A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithm...A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.展开更多
Pneumonia is part of the main diseases causing the death of children.It is generally diagnosed through chest Xray images.With the development of Deep Learning(DL),the diagnosis of pneumonia based on DL has received ex...Pneumonia is part of the main diseases causing the death of children.It is generally diagnosed through chest Xray images.With the development of Deep Learning(DL),the diagnosis of pneumonia based on DL has received extensive attention.However,due to the small difference between pneumonia and normal images,the performance of DL methods could be improved.This research proposes a new fine-grained Convolutional Neural Network(CNN)for children’s pneumonia diagnosis(FG-CPD).Firstly,the fine-grainedCNNclassificationwhich can handle the slight difference in images is investigated.To obtain the raw images from the real-world chest X-ray data,the YOLOv4 algorithm is trained to detect and position the chest part in the raw images.Secondly,a novel attention network is proposed,named SGNet,which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal images.Thirdly,the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of FG-CPD.The FG-CPD has been tested on the public Chest X-ray 2017 dataset,and the results show that it has achieved great effect.Then,the FG-CPD is tested on the real chest X-ray images from children aged 3–12 years ago from Tongji Hospital.The results show that FG-CPD has achieved up to 96.91%accuracy,which can validate the potential of the FG-CPD.展开更多
Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity rela...Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity relationships.The traditional ML models are always difficult to identify the structural differences among the single-atom systems with different modification methods,leading to the limitation of the potential application range.Aiming to the structural properties of several typical two-dimensional MA_(2)Z_(4)-based single-atom systems(bare MA_(2)Z_(4) and metal single-atom doped/supported MA_(2)Z_(4)),an improved crystal graph convolutional neural network(CGCNN)classification model was employed,instead of the traditional machine learning regression model,to address the challenge of incompatibility in the studied systems.The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer,surface layer,and bulk layer(ASB-GCNN).Through ML and DFT calculations,five potential single-atom hydrogen evolution reaction(HER)catalysts were screened from chemical space of 600 MA_(2)Z_(4)-based materials,especially V_(1)/HfSn_(2)N_(4)(S)with high stability and activity(Δ_(GH*)is 0.06 eV).Further projected density of states(pDOS)analysis in combination with the wave function analysis of the SAC-H bond revealed that the SAC-dz^(2)orbital coincided with the H-s orbital around the energy level of−2.50 eV,and orbital analysis confirmed the formation ofσbonds.This study provides an efficient multistep screening design framework of metal single-atom catalyst for HER systems with similar two-dimensional supports but different geometric configurations.展开更多
This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and itcan be very difficult to distinguish between them. Botanists and those who study plants, however, can ident...This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and itcan be very difficult to distinguish between them. Botanists and those who study plants, however, can identifythe type of seed at a glance. As far as we know, this is the first work to consider leguminous seeds images withdifferent backgrounds and different sizes and crowding. Machine learning is used to automatically classify andlocate 11 different seed types. We chose Leguminous seeds from 11 types to be the objects of this study. Thosetypes are of different colors, sizes, and shapes to add variety and complexity to our research. The images datasetof the leguminous seeds was manually collected, annotated, and then split randomly into three sub-datasetstrain, validation, and test (predictions), with a ratio of 80%, 10%, and 10% respectively. The images consideredthe variability between different leguminous seed types. The images were captured on five different backgrounds: white A4 paper, black pad, dark blue pad, dark green pad, and green pad. Different heights and shootingangles were considered. The crowdedness of the seeds also varied randomly between 1 and 50 seeds per image.Different combinations and arrangements between the 11 types were considered. Two different image-capturingdevices were used: a SAMSUNG smartphone camera and a Canon digital camera. A total of 828 images wereobtained, including 9801 seed objects (labels). The dataset contained images of different backgrounds, heights,angles, crowdedness, arrangements, and combinations. The TensorFlow framework was used to construct theFaster Region-based Convolutional Neural Network (R-CNN) model and CSPDarknet53 is used as the backbonefor YOLOv4 based on DenseNet designed to connect layers in convolutional neural. Using the transfer learningmethod, we optimized the seed detection models. The currently dominant object detection methods, Faster RCNN, and YOLOv4 performances were compared experimentally. The mAP (mean average precision) of the FasterR-CNN and YOLOv4 models were 84.56% and 98.52% respectively. YOLOv4 had a significant advantage in detection speed over Faster R-CNN which makes it suitable for real-time identification as well where high accuracy andlow false positives are needed. The results showed that YOLOv4 had better accuracy, and detection ability, as wellas faster detection speed beating Faster R-CNN by a large margin. The model can be effectively applied under avariety of backgrounds, image sizes, seed sizes, shooting angles, and shooting heights, as well as different levelsof seed crowding. It constitutes an effective and efficient method for detecting different leguminous seeds incomplex scenarios. This study provides a reference for further seed testing and enumeration applications.展开更多
