Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculat...In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculation after the grains are crushed,and reduce the producing time of specimen.In this work,several one-dimensional compressing simulations are carried out to investigate the effect of particle crushing on mechanical properties of granular materials under a wide range of stress.The results show that the crushing process of granular materials can be divided into four different stages according to er-logσy curves.At the end of the second stage,there exists a yield point,after which the physical and mechanical properties of specimens will change significantly.Under extremely high stress,particle crushing will wipe some initial information of specimens,and specimens with different initial gradings and void ratios present some similar characteristics.Particle crushing has great influence on grading,lateral pressure coefficient and compressibility of granular materials,and introduce extra irreversible volume deformation,which is necessary to be considered in modelling of granular materials in wide stress range.展开更多
Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes...Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes highly desirable.Here,dense SnO_(2) nanoparticles are uniformly grown on three-dimensional(3D)fiber network of carbon cloth(CC)by a facile dip-coating and calcination method.Importantly,Zn modification strategy is employed to restrain the growth of long-range order of SnO_(2) lattices and to produce rich grain boundaries.The hybrid architecture can act as a flexible electrode for CO_(2)-to-formate conversion,which delivers a high partial current of 18.8 m A cm-2 with a formate selectivity of 80%at a moderate cathodic potential of-0.947 V vs.RHE.The electrode exhibits remarkable stability over a 16 h continuous operation.The superior performance is attributed to the synergistic effect of ultrafine SnO_(2) nanoparticles with abundant active sites and 3D fiber network of the electrode for efficient mass transport and electron transfer.The sizeable electrodes hold promise for industrial applications.展开更多
Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate resul...Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.展开更多
Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seri...Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seriously exists in the co-channel Densely Deployed Femtocell Network(DDFN).Since the Femtocell Access Points(FAPs) are randomly deployed by their customers,the interference cannot be predicted in advance.Meanwhile,new characteristics such as the short radius of femtocell and the small number of users lead to the inefficiency of the traditional frequency reuse algorithms such as Fractional Frequency Reuse(FFR).Aiming for the downlink interference coordination in the DDFN,in this paper,we propose a User-oriented Graph based Frequency Allocation(UGFA)algorithm.Firstly,we construct the interference graph for users in the network.Secondly,we study the conventional graph based resources allocation algorithm.Then an improved two steps graph based frequency allocation mechanism is proposed.Simulation results show that UGFA has a high frequency reuse ratio mean while guarantees a better throughput.展开更多
The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing i...The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing in a DSC system for uplink of the DSC system. To further improve the uplink performance, the high-rank codebook is designed based on singular value decomposition （SVD） due to the unnecessary preservation of strict constant modulus in the DSC system. And according to the simulation result, the proposed codebook leads to significant sum-rate gain and appreciable block error rate （BLER） performance improvement in the DSC system.展开更多
This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in Qiina. In consideration of the severe environmental conditions of die densely built-up afeas, s...This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in Qiina. In consideration of the severe environmental conditions of die densely built-up afeas, such as the lack of green space and. open space, ecological disturbance in some areas, poor landscape quality, this paper focused on the ecological space optimization in the process of urban renewal Firstly, theories related to this field were analyzed, and a comprehensive ecological efficiency evaluation sjrstem was established based on disciplines such as urban ecology, landscape ecology, urban sociology, behavioral psychology, biology, urban planning and design. Secondly, this system was used to judge the ecological efficiency of typical blocks on GIS platform and to find out the key spatial nodes that need to be updated. Thirdly, in different cases, space optimization projects witii different theories were designed, and the spatial model of influence was used to comprehensively evaluate their ecological efficiency. Finally, the parameters under different conditions were corrected to get a systematic system for evaluating the green space system in densely built-up areas＊ Due to the lack of undetstanding of the ecological fonction of green space in the past, die environmental condition of densely built-up areas is not good. Therefore, the most important task of urban oigauic renewal is ecological restoiation. In this paper, die exploiation is based on the reservation for built-up afeas to avoid repeated reconstruction and interference. Authors of this paper tried to find out a way to rebuild green space system that performed more complex functions with limited spatial resources. The application of “micfo-ttansfotmation” of green space system in densely built-up areas turns out to improve the quality of landscape while reducing the construction costrds展开更多
To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates ...To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.展开更多
Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only cha...Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.展开更多
