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    2023,15(1):1-15, DOI: 10.13878/j.cnki.jnuist.2023.01.001
    Global climate change caused by excessive greenhouse gas (GHG) emissions has been widely concerned.Agricultural activities are the second largest source of GHGs emissions,so it is urgent to reduce agricultural GHGs emissions.Biochar,which has stable properties,abundant aromatic carbon and pores,is produced by pyrolysis of biomass under high temperature and limited oxygen conditions.The effect of biochar amendment on GHGs mitigation and soil carbon sequestration is excellent,and biochar application has the potential to participate in China's ongoing carbon trading of voluntary emission reduction (VER).However,the factors affecting the carbon sequestration and GHGs emission reduction effect of biochar are complicated,so it is necessary to systematically summarize the mitigation effect,influencing factors and research progress of biochar.This paper reviewed researches on the GHGs emission reduction and carbon sequestration effect of biochar through pot and field experiment as well as meta-analysis research.At the same time,CiteSpace software was used for visual analysis to explore the research hotspots and development trends in this field.The opportunities and challenges faced by biochar application projects participating in carbon trading were summarized based on the characteristics of domestic and foreign carbon trading market development and corresponding supporting policies.Corresponding solutions were also provided in this study,which offered scientific guidance and useful reference for the development of carbon sequestration and GHGs emission reduction research of biochar and the successful participation of biochar application projects in carbon trading.
    2023,15(1):16-23, DOI: 10.13878/j.cnki.jnuist.2023.01.002
    To explore the blocking impact of plant communities on atmospheric particulate matters,we monitored the PM2.5 and PM10 concentrations and related meteorological factors near plant communities in three functional areas of Zhengzhou's Jinshui district,which were garden area (Digital Park),residential area (Zhengzhou Blue Bay),and cultural & educational area (Henan Agricultural University).The monitoring period covered a whole winter from December 2020 to February 2021.The results indicated that the diurnal variation trends of PM2.5 and PM10 concentrations were basically the same for all sampling plots,which were generally high in early morning and low in evening;obvious differences in PM2.5 and PM10 concentrations were observed among plant communities,which were most significant between the square plot and other plots;for the three functional areas,the blocking of PM2.5 and PM10 were all the strongest by the combined structure of arbor,shrub and grass,followed by arbor & shrub and arbor & grass structures,and the lowest by structure of shrub & grass and single structure of grass;the PM2.5 and PM10 concentrations were observed to be negatively correlated with temperature and wind speed,and positively correlated with relative humidity.
    2023,15(1):24-33, DOI: 10.13878/j.cnki.jnuist.2023.01.003
    To explore the impact of climate change on the lake area of Selin Co,this paper used maximum likelihood method to extract the lake areas of Selin Co for the past 33 years (1988-2020) from Landsat data,then analyzed the variations in lake area,temperature,precipitation as well as snow cover depth in Selin Co basin using linear regression and Mann-Kendall test,and discussed the correlation between lake area and climate change by Pearson correlation.The results showed that,in the past 33 years,the Selin Co Lake expanded by 650.70 km2 at the rate of 203.34 km2/(10 a),mostly at northward and southward directions.The average annual temperature and precipitation increased significantly at the rates of 0.50 ℃/(10 a) and 17.32 mm/(10 a) (p<0.05),while the average maximum snow cover depth decreased significantly at the rate of 0.65 cm/(10 a) (p<0.05) during 1988-2020.An extremely significant correlation was found between the change of lake area and the rise of air temperature in the basin as well as the decreasing maximum snow cover depth in cold season (p<0.001),indicating that the Selin Co lake's expansion in the past 33 years was a consequence of the increasing water supply from ice-snow meltwater due to the rising air temperature in Selin Co basin.
    2023,15(1):34-41, DOI: 10.13878/j.cnki.jnuist.2023.01.004
    Here,a hierarchical autoregressive spatio-temporal model under the Bayesian framework is proposed to address the simultaneous multi-site PM2.5 prediction.The true daily average concentration of PM2.5 is regarded as a potential spatio-temporal process,then the temporal correlation is described by the first-order autoregressive process and the spatial correlation is captured based on the Matérn process,which greatly improves the efficiency in dimension reduction and synchronous prediction.In addition,meteorological factors such as daily maximum temperature,relative humidity and wind speed are used as explanatory variables to improve the prediction accuracy.The combination of Bayesian method and MCMC can realize parameter estimation and prediction process due to the model's hierarchical structure.The empirical analysis of daily PM2.5 concentration in Beijing shows that the proposed model has good interpolation or prediction performance in both spatial and temporal dimensions.
    2023,15(1):42-50, DOI: 10.13878/j.cnki.jnuist.2023.01.005
    In view of the problems that aspect level sentiment analysis tasks cannot give full consideration to syntactic comprehensiveness and semantic relevance,and the graph volume used in most studies only considers the top-down dissemination of information and ignores the bottom-up aggregation of information,this paper proposes a sentiment analysis model based on attention and dual channel network.While expanding the dependency representation,the model uses self attention to obtain the information matrix with semantic relevance,and uses a dual channel network to combine comprehensive syntactic and semantic relevance information.The dual channel network focuses on the semantic features of top-down propagation and the structural features of bottom-up aggregation respectively.The graph convolution output in the channel will interact with the information matrix,pay attention to complement the residual,and then complete the tasks in the channel through average pooling.Finally,the final sentiment classification features are obtained by the fusion of semantic based and structure based decision-makings.The experimental results show that the accuracy and F1 value of the model are improved on three public data sets.
