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AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2315-2347页

作者： Lirong yin Lei Wang Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani zhengtong yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization *** this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： Super-resolution feature extraction dynamic convolution attention mechanism gate control

DPAL-BERT:A Faster and Lighter Question Answering Model

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 771-786页

作者： Lirong yin Lei Wang Zhuohang Cai Siyu Lu Ruiyang Wang Ahmed AlSanad Salman A.AlQahtani Xiaobing Chen zhengtong yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA College of Resources and Environmental Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering ***,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced *** study aims to minimize the inference time of such models while maintaining computational *** also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and *** research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge *** experimental results showed that the distilled models greatly outperform models trained from *** addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the *** demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

关键词： DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory(BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing

Improved Blending Attention Mechanism in Visual Question Answering

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Computer Systems Science & Engineering 2023年第10期47卷 1149-1161页

作者： Siyu Lu Yueming Ding zhengtong yin Mingzhe Liu Xuan Liu Wenfeng Zheng Lirong yin School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325000China School of Public Affairs and Administration University of Electronic Science and Technology of ChinaChengdu611731China Department of Geography and Anthropology Louisiana State UniversityBaton Rouge70803LAUSA

Visual question answering(VQA)has attracted more and more attention in computer vision and natural language *** are committed to studying how to better integrate image features and text features to achieve better results in VQA *** of all features may cause information redundancy and heavy computational *** mechanism is a wise way to solve this ***,using single attention mechanism may cause incomplete concern of *** paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism *** the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as *** the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall *** results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.

关键词： Visual question answering spatial attention mechanism channel attention mechanism image feature processing text feature extraction

Convolution-Transformer for Image Feature Extraction

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 87-106页

作者： Lirong yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani zhengtong yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

基于MODIS数据乌江流域植被覆盖变化与气候变化关系研究

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林业资源管理 2017年第1期 127-134页

作者：石悦樾银正彤郑文锋贵州大学资源与环境工程学院成都电子科技大学自动化工程学院

分析乌江流域2001-2015年每年8月植被变化规律与气候间关系,得出植被覆盖时间和空间变化规律,为乌江流域生态保护提供参考依据,基于MOD13Q1,MODIS11 C3,TRMM3B43遥感数据产品,使用趋势分析法分析15年内NDVI变化趋势,简单相关分析、偏相... 详细信息

分析乌江流域2001-2015年每年8月植被变化规律与气候间关系,得出植被覆盖时间和空间变化规律,为乌江流域生态保护提供参考依据,基于MOD13Q1,MODIS11 C3,TRMM3B43遥感数据产品,使用趋势分析法分析15年内NDVI变化趋势,简单相关分析、偏相关分析和复相关分析得出NDVI变化与气候变化关系,结果如下:15年中乌江流域NDVI整体呈上升趋势;NDVI变化与气温呈负相关,NDVI变化与降水呈正相关,降水对NDVI的促进作用高于气温的抑制作用;气温、降水对耕地影响最大,常绿针叶林次之;高程在1 000~2 000m的植被覆盖变化最大;沿江地带植被覆盖减少与乌江流域梯级开发有关;植被覆盖减少多在城市区。乌江流域降水驱动型植被占研究区27.75%,对气候响应较大的植被类型以耕地为主;非气温降水驱动型比例为72.25%,人类活动对乌江流域植被覆盖变化起主导作用。

关键词：乌江流域 NDVI变化气候响应降水驱动型非气温降水驱动型

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基于遥感影像的贵阳市植被覆盖变化分析

贵州农业科学 2014年第2期42卷 145-148页

作者：郑永康银正彤何朝凤贵州大学资源与环境工程学院贵州贵阳550025

为了探明贵阳市植被覆盖的发展和变化，采用 ENVI 软件对贵阳市1990年和2007年的Landsat TM 遥感影像进行土地利用分类和植被覆盖度分级，分析植被和其他土地利用类型以及植被覆盖度各等级之间的转移情况。结果表明：较之1990年，2007... 详细信息

