To further clarify the proppant transport and placement law in multi-branched fractures induced by volume fracturing, proppant transport simulation experiments were performed with different fracture shapes, sand ratio...To further clarify the proppant transport and placement law in multi-branched fractures induced by volume fracturing, proppant transport simulation experiments were performed with different fracture shapes, sand ratios, branched fracture opening time and injection sequence of proppants in varied particle sizes. The results show that the settled proppant height increases and the placement length decreases in main fractures as the fracturing fluid diverts gradually to the branched fractures at different positions. The flow rate in branched fractures is the main factor affecting their filling. The diverion to branched fractures leads to low flow rate and poor filling of far-wellbore branched fractures. The inclined fracture wall exerts a frictional force on the proppant to slow its settlement, thus enhancing the vertical proppant distribution in the fracture. The increase of sand ratio can improve the filling of near-wellbore main fracture and far-wellbore branched fracture and also increase the settled proppant height in main fracture. Due to the limitation of fracture height, when the sand ratio increases to a certain level, the increment of fracture filling decreases. When branched fracture is always open(or extends continuously), the supporting effect on the branched fractures is the best, but the proppant placement length within the main fractures is shorter. The fractures support effect is better when it is first closed and then opened(or extends in late stage) than when it is first opened and then closed(or extends in early stage). Injecting proppants with different particle sizes in a specific sequence can improve the placement lengths of main fracture and branched fracture. Injection of proppants in an ascending order of particle size improves the near-wellbore fracture filling, to a better extent than that in a descending order of particle size.展开更多
Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges i...Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.展开更多
The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-bran...The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.展开更多
With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability....With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability.Currently,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false.However,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and accuracy.To this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification.First,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map.Second,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches.Finally,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels.We conduct sufficient ablation experiments and comparative experiments.The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.展开更多
In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based ...In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.展开更多
Macromolecular crystallography beamline BL17U1 at the Shanghai Synchrotron Radiation Facility has been relocated,upgraded,and given a new ID(BL02U1).It now delivers X-rays in the energy range of 6–16 keV,with a focus...Macromolecular crystallography beamline BL17U1 at the Shanghai Synchrotron Radiation Facility has been relocated,upgraded,and given a new ID(BL02U1).It now delivers X-rays in the energy range of 6–16 keV,with a focused beam of 11.6μm×4.8μm and photon flux greater than 1012 phs/s.The high credibility and stability of the beam and good timing synchronization of the equipment significantly improve the experimental efficiency.Since June 2021,when it officially opened to users,over 4200 h of beamtime have been provided to over 200 research groups to collect data at the beamline.Its good performance and stable operation have led to the resolution of several structures based on data collected at the beamline.展开更多
A new unsteady three-dimensional convective-diffusive mathematical model for the transportation of macromolecules and water across the arterial wall was proposed . After the formation of leaky junctions due to the mit...A new unsteady three-dimensional convective-diffusive mathematical model for the transportation of macromolecules and water across the arterial wall was proposed . After the formation of leaky junctions due to the mitosis of endothelial cell of the arterial wall, the macromolecular transport happens surrounding the leaky cells. The arterial wall was divided into four layers: the endothelial layer, the subendothelial intima, the internal elastic lamina and the media for the convenience of research. The time-dependent concentration growth, the effect of the shape of endothelial cell and the effect of physiological parameters were analyzed. The analytical solution of velocity field and pressure field of water flow across the arterial wall were obtained; and concentration distribution of three macromolecules; LDL, HRP and Albumin, were calculated with numerical simulation method. The new theory predicts, the maximum and distribution areas of time dependent concentration with round-shape endothelial cell are both larger than that with ellipse-shape endothelial cell. The model also predicts the concentration growth is much alike that of a two-dimensional model and it shows that the concentration reaches its peak at the leaky junction where atherosclerotic formation frequently occurs and falls down rapidly in a limited area beginning from its earlier-time growth to the state when macromolecular transfer approaches steadily. These predictions of the new model are in agreement with the experimental observation for the growth and concentration distribution of LDL and Albumin.展开更多
