Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood...Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.展开更多
With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public security.Vehicle Re-ID meets challenge attributable to the large intra-class diff...With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public security.Vehicle Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar appearances.Plentiful existing methods focus on local attributes by marking local locations.However,these methods require additional annotations,resulting in complex algorithms and insufferable computation time.To cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint loss.This model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in DenseNet121.At the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ability.Additionally,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training process.Experimental results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,respectively.Besides,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.展开更多
Co0.85Se magnetic nanoparticles supported on carbon nanotubes were prepared by a one‐step hydrothermal method.The saturation magnetization and coercivity of the MWCNTs/Co0.85Se nanocomposites increased due to a decre...Co0.85Se magnetic nanoparticles supported on carbon nanotubes were prepared by a one‐step hydrothermal method.The saturation magnetization and coercivity of the MWCNTs/Co0.85Se nanocomposites increased due to a decrease in the Co0.85Se nanoparticle size in the MWCNTs/Co0.85Se nanocomposites and an increase in the distance between the Co0.85Se nanoparticles,which increased the specific surface area,thereby benefiting the electrocatalytic performance of the catalyst.Moreover,the MWCNTs/Co0.85Se nanocomposites exhibited an excellent hydrogen evolution reaction performance owing to the presence of MWCNTs,which enhanced the mass transport during the electrocatalytic reactions.Furthermore,in an acid solution,the 30 wt%MWCNTs/Co0.85Se composite catalyst exhibited a current density of 10 mA cm^‒2 at a small overpotential of 266 mV vs.RHE,a small Tafel slope of 60.5 mV dec^‐1,and good stability for HER.展开更多
根据蓝印花布纹样的风格特征,文章提出一种端到端的蓝印花布纹样自动生成方法,实现简笔画图像向蓝印花布单纹样的自动迁移。针对蓝印花布的抽象风格和小数据集问题,重新构造CycleGAN生成网络中的编码器和解码器,使用SE(squeeze and exci...根据蓝印花布纹样的风格特征,文章提出一种端到端的蓝印花布纹样自动生成方法,实现简笔画图像向蓝印花布单纹样的自动迁移。针对蓝印花布的抽象风格和小数据集问题,重新构造CycleGAN生成网络中的编码器和解码器,使用SE(squeeze and excitation)注意力模块和残差模块与原始的卷积模块串联,提高特征提取能力和网络学习能力。同时减少生成网络中转换器的残差块层数,降低过拟合。实验结果表明,基于SE注意力CycleGAN网络方法自动生成的蓝印花布新纹样主观性上更贴合原始风格,与原图更加接近,有助于蓝印花布的数字化传承和创新。展开更多
近年来,中央银行数字货币(CBDC)受到全球多个国家和地区的高度关注.双离线交易作为CBDC的可选属性,在无网络连接的情况下进行支付,被认为具有较大的实用价值.面向CBDC的双离线匿名支付场景,基于可信执行环境(TEE)和安全单元(SE)技术,提...近年来,中央银行数字货币(CBDC)受到全球多个国家和地区的高度关注.双离线交易作为CBDC的可选属性,在无网络连接的情况下进行支付,被认为具有较大的实用价值.面向CBDC的双离线匿名支付场景,基于可信执行环境(TEE)和安全单元(SE)技术,提出了一种专为移动平台设计的高效双离线匿名支付方案(dual offline anonymous E-payment for mobile devices,OAPM).OAPM适用于资源受限的移动设备,允许移动付款者在不联网状态下安全地向收款者支付数字货币,且不向收款者及商业银行泄露个人隐私信息,付款者的支付行为也不会被链接,同时允许收款者设备处于离线状态,监管机构(如中央银行)在必要情况下能够识别匿名付款者的真实身份.该方案满足数字货币交易的多项重要属性,包括正确性、不可链接性、可追踪性、不可陷害性、机密性、真实性、防双花性以及可控匿名性等.实现了原型系统,并对可能的参数进行了评估.安全性分析和实验结果表明,该方案从安全性和效率两方面均能满足移动用户CBDC双离线交易的实际需求.展开更多
We present a detailed investigation of magnetic properties of colossal magnetoresistance material HgCr2Se4. While spontaneous magnetization and zero-field magnetic susceptibility are found to follow asymptotic scaling...We present a detailed investigation of magnetic properties of colossal magnetoresistance material HgCr2Se4. While spontaneous magnetization and zero-field magnetic susceptibility are found to follow asymptotic scaling laws for a narrow range of temperatures near the critical point, two methods with connections to the renormalization group theory provide analytical descriptions of the magnetic properties for much wider temperature ranges. Based on this, an analytical formula is obtained for the temperature dependence of the low field magnetoresistance in the paramagnetic phase.展开更多
基金This study was supported by the Fundamental Research Funds for the Central Universities(No.2572023DJ02).
