Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

食品中硒的DBDA测定方法的研究

1,2-二氨基-3,5-二溴代苯(DBDA)在酸性介质中与硒(IV)生成二溴代苤硒脑,在365nm有特征吸收峰,其A365nm值与硒(Ⅳ)浓度间存在良好的直线回归关系。此法实测最低浓度为0.01μg/ml,实测最低量为0.3μg,变异系数为2.5～4.7%.与气相色谱-电子捕获及荧光光度法测定的结果一致。本法简便、快速、选择性强、灵敏度和准确度均高。

导出类比设计系统DBDA的研究实现

讨论了设计领域中的导出类比方法，阐述了类比设计系统ＤＢＤＡ（ＤｅｓｉｇｎＢｙＤｅｒｉｖａｔｉｏｎａｌＡｎａｌｏｇｙ）的研究与实现。全文叙述了ＤＢＤＡ如何将设计计划表达成求精树，如何有效地检索源设计计划。

Performance Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on CEEMDAN-KPCA and DA-GRU Networks

In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.

两种带蒂皮瓣转移修复手指掌侧缺损的比较

[目的]比较指动脉背侧支皮瓣与掌背动脉返支皮瓣急诊修复手指中末节掌侧软组织缺损的临床效果。[方法]2015年5月—2020年8月,收治第2~5指中末节掌侧软组织缺损急诊入院患者46例。依据医患沟通结果,25例采用指背侧支皮瓣,21例采用掌背返支皮瓣。比较两组围手术期情况及随访结果。[结果]两组患者均顺利完成手术,无严重并发症。指背侧组采用植皮闭合供区,而掌背返支组供区闭合多采用直接缝合,前者显著不及后者(P<0.05)。两组手术时间、术后1周皮瓣成活面积、供区切口愈合等级、拆线时间及住院时间的差异均无统计学意义(P>0.05)。两组所有患者均获随访9~20个月,平均(13.6±2.4)个月。末次随访时,指背侧支组患者皮瓣静态两点辨别觉及外观满意度评分均优于掌背返支组(P<0.05),而两组伤指关节TAM的差异无统计学意义(P>0.05)。[结论]两种皮瓣均可有效修复手指中末节掌侧软组织缺损,相比之下,指动脉背侧支皮瓣感觉更好、外形更美观。