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    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
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    2014,6(5):405-419, DOI:
    [Abstract] (1689) [HTML] (0) [PDF 1.98 M] (21096)
    Abstract:
    With the rapid development of internet of things,cloud computing,and mobile internet,the rise of Big Data has attracted more and more concern,which brings not only great benefits but also crucial challenges on how to manage and utilize Big Data better.This paper describes the main aspects of Big Data including definition,data sources,key technologies,data processing tools and applications,discusses the relationship between Big Data and cloud computing,internet of things and mobile internet technology.Furthermore,the paper analyzes the core technologies of Big Data,Big Data solutions from industrial circles,and discusses the application of Big Data.Finally,the general development trend on Big Data is summarized.The review on Big Data is helpful to understand the current development status of Big Data,and provides references to scientifically utilize key technologies of Big Data.
    2013,5(5):385-396, DOI:
    [Abstract] (1459) [HTML] (0) [PDF 1.40 M] (7387)
    Abstract:
    Recently,coordinated control of multi-agent systems has been a hot topic in the control field,due to its wide application in cooperative control of multiple autonomous vehicles,traffic control of vehicles,formation control of unmanned aircrafts,resource allocation in networks and so on.Firstly,the introduction of background about multi-agent systems,the concepts of agents and the knowledge of the graph theory has been given.And then research status of swarming/flocking problems,formation control problems,consensus problems and network optimization are summarized and analyzed at home and abroad,including coordination control of multi-agent systems.Finally,some problems about multi-agent systems to be solved in future are proposed,in order to urge deep study on the theory and application in coordinated control of multi-agent systems.
    2010(5):410-413, DOI:
    [Abstract] (2275) [HTML] (0) [PDF 960.26 K] (7163)
    Abstract:
    设计了一个三维声源定位系统,提出了一个新的系统模型,并对传统的基于声波到达时间差TDOA的算法进行了优化。通过检测麦克风接收到信号的时间差,结合已知的阵列元的空间位置确定声源的位置。该系统声源采集部分由4个阵列成正四面体的麦克风组成。算法的硬件实现由TMS320C5416DSP芯片完成'整个系统实现了声源定位的功能。
    2017,9(2):159-167, DOI: 10.13878/j.cnki.jnuist.2017.02.006
    [Abstract] (1070) [HTML] (0) [PDF 1.56 M] (5685)
    Abstract:
    Various indoor positioning techniques have been developed and widely applied in both manufacturing processes and people's lives.Due to the electromagnetic interference and multipath effects,traditional Wi-Fi,Bluetooth and other wireless locating technologies are difficult to achieve high accuracy.Modulated white LED can provide both illumination and location information to achieve highly accurate indoor positioning.In this paper,we first introduce several modulation methods of visible light positioning systems and compare the characteristics of different modulation methods.Then,we propose a viable indoor positioning scheme based on visible light communications and discuss two different demodulation methods.In the following,we introduce several positioning algorithms used in visible light communication system.Finally,the problems and prospects of the visible light communication based indoor positioning are discussed.
    2012,4(4):351-361, DOI:
    [Abstract] (1339) [HTML] (0) [PDF 1.22 M] (5488)
    Abstract:
    In recent years,cloud computing as a new computing service model has become a research hotspot in computer science.This paper is to give a brief analysis and survey on the current cloud computing systems from the definition,deployment model,characteristics and key technologies.Then,the major international and domestic research enterprises and application products on cloud computing are compared and analyzed.Finally,the challenges and opportunities in current research of cloud computing are discussed,and the future directions are pointed out.So,it will help to provide a scientific analysis and references for use and operation of cloud computing.
    2017,9(2):174-178, DOI: 10.13878/j.cnki.jnuist.2017.02.008
    Abstract:
    With the deepening study of nonlinear effect in optical fiber,the distributed optical fiber sensor has been widely studied and applied.In this paper,the application of optical fiber sensor is introduced.To realize different types of fiber distributed sensing,the principle of three kinds of scattered light based on Brillouin scattering,Raman scattering,and Rayleigh scattering is summarized.Finally,the future development direction of fiber distributed sensing is prospected.
    2014,6(5):426-430, DOI:
    [Abstract] (1598) [HTML] (0) [PDF 1.04 M] (4007)
    Abstract:
    We propose a scheme to produce continuous-variable(CV) pair-entanglement frequency comb by nondegenerate optical parametric down-conversion in an optical oscillator cavity in which a multichannel variational period poled LiTaO3 locates as a gain crystal.Using the CV entanglement criteria,we prove that every pair generated from the corresponding channel is entangled.The characteristics of signal and idler entanglement are discussed.The CV pair-entanglement frequency comb may be very significant for the application in quantum communication and computation networks.