Piezoresistive pressure sensors have received considerable attention because of their simple structure,high sensitivity and low cost.Graphene,which is known for its outstanding mechanical and electrical properties,has...Piezoresistive pressure sensors have received considerable attention because of their simple structure,high sensitivity and low cost.Graphene,which is known for its outstanding mechanical and electrical properties,has shown great application potential as a sensor material.However,its durability and performance consistency in practical applications still require enhancement.In this study,magnetic graphene fibers(MGFs)are prepared via wet spinning,using graphene oxide(GO),doped with Fe_(3)O_(4)nanoparticles.The resulting MGFs exhibit a high tensile strength of 58.6 MPa,a strain of 5.3%and an electrical conductivity of 1.7×10^(4)S/m.These MGFs are utilised to construct a multilayer fabric for fabrication of flexible pressure sensors.The confinement within the spinning channel facilitates an ordered arrangement of GO sheets,resulting in MGFs with superior electrical and mechanical properties.The issuing MGFs pressure sensors demonstrate a wide detection range(0-120 kPa),high sensitivity(0.233 kPa^(−1),0-40 kPa)and rapid response/recovery times(121 ms/158 ms).In addition,it exhibits a remarkable durability,maintaining performance over 1300 cycles,during continuous operation,with negligible degradation.This sensor shows excellent capability in monitoring human physiological activities,indicating its substantial application potential in wearable devices.展开更多
The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.展开更多
Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without...Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.展开更多
The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications...The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications,both in research and industry.We report the two-dimensional dense packing patterns of bending and assembled rods,which are non-convexly deformed from simple objects and modeled as entangled particles.Monte Carlo simulations and further analytical constructions are carried out to explore possible densely packed structures.Two typical densely packed structures of C-bending rods are found,and their packing densities are identified as being functions of the aspect ratio and central angle.Six shapes of assembled rods,representing the combined deformations of rods,are employed in simulations with the packing structures classified into three types.The dense packing density of each packing pattern is derived as a function of different shape parameters.In contrast with the case of disordered packings,both the shape and order are verified to affect the packing density.展开更多
Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based elect...Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.展开更多
The Longmenshan(LMS)fault zone is located at the junction of the eastern Tibetan Plateau and the Sichuan Basin and is of great significance for studying regional tectonics and earthquake hazards.Although regional velo...The Longmenshan(LMS)fault zone is located at the junction of the eastern Tibetan Plateau and the Sichuan Basin and is of great significance for studying regional tectonics and earthquake hazards.Although regional velocity models are available for the LMS fault zone,high-resolution velocity models are lacking.Therefore,a dense array of 240 short-period seismometers was deployed around the central segment of the LMS fault zone for approximately 30 days to monitor earthquakes and characterize fine structures of the fault zone.Considering the large quantity of observed seismic data,the data processing workflow consisted of deep learning-based automatic earthquake detection,phase arrival picking,and association.Compared with the earthquake catalog released by the China Earthquake Administration,many more earthquakes were detected by the dense array.Double-difference seismic tomography was adopted to determine V_(p),V_(s),and V_(p)/V_(s)models as well as earthquake locations.The checkerboard test showed that the velocity models have spatial resolutions of approximately 5 km in the horizontal directions and 2 km at depth.To the west of the Yingxiu–Beichuan Fault(YBF),the Precambrian Pengguan complex,where most of earthquakes occurred,is characterized by high velocity and low V_(p)/V_(s)values.In comparison,to the east of the YBF,the Upper Paleozoic to Jurassic sediments,where few earthquakes occurred,show low velocity and high V_(p)/V_(s)values.Our results suggest that the earthquake activity in the LMS fault zone is controlled by the strength of the rock compositions.When the high-resolution velocity models were combined with the relocated earthquakes,we were also able to delineate the fault geometry for different faults in the LMS fault zone.展开更多
Currently,there is a lack of in-situ or model test results for cone penetration tests(CPTs)conducted in deep,dense sand layers under high overburden stresses,restricting the development of empirical relationships betw...Currently,there is a lack of in-situ or model test results for cone penetration tests(CPTs)conducted in deep,dense sand layers under high overburden stresses,restricting the development of empirical relationships between CPT results and the characteristics of such deep,dense sand layers.This study addresses this gap by proposing an empirical relationship to predict the relative density of dense silica sand based on stress level and cone tip resistance.The relationship was developed through CPTs performed in a calibration chamber using dense sand specimens(with relative densities of 74%-91%)subjected to high stresses(under overburden stresses of 0.5-2.0 MPa)and numerical simulations employing the large deformation finite element method.The Arbitrary Lagrangian Eulerian method was used to regularly regenerate the mesh to prevent soil element distortion around the cone tip.Additionally,the modified Mohr-Coulomb model was integrated to capture the stress-strain behavior of dense silica sand under high stresses.A reasonable agreement was achieved between the numerical and experimental penetration profiles,which verifies the reliability of the numerical model.A sufficient number of parametric analyses were carried out,and then an empirical equation was proposed to establish the relationship between the relative density of dense sand,stress level and cone resistance.The empirical equation provides predictions with acceptable accuracy,as the discrepancies between the predicted and measured relative density values fall within±30%.展开更多
Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dens...Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金The authors wish to thank the National Natural Science Foundation of China(No.11772117)the Fundamental Research Funds for the Central Universities(No.2015B37414)+1 种基金Henan Scientific and Technical Project under Grant(No.192102310480)Key Scientific Research Project of Colleges and Universities in Henan Province(CN)(21B560015)for financial support.