为了探明贵阳市植被覆盖的发展和变化，采用 ENVI 软件对贵阳市1990年和2007年的Landsat TM 遥感影像进行土地利用分类和植被覆盖度分级，分析植被和其他土地利用类型以及植被覆盖度各等级之间的转移情况。结果表明：较之1990年，2007年贵阳市的植被覆盖面积增加了11．10％，植被覆盖面积由中、低等级（0～3级）向高等级（4～5级）转移，转移面积达407．82 km2。

关键词：植被覆盖遥感影像土地利用分类植被覆盖度分级贵阳

智能助手多任务使用:信息模态和多任务类型的影响

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工业工程与管理 2023年第2期28卷 129-136页

作者：刘娜殷正同张生太仇泸毅北京邮电大学经济管理学院北京100876

研究智能助手多任务使用场景下多任务类型和信息模态对任务绩效和用户主观感受的影响。本研究采用两因子组内设计的人因学实验,测试了30名参试者在智能助手提供视觉-听觉双模态、视觉单模态、听觉单模态3种情况下,完成并行多任务和顺序... 详细信息

研究智能助手多任务使用场景下多任务类型和信息模态对任务绩效和用户主观感受的影响。本研究采用两因子组内设计的人因学实验,测试了30名参试者在智能助手提供视觉-听觉双模态、视觉单模态、听觉单模态3种情况下,完成并行多任务和顺序多任务的任务绩效、感知易用性、感知有用性、使用意愿和认知负荷,采用重复测量方差分析处理所得数据。结果表明:并行多任务的完成时间显著小于顺序多任务;在任务完成时间和主观感受方面,视觉-听觉双模态显著优于视觉单模态和听觉单模态;多任务类型和信息模态的交互作用并不显著。实验为智能助手信息呈现方式和任务组织方式的设计提供了理论依据和实践指导。

关键词：智能助手多任务信息模态人机交互

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基于数据融合的SRTM数据空洞填补方法

地理空间信息 2007年第3期5卷 62-64页

作者：阚瑷珂朱利东张瑞军郑文锋银正彤王绪本王成善西华师范大学国土资源学院成都理工大学沉积地质研究院四川成都610059 成都市国土规划地籍事务中心四川成都610031 中国地质大学(北京)地球科学与资源学院北京100083

总结国内外对SRTM数据空洞的填补方法,指出基于内插的填补方法无法满足大区域SRTM数据处理的需求。提出在内插的基础上进行数据融合的空洞填补方法,并以实例验证了该方法是一种获取完整地形数据的有效途径。

关键词： SRTM 数据空洞填补数据融合

壳聚糖增强纳米纤维素-蒙脱土复合膜的结构与性能

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复合材料学报 2024年第8期41卷 4299-4309页

作者：汪云逸邹楚文尹冉尤政通王海刚东北林业大学生物质材料科学与技术教育部重点实验室哈尔滨150040

利用纤维素和无机物模仿天然贝壳中高度有序的“砖-砂浆”结构制备高强度功能复合材料,是制备绿色包装膜的优秀选择,二者的界面结合是获得理想结构与性能的关键。本文以羧基化纤维素纳米纤维(CNFMG)和蒙脱土(MTM)纳米片制备膜材料,采用... 详细信息

利用纤维素和无机物模仿天然贝壳中高度有序的“砖-砂浆”结构制备高强度功能复合材料,是制备绿色包装膜的优秀选择,二者的界面结合是获得理想结构与性能的关键。本文以羧基化纤维素纳米纤维(CNFMG)和蒙脱土(MTM)纳米片制备膜材料,采用壳聚糖(CS)通过静电作用增强界面结合。研究了CS与CNFMG和MTM之间的静电相互作用对纳米复合材料结构、力学性能和热稳定性的影响。结果表明:复合膜中MTM以纳米片状形态有序地分散于CNFMG网络间。与CNFMG-MTM二元膜相比,CS加入后的三元膜拉伸强度达到119.2 MPa,强度提升1倍;断裂能达到10.9 MJ/m^(3),韧性提升5.4倍。复合膜为半透明状,具有良好的紫外屏蔽性,CS的加入也提升了复合膜的热稳定性。本文的研究结果可为纤维素基珍珠层仿生材料的研究和应用提供思路。

关键词：纳米纤维素蒙脱土壳聚糖复合膜珍珠层结构仿生