This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization pr...This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.展开更多
The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameteri...The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.展开更多
We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger(SSH)model with long-range hopping terms.By computing the quantum geometry tensor,we derive exact expression...We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger(SSH)model with long-range hopping terms.By computing the quantum geometry tensor,we derive exact expressions for the quantum metric and Berry curvature of the energy band electrons,and we obtain the phase diagram of the model marked by the first Chern number.Furthermore,we also obtain the topological Euler number of the energy band based on the Gauss-Bonnet theorem on the topological characterization of the closed Bloch states manifold in the first Brillouin zone.However,some regions where the Berry curvature is identically zero in the first Brillouin zone result in the degeneracy of the quantum metric,which leads to ill-defined non-integer topological Euler numbers.Nevertheless,the non-integer"Euler number"provides valuable insights and an upper bound for the absolute values of the Chern numbers.展开更多
By using the multi-taper method(MTM)of singular value decomposition(SVD),this study investigates the interdecadal evolution(10-to 30-year cycle)of precipitation over eastern China from 1951 to 2015 and its relationshi...By using the multi-taper method(MTM)of singular value decomposition(SVD),this study investigates the interdecadal evolution(10-to 30-year cycle)of precipitation over eastern China from 1951 to 2015 and its relationship with the North Pacific sea surface temperature(SST).Two significant interdecadal signals,one with an 11-year cycle and the other with a 23-year cycle,are identified in both the precipitation and SST fields.Results show that the North Pacific SST forcing modulates the precipitation distribution over China through the effects of the Pacific Decadal Oscillation(PDO)-related anomalous Aleutian low on the western Pacific subtropical high(WPSH)and Mongolia high(MH).During the development stage of the PDO cold phase associated with the 11-year cycle,a weakened WPSH and MH increased the precipitation over the Yangtze River Basin,whereas an intensified WPSH and MH caused the enhanced rain band to move northward to North China during the decay stage.During the development stage of the PDO cold phase associated with the 23-year cycle,a weakened WPSH and MH increased the precipitation over North China,whereas an intensified WPSH and the weakened MH increased the precipitation over South China during the decay stage.The 11-year and 23-year variabilities contribute differently to the precipitation variations in the different regions of China,as seen in the 1998flooding case.The 11-year cycle mainly accounts for precipitation increases over the Yangtze River Basin,while the 23-year cycle is responsible for the precipitation increase over Northeast China.These results have important implications for understanding how the PDO modulates the precipitation distribution over China,helping to improve interdecadal climate prediction.展开更多
The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive st...The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.展开更多
AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by ...AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.展开更多
Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal ...Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.展开更多
The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to a...The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.展开更多
A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Ni...A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Niño flavors,namely the Eastern-Pacific(EP)and Central-Pacific(CP)types,and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM.The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific,including the amplitude and spatial patterns of sea surface temperature(SST),zonal wind stress,and precipitation anomalies.An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events,respectively.Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events,the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter.In particular,the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere,while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American(PNA)pattern.As a result,different climatic impacts exist in North American regions,with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño,respectively.This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.展开更多
In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed metho...In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.展开更多
Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information ...Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.展开更多
This study comprehensively reviews the literature to deeply explore the role of computer science and internet technologies in addressing educational inequality and socio-psychological issues,with a particular focus on...This study comprehensively reviews the literature to deeply explore the role of computer science and internet technologies in addressing educational inequality and socio-psychological issues,with a particular focus on applications of 5G,artificial intelligence(AI),and augmented/virtual reality(AR/VR).By analyzing how these technologies are reshaping learning and their potential to ameliorate educational disparities,the study reveals challenges present in ensuring educational equity.The research methodology includes exhaustive reviews of applications of AI and machine learning,the Internet of Things and wearable technologies integration,big data analytics and data mining,and the effects of online platforms and social media on socio-psychological issues.Besides,the study discusses applications of these technologies in educational inequality and socio-psychological problem-solving through the lens of 5G,AI,and AR/VR,while also delineating challenges faced by these emerging technologies and future outlooks.The study finds that while computer science and internet technologies hold promise to bridge academic divides and address socio-psychological problems,the complexity of technology access and infrastructure,lack of digital literacy and skills,and critical ethical and privacy issues can impact widespread adoption and efficacy.Overall,the study provides a novel perspective to understand the potential of computer science and internet technologies in ameliorating educational inequality and socio-psychological issues,while pointing to new directions for future research.It also emphasizes the importance of cooperation among educational institutions,technology vendors,policymakers and researchers,and establishing comprehensive ethical guidelines and regulations to ensure the responsible use of these technologies.展开更多