文摘In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculation after the grains are crushed,and reduce the producing time of specimen.In this work,several one-dimensional compressing simulations are carried out to investigate the effect of particle crushing on mechanical properties of granular materials under a wide range of stress.The results show that the crushing process of granular materials can be divided into four different stages according to er-logσy curves.At the end of the second stage,there exists a yield point,after which the physical and mechanical properties of specimens will change significantly.Under extremely high stress,particle crushing will wipe some initial information of specimens,and specimens with different initial gradings and void ratios present some similar characteristics.Particle crushing has great influence on grading,lateral pressure coefficient and compressibility of granular materials,and introduce extra irreversible volume deformation,which is necessary to be considered in modelling of granular materials in wide stress range.
基金supported by the National Natural Science Foundation of China(51902204,22003041,21975163)Bureau of Industry and Information Technology of Shenzhen(201901171518)Shenzhen Science and Technology Program(KQTD20190929173914967)。
文摘Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes highly desirable.Here,dense SnO_(2) nanoparticles are uniformly grown on three-dimensional(3D)fiber network of carbon cloth(CC)by a facile dip-coating and calcination method.Importantly,Zn modification strategy is employed to restrain the growth of long-range order of SnO_(2) lattices and to produce rich grain boundaries.The hybrid architecture can act as a flexible electrode for CO_(2)-to-formate conversion,which delivers a high partial current of 18.8 m A cm-2 with a formate selectivity of 80%at a moderate cathodic potential of-0.947 V vs.RHE.The electrode exhibits remarkable stability over a 16 h continuous operation.The superior performance is attributed to the synergistic effect of ultrafine SnO_(2) nanoparticles with abundant active sites and 3D fiber network of the electrode for efficient mass transport and electron transfer.The sizeable electrodes hold promise for industrial applications.
基金This research is partially supported by grant from the National Natural Science Foundation of China(No.72071019)grant from the Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0185)grant from the Chongqing Graduate Education and Teaching Reform Research Project(No.yjg193096).
文摘Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.
基金supported by the National Natural Science Foundation of China under Grant No.61372092the China National Science and Technology Major Projects on New Generation Broadband Wireless Mobile Communications Network under Grants No.2011ZX03005-004,No.2012ZX03001029-003,No.2012ZX03001008-003
文摘Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seriously exists in the co-channel Densely Deployed Femtocell Network(DDFN).Since the Femtocell Access Points(FAPs) are randomly deployed by their customers,the interference cannot be predicted in advance.Meanwhile,new characteristics such as the short radius of femtocell and the small number of users lead to the inefficiency of the traditional frequency reuse algorithms such as Fractional Frequency Reuse(FFR).Aiming for the downlink interference coordination in the DDFN,in this paper,we propose a User-oriented Graph based Frequency Allocation(UGFA)algorithm.Firstly,we construct the interference graph for users in the network.Secondly,we study the conventional graph based resources allocation algorithm.Then an improved two steps graph based frequency allocation mechanism is proposed.Simulation results show that UGFA has a high frequency reuse ratio mean while guarantees a better throughput.
