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    2024,16(3):291-310, DOI: 10.13878/j.cnki.jnuist.20230905002
    Abstract:
    In recent years,deep learning has been a hot research topic in traffic flow prediction.Graph convolutional networks outperform traditional convolutional neural networks in spatial feature modeling,in view of their powerful capabilities in processing non-Euclidean data such as topological map,distance map and flow similarity map.Therefore,graph convolutional network and its variants have become a research hotspot in traffic flow prediction,and many attractive research results have been obtained.This article classifies and summarizes traffic flow prediction models based on graph convolutional networks in recent years.First,the graph convolution is elaborated by combining the definitions of spatial convolution and spectral convolution.Second,in view of the network structure of the prediction model,the graph convolutional network based traffic flow prediction models are divided into two major categories of combined type and improved type,each of which are analyzed and discussed in detail with representative model structures.In addition,typical datasets commonly used in traffic flow prediction for model performance comparison are reviewed,and a simulation test is conducted using one real dataset to demonstrate the prediction performance of four traffic flow prediction models based on graph convolutional networks.Finally,the future research hotspots and challenges in traffic flow prediction based on graph convolutional networks are prospected.
    2024,16(3):311-320, DOI: 10.13878/j.cnki.jnuist.20230822001
    Abstract:
    To address the low prediction accuracy in agricultural commodity futures due to their nonlinear and non-smooth features resulting from various influencing factors,this paper proposes a decomposition and ensemble forecasting approach based on CEEMDAN and Transformer-Encoder-TCN.First,the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to decompose the time series into multiscale Intrinsic Mode Function (IMF) and residuals,reducing the complexity of series modeling.Second,each subseries is predicted via Temporal Convolutional Network (TCN) incorporating multi-stage self-attention unit (Transformer-Encoder),which optimizes the modeling weights of significant features.Finally,the prediction results of each subseries are linearly summed and integrated to obtain the final prediction results.The soybean futures revenue index in the agricultural commodity index of South China Futures Company is used as the research object.The model is retrained by time-series cross-validation and parameter transfer.The ablation and comparison experimental results show that the proposed model has superiority in RMSE,MAE and DS,verifying its effectiveness in predicting agricultural commodity futures.
    2024,16(3):321-331, DOI: 10.13878/j.cnki.jnuist.20230810002
    Abstract:
    A prediction model based on ikPCA-FABAS-KELM is proposed to improve the short-term wind power prediction by traditional data-driven machine learning models.First,the principal component analysis is improved and the reversible kernel Principal Component Analysis (ikPCA) is proposed to reduce the complexity of input data while ensuring data features,with the purpose to advance the model in running speed.Second,the individual attraction strategies for Firefly Algorithm (FA) are used to improve the Beetle Antennae Search (BAS) thus a FABAS algorithm is proposed.Finally,the FABAS algorithm is used to optimize the regularization parameter C and kernel parameters γ of the Kernel Extreme Learning Machine (KELM),which can reduce the impact of manual parameter setting on blind model training thus improve model prediction accuracy.The simulation results show that the proposed model effectively improves the short-term wind power prediction accuracy.
    2024,16(3):332-340, DOI: 10.13878/j.cnki.jnuist.20230903002
    Abstract:
    To address the problems of stock price prediction due to its non-static,highly complex and random fluctuations,a combination model based on Variational Mode Decomposition (VMD)-Circle Sparrow Search Algorithm (CSSA)-Long Short-Term Memory (LSTM) neural network is established.The original stock closing data is decomposed into several Intrinsic Mode Function (IMF) components by VMD,and then the CSSA is used to optimize the parameters of hidden layer neurons,iteration number and learning rate of LSTM,and the optimal parameters are fitted into the LSTM,where each IMF component is modeled and predicted,and the prediction results of IMF component are superimposed to obtain the final result.Experiments show that the RMSE,MAE and MAPE of the proposed model are minimized on multiple stock datasets,the error of the predictied closing prices of individual stocks fluctuates around 0,which is more stable with better fitting and higher accuracy.
