Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic fire...Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.展开更多
Object detection is widely used in object tracking;anchor-free object tracking provides an end-to-end single-object-tracking approach.In this study,we propose a new anchor-free network,the Siamese center-prediction ne...Object detection is widely used in object tracking;anchor-free object tracking provides an end-to-end single-object-tracking approach.In this study,we propose a new anchor-free network,the Siamese center-prediction network(SiamCPN).Given the presence of referenced object features in the initial frame,we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations.Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction,SiamCPN directly obtains all information required for tracking,greatly simplifying the model.A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net.The model can accurately predict object location,implement appropriate corrections,and regress the size of the target bounding box.Compared to other leading Siamese networks,SiamCPN is simpler,faster,and more efficient as it uses fewer hyperparameters.Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks,and is comparable to other excellent trackers on LaSOT,VOT2016,and OTB-100 while improving inference speed 1.5 to 2 times.展开更多
The microinjection of Zebrafish embryos is significant to life science and biomedical research.In this article,a novel automated system is developed for cell microinjection.A sophisticated microfluidic chip is designe...The microinjection of Zebrafish embryos is significant to life science and biomedical research.In this article,a novel automated system is developed for cell microinjection.A sophisticated microfluidic chip is designed to transport,hold,and inject cells continuously.For the first time,a microinjector with microforce perception is proposed and integrated within the enclosed microfluidic chip to judge whether cells have been successfully punctured.The deep learning model is employed to detect the yolk center of zebrafish embryos and locate the position of the injection needle within the yolk,which enables enhancing the precision of cell injection.A prototype is fabricated to achieve automatic batch microinjection.Experimental results demonstrated that the injection efficiency is about 20 seconds per cell.Cell puncture success rate and cell survival rate are 100%and 84%,respectively.Compared to manual operation,this proposed system improves cell operation efficiency and cell survival rate.The proposed microinjection system has the potential to greatly reduce the workload of the experimenters and shorten the relevant study period.展开更多
文摘Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.
基金supported by the National Key R&D Program of China(Grant No.2018YFC0807500)the National Natural Science Foundation of China(Grant Nos.U20B2070 and 61832016).
文摘Object detection is widely used in object tracking;anchor-free object tracking provides an end-to-end single-object-tracking approach.In this study,we propose a new anchor-free network,the Siamese center-prediction network(SiamCPN).Given the presence of referenced object features in the initial frame,we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations.Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction,SiamCPN directly obtains all information required for tracking,greatly simplifying the model.A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net.The model can accurately predict object location,implement appropriate corrections,and regress the size of the target bounding box.Compared to other leading Siamese networks,SiamCPN is simpler,faster,and more efficient as it uses fewer hyperparameters.Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks,and is comparable to other excellent trackers on LaSOT,VOT2016,and OTB-100 while improving inference speed 1.5 to 2 times.
基金support from the National Natural Science Foundation of China(32101626)the Shandong Province Key R&D Plan Project(2022LZGC020)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20220490)the Fundamental Research Funds for the Central Universities under Grant No.226-2024-00227.
文摘The microinjection of Zebrafish embryos is significant to life science and biomedical research.In this article,a novel automated system is developed for cell microinjection.A sophisticated microfluidic chip is designed to transport,hold,and inject cells continuously.For the first time,a microinjector with microforce perception is proposed and integrated within the enclosed microfluidic chip to judge whether cells have been successfully punctured.The deep learning model is employed to detect the yolk center of zebrafish embryos and locate the position of the injection needle within the yolk,which enables enhancing the precision of cell injection.A prototype is fabricated to achieve automatic batch microinjection.Experimental results demonstrated that the injection efficiency is about 20 seconds per cell.Cell puncture success rate and cell survival rate are 100%and 84%,respectively.Compared to manual operation,this proposed system improves cell operation efficiency and cell survival rate.The proposed microinjection system has the potential to greatly reduce the workload of the experimenters and shorten the relevant study period.