基金supported by the National High-tech Research and Development Program of China(863 Program)(2012AA111902)the Shanghai Natural Science Foundation(12ZR1433900)
文摘The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing in a DSC system for uplink of the DSC system. To further improve the uplink performance, the high-rank codebook is designed based on singular value decomposition （SVD） due to the unnecessary preservation of strict constant modulus in the DSC system. And according to the simulation result, the proposed codebook leads to significant sum-rate gain and appreciable block error rate （BLER） performance improvement in the DSC system.
基金Sponsored by National Natural Science Fund of China(51578454)
文摘This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in Qiina. In consideration of the severe environmental conditions of die densely built-up afeas, such as the lack of green space and. open space, ecological disturbance in some areas, poor landscape quality, this paper focused on the ecological space optimization in the process of urban renewal Firstly, theories related to this field were analyzed, and a comprehensive ecological efficiency evaluation sjrstem was established based on disciplines such as urban ecology, landscape ecology, urban sociology, behavioral psychology, biology, urban planning and design. Secondly, this system was used to judge the ecological efficiency of typical blocks on GIS platform and to find out the key spatial nodes that need to be updated. Thirdly, in different cases, space optimization projects witii different theories were designed, and the spatial model of influence was used to comprehensively evaluate their ecological efficiency. Finally, the parameters under different conditions were corrected to get a systematic system for evaluating the green space system in densely built-up areas＊ Due to the lack of undetstanding of the ecological fonction of green space in the past, die environmental condition of densely built-up areas is not good. Therefore, the most important task of urban oigauic renewal is ecological restoiation. In this paper, die exploiation is based on the reservation for built-up afeas to avoid repeated reconstruction and interference. Authors of this paper tried to find out a way to rebuild green space system that performed more complex functions with limited spatial resources. The application of “micfo-ttansfotmation” of green space system in densely built-up areas turns out to improve the quality of landscape while reducing the construction costrds
基金the National Natural Science Foundation of China(No.81830052)the Shanghai Natural Science Foundation of China(No.20ZR1438300)the Shanghai Science and Technology Support Project(No.18441900500),China。
文摘To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62063004 and 61762033in part by the Hainan Provincial Natural Science Foundation of China under Grant 2019RC018 and 619QN246by the Postdoctoral Science Foundation under Grant 2020TQ0293.
文摘Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.
基金supported by Zhejiang Natural Science Foundation Technology Project(LGG22E030015)National Key Research and Development Program of China support by Ministry of Science and Technology(2022YFB3704504)+5 种基金Jiaxing Science and Technology Plan Project(2023AY11004)Jiaxing Project of Science and Technology(2023AY11014)Jiaxing University General Cultivation Project(00321056AL)Qinshen Youth Backbone Training Program of Jiaxing University(CD70623038)Jiaxing City Public Welfare Project(Young Talent Special Project)(2023AY40027)Key Laboratory of Yarn Materials Forming and Composite Processing Technology,Zhejiang Province(MTC-2022-06).
文摘Piezoresistive pressure sensors have received considerable attention because of their simple structure,high sensitivity and low cost.Graphene,which is known for its outstanding mechanical and electrical properties,has shown great application potential as a sensor material.However,its durability and performance consistency in practical applications still require enhancement.In this study,magnetic graphene fibers(MGFs)are prepared via wet spinning,using graphene oxide(GO),doped with Fe_(3)O_(4)nanoparticles.The resulting MGFs exhibit a high tensile strength of 58.6 MPa,a strain of 5.3%and an electrical conductivity of 1.7×10^(4)S/m.These MGFs are utilised to construct a multilayer fabric for fabrication of flexible pressure sensors.The confinement within the spinning channel facilitates an ordered arrangement of GO sheets,resulting in MGFs with superior electrical and mechanical properties.The issuing MGFs pressure sensors demonstrate a wide detection range(0-120 kPa),high sensitivity(0.233 kPa^(−1),0-40 kPa)and rapid response/recovery times(121 ms/158 ms).In addition,it exhibits a remarkable durability,maintaining performance over 1300 cycles,during continuous operation,with negligible degradation.This sensor shows excellent capability in monitoring human physiological activities,indicating its substantial application potential in wearable devices.
基金supported in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
文摘Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.