The combination of CrO4^2-anion and N,N′-dimethylformamide(DMF)-containing solvent helps to stabilize an atom-precise ultrasmall Ag6 kernel into a 52-nuclei silver shell,giving a core-shell Ag6@Ag52 wheel-like struct...The combination of CrO4^2-anion and N,N′-dimethylformamide(DMF)-containing solvent helps to stabilize an atom-precise ultrasmall Ag6 kernel into a 52-nuclei silver shell,giving a core-shell Ag6@Ag52 wheel-like structure(SD/Ag58b).The solution behavior and photocurrent response property were investigated in details.展开更多
卫星云图是气象预报的重要资源之一,可以显示云层的生消变化,对气象分析和预报工作有极大的作用.对云图进行一定时间段的预测有助于及时掌握云层的移动轨迹和变化情况,提高卫星云图资料的实用性.然而,当前卫星云图的预测面临诸多困难,例...卫星云图是气象预报的重要资源之一,可以显示云层的生消变化,对气象分析和预报工作有极大的作用.对云图进行一定时间段的预测有助于及时掌握云层的移动轨迹和变化情况,提高卫星云图资料的实用性.然而,当前卫星云图的预测面临诸多困难,例如,云团的变化大多是非平稳、非线性的;云图数据量小,实时性差等.因此,从时空序列的角度出发,提出一种基于3D卷积和自注意力机制的卫星云图预测模型,该模型在ST-LSTM(Spatiotemporal Long ShortTerm Memory)的基础上,在其单元内部引入3D卷积和自注意力机制,使模型能同时提取时间信息和空间特征,进一步增强云层短期趋势和长期依赖的联系;同时,在其外部框架使用空间和通道注意力机制,促进对云图空间特征的提取.在风云四号的卫星云图上进行评估,实验结果证明,该模型能够较准确地预测云层的形态变化和运动轨迹,各项评价指标均优于现有模型.展开更多
基金supported by the Science and technology project of State Grid Information&Telecommunication Group Co.,Ltd (SGTYHT/19-JS-218)
文摘A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.
基金supported in part by the Natural Science Foundation of China(NSFC)underGrant No.51805192,Major Special Science and Technology Project of Hubei Province under Grant No.2020AEA009sponsored by the State Key Laboratory of Digital Manufacturing Equipment and Technology(DMET)of Huazhong University of Science and Technology(HUST)under Grant No.DMETKF2020029.
文摘Pneumonia is part of the main diseases causing the death of children.It is generally diagnosed through chest Xray images.With the development of Deep Learning(DL),the diagnosis of pneumonia based on DL has received extensive attention.However,due to the small difference between pneumonia and normal images,the performance of DL methods could be improved.This research proposes a new fine-grained Convolutional Neural Network(CNN)for children’s pneumonia diagnosis(FG-CPD).Firstly,the fine-grainedCNNclassificationwhich can handle the slight difference in images is investigated.To obtain the raw images from the real-world chest X-ray data,the YOLOv4 algorithm is trained to detect and position the chest part in the raw images.Secondly,a novel attention network is proposed,named SGNet,which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal images.Thirdly,the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of FG-CPD.The FG-CPD has been tested on the public Chest X-ray 2017 dataset,and the results show that it has achieved great effect.Then,the FG-CPD is tested on the real chest X-ray images from children aged 3–12 years ago from Tongji Hospital.The results show that FG-CPD has achieved up to 96.91%accuracy,which can validate the potential of the FG-CPD.
基金supported by the National Key R&D Program of China(2021YFA1500900)National Natural Science Foundation of China(U21A20298,22141001).
文摘Machine learning(ML)integrated with density functional theory(DFT)calculations have recently been used to accelerate the design and discovery of single-atom catalysts(SACs)by establishing deep structure–activity relationships.The traditional ML models are always difficult to identify the structural differences among the single-atom systems with different modification methods,leading to the limitation of the potential application range.Aiming to the structural properties of several typical two-dimensional MA_(2)Z_(4)-based single-atom systems(bare MA_(2)Z_(4) and metal single-atom doped/supported MA_(2)Z_(4)),an improved crystal graph convolutional neural network(CGCNN)classification model was employed,instead of the traditional machine learning regression model,to address the challenge of incompatibility in the studied systems.The CGCNN model was optimized using crystal graph representation in which the geometric configuration was divided into active layer,surface layer,and bulk layer(ASB-GCNN).Through ML and DFT calculations,five potential single-atom hydrogen evolution reaction(HER)catalysts were screened from chemical space of 600 MA_(2)Z_(4)-based materials,especially V_(1)/HfSn_(2)N_(4)(S)with high stability and activity(Δ_(GH*)is 0.06 eV).Further projected density of states(pDOS)analysis in combination with the wave function analysis of the SAC-H bond revealed that the SAC-dz^(2)orbital coincided with the H-s orbital around the energy level of−2.50 eV,and orbital analysis confirmed the formation ofσbonds.This study provides an efficient multistep screening design framework of metal single-atom catalyst for HER systems with similar two-dimensional supports but different geometric configurations.