The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a c...The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.展开更多
An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport(KGT)and the Kuroshio/Ryu...An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport(KGT)and the Kuroshio/Ryukyu Current system.The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea-(ECS-)Kuroshio and the Ryukyu Current.The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio.Additionally,it contributed approximately 37%to the variability of the Ryukyu Current.The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory.It was dominated by its subsurface-intensified flow core,and the upper layer transport made a weaker negative contribution to the total KGT.The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap,and the pressure head was further dominated by the subsurface density anomalies on the Pacific side.These density anomalies could be traced back to the eastern open ocean,and their propagation speed was estimated to be about 7.4 km/d,which is consistent with the speed of the local first-order baroclinic Rossby wave.When the negative(positive)density anomaly signal reached the southern region of the Kerama Gap,it triggered the increase(decrease)of the KGT towards the Pacific side and the formation of an anticyclonic(cyclonic)vortex by baroclinic adjustment.Meanwhile,there is an increase(decrease)in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases(increases)the downstream Ryukyu Current.展开更多
This paper is concerned with a third order in time linear Moore-Gibson-Thompson equation which describes the acoustic velocity potential in ultrasound wave program.Influenced by the work of Kaltenbacher,Lasiecka and M...This paper is concerned with a third order in time linear Moore-Gibson-Thompson equation which describes the acoustic velocity potential in ultrasound wave program.Influenced by the work of Kaltenbacher,Lasiecka and Marchand(Control Cybernet.2011,40:971-988),we establish an observability inequality of the conservative problem,and then discuss the equivalence between the exponential stabilization of a dissipative system and the internal observational inequality of the corresponding conservative system.展开更多
Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural a...Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural and electrical properties of nonpolar p-type GaN films were investigated in detail. It is found that all the surface morphology, crystalline quality, strains, and electrical properties of nonpolar a-plane p-type GaN films are interconnected, and are closely related to the Mg-doping temperature. This means that a proper performance of nonpolar p-type GaN can be expected by optimizing the Mg-doping temperature. In fact, a hole concentration of 1.3×10^(18)cm^(-3), a high Mg activation efficiency of 6.5%,an activation energy of 114 me V for Mg acceptor, and a low anisotropy of 8.3% in crystalline quality were achieved with a growth temperature of 990℃. This approach to optimizing the Mg-doping temperature of the nonpolar a-plane p-type GaN film provides an effective way to fabricate high-efficiency optoelectronic devices in the future.展开更多
High spatiotemporal resolution radiances from the advanced imagers onboard the new generation of geostationary weather satellites provide a unique opportunity to evaluate the abilities of various reanalysis datasets t...High spatiotemporal resolution radiances from the advanced imagers onboard the new generation of geostationary weather satellites provide a unique opportunity to evaluate the abilities of various reanalysis datasets to depict multilayer tropospheric water vapor(WV),thereby enhancing our understanding of the deficiencies of WV in reanalysis datasets.Based on daily measurements from the Advanced Himawari Imager(AHI)onboard the Himawari-8 satellite in 2016,the bias features of multilayer WV from six reanalysis datasets over East Asia are thoroughly evaluated.The assessments show that wet biases exist in the upper troposphere in all six reanalysis datasets;in particular,these biases are much larger in summer.Overall,we find better depictions of WV in the middle troposphere than in the upper troposphere.The accuracy of WV in the ERA5 dataset is the highest,in terms of the bias magnitude,dispersion,and pattern similarity.The characteristics of the WV bias over the Tibetan Plateau are significantly different from those over other parts of East Asia.In addition,the reanalysis datasets all capture the shift of the subtropical high very well,with ERA5 performing better overall.展开更多
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
Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-sec...Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-section(σ_(p))of H1 are determined to be 0.75 eV and 4.67×10^(−15)cm^(2),respectively.Distribution of apparent trap concentration in space charge region is demonstrated.Temperature-enhanced emission process is revealed by decrease of emission time constant.Electricfield-boosted trap emission kinetics are analyzed by the Poole−Frenkel emission(PFE)model.In addition,H1 shows point defect capture properties and temperature-enhanced capture kinetics.Taking both hole capture and emission processes into account during laser beam incidence,H1 features a trap concentration of 2.67×10^(15)cm^(−3).The method and obtained results may facilitate understanding of minority carrier trap properties in wide bandgap semiconductor material and can be applied for device reliability assessment.展开更多
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBGP003in part by the National Natural Science Foundation of China(NSFC)under Grant 62071033in part by ZTE IndustryUniversity-Institute Cooperation Funds under Grant No.IA20230217003。
文摘This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.
