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Insight into SO_(2) poisoning and regeneration of one-pot synthesized Cu-SSZ-13 catalyst for selective reduction of NO_(x) by NH_(3)

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Chinese Journal of Chemical Engineering 2022年第6期35卷 184-193页

作者： Xin Yong Hong Chen Huawang zhao Miao Wei yingnan zhao Yongdan Li School of Environmental Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin Key Laboratory of Applied Catalysis Science and TechnologyState Key Laboratory of Chemical Engineering(Tianjin University)School of Chemical EngineeringTianjin UniversityTianjin 300072China Department of Chemical and Metallurgical Engineering School of Chemical EngineeringAalto UniversityEspoo 02150Finland

The effects of SO_(2) on an one-pot synthesized Cu-SSZ-13 catalyst for selective reduction of NO_(x) by NH_(3) were examined.The addition of SO_(2) inhibited NO_(x) conversion significantly below 300℃,while no effect on NO_(x) conversion was observed above 300℃.TGA,TPD,and XPS results showed that the deactivation was caused by the formation of(NH4)2SO_(4),SO_(2) chemisorption on the isolated Cu^(2+)ion sites,as well as the formation of CuSO_(4)-like species.Among them,the site-blocking effect of(NH_(4))_(2)SO_(4) on Cu^(2+)was the primary reason for deactivation.Fortunately,89%of deNO_(x) activity of the poisoned catalyst was recovered after thermal treatment at 500℃ in air,where(NH_(4))_(2)SO_(4) was completely decomposed.The activity was further recovered with regeneration temperature increasing to 600℃,removing the adsorbed SO_(2) on the Cu^(2+)sites.The regeneration at 600℃ could not recover the activity completely,because of the high stability of CuSO_(4)-like species.

关键词： Emission control SCR One-pot synthesis of Cu-SSZ-13 SO_(2)poisoning Activity regeneration

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

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Computer Systems Science & Engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu yingnan zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning YOLOv5 Attention Mechanism

Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

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Computer Systems Science & Engineering 2022年第11期43卷 719-735页

作者： yingnan zhao Guanlan Ji Fei Chen Peiyuan Ji Yi Cao School of Computer and Science Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.

关键词： Short-term wind speed prediction variational mode decomposition attention mechanism SENet BiLSTM

Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

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Computer Systems Science & Engineering 2021年第11期39卷 197-209页

作者： Jingcheng Qian Mingfang Zhu yingnan zhao Xiangjian He Wujiang Power Supply Company Suzhou320500China School of Computer&Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computing and Communications University of TechnologySydneyAustralia

Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power.Temporal-spatial wind speed features contain rich information;however,their use to predict wind speed remains one of the most challenging and less studied areas.This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attentionbased long short-term memory(LSTM),termed 2Attn-LSTM,a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data.To eliminate the unevenness of the original wind speed,we initially decompose the preprocessing data into IMF components by variational mode decomposition(VMD).Then,it encodes the spatial features of IMF components at the bottom of the model and decodes the temporal features to obtain each component's predicted value on the second layer.Finally,we obtain the ultimate prediction value after denormalization and superposition.We have performed extensive experiments for short-term predictions on real-world data,demonstrating that 2Attn-LSTM outperforms the four baseline methods.It is worth pointing out that the presented 2Atts-LSTM is a general model suitable for other spatial-temporal features.

关键词： Wind speed prediction temporal-spatial features VMD LSTM attention mechanism

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混合特征及多头注意力的中文短文本分类

计算机工程与应用 2024年第9期60卷 237-243页

作者：江结林朱永伟许小龙崔燕赵英男南京信息工程大学软件学院南京210044 南京信息工程大学江苏省大气环境与装备技术协同创新中心南京210044 南京特殊教育师范学院数学与信息科学学院南京210038 南京信息工程大学计算机学院、网络空间安全学院南京210044

