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Anion exchange membranes with a semi-interpenetrating polymer network using 1,6-dibromohexane as bifunctional crosslinker
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作者 Aijie Li Zhanliang Wang +6 位作者 Zhihao Si Lu Lu Peipei Huang Jinhong Liu Songyuan Yao Peiyong Qin Xinmiao Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第8期199-208,共10页
An anion exchange membrane(AEM)is generally expected to possess high ion exchange capacity(IEC),low water uptake(WU),and high mechanical strength when applied to electrodialysis desalination.Among different types of A... An anion exchange membrane(AEM)is generally expected to possess high ion exchange capacity(IEC),low water uptake(WU),and high mechanical strength when applied to electrodialysis desalination.Among different types of AEMs,semi-interpenetrating polymer networks(SIPNs)have been suggested for their structural superiorities,i.e.,the tunable local density of ion exchange groups for IEC and the restrained leaching of hygroscopic groups by insolubility for WU.Unfortunately,the conventional SIPN AEMs still struggle to balances IEC,WU,and mechanical strength simultaneously,due to the lack of the compact crosslinking region.In this work,we proposed a novel SIPN structure of polyvinylidene difluoride/polyvinylimidazole/1,6-dibromohexane(PVDF/PVIm/DBH).On the one hand,DBH with two cationic groups of imidazole groups are introduced to enhance the ion conductivity,which is different from the conventional monofunctional modifier with only one cationic group.On the other hand,DBH has the ability to bridge with PVIm,where the mechanical strength of the resulting AEM is increased by the increase of crosslinking degree.Results show that a low WU of 38.1%to 62.6%,high IEC of 2.12—2.22 mmol·g^(-1),and excellent tensile strength of 3.54—12.35 MPa for PVDF/PVIm/DBH membrane are achieved.This work opens a new avenue for achieving the high-quality AEMs. 展开更多
关键词 Anion exchange membrane Polyvinylidene difluoride ELECTRODIALYSIS semi-interpenetrating polymer networks
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Fluorinated semi-interpenetrating polymer networks for enhancing the mechanical performance and storage stability of polymer-bonded explosives by controlling curing and phase separation rates
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作者 Chao Deng Huihui Liu +1 位作者 Yongping Bai Zhen Hu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第8期58-66,共9页
Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepare... Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepared using different catalyst amounts(denoted as F23-CLF-30-D). The involved curing and phase separation processes were monitored using Fourier-transform infrared spectroscopy, differential scanning calorimetry, a haze meter and a rheometer. Curing rate constant and activation energy were calculated using a theoretical model and numerical method, respectively. Results revealed that owing to its co-continuous micro-phase separation structure, the F23-CLF-30-D3 semi-IPN exhibited considerably higher tensile strength and elongation at break than pure fluororubber F2314 and the F23-CLF-30-D0 semi-IPN because the phase separation and curing rates matched in the initial stage of curing.An arc Brazilian test revealed that F23-CLF-30-D-based composites used as mock materials for PBXs exhibited excellent mechanical performance and storage stability. Thus, the matched curing and phase separation rates play a crucial role during the fabrication of high-performance semi-IPNs;these factors can be feasibly controlled using an appropriate catalyst amount. 展开更多
关键词 semi-interpenetrating polymer networks FLUOROPOLYMER Curing rate Phase separation rate Polymer-bonded explosives
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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles
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作者 Dun Cao Jia Ru +3 位作者 Jian Qin Amr Tolba Jin Wang Min Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1365-1384,共20页
Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transp... Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety. 展开更多
关键词 Internet of vehicles road networks 3D road model structure recognition GIS
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Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction
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作者 Chuyuan Wei Jinzhe Li +2 位作者 Zhiyuan Wang Shanshan Wan Maozu Guo 《Computers, Materials & Continua》 SCIE EI 2024年第5期3299-3314,共16页
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,... Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous. 展开更多
关键词 Relation extraction graph convolutional neural networks dependency tree dynamic structure attention
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Analysis of Urban Agglomeration Network Structure Based on Baidu Migration Data: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Urban Agglomeration
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作者 XIA Yuan WANG Bin 《Journal of Landscape Research》 2024年第4期47-50,共4页