基金supported by the National Natural Science Foundation of China(Grant Nos.42175099,42027804,42075073)the Innovative Project of Postgraduates in Jiangsu Province in 2023(Grant No.KYCX23_1319)+3 种基金supported by the National Natural Science Foundation of China(Grant No.42205080)the Natural Science Foundation of Sichuan(Grant No.2023YFS0442)the Research Fund of Civil Aviation Flight University of China(Grant No.J2022-037)supported by the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(Earth Lab)。
文摘The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.
基金Project supported by the Beijing Natural Science Foundation(Grant No.1232026)the Qinxin Talents Program of BISTU(Grant No.QXTCP C201711)+2 种基金the R&D Program of Beijing Municipal Education Commission(Grant No.KM202011232017)the National Natural Science Foundation of China(Grant No.12304190)the Research fund of BISTU(Grant No.2022XJJ32).
文摘We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger(SSH)model with long-range hopping terms.By computing the quantum geometry tensor,we derive exact expressions for the quantum metric and Berry curvature of the energy band electrons,and we obtain the phase diagram of the model marked by the first Chern number.Furthermore,we also obtain the topological Euler number of the energy band based on the Gauss-Bonnet theorem on the topological characterization of the closed Bloch states manifold in the first Brillouin zone.However,some regions where the Berry curvature is identically zero in the first Brillouin zone result in the degeneracy of the quantum metric,which leads to ill-defined non-integer topological Euler numbers.Nevertheless,the non-integer"Euler number"provides valuable insights and an upper bound for the absolute values of the Chern numbers.
基金supported by the National Natural Science Foundation of China(Grant No.42030410)Laoshan Laboratory(No.LSKJ202202403-2)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)the Startup Foundation for Introducing Talent of NUIST。
文摘By using the multi-taper method(MTM)of singular value decomposition(SVD),this study investigates the interdecadal evolution(10-to 30-year cycle)of precipitation over eastern China from 1951 to 2015 and its relationship with the North Pacific sea surface temperature(SST).Two significant interdecadal signals,one with an 11-year cycle and the other with a 23-year cycle,are identified in both the precipitation and SST fields.Results show that the North Pacific SST forcing modulates the precipitation distribution over China through the effects of the Pacific Decadal Oscillation(PDO)-related anomalous Aleutian low on the western Pacific subtropical high(WPSH)and Mongolia high(MH).During the development stage of the PDO cold phase associated with the 11-year cycle,a weakened WPSH and MH increased the precipitation over the Yangtze River Basin,whereas an intensified WPSH and MH caused the enhanced rain band to move northward to North China during the decay stage.During the development stage of the PDO cold phase associated with the 23-year cycle,a weakened WPSH and MH increased the precipitation over North China,whereas an intensified WPSH and the weakened MH increased the precipitation over South China during the decay stage.The 11-year and 23-year variabilities contribute differently to the precipitation variations in the different regions of China,as seen in the 1998flooding case.The 11-year cycle mainly accounts for precipitation increases over the Yangtze River Basin,while the 23-year cycle is responsible for the precipitation increase over Northeast China.These results have important implications for understanding how the PDO modulates the precipitation distribution over China,helping to improve interdecadal climate prediction.
基金supported by the EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,Grant number:101109045)National Key R&D Program of China with Grant number 2018YFB1800804+2 种基金the National Natural Science Foundation of China(Nos.NSFC 61925105,and 62171257)Tsinghua University-China Mobile Communications Group Co.,Ltd,Joint Institutethe Fundamental Research Funds for the Central Universities,China(No.FRF-NP-20-03)。
文摘The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.
基金Project supported in part by the National Key Research and Development Program of China(Grant No.2021YFA0716400)the National Natural Science Foundation of China(Grant Nos.62225405,62150027,61974080,61991443,61975093,61927811,61875104,62175126,and 62235011)+2 种基金the Ministry of Science and Technology of China(Grant Nos.2021ZD0109900 and 2021ZD0109903)the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving ElectronicsTsinghua University Initiative Scientific Research Program.
