WANG Ranghui , ZHANG Weidong , ZHOU Limin , LIU Chunwei , PENG Qing , TIAN Chang , DING Liguo
2022, 14(5):509-515. DOI: 10.13878/j.cnki.jnuist.2022.05.001
Abstract:The essence of ecological security focuses on ecological risk and ecological vulnerability,and the study of ecological security is the hot points of low-carbon green development,which has important theoretical value and practical significance for optimizing landscape ecological spatial structure and maintaining ecological sustainability.Based on the interdisciplinary principles and methods involved in geography,ecology,remote sensing and GIS,this paper studies the characteristics and laws of climate change and landscape pattern,the coupling relationship between climate change and ecological process,landscape pattern and functional properties.The relationship between landscape pattern,climate factors and soil,hydrological processes and vegetation processes in the ecosystem is analyzed,and the effects of climate change on carbon storage,NPP and ecosystem services are studied to reveal the impact of climate change on ecological processes.The comprehensive analysis shows that climate factors affect the characteristics and changes of ecological security.Meanwhile,landscape pattern is the basis of restricting regional ecological security,and the effects of climate change and landscape pattern on ecological security mechanism are complex.
SHAN Haiyan , PI Wenjie , XU Jili
2022, 14(5):516-526. DOI: 10.13878/j.cnki.jnuist.2022.05.002
Abstract:Small and medium-sized service enterprises were particularly vulnerable during COVID-19.To help them survive the pandemic,local government introduced series of support scheme,such as the vouchers jointly granted by government and small and medium-sized service enterprises.This paper constructs a tripartite evolutionary game model involving local government,small and medium-sized service enterprises,and consumers,to study the influencing factors of paid subsidies,government credibility,and voucher deduction amount on the strategies of each stakeholder.The results show that the low subsidy and the paid characteristic of consumer vouchers have a great impact on the government credibility,which will prompt government to choose paid support for enterprises;the enterprises are inclined to choose positive self-help strategy due to higher paid subsidies and less deduction for consumption vouchers;that the vouchers hardly influence the actual consumption amount,and the more deductions of consumption vouchers by enterprises of positive self-help strategy,will motivate consumers to choose voucher consumption.It can be concluded that a differentiated support scheme combined with precise delivery of consumption vouchers can promote economic recovery.
JIANG Jianfeng , ZHANG Yunsong , ZHANG Xian , ZHANG Chenxiang , AN Shumei
2022, 14(5):527-534. DOI: 10.13878/j.cnki.jnuist.2022.05.003
Abstract:The development of 5G network promotes the strategic transformation and upgrading of the vertical industry of the industrial Internet.The 5G SA (Stand-Alone) network sinks the User Plane Function (UPF) into the business area,and retains the control plane in the center of the large area.All business data is accessed through the 5G Multi-access Edge Computing (MEC) interface to the business application server,while all traffic for users to access their own servers is completed internally,which guarantees the network security and minimizes the business path delay.Through the optimized Particle Swarm Optimization (PSO),a fitness function based on network physical node resources and link resources is constructed,in which the Bayesian evaluation is introduced to calculate the physical node isolation factor.The average isolation factor of all nodes is set as the threshold then used as constraint condition to ensure the isolation performance of network slices,and the iterative solution is used to adjust the path resources between network slices to improve network performance.The results of actual 5G network test on smart mine show that the algorithm can increase the revenue-cost ratio by 10%,improve the link utilization rate by 19% and the uplink rate by 20%-50%,which fully guarantees the quality of network service in mining areas.
ZHENG Zheng , TAN Lei , ZHOU Nan , HAN Junwei , GAO Jing , WENG Liguo
2022, 14(5):535-542. DOI: 10.13878/j.cnki.jnuist.2022.05.004
Abstract:Predicting residential energy consumption is tantamount to forecasting a multivariate time series.A specific window for several sensor signals can extract various features to forecast the energy consumption by using a prediction model.However,it is still a challenging task because of irregular patterns inside including hidden correlations between power attributes.In order to extract the complicated irregular energy patterns and selectively learn the spatiotemporal features to reduce the translational variance between energy attributes,we propose a deep learning model based on the multi-headed attention with the convolutional recurrent neural network.Compared with the simple time series model,the proposed model uses convolution and weighting mechanism to model the local correlation between power attributes and active power.It exploits the attention scores calculated with softmax and dot product operation in the network to model the transient and impulsive nature of energy demand,predicting the instantaneous pulse power consumption effectively.Experiments with the dataset of University of California,Irvine (UCI) household electric power consumption consisting of a total 2,075,259 time-series show that the proposed model greatly improves the prediction accuracy compared to the state-of-the-art deep learning models.