The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure ... The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data. 展开更多
关键词 Baidu migration data Social network analysis Urban agglomeration network structure Greater Bay Area urban agglomeration
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Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations 被引量:1
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作者 Jifeng QI Guimin SUN +2 位作者 Bowen XIE Delei LI Baoshu YIN 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2024年第2期377-389,共13页
Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS... Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques. 展开更多
关键词 machine learning convolutional neural network(CNN) ocean subsurface salinity structure(OSSS) Indian Ocean satellite observations
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Influence of Al_(2)O_(3)/SiO_(2) Ratio on the Structure and Properties of Na^(+)/K^(+)Ion Exchange Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) Glasses
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作者 吴建磊 CHEN Junzhu +4 位作者 TIAN Xiaokun LI Jiahao GAO Wenkai YUE Yunlong 康俊峰 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第3期606-612,共7页
In this work,the structure,viscosity and ion-exchange process of Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) glasses with different Al_(2)O_(3)/SiO_(2) molar ratios were investigated.The results showed that,with increasing Al_(2)... In this work,the structure,viscosity and ion-exchange process of Na_(2)O-MgO-Al_(2)O_(3)-SiO_(2) glasses with different Al_(2)O_(3)/SiO_(2) molar ratios were investigated.The results showed that,with increasing Al_(2)O_(3)/SiO_(2) ratio,the simple structural units Q_(1) and Q_(2) transformed into highly aggregated structural units Q_(3) and Q_(4),indicating the increase of polymerization degree of glass network.Meanwhile,the coefficient of thermal expansion decreased from 9.23×10^(-6)℃^(-1) to 8.88×10^(-6)℃^(-1).The characteristic temperatures such as melting,forming,softening and glass transition temperatures increased with the increase of Al_(2)O_(3)/SiO_(2) ratio,while the glasses working temperature range became narrow.The increasing Al_(2)O_(3)/SiO_(2) ratio and prolonging ion-exchange time enhanced the surface compressive stress(CS)and depth of stress layer(DOL).However,the increase of ion exchange temperature increased the DOL and decreased the CS affected by stress relaxation.There was a good linear relationship between stress relaxation and surface compressive stress.Chemical strengthening significantly improved the hardness of glasses,which reached the maximum value of(622.1±10)MPa for sample with Al_(2)O_(3)/SiO_(2) ratio of 0.27 after heat treated at 410℃for 2 h. 展开更多
关键词 network structure viscosity ion exchange aluminosilicate glass
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Effect of Fe_(2)O_(3)on the Structure,Physical Properties and Crystallization of CaO-Al_(2)O_(3)-SiO_(2)Glass
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作者 张峰 XIONG Dehua +7 位作者 谢俊 张继红 HAN Jianjun CHEN Dequan WEN Zhongquan FAN Zhenhua CHEN Lina SUN Tengfei 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第4期954-961,共8页
The calcium aluminosilicate-based glasses(CaO-Al_(2)O_(3)-SiO_(2),CAS)with different Fe_(2)O_(3)content(0.10wt%,0.50wt%,0.90wt%,and 1.30wt%)were prepared by traditional melt-quenching method.The glass network structur... The calcium aluminosilicate-based glasses(CaO-Al_(2)O_(3)-SiO_(2),CAS)with different Fe_(2)O_(3)content(0.10wt%,0.50wt%,0.90wt%,and 1.30wt%)were prepared by traditional melt-quenching method.The glass network structure,thermal and mechanical properties,and crystallization behavior changes were investigated by nuclear magnetic resonance spectrometer,Fourier-transform infrared spectro-photometer,X-ray diffractometer,differential scanning calorimetry and field emission scanning electron microscope measurements.The change of Q^(n)in glass structures reveals the glass network connectivity decreases due to the increasing content of Fe_(2)O_(3)addition,resulting in the increasing of non-bridging number in glass structure.The glass densities slightly rise from 2.644 to 2.681 g/cm^(3),while Vickers’s hardness increases at first,from 6.469 to 6.901 GPa,then slightly drops to 6.745 GPa,with Fe_(2)O_(3)content increase.There is almost no thermal expansion coefficient change from different Fe_(2)O_(3)content.The glass transmittance in visible range gradually decreases with higher Fe_(2)O_(3)content,resulting from the strong absorption of Fe^(2+)and Fe^(3+)ions.The calculated activation energy from thermal analysis results first decreases from 282.70 to 231.18 kJ/mol,and then increases to 244.02 kJ/mol,with the Fe_(2)O_(3)content increasing from 0.10wt%to 1.30wt%.Meanwhile,the maximum Avrami constant of 2.33 means the CAS glasses exhibit two-dimensional crystallization.All of the CAS glass-ceramics samples contain main crystal phase of anorthite,the microstructure appears lamellar and columnar crystals. 展开更多
关键词 calcium aluminosilicate glass network structure physical properties CRYSTALLIZATION