文摘AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.
基金the National Natural Science Foundation of PRChina(42075130)Nari Technology Co.,Ltd.(4561655965)。
文摘Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.
基金the National Social Science Foundation of China(Grant No.22BTJ035).
文摘The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.
基金supported by the National Natural Science Foundation of China(NSFCGrant No.42275061)+3 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)the Laoshan Laboratory(Grant No.LSKJ202202404)the NSFC(Grant No.42030410)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology.
文摘A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Niño flavors,namely the Eastern-Pacific(EP)and Central-Pacific(CP)types,and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM.The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific,including the amplitude and spatial patterns of sea surface temperature(SST),zonal wind stress,and precipitation anomalies.An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events,respectively.Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events,the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter.In particular,the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere,while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American(PNA)pattern.As a result,different climatic impacts exist in North American regions,with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño,respectively.This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.
基金supported by the National Science Fund for Distinguished Young Scholars (62225303)the Fundamental Research Funds for the Central Universities (buctrc202201)+1 种基金China Scholarship Council,and High Performance Computing PlatformCollege of Information Science and Technology,Beijing University of Chemical Technology。
文摘In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.
基金supported by the National Natural Science Foundation of China(No.62101439)the Key Research and Development Program of Shaanxi(No.2023-YBSF-289).
文摘Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.
文摘This study comprehensively reviews the literature to deeply explore the role of computer science and internet technologies in addressing educational inequality and socio-psychological issues,with a particular focus on applications of 5G,artificial intelligence(AI),and augmented/virtual reality(AR/VR).By analyzing how these technologies are reshaping learning and their potential to ameliorate educational disparities,the study reveals challenges present in ensuring educational equity.The research methodology includes exhaustive reviews of applications of AI and machine learning,the Internet of Things and wearable technologies integration,big data analytics and data mining,and the effects of online platforms and social media on socio-psychological issues.Besides,the study discusses applications of these technologies in educational inequality and socio-psychological problem-solving through the lens of 5G,AI,and AR/VR,while also delineating challenges faced by these emerging technologies and future outlooks.The study finds that while computer science and internet technologies hold promise to bridge academic divides and address socio-psychological problems,the complexity of technology access and infrastructure,lack of digital literacy and skills,and critical ethical and privacy issues can impact widespread adoption and efficacy.Overall,the study provides a novel perspective to understand the potential of computer science and internet technologies in ameliorating educational inequality and socio-psychological issues,while pointing to new directions for future research.It also emphasizes the importance of cooperation among educational institutions,technology vendors,policymakers and researchers,and establishing comprehensive ethical guidelines and regulations to ensure the responsible use of these technologies.
基金support of the National Key R&D Program of China(No.2022YFC2803903)the Key R&D Program of Zhejiang Province(No.2021C03013)the Zhejiang Provincial Natural Science Foundation of China(No.LZ20F020003).
文摘The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.
基金The Fundamental Research Funds for the Central Universities under contract No.B220201024.
文摘An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport(KGT)and the Kuroshio/Ryukyu Current system.The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea-(ECS-)Kuroshio and the Ryukyu Current.The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio.Additionally,it contributed approximately 37%to the variability of the Ryukyu Current.The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory.It was dominated by its subsurface-intensified flow core,and the upper layer transport made a weaker negative contribution to the total KGT.The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap,and the pressure head was further dominated by the subsurface density anomalies on the Pacific side.These density anomalies could be traced back to the eastern open ocean,and their propagation speed was estimated to be about 7.4 km/d,which is consistent with the speed of the local first-order baroclinic Rossby wave.When the negative(positive)density anomaly signal reached the southern region of the Kerama Gap,it triggered the increase(decrease)of the KGT towards the Pacific side and the formation of an anticyclonic(cyclonic)vortex by baroclinic adjustment.Meanwhile,there is an increase(decrease)in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases(increases)the downstream Ryukyu Current.
基金Supported by the National Natural Science Foundation of China(11771216)the Key Research and Development Program of Jiangsu Province(Social Development)(BE2019725)the Qing Lan Project of Jiangsu Province。
文摘This paper is concerned with a third order in time linear Moore-Gibson-Thompson equation which describes the acoustic velocity potential in ultrasound wave program.Influenced by the work of Kaltenbacher,Lasiecka and Marchand(Control Cybernet.2011,40:971-988),we establish an observability inequality of the conservative problem,and then discuss the equivalence between the exponential stabilization of a dissipative system and the internal observational inequality of the corresponding conservative system.
