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    2021,13(6):635-644, DOI: 10.13878/j.cnki.jnuist.2021.06.001
    Abstract:
    Road intersections are important parts of road traffic network, the location and type of which are the basic data for various application services such as high-definition map and automatic driving.However, little attention has been paid to classify road intersections compared with the great number of researches on the road boundary extraction from mobile laser scanning point clouds.Here, we propose a road intersection classification method based on dynamic graph neural network.First, we employ geometric and spatial distribution differences of supervoxels to extract road boundaries from ground.Then we calculate the curvature of road boundary points and detect road intersections according to the curvature difference in sliding windows.Finally, we build a dynamic graph neural network to identify the T junction and regular intersections.The experimental results show the proposed method can accurately detect most road intersections.
    2021,13(6):645-652, DOI: 10.13878/j.cnki.jnuist.2021.06.002
    Abstract:
    Retrieving surface Soil Moisture (SM) from CYGNSS has attracted great attention in recent years, yet its accuracy and efficiency should be further improved.Here, a pre-classification strategy combined with Artificial Intelligence (AI) algorithm is proposed to predict SM from CYGNSS data.This strategy can improve the accuracy of SM estimation due to the use of AI algorithm and is versatile and easy to use.The field SM data of China in 2018 are used as real ground truth values for modeling and prediction.The results show that the predicted SM is in good agreement with the referenced SM.The correlation coefficient (R) between SM retrieved from CYGNSS and ground truth data is as high as 0.8, and the mean values of Root Mean Square Error (RMSE) and unbiased root mean square error (ubRMSE) are 0.059 cm3/cm3 and 0.050 cm3/cm3, respectively.Meanwhile, the results show that the AI-based pre-classification strategy not only significantly improves the accuracy of SM estimation from CYGNSS, but is applicable to other regression and prediction fields for its good generalization and expansibility.
    2021,13(6):653-660, DOI: 10.13878/j.cnki.jnuist.2021.06.003
    Abstract:
    At present, deep learning has been widely applied in object detection, such as vehicle detection.In this paper, the deep learning EfficientDet model was analyzed, and its advantages in vehicle detection were confirmed.A phased adaptive training model was constructed to avoid local optimum in training process, then it was used to detect vehicles from both short and long distance.The detection results showed that compared with detection methods based on Cascade R-CNN and CenterNet, the proposed model was superior in terms of computational complexity, time consumption and detection accuracy.Meanwhile, further analysis figured out the optimal detection distance and angle.Finally, an example is given to verify that the proposed method can be applied to a large range of vehicle detection.
    2021,13(6):661-668, DOI: 10.13878/j.cnki.jnuist.2021.06.004
    Abstract:
    At present, the research of road extraction using vehicle-borne point cloud mainly focuses on structured roads with regular road cuts.However, some roads in reality are flanked by grass, and the extraction method of structured roads is no longer applicable.To address this problem, an automatic road surface extraction method integrating the moving window height difference and neighboring point serial number difference is proposed.First, the adjacent point space is used to realize the extraction of scan lines, and the road boundary points are extracted according to the moving window height difference as well as the adjacent point serial number difference.Next, the road boundary points are fitted with RANSAC algorithm.Afterwards, the road surface point cloud is extracted according to the linear programming principle and filter processing.Finally, two sets of road data are used to test the proposed method.The completeness of road surface extraction is 99.79% and 99.52%, the correctness is 99.91% and 99.62%, and the quality is 99.70% and 99.15%, for the two datasets respectively.The experimental results show that the proposed method is applicable for road surface extraction of structured road and unstructured road with grass on the roadside.
    2021,13(6):669-677, DOI: 10.13878/j.cnki.jnuist.2021.06.005
    Abstract:
    Architectural models can be constructed from 3D point cloud data due to the advances in spatial information acquisition technology.However, most of the existing model construction methods rely on manual interaction, which is extremely laborious and time consuming, thus are not applicable to the construction of large-scale urban models and related applications.This paper proposes an approach to build the main structure model based on wireframe analysis.First, filter and normalize the original building point cloud, and extract the multi-level boundary polygons of the building to comprehensively describe the building boundary structure; then divide the boundary polygons at each level into multiple rectangular primitives that are simple and easy to control.A robust hierarchical rectangular connection and analysis algorithm is used to generate a complete main structure model of the building.Finally, experiments on three sets of complex structure and severely occluded building point cloud data show that the approach has good performance and can robustly handle the point cloud data of buildings with missing data and different point density.The proposed approach takes into account the structural differences at different heights of the building, and realizes the high-precision construction of the wireframe model for the main building structure.
    2021,13(6):678-685, DOI: 10.13878/j.cnki.jnuist.2021.06.006
    Abstract:
    Airborne LiDAR Bathymetry (ALB) system can quickly and efficiently obtain the integrated overwater and underwater data of sea islands, reefs and their adjacent areas.However, due to the fact that most of the measurement areas are shallow near-shore waters with slow terrain changes, the obtained point cloud is low in density and large in thickness, resulting in rare registration characteristics.Few studies have been done on the registration of ALB data due to the difficulty in extracting their homonymous features.To address this problem, we employ three registration methods including Fast Point Feature Histograms (FPFH), Longest Common Subsequence (LCSS) and Generalized Iterative Closest Point (GICP) to register the ALB point cloud data in the South China Sea.The registration performance comparison shows that the LCSS line sequence outperforms the other two methods in registration accuracy and reliability.Moreover, the LCSS can tackle the problems of single information and noise in the corresponding feature matching, improve the robust estimation of corresponding points in the feature curve, and enhance the robustness of airstrip data registration.It can be concluded that the LCSS is an effective solution for ALB data registration.