LI Min , CHEN Fulong , PANG Hui
2022, 14(5):543-550. DOI: 10.13878/j.cnki.jnuist.2022.05.005
Abstract:With the rapid development of ICV (Intelligent Connected Vehicle) industry,data exchange between vehicle and human,vehicle and vehicle as well as between vehicle and external environment has become common,which imposes serious threat to automobile security.Security certification of vehicle PEPS (Passive Entry Passive Start) and EMS (Engine Management System) is the prerequisite to ensure the safe operation of the vehicle.However,the widely used 128 bits AES for PEPS and EMS security certification is complex in algorithm,time consuming in encryption and decryption,and occupies more MCU resources,compared with encryption algorithm SM4.Here,the SM4 algorithm is used to carry out security certification of vehicle PEPS and EMS,which can shorten the encryption and decryption time,and effectively improve the data transmission efficiency.Then,it is implemented by advance language and transplanted to domestic MCU GD32F103.The proposed approach applies encryption algorithm SM4 and provide a research basis for ICV security certification.
HU Xuyang , GAO Shangbing , WANG Changchun , HU Liwei , LI Shaofan
2022, 14(5):551-558. DOI: 10.13878/j.cnki.jnuist.2022.05.006
Abstract:Lane detection plays an important role in intelligent transportation.The accurate and fast lane detection is important for assisted driving and automatic driving.In view of the poor accuracy and slow speed of deep learning methods for lane line recognition,a method abbreviated as LaneSegNet is proposed for efficient lane line segmentation.First,based on the principle of encoding and decoding network,a backbone network Lane-Net is constructed to extract the lane line features and segment the lane lines.Then,the multi-scale dilated convolution feature fusion network is used to greatly expand the receptive field of the model and extract the global features.Finally,the hybrid attention network is used to obtain rich lane line features and enhance the information related to the current task.The experimental results show that the accuracy of this method is 97.6% on TuSimple dataset,while on the CULane dataset,the detection accuracies are 92.5% and 75.2% for standard pavement and multiple pavements,respectively.Compared with other models,the proposed LaneSegNet has better segmentation accuracy and reasoning speed,and has stronger adaptability and robustness.
2022, 14(5):559-565. DOI: 10.13878/j.cnki.jnuist.2022.05.007
Abstract:3D morphable model (3DMM) has been widely used in 3D modeling,image synthesis and related fields.However,it is perplexed by over-constraint due to the influence from size,types,and principal components of training data,thus cannot provide enough flexibility to represent high-frequency deformation.Here,we embed the 3DMM into deep neural network to improve its representation ability in 3D face reconstruction.A dual-path neural network is constructed and improved in efficiency of network learning,which achieves balance between global path and local path.Then the nonlinear 3DMM is improved in both learning objectives and network structure,so as to capture more details than linear or previous nonlinear models.The comparison and simulation experiments show that the proposed algorithm has lower normalized average error in 3D face reconstruction,and the generated 3D face model has good robustness and accurate details.
ZHUANG Zhihao , WANG Min , WANG Kang , LI Sheng , WU Jia
2022, 14(5):566-578. DOI: 10.13878/j.cnki.jnuist.2022.05.008
Abstract:With the rapid development of deep learning,its automatic learning characteristics and accurate prediction ability make it successful in ground-based cloud classification.More complex and better deep learning networks are applied and studied in the field of ground-based cloud classification.In the past two years,some large-scale ground-based cloud classification data sets have been published,yet there is no literature on fully introduction and use of these large data sets.Here,we list the lately issued massive data sets,then introduce the cloud classification technology,especially detail the latest research progress in deep learning-based cloud classification,and finally assess and compare several classic convolution neural network learning models on their ground-based cloud classification performance.The convolution neural network is verified to be effective in the field of ground-based cloud classification.
KE Jielong , ZHANG Yu , ZHU Penghui , HUANG Chikun , WU Keting
2022, 14(5):579-586. DOI: 10.13878/j.cnki.jnuist.2022.05.009
Abstract:Increasingly frequent bird activities have brought serious threat on the safe operation of transmission lines,and the existing audio bird-repelling device cannot perennially effectively drive birds due to the lack of intellectuality.In order to solve the above problems,this paper presents an audio bird-repelling strategy based on improved Q-learning algorithm.First of all,in order to evaluate the effect of each audio,the behavior of birds after hearing the audio is quantified into different bird response types by combining with the fuzzy theory.Then,an audio bird-repelling experiment is designed,the data of each audio bird-repelling effect is counted,and the initial weight of each audio is obtained,which provides experimental basis for the audio selection of audio bird-repelling device.In order to make the audio weight more consistent with the actual experimental situation,the weight calculation formula of CRITIC (Criteria Importance Though Intercrieria Correlation) is optimized.Finally,the Q-learning algorithm is improved via the audio weights obtained from the above experiment,and a contrast experiment with other audio bird-repelling strategies is designed.Experimental results show that the improved Q-learning algorithm outperforms other audio bird-repelling strategies,characterized by fast convergence,stable bird-repelling performance,and reducing the adaptability of birds.