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Structural Characteristics and Evolution of a Weighted Sino-US Container Shipping Network
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作者 ZHANG Tiantian XI Daping +3 位作者 JIANG Wenping FENG Yuhao WANG Chuyuan HU Xini 《Chinese Geographical Science》 SCIE CSCD 2024年第5期810-828,共19页
This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constru... This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural fea-tures.The network’s evolution is examined from three perspectives,namely,time,space,and event influence,aiming to comprehens-ively explore the network’s evolution mechanism.The results revealed that:1)the weighted Sino-US container shipping network exhib-its small-world and scale-free properties.Key hub ports in the United States include NEW YORK NY,SAVANNAH GA,LOS ANGELES CA,and OAKLAND CA,whereas SHANGHAI serving as the hub port in China.The geographical distribution of these hub ports is uneven.2)Concerning the evolution of the weighted Sino-US container shipping network,from a temporal perspective,the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and de-velopment,with a need for further enhancement in competitiveness and development speed.The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage.The shipping development in Northern China,the Western and Eastern United States,and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances.From a spatial perspective,the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani(BBV)model are confirmed through theoretical derivation.The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network.From an event impact per-spective,the Corona Virus Disease 2019(COVID-19)pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity.The network lacks sufficient resilience and stability in emergency situations.3)Based on the analysis of the structural characteristics and evolution of the weighted Sino-US con-tainer shipping network,recommendations for network development are proposed from three aspects:emphasizing the development of hub ports,focusing on the balanced development of the network,and optimizing the layout of Chinese ports. 展开更多
关键词 container shipping network structure characteristics network evolution voyage weighting improved Barrat-Barthelemy-Vespignani(BBV)model
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Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image
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作者 Yiwei Chen Yi He +3 位作者 Hong Ye Lina Xing Xin Zhang Guohua Shi 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2024年第3期105-113,共9页
The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera im... The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error. 展开更多
关键词 Fundus fluorescein angiography image fundus structure image image translation unified deep learning model generative adversarial networks
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Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis
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作者 Sihong Huang Jungong Han +4 位作者 Hairong Zheng Mengjun Li Chuxin Huang Xiaoyan Kui Jun Liu 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第7期1553-1558,共6页
Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely u... Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury. 展开更多
关键词 cognitive function CROSS-SECTION FOLLOW-UP functional connectivity graph theory longitudinal study mild traumatic brain injury prediction small-worldness structural connectivity subnetworks whole brain network
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Modeling of multiphase flow in low permeability porous media:Effect of wettability and pore structure properties
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作者 Xiangjie Qin Yuxuan Xia +3 位作者 Juncheng Qiao Jiaheng Chen Jianhui Zeng Jianchao Cai 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第4期1127-1139,共13页
Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the ef... Multiphase flow in low permeability porous media is involved in numerous energy and environmental applications.However,a complete description of this process is challenging due to the limited modeling scale and the effects of complex pore structures and wettability.To address this issue,based on the digital rock of low permeability sandstone,a direct numerical simulation is performed considering the interphase drag and boundary slip to clarify the microscopic water-oil displacement process.In addition,a dual-porosity pore network model(PNM)is constructed to obtain the water-oil relative permeability of the sample.The displacement efficiency as a recovery process is assessed under different wetting and pore structure properties.Results show that microscopic displacement mechanisms explain the corresponding macroscopic relative permeability.The injected water breaks through the outlet earlier with a large mass flow,while thick oil films exist in rough hydrophobic surfaces and poorly connected pores.The variation of water-oil relative permeability is significant,and residual oil saturation is high in the oil-wet system.The flooding is extensive,and the residual oil is trapped in complex pore networks for hydrophilic pore surfaces;thus,water relative permeability is lower in the water-wet system.While the displacement efficiency is the worst in mixed-wetting systems for poor water connectivity.Microporosity negatively correlates with invading oil volume fraction due to strong capillary resistance,and a large microporosity corresponds to low residual oil saturation.This work provides insights into the water-oil flow from different modeling perspectives and helps to optimize the development plan for enhanced recovery. 展开更多
