the Key Research and Development Program of Shaanxi (2023-YBGY-076)+1 种基金the National Key R&D Program of China (2020YFA0713404)the China Uranium Industry and East China University of Technology Joint Innovation Fund (NRE202107)。
文摘Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.
基金supported by the Australian Centre for International Agricultural Research(ACIAR,ADP/2017/024)。
文摘Rural transformation can improve poverty reduction,living standards,and health outcomes in developing countries.However,impacts associated with rural transformation vary by region,household,and individual trait(including gender).While research on rural transformation has been increasing over the last decade,there has been no comprehensive review conducted on the relationships between gender and rural transformation.Here,we conduct a systematic literature review to investigate the impacts of rural transformation on gender and the influence of gender inclusiveness on rural transformation.We reviewed 82 studies from 1960-2021 that explore the relationships between rural transformation and gender.We then developed a framework that captures incidences and flow directions between indicators.Results show that most studies examined the impacts of rural transformation on women and between gender indicators.Few investigated the role of women and the influence of gender inclusiveness on rural transformation.Overall,studies showed that rural transformation typically leads to positive outcomes for women regarding employment,income,and empowerment.However,negative impacts on women’s control over income,stability of new income sources,and access to healthy food are also common.Tailoring future development policies and programs to explicitly account for gender inclusiveness can lead to more successful rural transformation.
基金supported by the National Natural Science Foundation of China (Grant No. 61672124)the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund,China (Grant No. MMJJ20170203)+3 种基金the Liaoning Provincial Science and Technology Innovation Leading Talents Program Project,China (Grant No. XLYC1802013)the Key Research and Development Projects of Liaoning Province,China (Grant No. 2019020105-JH2/103)the Jinan City ‘20 universities’ Funding Projects Introducing Innovation Team Program,China (Grant No. 2019GXRC031)the “Double First-rate”Construction Project (“Innovation Project”),China (Grant No. SSCXXM013)。
文摘The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box(S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR(XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.
文摘依据FFT→优化窗→IFFT思路,突破线性时频变换的窗函数积分性能桎梏,实现高性能优化窗函数的线性时频变换应用,建立新型时频变换算法——K-S变换.对信号x(t)的FFT频谱向量进行频移处理后,与该频移点下Kaiser优化窗的频谱向量进行Hadamard乘积,再将乘积结果进行FFT逆变换(IFFT),构造出K-S变换复时频矩阵,由此获得x(t)的时间-频率-幅值、时间-频率-相位三维信息;给出逆变换的数学推导与局部性质、线性性质和变分辨率特性;0~150 kHz电网的稳态与时变超谐波信号仿真实验表明,K-S变换的时域、频域分辨能力均优于流行的短时傅里叶变换、S变换,具有优良的变分辨率性能;0~40 kHz超谐波信号的实测证明,基于K-S变换的超谐波电压幅值测量绝对误差均小于0.032 3 V.