基金the National Natural Science Foundation of China(Nos.11602088,11672110 and 11472110)Natural Science Foundation of Guangdong Province(No.2017A030313014)+1 种基金the opening project of the State Key Lab-oratory for Strength and Vibration of Mechanical Structures(Xi'an Jiaotong University)(Nos.SV2018-KF-33 and SV2017-KF-04)Fundamental Research Funds for the Central Universities(Nos.2017BQ094 and 2018PY21).
文摘The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications,both in research and industry.We report the two-dimensional dense packing patterns of bending and assembled rods,which are non-convexly deformed from simple objects and modeled as entangled particles.Monte Carlo simulations and further analytical constructions are carried out to explore possible densely packed structures.Two typical densely packed structures of C-bending rods are found,and their packing densities are identified as being functions of the aspect ratio and central angle.Six shapes of assembled rods,representing the combined deformations of rods,are employed in simulations with the packing structures classified into three types.The dense packing density of each packing pattern is derived as a function of different shape parameters.In contrast with the case of disordered packings,both the shape and order are verified to affect the packing density.
基金funded by the Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U21A20143)the National Science Fund for Excellent Young Scholars(52322607)the Excellent Youth Foundation of Heilongjiang Scientific Committee(YQ2022E028)。
文摘Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.
基金supported by the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology under Grant 2024yjrc64the National Key R&D Program of China under Grant 2018YFC1504102。
文摘The Longmenshan(LMS)fault zone is located at the junction of the eastern Tibetan Plateau and the Sichuan Basin and is of great significance for studying regional tectonics and earthquake hazards.Although regional velocity models are available for the LMS fault zone,high-resolution velocity models are lacking.Therefore,a dense array of 240 short-period seismometers was deployed around the central segment of the LMS fault zone for approximately 30 days to monitor earthquakes and characterize fine structures of the fault zone.Considering the large quantity of observed seismic data,the data processing workflow consisted of deep learning-based automatic earthquake detection,phase arrival picking,and association.Compared with the earthquake catalog released by the China Earthquake Administration,many more earthquakes were detected by the dense array.Double-difference seismic tomography was adopted to determine V_(p),V_(s),and V_(p)/V_(s)models as well as earthquake locations.The checkerboard test showed that the velocity models have spatial resolutions of approximately 5 km in the horizontal directions and 2 km at depth.To the west of the Yingxiu–Beichuan Fault(YBF),the Precambrian Pengguan complex,where most of earthquakes occurred,is characterized by high velocity and low V_(p)/V_(s)values.In comparison,to the east of the YBF,the Upper Paleozoic to Jurassic sediments,where few earthquakes occurred,show low velocity and high V_(p)/V_(s)values.Our results suggest that the earthquake activity in the LMS fault zone is controlled by the strength of the rock compositions.When the high-resolution velocity models were combined with the relocated earthquakes,we were also able to delineate the fault geometry for different faults in the LMS fault zone.
基金National Natural Science Foundation of China(Nos.42025702,52394251)。
文摘Currently,there is a lack of in-situ or model test results for cone penetration tests(CPTs)conducted in deep,dense sand layers under high overburden stresses,restricting the development of empirical relationships between CPT results and the characteristics of such deep,dense sand layers.This study addresses this gap by proposing an empirical relationship to predict the relative density of dense silica sand based on stress level and cone tip resistance.The relationship was developed through CPTs performed in a calibration chamber using dense sand specimens(with relative densities of 74%-91%)subjected to high stresses(under overburden stresses of 0.5-2.0 MPa)and numerical simulations employing the large deformation finite element method.The Arbitrary Lagrangian Eulerian method was used to regularly regenerate the mesh to prevent soil element distortion around the cone tip.Additionally,the modified Mohr-Coulomb model was integrated to capture the stress-strain behavior of dense silica sand under high stresses.A reasonable agreement was achieved between the numerical and experimental penetration profiles,which verifies the reliability of the numerical model.A sufficient number of parametric analyses were carried out,and then an empirical equation was proposed to establish the relationship between the relative density of dense sand,stress level and cone resistance.The empirical equation provides predictions with acceptable accuracy,as the discrepancies between the predicted and measured relative density values fall within±30%.
基金supported in part by the National Science Foundation of China(52371372)the Project of Science and Technology Commission of Shanghai Municipality,China(22JC1401400,21190780300)the 111 Project,China(D18003)
文摘Dear Editor,This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging.
