China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various rel...China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.展开更多
The N-fold Darboux transformation(DT) T_n^([N]) of the nonlinear self-dual network equation is given in terms of the determinant representation. The elements in determinants are composed of the eigenvalues λ_j(j = 1,...The N-fold Darboux transformation(DT) T_n^([N]) of the nonlinear self-dual network equation is given in terms of the determinant representation. The elements in determinants are composed of the eigenvalues λ_j(j = 1, 2..., N)and the corresponding eigenfunctions of the associated Lax equation. Using this representation, the N-soliton solutions of the nonlinear self-dual network equation are given from the zero "seed" solution by the N-fold DT. A general form of the N-degenerate soliton is constructed from the determinants of N-soliton by a special limit λ_j →λ_1 and by using the higher-order Taylor expansion. For 2-degenerate and 3-degenerate solitons, approximate orbits are given analytically,which provide excellent fit of exact trajectories. These orbits have a time-dependent "phase shift", namely ln(t^2).展开更多
Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been pr...Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.展开更多
Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has ...Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has been difficult to strike a balance between the robustness and flexibility of coatings constructed by a single cross-linked network design.To overcome the conundrum,this innovative approach effectively combines two distinct cross-linked networks with unique functions,thus overcoming the challenge.Through a tightly interwoven structure comprised of added crosslinking sites,the coating achieves improved liquid repellency(WCA>100°,OSA<10°),increased durability(withstands 2,000 cycles of cotton wear),enhanced flexibility(endures 5,000 cycles of bending with a bending radius of 1 mm),and maintains high transparency(over 98%in the range of 410 nm to 760 nm).Additionally,the coating with remarkable adhesion can be applied to multiple substrates,enabling large-scale preparation and easy cycling coating,thus expanding its potential applications.The architecture of this fluoride-free dual cross-linked network not only advances liquid-repellent surfaces but also provides valuable insights for the development of eco-friendly materials in the future.展开更多
As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many de...As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such r...Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such reservoirs is mainly determined by micro-fractures, but conventional laboratory experimentalmethods are difficult to measure it, which is attribute to the dynamic cracking of these micro-fractures.The emerging digital core technology in recent years can solve this problem by developing an accuratepore network model and a rational simulation approach. In this study, a novel pore-fracture dualnetwork model was established based on percolation theory. Fluid flow in the pore of two scales, microfracture and matrix pore, were considered, also with the impact of micro-fracture opening and closingduring flow. Some seepage characteristic parameters, such as fluid saturations, capillary pressure, relative permeabilities, displacement efficiency in different flow stage, can be predicted by proposedcalculating method. Through these work, seepage characteristics of dual porous media can be achieved.展开更多
A facile method to fabricate tough and highly stretchable polyacrylamide （PAM） nanocomposite physical hydrogel （NCP gel） was proposed. The hydrogels are dually crosslinked single network with the PAM grafted vinyl...A facile method to fabricate tough and highly stretchable polyacrylamide （PAM） nanocomposite physical hydrogel （NCP gel） was proposed. The hydrogels are dually crosslinked single network with the PAM grafted vinyl hybrid silica nanoparticles （VSNPs） as the analogous covalent crosslinking points and the reversible hydrogen bonds among the PAM chains as the physical crosslinking points. In order to further elucidate the toughening mechanism of the PAM NCP gel, especially to understand the role of the dual crosslinking points, the PAM hybrid hydrogels （H gels） and a series of poly（acrylamide-co-dimethylacrylamide） （P（AM-co-DMAA）） NCP gels were designed and fabricated. Their mechanical properties were compared with those of the PAM NCP gels. The PAM H gels are prepared by simply mixing the PAM chains with bare silica nanoparticles （SNPs）. Relative to the poor mechanical properties of the PAM H gel, the PAM NCP gel is remarkably tough and stretchable and also generates large number of micro-cracks to stop notch propagation, indicating the important role of PAM grafted VSNPs in toughening the NCP gel. In the P（AM-co-DMAA） NCP gels, the P（AM-co- DMAA） chains are grafted on VSNPs and the polydimethylacrylamide （PDMAA） only forms very weak hydrogen bonds between themselves. It is found that mechanical properties of the PAM NCP gel, such as the tensile strength and the elongation at break, are enhanced significantly, but those of the P（AM-co-DMAA） NCP gels decreased rapidly with decreasing AM content. This result reveals the role of the hydrogen bonds among the grafted polymer chains as the physical crosslinking points in toughening the NCP gel.展开更多