关键词 Low permeability porous media Water-oil flow WETTABILITY Pore structures Dual porosity pore network model(PNM) Free surface model
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Deformation,structure and potential hazard of a landslide based on InSAR in Banbar county,Xizang(Tibet)
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作者 Guan-hua Zhao Heng-xing Lan +4 位作者 Hui-yong Yin Lang-ping Li Alexander Strom Wei-feng Sun Chao-yang Tian 《China Geology》 CAS CSCD 2024年第2期203-221,共19页
The Tibetan Plateau is characterized by complex geological conditions and a relatively fragile ecological environment.In recent years,there has been continuous development and increased human activity in the Tibetan P... The Tibetan Plateau is characterized by complex geological conditions and a relatively fragile ecological environment.In recent years,there has been continuous development and increased human activity in the Tibetan Plateau region,leading to a rising risk of landslides.The landslide in Banbar County,Xizang(Tibet),have been perturbed by ongoing disturbances from human engineering activities,making it susceptible to instability and displaying distinct features.In this study,small baseline subset synthetic aperture radar interferometry(SBAS-InSAR)technology is used to obtain the Line of Sight(LOS)deformation velocity field in the study area,and then the slope-orientation deformation field of the landslide is obtained according to the spatial geometric relationship between the satellite’s LOS direction and the landslide.Subsequently,the landslide thickness is inverted by applying the mass conservation criterion.The results show that the movement area of the landslide is about 6.57×10^(4)m^(2),and the landslide volume is about 1.45×10^(6)m^(3).The maximum estimated thickness and average thickness of the landslide are 39 m and 22 m,respectively.The thickness estimation results align with the findings from on-site investigation,indicating the applicability of this method to large-scale earth slides.The deformation rate of the landslide exhibits a notable correlation with temperature variations,with rainfall playing a supportive role in the deformation process and displaying a certain lag.Human activities exert the most substantial influence on the spatial heterogeneity of landslide deformation,leading to the direct impact of several prominent deformation areas due to human interventions.Simultaneously,utilizing the long short-term memory(LSTM)model to predict landslide displacement,and the forecast results demonstrate the effectiveness of the LSTM model in predicting landslides that are in a continuous development and movement phase.The landslide is still active,and based on the spatial heterogeneity of landslide deformation,new recommendations have been proposed for the future management of the landslide in order to mitigate potential hazards associated with landslide instability. 展开更多
关键词 LANDSLIDE INSAR Human activity DEFORMATION structure LSTM model Engineering construction Thickness Neural network Machine learning Prediction and prevention Tibetan Plateau Geological hazards survey engineering
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Input Structure Design for Structural Controllability of Complex Networks 被引量:1
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作者 Lifu Wang Zhaofei Li +2 位作者 Guotao Zhao Ge Guo Zhi Kong 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第7期1571-1581,共11页
This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly con... This paper addresses the problem of the input design of large-scale complex networks.Two types of network components,redundant inaccessible strongly connected component(RISCC)and intermittent inaccessible strongly connected component(IISCC)are defined,and a subnetwork called a driver network is developed.Based on these,an efficient method is proposed to find the minimum number of controlled nodes to achieve structural complete controllability of a network,in the case that each input can act on multiple state nodes.The range of the number of input nodes to achieve minimal control,and the configuration method(the connection between the input nodes and the controlled nodes)are presented.All possible input solutions can be obtained by this method.Moreover,we give an example and some experiments on real-world networks to illustrate the effectiveness of the method. 展开更多
关键词 Complex network input configuration minimum controlled node set structural controllability