传统的短文本分类研究方法存在两方面不足,一是不能全面地表示文本的语义信息,二是无法充分地提取和融合文本全局和局部信息。基于此,提出一种混合特征及多头注意力(HF-MHA)的中文短文本分类方法。该方法利用预训练模型计算中文短文本... 详细信息

传统的短文本分类研究方法存在两方面不足,一是不能全面地表示文本的语义信息,二是无法充分地提取和融合文本全局和局部信息。基于此,提出一种混合特征及多头注意力(HF-MHA)的中文短文本分类方法。该方法利用预训练模型计算中文短文本的字符级向量和词级向量表示,以得到更全面的文本特征向量表示;采用多头注意力机制捕捉文本序列中的依赖关系,以提高文本的语义理解;通过卷积神经网络分别提取两种向量表示的特征,并将其融合为一个特征向量,以整合文本的全局和局部信息;通过输出层得到分类结果。在三个公开数据集上的实验表明,HF-MHA能够有效地提升中文短文本分类的性能。

关键词：中文短文本分类注意力机制词级向量字符级向量

哈萨克斯坦进口3种食用植物油的营养组分特征研究

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中国粮油学报 2024年第2期39卷 120-124页

作者：柴成梁郭咪咪朱莹丹赵英男段章群国家粮食和物资储备局科学研究院粮油加工研究所北京100037 黑龙江八一农垦大学食品学院大庆163000

哈萨克斯坦是“一带一路”经济带上的油脂油料生产大国,与中国贸易基础深厚,具有成为我国油脂油料进口重要伙伴的巨大潜力。研究系统分析了哈萨克斯坦进口菜籽油、葵花籽油及亚麻籽油的营养组分组成及含量特征。结果表明,3种哈萨克斯坦... 详细信息

哈萨克斯坦是“一带一路”经济带上的油脂油料生产大国,与中国贸易基础深厚,具有成为我国油脂油料进口重要伙伴的巨大潜力。研究系统分析了哈萨克斯坦进口菜籽油、葵花籽油及亚麻籽油的营养组分组成及含量特征。结果表明,3种哈萨克斯坦进口的食用油脂中的必需脂肪酸的含量水平,均符合我国规定的同类油品中的必需脂肪酸的含量要求,其中,葵花籽油和亚麻籽油的亚油酸质量分数分别为66.272%和18.332%,与我国规定的48.3%~74.0%和10.0%~20.0%相比,处于较高水平。与国际食品法典委员会规定值相比,其营养物质生育酚,尤其是γ-生育酚和δ-生育酚的含量十分丰富,其中,菜籽油中γ-生育酚和δ-生育酚的含量为825.5、25.3 mg/kg,分别比国际食品法典委员会规定的最大值高出9.6%和15.0%;葵花籽油中γ-生育酚和δ-生育酚的含量为94.1、21.5 mg/kg,分别比国际食品法典委员会规定的最大值高出176.8%和207.1%;亚麻籽油中γ-生育酚和δ-生育酚的含量为802.6、22.5 mg/kg,分别比国际食品法典委员会规定的最大值高出12.7%和60.7%。

关键词：油脂进口菜籽油葵花籽油亚麻籽油营养组分

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铝合金蜂窝吸能结构参数优化设计

科技创新与应用 2024年第15期14卷 21-24页

作者：赵庆龙蔡茂赵英男中车成型科技(青岛)有限公司山东青岛266000

基于铝合金蜂窝结构轴向平压试验和数值模拟,对不同结构参数的铝合金蜂窝结构吸能特性展开评估,通过对参数质量比吸能增率的引入,表明吸能增率与质量增率为一次线性关系。归纳得出铝合金蜂窝吸能结构参数优化设计的经验公式,经验证明,... 详细信息

基于铝合金蜂窝结构轴向平压试验和数值模拟,对不同结构参数的铝合金蜂窝结构吸能特性展开评估,通过对参数质量比吸能增率的引入,表明吸能增率与质量增率为一次线性关系。归纳得出铝合金蜂窝吸能结构参数优化设计的经验公式,经验证明,计算精度超过90%,可实现铝蜂窝吸能结构参数快速优化设计,为轻量化吸能结构的设计提供理论依据。