基金Supported by the National Natural Science Foundation of China (52074332,52204024)Outstanding Youth Foundation of Shandong Province (ZR2020YQ36)China Postdoctoral Science Foundation (M710225)。
文摘To further clarify the proppant transport and placement law in multi-branched fractures induced by volume fracturing, proppant transport simulation experiments were performed with different fracture shapes, sand ratios, branched fracture opening time and injection sequence of proppants in varied particle sizes. The results show that the settled proppant height increases and the placement length decreases in main fractures as the fracturing fluid diverts gradually to the branched fractures at different positions. The flow rate in branched fractures is the main factor affecting their filling. The diverion to branched fractures leads to low flow rate and poor filling of far-wellbore branched fractures. The inclined fracture wall exerts a frictional force on the proppant to slow its settlement, thus enhancing the vertical proppant distribution in the fracture. The increase of sand ratio can improve the filling of near-wellbore main fracture and far-wellbore branched fracture and also increase the settled proppant height in main fracture. Due to the limitation of fracture height, when the sand ratio increases to a certain level, the increment of fracture filling decreases. When branched fracture is always open(or extends continuously), the supporting effect on the branched fractures is the best, but the proppant placement length within the main fractures is shorter. The fractures support effect is better when it is first closed and then opened(or extends in late stage) than when it is first opened and then closed(or extends in early stage). Injecting proppants with different particle sizes in a specific sequence can improve the placement lengths of main fracture and branched fracture. Injection of proppants in an ascending order of particle size improves the near-wellbore fracture filling, to a better extent than that in a descending order of particle size.
基金Double First-Class Innovation Research Project for People’s Public Security University of China(2023SYL08).
文摘Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.
基金This work was funded by the project of State Grid Hunan Electric Power Research Institute(No.SGHNDK00PWJS2210033).
文摘The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.
基金supported by the 2023 Open Project of Key Laboratory of Ministry of Public Security for Artificial Intelligence Security(RGZNAQ-2304)the Fundamental Research Funds for the Central Universities of PPSUC(2023JKF01ZK08).
文摘With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability.Currently,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false.However,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and accuracy.To this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification.First,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map.Second,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches.Finally,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels.We conduct sufficient ablation experiments and comparative experiments.The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.
文摘In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.
基金supported by the National Key Research and Development Program of China(No.2021YFC2301405)the National Natural Science Foundation of China(No.31971121)Shanghai Science and Technology Plan Project(No.21ZR14718)。
文摘Macromolecular crystallography beamline BL17U1 at the Shanghai Synchrotron Radiation Facility has been relocated,upgraded,and given a new ID(BL02U1).It now delivers X-rays in the energy range of 6–16 keV,with a focused beam of 11.6μm×4.8μm and photon flux greater than 1012 phs/s.The high credibility and stability of the beam and good timing synchronization of the equipment significantly improve the experimental efficiency.Since June 2021,when it officially opened to users,over 4200 h of beamtime have been provided to over 200 research groups to collect data at the beamline.Its good performance and stable operation have led to the resolution of several structures based on data collected at the beamline.
文摘A new unsteady three-dimensional convective-diffusive mathematical model for the transportation of macromolecules and water across the arterial wall was proposed . After the formation of leaky junctions due to the mitosis of endothelial cell of the arterial wall, the macromolecular transport happens surrounding the leaky cells. The arterial wall was divided into four layers: the endothelial layer, the subendothelial intima, the internal elastic lamina and the media for the convenience of research. The time-dependent concentration growth, the effect of the shape of endothelial cell and the effect of physiological parameters were analyzed. The analytical solution of velocity field and pressure field of water flow across the arterial wall were obtained; and concentration distribution of three macromolecules; LDL, HRP and Albumin, were calculated with numerical simulation method. The new theory predicts, the maximum and distribution areas of time dependent concentration with round-shape endothelial cell are both larger than that with ellipse-shape endothelial cell. The model also predicts the concentration growth is much alike that of a two-dimensional model and it shows that the concentration reaches its peak at the leaky junction where atherosclerotic formation frequently occurs and falls down rapidly in a limited area beginning from its earlier-time growth to the state when macromolecular transfer approaches steadily. These predictions of the new model are in agreement with the experimental observation for the growth and concentration distribution of LDL and Albumin.