    2021,13(6):686-692, DOI: 10.13878/j.cnki.jnuist.2021.06.007
    Abstract:
    Water level information is an important parameter for studying changes in the water cycle, even the climate and ecological environments.It is of great significance to monitor the water level changes in near real-time and with high precision.However, traditional water level gauge is a relative water level measurement approach in a small range and has a high cost.The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) uses the Signal-to-Noise Ratio (SNR) data from the GNSS receiver installed on the coast to estimate the water level change, which provides a new monitoring approach for water level measurement with high accuracy and near real-time.Here, 30 days of GPS, BDS and GLONASS SNR data from a multi-GNSS station at Badong station in the upper reaches of the Yangtze River are used to estimate the water level changes, which are then compared with the in-suit water level station observations.The results show that GNSS-IR obtains centimeter-level water level results with the minimum RMSE of 6.43 cm.The estimation results of GPS and GLONASS system at L1 frequency and BDS system at B1 frequency are better, and the accuracy of GLONASS L2 frequency is lower than that of other frequencies.The joint multi-GNSS system improves the time resolution of GNSS-IR estimation of water level changes.
    2021,13(6):693-706, DOI: 10.13878/j.cnki.jnuist.2021.06.008
    Abstract:
    Soil moisture is a key parameter of the water cycle and energy budget in terrestrial ecosystems.Land data assimilation system can provide spatio-temporally continuous soil moisture data, however, its low spatial resolution limits the further application.Here, the soil moisture output in 0-10 cm soil layer from China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0) was downscaled from 6 km to 1 km in North China by three single models (gradient boosting machine, deep feedforward neural network and random forest) and a Stacking ensemble learning method.The downscaled results for period of April to October in 2019 show that the four downscaling methods can reflect the temporal and spatial variation of soil moisture in North China and somehow alleviate the overestimation of CLDAS products.Both the spatial distribution details and accuracies are improved compared with original CLDAS soil moisture data.Furthermore, the Stacking ensemble learning method outperforms the other three in downscaling performance, including its highest correlation coefficient with observed data (R=0.756 8) and lowest error (RMSE=0.050 5 m3/m3, Bias=-0.005 2 m3/m3).Meanwhile, the downscaled results by Stacking ensemble learning are also highly correlated with the dynamic changes of soil moisture, with lowest RMSE and bias compared with station observations, followed by random forest and deep feedforward neural network.
    2021,13(6):707-719, DOI: 10.13878/j.cnki.jnuist.2021.06.009
    Abstract:
    In recent years, ozone has become the primary air pollution in major cities of China.Therefore, tropospheric ozone products are very important for monitoring near surface ozone concentration.However, existing ozone products cannot meet the requirements of high spatial & temporal resolution monitoring.Here, we use a spatial & temporal fitting method to repair the total ozone data from OMI, and then inverse the tropospheric ozone distribution by a residual approach.The results show that the spatial & temporal fitting method is qualitatively and quantitatively superior to both Kriging interpolation and inverse distance weighted interpolation, indicated by its smaller RMSE and MAE.The tropospheric ozone profile obtained by the proposed method is consistent with official ozone products of OMI/MLS according to their correlation coefficient (R) up to 0.82.
    2021,13(6):720-729, DOI: 10.13878/j.cnki.jnuist.2021.06.010
    Abstract:
    Here, we try to realize the spatialization of the added values of the secondary and tertiary industries in Fujian province, by adding thermal infrared remote sensing data to land use data and night light remote sensing data, and considering surface temperature as well.The results show that compared with previous method of using land use data plus night light remote sensing data, the adding of thermal infrared remote sensing data and consideration of surface temperature improves the GDP spatialization model in coefficients of determination (R2, 0.966 vs. 0.743, 0.870 vs. 0.776 for the secondary and tertiary industry, respectively) and simulation accuracy (MRE, 20.45% vs. 72.60%, 19.82% vs. 60.10%, for the secondary and tertiary industry, respectively).Further, we take Xiamen as an example to show the potential of thermal infrared remote sensing data in GDP spatialization model.It is found that the proposed method can greatly improve the spatialization of the added values of the secondary and tertiary industries, indicated by its high consistency with reality.The results of this paper can provide reference for regional economic development planning.
    2021,13(6):730-743, DOI: 10.13878/j.cnki.jnuist.2021.06.011
    Abstract:
    Human action recognition has always been a hot topic in computer vision research and widely applied in virtual reality, short video, etc.Meanwhile, the fast development of deep learning in recent years has also inspired the action recognition algorithms.Compared with traditional methods, the action recognition algorithms based on deep learning have advantages of strong robustness and high accuracy.Here, we make a survey on the action recognition algorithms based on deep learning proposed in recent years, and focus on those developed from two-stream network and 3D convolutional network, then summarize their performances and positive results, and finally make prospects in this field.
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    2014,6(5):405-419, DOI:
    [Abstract] (1627) [HTML] (0) [PDF 1.98 M] (20645)
    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] (1441) [HTML] (0) [PDF 1.40 M] (6997)
    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] (2250) [HTML] (0) [PDF 960.26 K] (6765)
    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] (1047) [HTML] (0) [PDF 1.56 M] (5322)
    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] (1327) [HTML] (0) [PDF 1.22 M] (5130)
    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] (1585) [HTML] (0) [PDF 1.04 M] (3666)
    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] (911) [HTML] (0) [PDF 1.56 M] (3530)
    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] (862) [HTML] (0) [PDF 1.33 M] (3495)
    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] (1783) [HTML] (0) [PDF 1.29 M] (3248)
    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] (1307) [HTML] (0) [PDF 1.18 M] (3015)
    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] (927) [HTML] (0) [PDF 1.04 M] (2992)
    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.

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