    2013,5(6):544-547, DOI:
    [Abstract] (917) [HTML] (0) [PDF 1.56 M] (3914)
    Abstract:
    On account of the power quality signal under stable state,this paper integrates the function of Hanning window with Fast Fourier Transform(FFT),and uses it to harmonic analysis for power quality.Matlab simulation is carried out for the feasibility of the proposed windowed FFT method,and results show that the integration of Hanning window function with FFT can significantly reduce the harmonic leakage,effectively weaken the interference between the harmonics,and accurately measure the amplitude and phase of power signal.
    2014,6(3):226-230, DOI:
    [Abstract] (871) [HTML] (0) [PDF 1.33 M] (3860)
    Abstract:
    As a modulation with relatively strong anti-interference capacity,quadrature phase shift keying(QPSK) has been extensively used in wireless satellite communication.This paper describes the Matlab simulation of QPSK demodulation,and designs an all-digital QPSK demodulation with FPGA.The core of demodulation is synchronization,which includes carrier synchronization and signal synchronization.The carrier synchronization is completed through numerical Costas loop,while signal synchronization through modulus square spectrum analysis,and the results are simulated on Matlab.The communication functions are implemented by upgradable or substitutable softwares as many as possible,based on the idea of software radio communication.The parameter values through Matlab simulation,combined with appropriate hardware system,technically realize the design of the proposed all-digital meteorological satellite demodulator based on FPGA.
    2011(1):1-22, DOI:
    [Abstract] (1791) [HTML] (0) [PDF 1.29 M] (3647)
    Abstract:
    System identification is the theory and methods of establishing mathematical models of systems.The mathematical modeling has a long research history,but the system identification discipline has only several tens of years.In this short decades,system identification has achieved great developments,new identification methods are born one after another,and the research results cover the theory and applications of natural science and social sciences,including physics,biology,earth science,meteorology,computer science,economics,psychology,political science and so on.In this context,we come back to ponder some basic problems of system identification,which is not without benefits for the development of system identification.This is a paper of an introduction to system identification which briefly introduces the definition of identification,system models and identification models,the basic steps and purposes of identification,including the experimental design of identification and data preprocessing,and the types of identification methods,including the least squares identification methods,gradient identification methods,auxiliary model based identification methods,and multi innovation identification methods,and hierarchical identification methods,etc
    2017,9(6):575-582, DOI: 10.13878/j.cnki.jnuist.2017.06.002
    [Abstract] (1324) [HTML] (0) [PDF 1.18 M] (3329)
    Abstract:
    Knowledge graph technology is widely concerned and studied during recent years,in this paper we introduce the construction methods,recent development of knowledge graph in details,we also summarize the interdisciplinary applications of knowledge graph and future directions of research.This paper details the key technologies of textual,visual and multi-modal knowledge graph,such as information extraction,knowledge fusion and knowledge representation.As an important part of the knowledge engineering,knowledge graph,especially the development of multi-modal knowledge graph,is of great significance for efficient knowledge management,knowledge acquisition and knowledge sharing in the era of big data.
    2013,5(5):414-420, DOI:
    [Abstract] (937) [HTML] (0) [PDF 1.04 M] (3316)
    Abstract:
    With the continuous increase of road vehicles,occasional congestion caused by traffic accidents seriously affect the commuting efficiency of traveler and the overall operation level of road network.Real-time and exact forecasting of short-term traffic flow volume is the key point to intelligent traffic system and precondition to solve the congestion situation by route guidance and clearing.According to the uncertain and non-linear features of traffic volume,a model integrated of the improved BP neural network and autoregressive integrated moving average (ARIMA) model is established to forecast the short-term traffic flow.The case application result shows that the combined model has an advantage over the single models in forecasting performance and forecasting accuracy.
    2014,6(6):515-519, DOI:
    Abstract:
    This paper proposes a two-step detection scheme that begins thick and ends thin,to mine the outliers of multivariable time series (MTS).According to the confidence interval of the data in sliding window,characteristics of both variation trend value and relevant variation trend value were constructed,which were then used in the two detection processes.Meanwhile,the rapid extraction algorithm for characteristics is studied.The outlier detection scheme is then applied to mine outliers before and after an accident happened at a 110 kV Grid Transformer Substation in Jiangsu province.Data sets of various equipment tables,which were collected by OPEN3000 data surveillance system,were checked by the proposed detection scheme,and experiment result indicates that this algorithm can rapidly and precisely locate the outliers.

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