文摘Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.
基金supported,in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public security.Vehicle Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar appearances.Plentiful existing methods focus on local attributes by marking local locations.However,these methods require additional annotations,resulting in complex algorithms and insufferable computation time.To cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint loss.This model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in DenseNet121.At the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ability.Additionally,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training process.Experimental results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,respectively.Besides,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.
文摘Co0.85Se magnetic nanoparticles supported on carbon nanotubes were prepared by a one‐step hydrothermal method.The saturation magnetization and coercivity of the MWCNTs/Co0.85Se nanocomposites increased due to a decrease in the Co0.85Se nanoparticle size in the MWCNTs/Co0.85Se nanocomposites and an increase in the distance between the Co0.85Se nanoparticles,which increased the specific surface area,thereby benefiting the electrocatalytic performance of the catalyst.Moreover,the MWCNTs/Co0.85Se nanocomposites exhibited an excellent hydrogen evolution reaction performance owing to the presence of MWCNTs,which enhanced the mass transport during the electrocatalytic reactions.Furthermore,in an acid solution,the 30 wt%MWCNTs/Co0.85Se composite catalyst exhibited a current density of 10 mA cm^‒2 at a small overpotential of 266 mV vs.RHE,a small Tafel slope of 60.5 mV dec^‐1,and good stability for HER.
文摘根据蓝印花布纹样的风格特征,文章提出一种端到端的蓝印花布纹样自动生成方法,实现简笔画图像向蓝印花布单纹样的自动迁移。针对蓝印花布的抽象风格和小数据集问题,重新构造CycleGAN生成网络中的编码器和解码器,使用SE(squeeze and excitation)注意力模块和残差模块与原始的卷积模块串联,提高特征提取能力和网络学习能力。同时减少生成网络中转换器的残差块层数,降低过拟合。实验结果表明,基于SE注意力CycleGAN网络方法自动生成的蓝印花布新纹样主观性上更贴合原始风格,与原图更加接近,有助于蓝印花布的数字化传承和创新。
文摘近年来,中央银行数字货币(CBDC)受到全球多个国家和地区的高度关注.双离线交易作为CBDC的可选属性,在无网络连接的情况下进行支付,被认为具有较大的实用价值.面向CBDC的双离线匿名支付场景,基于可信执行环境(TEE)和安全单元(SE)技术,提出了一种专为移动平台设计的高效双离线匿名支付方案(dual offline anonymous E-payment for mobile devices,OAPM).OAPM适用于资源受限的移动设备,允许移动付款者在不联网状态下安全地向收款者支付数字货币,且不向收款者及商业银行泄露个人隐私信息,付款者的支付行为也不会被链接,同时允许收款者设备处于离线状态,监管机构(如中央银行)在必要情况下能够识别匿名付款者的真实身份.该方案满足数字货币交易的多项重要属性,包括正确性、不可链接性、可追踪性、不可陷害性、机密性、真实性、防双花性以及可控匿名性等.实现了原型系统,并对可能的参数进行了评估.安全性分析和实验结果表明,该方案从安全性和效率两方面均能满足移动用户CBDC双离线交易的实际需求.
基金Supported by the National Natural Science Foundation of China under Grant Nos 61425015,11474330 and 11374337the National Basic Research Program of China under Grant Nos 2012CB921703 and 2015CB921102the Chinese Academy of Sciences
文摘We present a detailed investigation of magnetic properties of colossal magnetoresistance material HgCr2Se4. While spontaneous magnetization and zero-field magnetic susceptibility are found to follow asymptotic scaling laws for a narrow range of temperatures near the critical point, two methods with connections to the renormalization group theory provide analytical descriptions of the magnetic properties for much wider temperature ranges. Based on this, an analytical formula is obtained for the temperature dependence of the low field magnetoresistance in the paramagnetic phase.