    2024,16(3):341-351, DOI: 10.13878/j.cnki.jnuist.20230402001
    Abstract:
    Illegally parked vehicles reduce road traffic efficiency,and cause traffic congestion even traffic accidents.Traditional vehicle detection methods are perplexed by a large number of parameters and low accuracy.Here,we propose a method using the improved YOLOv5 model and ray method to detect illegally parked vehicles.First,a lightweight feature extraction module is designed to reduce the amount of model parameters.Second,the attention mechanism is added to the model to enhance its feature extraction ability from both channel dimension and spatial dimension to ensure the model's accuracy.Then,the mixed data is used to enhance and enrich the dataset samples thus improve the detection performance in complex backgrounds,and EIoU is selected as the loss function to improve the model's positioning performance.Experiments show that the mean accuracy of the improved YOLOv5 model reaches 91.35%,which is 1.01 percentage points higher than that of the original YOLOv5s,and the number of parameters is reduced by 35.79%.Finally,the improved YOLOv5 model is combined with the ray method,which can reach real-time inspection speed of 28 frames per second on the embedded platform of Jetson Xavier NX.
    2024,16(3):352-363, DOI: 10.13878/j.cnki.jnuist.20220420001
    Abstract:
    Although traditional vision-based SLAM (VSLAM) technologies have achieved impressive results,they are less satisfactory in challenging environments.Deep learning promotes the rapid development of computer vision and shows prominent advantages in image processing.It's a hot spot to combine deep learning with VSLAM,which is promising through the efforts of many researchers.Here,we introduce the combination of deep learning and traditional VSLAM algorithm,starting from the classical neural networks of deep learning.The achievements of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in depth estimation,pose estimation and closed-loop detection are summarized.The advantages of neural network in semantic information extraction are elaborated,and the future development of VSLAM is also prospected.
    2024,16(3):364-373, DOI: 10.13878/j.cnki.jnuist.20230717003
    Abstract:
    To address the low segmentation accuracy of multifidus muscle lesion sites in MRI images of patients with lumbar disc herniation,this paper proposes a new model to improve the U2-Net network with the goal that the encoding and decoding subnetworks are interconnected by a series of nested jump paths.To reduce the semantic missing of feature maps in the encoding and decoding subnetworks,the jump connections in the middle of RSU-7,RSU-6,RSU-5,and RSU-4 in the U2-Net model are redesigned,while the RSU-4F part remains unchanged.In addition,the channel attention module is added to enable the net to focus on channels of higher contribution to task,thus extract high quality multifractal muscle features.The experiments on the multifidus muscle MRI image dataset show that the improved U2-Net outperforms U-Net,U2-Net and U-Net++ network in indicators of Dice,HD and MIoU.It can be concluded that the proposed approach has good performance on MRI image segmentation of multifidus muscle,which can assist doctors to make diagnosis.
    2024,16(3):374-385, DOI: 10.13878/j.cnki.jnuist.20230308002
    Abstract:
    Southeast Asia holds great importance as an essential passage of the Belt and Road Initiative and a key node region for China's foreign trade.Here,the spatial and temporal characteristics of economic development of Southeast Asian countries from 2012 to 2021 are analyzed using NPP/VIIRS nighttime lighting data and spatial analysis methods such as centroid,standard deviation ellipse and Moran index.The results indicate a significant correlation between GDP and nighttime light data,and a generally northwest bound transfer of economic centers of Southeast Asian countries.Most of the nightlight clusters have moved 476 kilometers to the northwest of Southeast Asia (that is,to the land border between Southeast Asia and China),suggesting a relationship between the movement of economic centers and the Belt and Road Initiative.The overall economic volume of Southeast Asia has increased,and economic development has been clustering in the region with evident directional characteristics.In terms of the spatial features,high-high aggregation and low-low aggregation are the two most significant spatial aggregation characteristics.The high-high agglomeration areas have played a good role and driven the economic development of the surrounding areas,the low-high agglomeration areas have greater development potential,while the low-low agglomeration areas in northern Southeast Asia have decreased significantly during 2012-2021.