文摘This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and itcan be very difficult to distinguish between them. Botanists and those who study plants, however, can identifythe type of seed at a glance. As far as we know, this is the first work to consider leguminous seeds images withdifferent backgrounds and different sizes and crowding. Machine learning is used to automatically classify andlocate 11 different seed types. We chose Leguminous seeds from 11 types to be the objects of this study. Thosetypes are of different colors, sizes, and shapes to add variety and complexity to our research. The images datasetof the leguminous seeds was manually collected, annotated, and then split randomly into three sub-datasetstrain, validation, and test (predictions), with a ratio of 80%, 10%, and 10% respectively. The images consideredthe variability between different leguminous seed types. The images were captured on five different backgrounds: white A4 paper, black pad, dark blue pad, dark green pad, and green pad. Different heights and shootingangles were considered. The crowdedness of the seeds also varied randomly between 1 and 50 seeds per image.Different combinations and arrangements between the 11 types were considered. Two different image-capturingdevices were used: a SAMSUNG smartphone camera and a Canon digital camera. A total of 828 images wereobtained, including 9801 seed objects (labels). The dataset contained images of different backgrounds, heights,angles, crowdedness, arrangements, and combinations. The TensorFlow framework was used to construct theFaster Region-based Convolutional Neural Network (R-CNN) model and CSPDarknet53 is used as the backbonefor YOLOv4 based on DenseNet designed to connect layers in convolutional neural. Using the transfer learningmethod, we optimized the seed detection models. The currently dominant object detection methods, Faster RCNN, and YOLOv4 performances were compared experimentally. The mAP (mean average precision) of the FasterR-CNN and YOLOv4 models were 84.56% and 98.52% respectively. YOLOv4 had a significant advantage in detection speed over Faster R-CNN which makes it suitable for real-time identification as well where high accuracy andlow false positives are needed. The results showed that YOLOv4 had better accuracy, and detection ability, as wellas faster detection speed beating Faster R-CNN by a large margin. The model can be effectively applied under avariety of backgrounds, image sizes, seed sizes, shooting angles, and shooting heights, as well as different levelsof seed crowding. It constitutes an effective and efficient method for detecting different leguminous seeds incomplex scenarios. This study provides a reference for further seed testing and enumeration applications.
基金supported by the National Natural Science Foundation of China(21822107,21571115,21827801,21671172)the Natural Science Foundation of Shandong Province(JQ201803,ZR2017MB061)+2 种基金the Taishan Scholar Project of Shandong Province of Chinathe Qilu Youth Scholar Funding of Shandong Universitythe Fundamental Research Funds of Shandong University(104.205.2.5)
文摘The combination of CrO4^2-anion and N,N′-dimethylformamide(DMF)-containing solvent helps to stabilize an atom-precise ultrasmall Ag6 kernel into a 52-nuclei silver shell,giving a core-shell Ag6@Ag52 wheel-like structure(SD/Ag58b).The solution behavior and photocurrent response property were investigated in details.
文摘卫星云图是气象预报的重要资源之一,可以显示云层的生消变化,对气象分析和预报工作有极大的作用.对云图进行一定时间段的预测有助于及时掌握云层的移动轨迹和变化情况,提高卫星云图资料的实用性.然而,当前卫星云图的预测面临诸多困难,例如,云团的变化大多是非平稳、非线性的;云图数据量小,实时性差等.因此,从时空序列的角度出发,提出一种基于3D卷积和自注意力机制的卫星云图预测模型,该模型在ST-LSTM(Spatiotemporal Long ShortTerm Memory)的基础上,在其单元内部引入3D卷积和自注意力机制,使模型能同时提取时间信息和空间特征,进一步增强云层短期趋势和长期依赖的联系;同时,在其外部框架使用空间和通道注意力机制,促进对云图空间特征的提取.在风云四号的卫星云图上进行评估,实验结果证明,该模型能够较准确地预测云层的形态变化和运动轨迹,各项评价指标均优于现有模型.