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An aerial ammunition ad hoc network collaborative localization algorithm based on relative ranging and velocity measurement in a highly-dynamic topographic structure 被引量:1
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作者 Hao Wu Peng-fei Wu +2 位作者 Zhang-song Shi Shi-yan Sun Zhong-hong Wu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第7期231-248,共18页
In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of... In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of munitions with an aerial three-dimensional(3D) highly-dynamic topographic structure under a satellite denied environment. As for aerial networked munitions, the measurement of munitions is objectively incomplete due to the degenerated and interrupted link of munitions. For this reason, a cluster-oriented collaborative localization method is put forward in this paper. Multidimensional scaling(MDS) was first integrated with a trilateration localization method(TLM) to construct a relative localization algorithm for determining the relative location of a mobile cluster network. The information related to relative velocity was then combined into a collaborative localization framework to devise a TLM-vMDS algorithm. Finally, an iterative refinement algorithm based on scaling by majorizing a complicated function(SMACOF) was employed to effectively eliminate the influence of incomplete link observation on localization accuracy. Compared with the currently available advanced algorithms, the proposed TLM-vMDS algorithm achieves higher localization accuracy and faster convergence for a cluster of extensively networked munitions, and also offers better numerical stability and robustness for highspeed motion models. 展开更多
关键词 Highly-dynamic topographic structure MDS Relative ranging Aerial ammunition ad hoc network
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DHSEGATs:distance and hop-wise structures encoding enhanced graph attention networks 被引量:1
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作者 HUANG Zhiguo 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第2期350-359,共10页
Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can signi... Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result. 展开更多
关键词 graph attention network(GAT) graph structure information label propagation
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Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group
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作者 Yadong Xu Weixing Hong +3 位作者 Mohammad Noori Wael A.Altabey Ahmed Silik Nabeel S.D.Farhan 《Structural Durability & Health Monitoring》 EI 2024年第6期763-783,共21页
This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemb... This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure. 展开更多
关键词 structural Health Monitoring(SHM) BRIDGES big model Convolutional Neural network(CNN) Finite Element Method(FEM)
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The Study on the Evolution of Urban Spatial Structure in Zhuhai City Based on Spatial Syntax
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作者 Weiqiang Zhou Fangting Liang 《Journal of Architectural Research and Development》 2024年第4期40-44,共5页
With the deepening of the Guangdong-Hong Kong-Macao Greater Bay Area strategy and the accelerated integration and development of the east and west sides of the Pearl River Estuary,Zhuhai’s hub position is becoming mo... With the deepening of the Guangdong-Hong Kong-Macao Greater Bay Area strategy and the accelerated integration and development of the east and west sides of the Pearl River Estuary,Zhuhai’s hub position is becoming more and more prominent.The city of Zhuhai has a dense water network and is divided into two urban areas,the east and the west,under the influence of the Mordor Gate waterway.Based on the theory of spatial syntax,this paper carries out an analytical study on the urban spatial structure of Zhuhai,identifies the distribution characteristics of urban POIs,and provides theoretical support for the urban development of Zhuhai. 展开更多
关键词 Spatial syntax POI data Transport network Urban spatial structure
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Network autoregression model with grouped factor structures
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作者 ZHANG Zhiyuan ZHU Xuening 《中山大学学报(自然科学版)(中英文)》 CAS CSCD 北大核心 2023年第5期24-37,共14页
Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group stru... Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market. 展开更多
关键词 network autoregression factor structure HETEROGENEITY latent group structure network time series
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Sensor Network Structure Recognition Based on P-law
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作者 Chuiju You Guanjun Lin +3 位作者 Jinming Qiu Ning Cao Yundong Sun Russell Higgs 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1277-1292,共16页
A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor net... A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition. 展开更多
关键词 Sensor network graph network P-law unit subgroup structure recognition
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