Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,si...Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.展开更多
光电化学(PEC)催化CO_(2)转化合成高附加值多碳化合物具有广阔的应用前景.本文将Au纳米晶修饰N掺杂TiO_(2)纳米片的光阳极与Zn掺杂Cu_(2)O阴极相结合,构筑了高效PECCO_(2)转化体系.该体系在较低外加偏压(0.5 V vs.Ag/AgCl)和光照条件下...光电化学(PEC)催化CO_(2)转化合成高附加值多碳化合物具有广阔的应用前景.本文将Au纳米晶修饰N掺杂TiO_(2)纳米片的光阳极与Zn掺杂Cu_(2)O阴极相结合,构筑了高效PECCO_(2)转化体系.该体系在较低外加偏压(0.5 V vs.Ag/AgCl)和光照条件下能够实现CO_(2)高效转化合成CH3COOH,其法拉第效率高达58.1%(碳产物的选择性为91.5%).研究人员进一步利用程序升温脱附和原位拉曼光谱发现阴极上Zn的引入能够在选择性催化CO_(2)转化合成CH3COOH过程中起到关键作用:(1)优化Cu_(2)O局部电子结构;(2)增加表面反应活性位点;(3)促进C-C偶联中间体CH2/*CH3的形成.而Au纳米晶修饰N掺杂TiO_(2)纳米片优异的光响应则能够为反应提供更多的光生电子,进而提高催化反应速率.这项工作不仅实现了高选择性光电化学催化CO_(2)向高附加值产物的转化,同时为高效PEC CO_(2)转化体系的设计提供了新的思路.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61973055Fundamental Research Funds for the Central Universities under Grant No.ZYGX2020J011Regional Innovation Cooperation Funds of Sichuan under Grant No.2024YFHZ0089.
文摘Data augmentation is an important task of using existing data to expand data sets.Using generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated samples.However,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator features.To solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator samples.The attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect samples.The experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.
基金financially supported in part by the National Key R&D Program of China (2017YFA0207301, and 2017YFA0403402)the National Natural Science Foundation of China (21725102, 91961106, U1832156, 22075267, 21803002, 91963108, 21950410514, and U1732272)+5 种基金CAS Key Research Program of Frontier Sciences (QYZDB-SSW-SLH018)Science and Technological Fund of Anhui Province for Outstanding Youth (2008085 J05)Youth Innovation Promotion Association of CAS (2019444)Young Elite Scientist Sponsorship Program by CAST, China Postdoctoral Science Foundation (2019 M652190, 2020 T130627)Chinese Universities Scientific Fund (WK2060190096), MOST (2018YFA0208603)DNL Cooperation Fund, CAS (DNL201922, DNL180201)
文摘光电化学(PEC)催化CO_(2)转化合成高附加值多碳化合物具有广阔的应用前景.本文将Au纳米晶修饰N掺杂TiO_(2)纳米片的光阳极与Zn掺杂Cu_(2)O阴极相结合,构筑了高效PECCO_(2)转化体系.该体系在较低外加偏压(0.5 V vs.Ag/AgCl)和光照条件下能够实现CO_(2)高效转化合成CH3COOH,其法拉第效率高达58.1%(碳产物的选择性为91.5%).研究人员进一步利用程序升温脱附和原位拉曼光谱发现阴极上Zn的引入能够在选择性催化CO_(2)转化合成CH3COOH过程中起到关键作用:(1)优化Cu_(2)O局部电子结构;(2)增加表面反应活性位点;(3)促进C-C偶联中间体CH2/*CH3的形成.而Au纳米晶修饰N掺杂TiO_(2)纳米片优异的光响应则能够为反应提供更多的光生电子,进而提高催化反应速率.这项工作不仅实现了高选择性光电化学催化CO_(2)向高附加值产物的转化,同时为高效PEC CO_(2)转化体系的设计提供了新的思路.