基金Project supported by the National Key Research and Development Program of China (Grant Nos.2021YFB3601000 and 2021YFB3601002)the National Natural Science Foundation of China (Grant Nos.62074077,61921005,61974062,62204121,and 61904082)+1 种基金Leading-edge Technology Program of Jiangsu Natural Science Foundation (Grant No.BE2021008-2)the China Postdoctoral Science Foundation (Grant No.2020M671441)。
文摘Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural and electrical properties of nonpolar p-type GaN films were investigated in detail. It is found that all the surface morphology, crystalline quality, strains, and electrical properties of nonpolar a-plane p-type GaN films are interconnected, and are closely related to the Mg-doping temperature. This means that a proper performance of nonpolar p-type GaN can be expected by optimizing the Mg-doping temperature. In fact, a hole concentration of 1.3×10^(18)cm^(-3), a high Mg activation efficiency of 6.5%,an activation energy of 114 me V for Mg acceptor, and a low anisotropy of 8.3% in crystalline quality were achieved with a growth temperature of 990℃. This approach to optimizing the Mg-doping temperature of the nonpolar a-plane p-type GaN film provides an effective way to fabricate high-efficiency optoelectronic devices in the future.
基金partly supported by the National Natural Science Foundation of China(Grant Nos.41975020 and 41975031)(Jun LI)。
文摘High spatiotemporal resolution radiances from the advanced imagers onboard the new generation of geostationary weather satellites provide a unique opportunity to evaluate the abilities of various reanalysis datasets to depict multilayer tropospheric water vapor(WV),thereby enhancing our understanding of the deficiencies of WV in reanalysis datasets.Based on daily measurements from the Advanced Himawari Imager(AHI)onboard the Himawari-8 satellite in 2016,the bias features of multilayer WV from six reanalysis datasets over East Asia are thoroughly evaluated.The assessments show that wet biases exist in the upper troposphere in all six reanalysis datasets;in particular,these biases are much larger in summer.Overall,we find better depictions of WV in the middle troposphere than in the upper troposphere.The accuracy of WV in the ERA5 dataset is the highest,in terms of the bias magnitude,dispersion,and pattern similarity.The characteristics of the WV bias over the Tibetan Plateau are significantly different from those over other parts of East Asia.In addition,the reanalysis datasets all capture the shift of the subtropical high very well,with ERA5 performing better overall.
基金supported by the National Natural Science Foundation of China[grant number 42088101] and the National Natural Science Foundation of China[grant number 42005020].
基金the National Natural Science Foundation of China(Nos.62272063,62072056 and 61902041)the Natural Science Foundation of Hunan Province(Nos.2022JJ30617 and 2020JJ2029)+4 种基金Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications(No.JZNY202102)the Traffic Science and Technology Project of Hunan Province,China(No.202042)Hunan Provincial Key Research and Development Program(No.2022GK2019)this work was funded by the Researchers Supporting Project Number(RSPD2023R681)King Saud University,Riyadh,Saudi Arabia.
文摘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.
基金supported by ShanghaiTech University Startup Fund 2017F0203-000-14the National Natural Science Foundation of China(Grant No.52131303)+1 种基金Natural Science Foundation of Shanghai(Grant No.22ZR1442300)in part by CAS Strategic Science and Technology Program(Grant No.XDA18000000).
文摘Emission and capture characteristics of a deep hole trap(H1)in n-GaN Schottky barrier diodes(SBDs)have been investigated by optical deep level transient spectroscopy(ODLTS).Activation energy(Eemi)and capture cross-section(σ_(p))of H1 are determined to be 0.75 eV and 4.67×10^(−15)cm^(2),respectively.Distribution of apparent trap concentration in space charge region is demonstrated.Temperature-enhanced emission process is revealed by decrease of emission time constant.Electricfield-boosted trap emission kinetics are analyzed by the Poole−Frenkel emission(PFE)model.In addition,H1 shows point defect capture properties and temperature-enhanced capture kinetics.Taking both hole capture and emission processes into account during laser beam incidence,H1 features a trap concentration of 2.67×10^(15)cm^(−3).The method and obtained results may facilitate understanding of minority carrier trap properties in wide bandgap semiconductor material and can be applied for device reliability assessment.