2022, 14(5):587-594. DOI: 10.13878/j.cnki.jnuist.2022.05.010
Abstract:In this paper,the problem of cooperative state feedback control for a class of large-scale linear systems described by delta operator is studied.First,according to the independent subsystem described by delta operator,an interconnected closed-loop control system is given through cooperative state feedback controller.Then,based on linear matrix inequality,the sufficient conditions for the design of coordinated state feedback stabilizing controller and cooperative state feedback guaranteed cost controller are given,and the validity of the proposed method is proved by Lyapunov stability theory.Finally,simulation examples show the effectiveness and superiority of the proposed algorithm.
2022, 14(5):595-603. DOI: 10.13878/j.cnki.jnuist.2022.05.011
Abstract:The energy storage is perplexed by low utilization efficiency and poor profit due to its high cost in energy storage and lack of trading platform.Here,a non-cooperative game trading model based on blockchain is designed for shared energy storage.Taking microgrids as nodes,shared energy storage uses digital signature to verify node identity,which can effectively guarantee the transaction security.The trading mechanism for shared energy storage is established on the self-driven and self-executing smart contract.The non-cooperative game model can maximize the benefits of each node,improve energy storage profit,and motivate the node users to participate in transactions,thus help optimize the industrial structure.Example analysis shows that the blockchain-based non-cooperative game trading model can effectively realize participation of energy storage in market transactions,improve energy storage utilization efficiency,increase energy storage profit,and provide technical and theoretical support for further development of energy storage industry.
Yisilamu Wulayin , MIAO Junfeng , FENG Wen
2022, 14(5):604-615. DOI: 10.13878/j.cnki.jnuist.2022.05.012
Abstract:Sea Breeze Front (SBF) is the landward edge of sea breeze circulation,which has similar characteristics to the cold front.SBF has always been a key object of coastal meteorological research since it can trigger strong convection even disastrous weather such as thunderstorms in coastal areas.Numerical simulation has become an important tool in meteorological research with the rapid development of numerical models and computer technology.The numerical simulation of SBF has attracted much attention and yielded fruitful results.Here,we summarize the numerical simulation of SBF carried out in China in the past 40 years,and detail on the effects of environmental flow,topography,urban heat island and atmosphere stability on SBF.In addition,the influence of physical parameterization on SBF and the work of large eddy simulation are summarized.Finally,we sum up some issues which are needed to be addressed for further study.
MO Zhixiang , LI Jiahao , Zhou Lü , HUANG Liangke , LIU Lilong
2022, 14(5):616-624. DOI: 10.13878/j.cnki.jnuist.2022.05.013
Abstract:Atmospheric weighted mean temperature (Tm) plays a key role in GNSS atmospheric precipitable water vapor (PWV) retrieval.In view of the poor applicability of the existing Tm models in Tibetan Plateau,a new Tm model considering surface temperature,altitude,latitude and seasonal variation,named as TPTm,is established using the observation data of 13 radiosonde stations from 2014 to 2017 in Tibetan Plateau.Then,the TPTm model is assessed by comparing with the widely used Bevis model,the local refined Bevis model (Bevis-TP model) and GPT2w model using the radiosonde data in 2018 as reference values.The results show that the TPTm model has better performance with annual bias and Root Mean Square (RMS) error being 0.07 K and 2.76 K,respectively,of which the RMS errors is improved by 54.5%,30.8%,36.3% and 27.6% compared with Bevis,Bevis-TP,GPT2w-5(5° resolution) and GPT2w-1(1° resolution) models,respectively.In addition,when used to estimate GNSS-PWV,the TPTm model has theoretical ERMS,PWV and ERMS,PWV/VPW values of 0.10 mm and 1.02%,respectively.Therefore,the TPTm model will have critical applications in GNSS-PWV retrieval in Tibetan Plateau.
FAN Jingwei , ZHOU Weican , FENG Yecheng , GUAN Yuanhong
2022, 14(5):625-634. DOI: 10.13878/j.cnki.jnuist.2022.05.014
Abstract:In this study,the kinetic energy equation is derived from the basic equation in the rotating coordinate system,and then the energy functional describing the change rate of Tropical Cyclone (TC) intensity is defined by the local change rate of kinetic energy.Afterwards,the Euler-Lagrange equation is obtained by taking the variation of the functional.The equation shows that when the change rate of TC intensity reaches the maximum,the friction,the pressure gradient force,the gravity and the gradient of kinetic energy are balanced.Therefore,the vector determined by these four forces in the balance equation can be used as a predictor of TC intensity,which can more accurately determine the time when the intensity change rate of TCs reaches the maximum.Furthermore,the maximum vortex in a rotational field is extracted by the variational decomposition of wind field,and the analytical solution of vorticity and flow field are obtained when TC intensity changes the fastest.The conclusion has certain practical value for studying the variation trend of velocity and the spatial structure of TC in the evolution of TC,and provides certain theoretical guidance for TC track and intensity forecast.
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