To achieve smart and personalized medicine, the development of hydrogel dressings with sensing properties and biotherapeutic properties that can act as a sensor to monitor of human health in real-time while speeding u...To achieve smart and personalized medicine, the development of hydrogel dressings with sensing properties and biotherapeutic properties that can act as a sensor to monitor of human health in real-time while speeding up wound healing face great challenge. In the present study, a biocompatible dual-network composite hydrogel(DNCGel) sensor was obtained via a simple process. The dual network hydrogel is constructed by the interpenetration of a flexible network formed of poly(vinyl alcohol)(PVA) physical cross-linked by repeated freeze-thawing and a rigid network of iron-chelated xanthan gum(XG) impregnated with Fe^(3+) interpenetration. The pure PVA/XG hydrogels were chelated with ferric ions by immersion to improve the gel strength(compressive modulus and tensile modulus can reach up to 0.62 MPa and0.079 MPa, respectively), conductivity(conductivity values ranging from 9 × 10^(-4) S/cm to 1 × 10^(-3)S/cm)and bacterial inhibition properties(up to 98.56%). Subsequently, the effects of the ratio of PVA and XG and the immersion time of Fe^(3+) on the hydrogels were investigated, and DNGel3 was given the most priority on a comprehensive consideration. It was demonstrated that the DNCGel exhibit good biocompatibility in vitro, effectively facilitate wound healing in vivo(up to 97.8% healing rate) under electrical stimulation, and monitors human movement in real time. This work provides a novel avenue to explore multifunctional intelligent hydrogels that hold great promise in biomedical fields such as smart wound dressings and flexible wearable sensors.展开更多
Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm...Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.展开更多
A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatm...A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatment outcomes,develop more effective medical devices,or arrive at a more accurate diagnosis.This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction.The classification process was conducted with the aid of a convolu-tional neural network(CNN)with dual graphs.Evaluation of the performance of the fused model is carried out with various methods.In the initial input Com-puter Tomography(CT)image,150 images are pre-processed and segmented to identify cancerous and non-cancerous nodules.The geometrical,statistical,struc-tural,and texture features are extracted from the preprocessed segmented image using various methods such as Gray-level co-occurrence matrix(GLCM),Histo-gram-oriented gradient features(HOG),and Gray-level dependence matrix(GLDM).To select the optimal features,a novel fusion approach known as Whale-Bacterial Foraging Optimization is proposed.For the classification of lung cancer,dual graph convolutional neural networks have been employed.A com-parison of classification algorithms and optimization algorithms has been con-ducted.According to the evaluated results,the proposed fused algorithm is successful with an accuracy of 98.72%in predicting lung tumors,and it outper-forms other conventional approaches.展开更多
To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)...To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.展开更多
In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. A...In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. As the difference of hybrid energy source in stability and economy, thus, this paper focuses on the network with hybrid energy source, and design the utility of each user in the hybrid energy source system from the perspective of stability, economy and environment pollution. A dual power allocation algorithm based on Stackelberg game to maximize the utilities of users and networks is proposed. In addition, an iteration method is proposed which enables all players to reach the Stackelberg equilibrium(SE). Simulation results validate that players can reach the SE and the utilities of users and networks can be maximization, and the green energy can be efficiently used.展开更多
A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In con...A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.展开更多
Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversati...Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2019YFF0301400)the National Natural Science Foundation of China(Grant Nos.61671031,61722102,41722103,and 61961146005)。
文摘China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.
基金Supported by the Natural Science Foundation of Zhejiang Province under Grant No.LY15A010005the Natural Science Foundation of Ningbo under Grant No.2018A610197+1 种基金the NSF of China under Grant No.11671219K.C.Wong Magna Fund in Ningbo University
文摘The N-fold Darboux transformation(DT) T_n^([N]) of the nonlinear self-dual network equation is given in terms of the determinant representation. The elements in determinants are composed of the eigenvalues λ_j(j = 1, 2..., N)and the corresponding eigenfunctions of the associated Lax equation. Using this representation, the N-soliton solutions of the nonlinear self-dual network equation are given from the zero "seed" solution by the N-fold DT. A general form of the N-degenerate soliton is constructed from the determinants of N-soliton by a special limit λ_j →λ_1 and by using the higher-order Taylor expansion. For 2-degenerate and 3-degenerate solitons, approximate orbits are given analytically,which provide excellent fit of exact trajectories. These orbits have a time-dependent "phase shift", namely ln(t^2).
基金supported by the Key Research and Development Program of Jiangsu Province under Grant BE2022059-3,CTBC Bank through the Industry-Academia Cooperation Project,as well as by the Ministry of Science and Technology of Taiwan through Grants MOST-108-2218-E-002-055,MOST-109-2223-E-009-002-MY3,MOST-109-2218-E-009-025,and MOST431109-2218-E-002-015.