    2024,16(3):386-393, DOI: 10.13878/j.cnki.jnuist.20230219002
    Abstract:
    Scenic spots are most distinctive nature reserves in China,and the main carrier of tourism resources.Here,the spatial distribution characteristics and main influencing factors of scenic spots in China's cities above prefecture level are deeply explored via ArcGIS spatial analysis and SPSS multiple regression statistical analysis,using data of POI (Point of Interest) of national scenic spots as well as urban construction statistical yearbook.The results show a general pattern of strong southeast and weak northwest of China's scenic spots,with contiguous distribution stretching around major urban agglomerations,belt distribution of scenic spots above A level,and point distribution of national scenic spots.Three low-quality distribution belts along Shanxi-Hubei-Guangdong,Inner Mongolia-Qinghai-Xizang and the southeast coast have formed in the scenic spots;and two low-quality gathering belts of Inner Mongolia-Qinghai-Xizang and the southeast coast hold strong construction advantages and great potential for landscape quality improvement.Urban construction scale,urban population,government financial support,tertiary industry development and urban economic status are the main influencing factors of the spatial distribution of urban scenic spots.
    2024,16(3):394-404, DOI: 10.13878/j.cnki.jnuist.20230228001
    Abstract:
    Traditional drought indices mainly consider a single factor and often cannot comprehensively reflect the drought condition.Based on data of MODIS and CLDAS (CMA Land Data Assimilation System),a daily scale integrated drought monitoring model was established by Gradient Boosting Machine (GBM) with multiple influencing factors and drought index as independent variables and comprehensive meteorological drought index (CI) as dependent variable.It was researched by taking drought in North China from 2015 to 2018 as a case.The results show that the model monitoring results are significantly correlated with the calculated CI values of the observation stations.The coefficient of determination is 0.945 and 0.655,and the Root Mean Square Error (RMSE) is 0.033 and 0.082 for training and test sets,respectively,indicating the high accuracy of the proposed integrated drought monitoring model.The consistency rate between the model monitored CI and calculated CI values is above 65%,and the correlation coefficient with Standard Precipitation Evapotranspiration Index (SPEI) and Relative Soil Moisture (RSM) is 0.68 and 0.6,respectively,showing its capacity to reflect both the meteorological drought and the agricultural drought.Monitoring of typical drought condition shows that the integrated drought monitoring model can accurately identify the drought occurrence,and represent the situation of comprehensive drought via considering various drought influencing factors.
    2024,16(3):405-415, DOI: 10.13878/j.cnki.jnuist.20230726002
    Abstract:
    Phytoplankton is a major participant in the material and energy cycles of lake ecosystems,and the information of its community structure is of great significance in coping with and regulating lake ecosystems.In this study,the community characteristics of eukaryotic phytoplankton in the western Chaohu Lake in winter and summer were obtained via high-throughput sequencing.A total of 7 phyla and 71 genera of eukaryotic phytoplankton were detected during the survey,including 7 phyla and 59 genera in summer and 5 phyla and 27 genera in winter,dominated by Chlorophyta and Bacillariophyta,and the dominant genera varied greatly in winter compared with those in summer.The mean values of Shannon-Wiener index in summer and winter were 1.83 and 1.88 respectively,and the Pielou index were averaged 0.75 and 0.83 for summer and winter respectively.The results of water quality analysis indicated that TN and TP were relatively high in the western Chaohu Lake during the study period,and the physicochemical factors of the water body varied significantly between summer and winter (P≤0.05).Redundancy analysis showed that the eukaryotic phytoplankton community can be roughly explained by PO4--P,TN,TP and NH4+-N,especially the PO4--P (P≤0.05).Mantel correlation analysis showed a close correlation between eukaryotic phytoplankton abundance and WT,DO,pH,NH4+-N,TN and Chl.a.Variance partitioning analysis showed that seasonal factors explained most of the eukaryotic phytoplankton community dynamics.