关键词：蜂窝铝吸能特性质量比吸能增率结构参数优化吸能结构

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碳纤维麦弗逊悬架控制臂铺层优化

科技创新与应用 2024年第12期14卷 28-31页

作者：蔡茂赵庆龙赵英男王鹏中车成型科技(青岛)有限公司山东青岛266000

根据3种极限工况的悬架硬点载荷,结合复合材料经典层合板理论和对称平衡的铺层设计方法,开展复合材料控制臂的纤维铺层角度设计,并使用OptiStruct软件以1个超级层关联4个单层铺层的方法进行铺层优化设计。进而通过拓扑分析的方法获得需... 详细信息

根据3种极限工况的悬架硬点载荷,结合复合材料经典层合板理论和对称平衡的铺层设计方法,开展复合材料控制臂的纤维铺层角度设计,并使用OptiStruct软件以1个超级层关联4个单层铺层的方法进行铺层优化设计。进而通过拓扑分析的方法获得需要加强的区域,利用铺层尺寸优化方法对控制臂进行形貌优化设计,在满足使用要求的前提下实现较原金属结构减重64%。提出一种非均匀截面结构的铺层优化方法,对复杂截面复合材料结构铺层优化具有一定的指导意义。

关键词：碳纤维复合材料控制臂铺层优化形貌优化非均匀截面结构

马齿苋提取物对α-淀粉酶和α-葡萄糖苷酶的抑制作用

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食品科技 2024年第3期49卷 243-249页

作者：张义涛王金兰张树军赵英楠王丹赵明时志春李军齐齐哈尔大学化学与化学工程学院黑龙江齐齐哈尔161006 黑龙江省工业大麻加工技术创新中心国家市场监管技术创新中心(工业大麻)黑龙江齐齐哈尔161006

目的:进一步研究干、鲜马齿苋的降血糖活性及降糖机制。方法:分别将干燥和新鲜马齿苋用乙醇为溶剂浸泡提取,制备干燥马齿苋乙醇提取物(Dried Portulaca oleracea L.ethanol extracts,DEP)和新鲜马齿苋乙醇提取物(Fresh Portulaca olerac... 详细信息

目的:进一步研究干、鲜马齿苋的降血糖活性及降糖机制。方法:分别将干燥和新鲜马齿苋用乙醇为溶剂浸泡提取,制备干燥马齿苋乙醇提取物(Dried Portulaca oleracea L.ethanol extracts,DEP)和新鲜马齿苋乙醇提取物(Fresh Portulaca oleracea L.ethanol extracts,FEP)。再分别将DEP和FEP用水混悬,依次用正己烷、乙酸乙酯、正丁醇进行萃取,从DEP得到H-DEP、E-DEP、D-DEP和W-DEP,从FEP得到H-FEP、E-FEP、D-FEP和W-FEP。分别对DEP和FEP以及所得各种萃取物进行酶抑制活性测试,结果:E-FEP对2种酶的抑制作用最好,IC50值分别为(0.094±0.001)mg/mL和(33.63±0.42)μg/mL。绘制Lineweaver-Burk曲线确定酶抑制类型:对于α-淀粉酶,H-DEP、E-DEP、H-FEP及E-FEP为非竞争性抑制,DEP、FEP、D-DEP、W-DEP、D-FEP及W-FEP为竞争性和非竞争性混合抑制;对于α-葡萄糖苷酶,H-DEP、E-DEP、H-FEP及E-FEP为非竞争性抑制,DEP、FEP、D-DEP、W-DEP、D-FEP及W-FEP为反竞争性和非竞争性混合的抑制类型。结论:马齿苋具有良好的降血糖活性,该研究结果可为综合利用马齿苋资源提供理论依据,对研究开发新型降糖食物、推动降血糖活性研究进展等具有重要意义。

关键词：马齿苋 α-淀粉酶 α-葡萄糖苷酶抑制作用酶动力学