There are three major research hotspots in efficient visualization techniques of high resolution remotely sensed data in network environment: the data organiza-tion and access in disk storage,the image data stitching ...There are three major research hotspots in efficient visualization techniques of high resolution remotely sensed data in network environment: the data organiza-tion and access in disk storage,the image data stitching and fitting methods,and the network transfers and access. In this paper a new method of "Big File" organi-zation for improving the storage access efficiency of high resolution remote data is presented; a "virtual data source" concept is introduced to solve the stitching problem of remotely sensed data from different sources with different resolutions; a remotely sensed data access engine design based on ATL technique is discussed to process the network transfers and access of remotely sensed data. All these techniques have been adopted in a prototype of digital China named "ChinaStar".展开更多
针对电动车驾驶人员未佩戴头盔的现象,提出了一种改进YOLOv4(You only look once)算法的电动车驾驶人员头盔佩戴检测方法。将数据集利用K-means算法进行聚类以获得先验框,提高先验框与特征图的匹配程度;在CSPDarknet53主干特征提取网络...针对电动车驾驶人员未佩戴头盔的现象,提出了一种改进YOLOv4(You only look once)算法的电动车驾驶人员头盔佩戴检测方法。将数据集利用K-means算法进行聚类以获得先验框,提高先验框与特征图的匹配程度;在CSPDarknet53主干特征提取网络的输出层增加卷积层,并在PANet(Path Aggregation Network)网络部分增加SPP(Spatial Pyramid Pooling)空间池化金字塔增加感受野,提升特征提取和融合能力,提高对电动车驾驶员是否佩戴头盔的检测能力。实验表明,在是否佩戴头盔检测任务中,改进后框架算法的全类别mAP(mean average precision)达到96.63%,比原框架提高2.4%;其中改进后佩戴头盔类别的AP(Average Precision)比原框架提高4%,未佩戴头盔类别AP比原框架提高1%;F(F-Measure)值比原算法均提高4%,改进后的算法更满足头盔佩戴检测任务。展开更多
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network...With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.展开更多
Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to ...Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.展开更多
基金the National Basic Research Program of China ("973") (Grant No. 2005CB321900)the National Natural Science Foundation of China (Grant No. 40701134)
文摘There are three major research hotspots in efficient visualization techniques of high resolution remotely sensed data in network environment: the data organiza-tion and access in disk storage,the image data stitching and fitting methods,and the network transfers and access. In this paper a new method of "Big File" organi-zation for improving the storage access efficiency of high resolution remote data is presented; a "virtual data source" concept is introduced to solve the stitching problem of remotely sensed data from different sources with different resolutions; a remotely sensed data access engine design based on ATL technique is discussed to process the network transfers and access of remotely sensed data. All these techniques have been adopted in a prototype of digital China named "ChinaStar".
基金funded by the Fundamental Research Project of CNPC Geophysical Key Lab(2022DQ0604-4)the Strategic Cooperation Technology Projects of China National Petroleum Corporation and China University of Petroleum-Beijing(ZLZX 202003)。
文摘With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.
基金supported by the National Natural Science Foundation of China(Nos.U19A208162202320)+2 种基金the Fundamental Research Funds for the Central Universities(No.SCU2023D008)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129)the Key Laboratory of Data Protection and Intelligent Management(Sichuan University),Ministry of Education.
文摘Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.