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    2014,6(5):405-419, DOI:
    [Abstract] (1948) [HTML] (0) [PDF 1.98 M] (24331)
    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.
    2009(1):1-15, DOI:
    [Abstract] (2463) [HTML] (0) [PDF 1.11 M] (15978)
    Abstract:
    根据个人学习研究稳定性的心得体会,首先介绍了前苏联伟大的数学力学家Lyapunov院士的博士论文《运动稳定性的一般问题》在全世界产生的超过1个世纪的巨大影响.叙述了由该博士论文首创的几个巨大成就何以能奠定1门学科的基础,从而开创了1个新的重要的研究方向,以及留给后人很多很多研究的课题的理由.特别地,用事实和科学断语回答了“Lyapunov稳定性已领风骚100多年,余晖还几何”的问题.明确表明1个观点:稳定性将是1个“永恒的主题”,不老的科学,定将永恒地给人启迪,洞察力,智慧和思想.
    2011(1):1-22, DOI:
    [Abstract] (2003) [HTML] (0) [PDF 1.29 M] (10371)
    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
    2013,5(5):385-396, DOI:
    [Abstract] (1773) [HTML] (0) [PDF 1.40 M] (9760)
    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] (2464) [HTML] (0) [PDF 960.26 K] (9593)
    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] (1323) [HTML] (0) [PDF 1.56 M] (7840)
    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] (1758) [HTML] (0) [PDF 1.22 M] (7785)
    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] (1061) [HTML] (0) [PDF 830.02 K] (6419)
    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.
    2013,5(6):544-547, DOI:
    [Abstract] (1010) [HTML] (0) [PDF 1.56 M] (5917)
    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(5):426-430, DOI:
    [Abstract] (1646) [HTML] (0) [PDF 1.04 M] (5915)
    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.
    2014,6(3):226-230, DOI:
    [Abstract] (889) [HTML] (0) [PDF 1.33 M] (5771)
    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.
    2017,9(6):575-582, DOI: 10.13878/j.cnki.jnuist.2017.06.002
    [Abstract] (1429) [HTML] (0) [PDF 1.18 M] (5403)
    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.
    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.
    2013,5(5):414-420, DOI:
    [Abstract] (1275) [HTML] (0) [PDF 1.04 M] (5224)
    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.
    2011(1):23-27, DOI:
    [Abstract] (3824) [HTML] (0) [PDF 1.11 M] (4753)
    Abstract:
    In order to solve the sudoku more efficiently,a novel approach was proposed.We employed the real number coding to get rid of the integer constraint,meanwhile used the L0 norm to guarantee the sparsity of the solution.Moreover,the L1 norm was used to approximate the L0 norm on the basis of RIP and KGG condition.Finally,the slack vectors were introduced to transfer the L1 norm into a convex linear programming problem,which was solved by the primal dual interior point method.Experiments demonstrate that this algorithm reach 100% success rate on easy,medium,difficult,and evil levels,and reach 864% success rate on only 17 clue sudokus.Besides,the average computation time is quite short,and has nothing to do with the difficulty of sudoku itself.In all,this algorithm is superior to both constraint programming and Sinkhorn algorithm in terms of success rate and computation time.
    2015,7(1):86-91, DOI:
    [Abstract] (924) [HTML] (0) [PDF 4.24 M] (4696)
    Abstract:
    Based upon GDAS and GBL NCEP reanalysis data with resolution 1°×1°and 2.5°×2.5°respectively,the trajectory of the air mass at 100 m altitude over Hetian meteorological station is simulated by HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model),which is developed by Air Source Laboratory of NOAA,to estimate the effect of integration error and resolution error on the trajectory calculation error.The contribution of the integration error is found to be very small,which increases slightly with the integration time length and has no relation to the resolution of the meteorological data.The resolution error varies at different time point,and is found to be related to the topography,the weather system and the interpolation.The simulated trajectories using datasets with different resolution differed with each other significantly,indicating that the resolution error contributes more to the trajectory calculation error than calculation error.

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