As an alternative to conventional encapsulation concepts for a double glass photovoltaic(PV)module,we introduce an innovative ionomer-based multi-layer encapsulant,by which the application of additional edge sealing t...As an alternative to conventional encapsulation concepts for a double glass photovoltaic(PV)module,we introduce an innovative ionomer-based multi-layer encapsulant,by which the application of additional edge sealing to prevent moisture penetration is not required.The spontaneous moisture absorption and desorption of this encapsulant and its raw materials,poly(ethylene-co-acrylic acid)and an ionomer,are analyzed under different climatic conditions in this work.The relative air humidity is thermodynamically the driving force for these inverse processes and determines the corresponding equilibrium moisture content(EMC).Higher air humidity results in a larger EMC.The homogenization of the absorbed water molecules is a diffusion-controlled process,in which temperature plays a dominant role.Nevertheless,the diffusion coefficient at a higher temperature is still relatively low.Hence,under normal climatic conditions for the application of PV modules,we believe that the investigated ionomer-based encapsulant can“breathe”the humidity:During the day,when there is higher relative humidity,it“inhales”(absorbs)moisture and restrains it within the outer edge of the module;then at night,when there is a lower relative humidity,it“exhales”(desorbs)the moisture.In this way,the encapsulant protects the cell from moisture ingress.展开更多
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
The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced usin...The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.展开更多
文摘As an alternative to conventional encapsulation concepts for a double glass photovoltaic(PV)module,we introduce an innovative ionomer-based multi-layer encapsulant,by which the application of additional edge sealing to prevent moisture penetration is not required.The spontaneous moisture absorption and desorption of this encapsulant and its raw materials,poly(ethylene-co-acrylic acid)and an ionomer,are analyzed under different climatic conditions in this work.The relative air humidity is thermodynamically the driving force for these inverse processes and determines the corresponding equilibrium moisture content(EMC).Higher air humidity results in a larger EMC.The homogenization of the absorbed water molecules is a diffusion-controlled process,in which temperature plays a dominant role.Nevertheless,the diffusion coefficient at a higher temperature is still relatively low.Hence,under normal climatic conditions for the application of PV modules,we believe that the investigated ionomer-based encapsulant can“breathe”the humidity:During the day,when there is higher relative humidity,it“inhales”(absorbs)moisture and restrains it within the outer edge of the module;then at night,when there is a lower relative humidity,it“exhales”(desorbs)the moisture.In this way,the encapsulant protects the cell from moisture ingress.
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This research was supported by the Science and Technology Department of Jilin Province[20210202128NC http://kjt.jl.gov.cn]The People’s Republic of China Ministry of Science and Technology[2018YFF0213606-03 http://www.most.gov.cn]+1 种基金the Jilin Province Development and Reform Commission[2019C021 http://jldrc.jl.gov.cn]the Science and Technology Bureau of Changchun City[21ZGN27 http://kjj.changchun.gov.cn].
文摘The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.