文摘Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial expressions.Numerous deep neural networks(DNNs)with convolutional structures have been proposed.However,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small datasets.Still,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective features.To address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME Recognition.The proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective features.The outputs are then fused,concatenated,and classified using the XgBoost algorithm.This comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of XgBoost.The proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent models.The accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,respectively.The proposed method has the advantage of operational efficiency and less computational time.
基金financially supported by the National Natu-ral Science Foundation of China(Nos.22375047,22378068,and 22075046)the Natural Science Foundation of Fujian Province(No.2022J01568)+2 种基金the National Key Research and Development Program of China(Nos.2022YFB3804905 and 2022YFB3804900)China Postdoctoral Science Foundation(No.2023M743437)start-up funding from Wenzhou Institute,University of Chinese Academy of Sciences(No.WIUCASQD2019002).
文摘Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has been difficult to strike a balance between the robustness and flexibility of coatings constructed by a single cross-linked network design.To overcome the conundrum,this innovative approach effectively combines two distinct cross-linked networks with unique functions,thus overcoming the challenge.Through a tightly interwoven structure comprised of added crosslinking sites,the coating achieves improved liquid repellency(WCA>100°,OSA<10°),increased durability(withstands 2,000 cycles of cotton wear),enhanced flexibility(endures 5,000 cycles of bending with a bending radius of 1 mm),and maintains high transparency(over 98%in the range of 410 nm to 760 nm).Additionally,the coating with remarkable adhesion can be applied to multiple substrates,enabling large-scale preparation and easy cycling coating,thus expanding its potential applications.The architecture of this fluoride-free dual cross-linked network not only advances liquid-repellent surfaces but also provides valuable insights for the development of eco-friendly materials in the future.
基金funded by the Ministry of Public Security Science and Technology Program Project(No.2023LL35)the Key Laboratory of Smart Policing and National Security Risk Governance,Sichuan Province(No.ZHZZZD2302).
文摘As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
基金The writers greatly appreciate the financial support of the Major Special Project of PetroChina Co Ltd.(2017E-0406)the National Science and Technology Major Project during the 13th Five-year Plan Period(2016ZX05010-00504).
文摘Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such reservoirs is mainly determined by micro-fractures, but conventional laboratory experimentalmethods are difficult to measure it, which is attribute to the dynamic cracking of these micro-fractures.The emerging digital core technology in recent years can solve this problem by developing an accuratepore network model and a rational simulation approach. In this study, a novel pore-fracture dualnetwork model was established based on percolation theory. Fluid flow in the pore of two scales, microfracture and matrix pore, were considered, also with the impact of micro-fracture opening and closingduring flow. Some seepage characteristic parameters, such as fluid saturations, capillary pressure, relative permeabilities, displacement efficiency in different flow stage, can be predicted by proposedcalculating method. Through these work, seepage characteristics of dual porous media can be achieved.
基金financially supported by the National Natural Science Foundation of China(Nos.21474058 and 51633003)State Key Laboratory for Modification of Chemical Fibers and Polymer Materials,Donghua University(No.LK1404)+1 种基金Tsinghua University Scientific Research Project(No.2014Z22069)State Key Laboratory of Organic-Inorganic Composites,Beijing University of Chemical Technology(No.OIC-201601006)
文摘A facile method to fabricate tough and highly stretchable polyacrylamide （PAM） nanocomposite physical hydrogel （NCP gel） was proposed. The hydrogels are dually crosslinked single network with the PAM grafted vinyl hybrid silica nanoparticles （VSNPs） as the analogous covalent crosslinking points and the reversible hydrogen bonds among the PAM chains as the physical crosslinking points. In order to further elucidate the toughening mechanism of the PAM NCP gel, especially to understand the role of the dual crosslinking points, the PAM hybrid hydrogels （H gels） and a series of poly（acrylamide-co-dimethylacrylamide） （P（AM-co-DMAA）） NCP gels were designed and fabricated. Their mechanical properties were compared with those of the PAM NCP gels. The PAM H gels are prepared by simply mixing the PAM chains with bare silica nanoparticles （SNPs）. Relative to the poor mechanical properties of the PAM H gel, the PAM NCP gel is remarkably tough and stretchable and also generates large number of micro-cracks to stop notch propagation, indicating the important role of PAM grafted VSNPs in toughening the NCP gel. In the P（AM-co-DMAA） NCP gels, the P（AM-co- DMAA） chains are grafted on VSNPs and the polydimethylacrylamide （PDMAA） only forms very weak hydrogen bonds between themselves. It is found that mechanical properties of the PAM NCP gel, such as the tensile strength and the elongation at break, are enhanced significantly, but those of the P（AM-co-DMAA） NCP gels decreased rapidly with decreasing AM content. This result reveals the role of the hydrogen bonds among the grafted polymer chains as the physical crosslinking points in toughening the NCP gel.
基金supported by Physical Chemical Materials Analytical&Testing Center of Shandong University at Weihai,Natural Science Foundation of Shandong Province(No.ZR2022QD057)Open Project Fund for Hubei Key Laboratory of Oral and Maxillofacial Development and Regeneration(No.2021kqhm003)+1 种基金State Key Laboratory of Advanced Technology for Materials Synthesis and Processing(Wuhan University of Technology)the Science Fund of Shandong Laboratory of Advanced Materials and Green Manufacturing(Yantai,No.AMGM2021F02)。
文摘To achieve smart and personalized medicine, the development of hydrogel dressings with sensing properties and biotherapeutic properties that can act as a sensor to monitor of human health in real-time while speeding up wound healing face great challenge. In the present study, a biocompatible dual-network composite hydrogel(DNCGel) sensor was obtained via a simple process. The dual network hydrogel is constructed by the interpenetration of a flexible network formed of poly(vinyl alcohol)(PVA) physical cross-linked by repeated freeze-thawing and a rigid network of iron-chelated xanthan gum(XG) impregnated with Fe^(3+) interpenetration. The pure PVA/XG hydrogels were chelated with ferric ions by immersion to improve the gel strength(compressive modulus and tensile modulus can reach up to 0.62 MPa and0.079 MPa, respectively), conductivity(conductivity values ranging from 9 × 10^(-4) S/cm to 1 × 10^(-3)S/cm)and bacterial inhibition properties(up to 98.56%). Subsequently, the effects of the ratio of PVA and XG and the immersion time of Fe^(3+) on the hydrogels were investigated, and DNGel3 was given the most priority on a comprehensive consideration. It was demonstrated that the DNCGel exhibit good biocompatibility in vitro, effectively facilitate wound healing in vivo(up to 97.8% healing rate) under electrical stimulation, and monitors human movement in real time. This work provides a novel avenue to explore multifunctional intelligent hydrogels that hold great promise in biomedical fields such as smart wound dressings and flexible wearable sensors.
基金supported by the National Natural Science Foundation of China(62173352,62103112)the Guangdong Basic and Applied Basic Research Foundation(2021A1515012314)+1 种基金the Open Project of Shenzhen Institute of Artificial Intelligence and Robotics for Society(AC01202005006)the Key-Area Research and Development Program of Guangzhou(202007030004)。
文摘Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.
文摘A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatment outcomes,develop more effective medical devices,or arrive at a more accurate diagnosis.This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction.The classification process was conducted with the aid of a convolu-tional neural network(CNN)with dual graphs.Evaluation of the performance of the fused model is carried out with various methods.In the initial input Com-puter Tomography(CT)image,150 images are pre-processed and segmented to identify cancerous and non-cancerous nodules.The geometrical,statistical,struc-tural,and texture features are extracted from the preprocessed segmented image using various methods such as Gray-level co-occurrence matrix(GLCM),Histo-gram-oriented gradient features(HOG),and Gray-level dependence matrix(GLDM).To select the optimal features,a novel fusion approach known as Whale-Bacterial Foraging Optimization is proposed.For the classification of lung cancer,dual graph convolutional neural networks have been employed.A com-parison of classification algorithms and optimization algorithms has been con-ducted.According to the evaluated results,the proposed fused algorithm is successful with an accuracy of 98.72%in predicting lung tumors,and it outper-forms other conventional approaches.
基金Supported by the National Natural Science Foundation of China(60905012,60572058)
文摘To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.
基金supported by the Beijing Natural Science Foundation (4142049)863 project No. 2014AA01A701the Fundamental Research Funds for Central Universities of China No. 2015XS07
文摘In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. As the difference of hybrid energy source in stability and economy, thus, this paper focuses on the network with hybrid energy source, and design the utility of each user in the hybrid energy source system from the perspective of stability, economy and environment pollution. A dual power allocation algorithm based on Stackelberg game to maximize the utilities of users and networks is proposed. In addition, an iteration method is proposed which enables all players to reach the Stackelberg equilibrium(SE). Simulation results validate that players can reach the SE and the utilities of users and networks can be maximization, and the green energy can be efficiently used.
基金National Natural Science Foundation of China(No.61203184)
文摘A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.
基金the National Natural Science Foundation of China (Nos. 10672040 and10372022)the Natural Science Foundation of Fujian Province of China (No. E0410008)
文摘Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.