    2023,15(1):51-65, DOI: 10.13878/j.cnki.jnuist.2023.01.006
    The process of screening and developing new drugs through experiments is very slow and requires a lot of manpower and material resources,and the use of computer-aided prediction of the molecular properties of drugs can greatly save time and cost of drug development.Therefore,in order to enable anti-breast cancer candidate drugs to have good biological activity and ADMET properties for inhibiting ERα,the random forest classifier was first used for the collected 1 974 compounds to screen the top 20 molecular descriptors with the most significant effects on biological activity.Then a QSAR model was established using this and pIC50 value as characteristic data.The biological activity values of 50 new compounds were predicted via the PSO optimized BP neural network,with the model fit of 0.833 7 and the root mean square error of 0.731 5,which were more consistent with the actual values than the predicted results of the BP neural network.Subsequently,in order to improve the success rate of drug development,the ADMET classification prediction model was constructed using PSO to optimize the SVM based on the existing ADMET property data.The algorithm cross-validation CV accuracy rate reached 94.076 7%,and the prediction accuracy rates of the five index models were all above 79%.The results show that the proposed model has better prediction performance than the benchmark model,and the adopted prediction strategy is effective,which can provide reference for the discovery and development of anti-breast cancer drugs.
    2023,15(1):66-75, DOI: 10.13878/j.cnki.jnuist.2023.01.007
    Introducing regularization into the correlation filter tracking algorithm can effectively improve the tracking efficiency,but it takes a lot of effort to adjust the predefined parameters.In addition,the target response occurring in non-target areas will lead to tracking drift.Therefore,an Automatic Global Context Awareness Correlation Filter (AGCACF) tracking algorithm is proposed.First,during the tracking process,the automatic spatial regularization is realized using the target local response change,then its module is added into the target function to enable the filter to focus on the learning of the target object.Second,the tracker utilizes the global context information of the target,which can avail the filter learn more information related to the target and reduce the impact of background on tracking performance. Then a temporal regularization term is added to the filter to fully learn the change of targets between adjacent frames to obtain more accurate model samples.Experimental results show that the proposed AGCACF tracking algorithm has better tracking effect in distance accuracy and success rate compared with other tracking algorithms.
    2023,15(1):76-84, DOI: 10.13878/j.cnki.jnuist.2023.01.008
    In view of the increasing concern on model efficiency in computer vision,this paper proposed several optimization schemes to improve the flame detection models in model efficiency as well as the detection performance.A backbone network (FIRE-Net) was constructed from a multi-convolution combined structure,which can efficiently extract rich flame features from multiple scales.Then an improved weighted bidirectional feature pyramid network (BiFPN-mini) was used to quickly achieve multi-scale feature fusion.In addition,a new attention mechanism (FIRE-Attention) was proposed to make the detector more sensitive to flame characteristics.The above optimizations were combined to develop a new flame detector abbreviated as FIRE-DET,which was then trained on self-built dataset and tested on internet videos.The experimental results showed that the FIRE-DET outperformed mainstream algorithms by its flame recognition accuracy of 97% and frame rate of 85 FPS,thus provides a more common solution to solve the flame detection.
    2023,15(1):85-93, DOI: 10.13878/j.cnki.jnuist.2023.01.009
    In the past few years,robots have become an important means of substation inspection,and robotic inspection technology for non-fixed lines has received increasing attention in order to perform inspection tasks more flexibly.How to achieve high-precision positioning in complex substation environment is one of the core problems to be solved.It is difficult for a single sensor to meet the requirements of reliable positioning in substations,therefore,this paper designs a multi-sensor fusion LINS-GNSS positioning method.Its front-end tightly couples LiDAR and inertial navigation based on an iterative error-state Kalman filter framework,which recursively corrects the estimated state by generating new feature correspondences in each iteration.The back-end uses a factor graph optimization approach to loosely couple the localization results from the satellite navigation with the localization results output from the LINS back-end.The optimization process first aligns the local coordinate system with the global coordinate system,then adds the position constraints of the GNSS as a priori edge to the factor graph in the back-end,and finally outputs the positioning results in the global coordinate system.In order to evaluate the performance of the LINS-GNSS system in the substation environment,this paper conducted field tests under real scenarios.The experimental results show that the LINS-GNSS system can achieve a positioning accuracy better than 0.5 m in the substation environment,better than LIO-SAM.
    2023,15(1):94-103, DOI: 10.13878/j.cnki.jnuist.2023.01.010
    Aiming at the collaborative optimization of multi-UAV reconnaissance and communication service for multiple heterogeneous targets,the Stackelberg game model is constructed by considering the mission requirements and target values,as well as the restriction between multi-UAV coordination gain and task behavior.The upper-level drone is established as the leader of the game,while the lower-level drones are established as the followers of the game,and then a distributed strategy update iterative algorithm is proposed,which realizes the stable convergence of the multi-UAV task allocation scheme and the optimization of the task revenue.Simulation results show that the proposed approach can effectively improve the efficiency of multi-UAV systems to complete multiple tasks at the same time,and can achieve efficient collaboration for the values of heterogeneous tasks in different environments.
    2023,15(1):104-110, DOI: 10.13878/j.cnki.jnuist.2023.01.011
    In order to timely identify the changing trend of marine environment and reduce the influence of long-term accumulated marine environment data on prediction model,an online prediction model of marine environment data based on recurrent online sequential extreme learning machine (R-OSELM) is proposed.The marine environment data training set is initialized by an online method,the existing marine environment data is input block by block via online sequential extreme learning machine algorithm,and the input weight is cyclically processed by automatic coding technology of extreme learning machine and a normalized method,which realize the online update of the prediction model.Finally,online prediction of marine environment data is completed.The model is then used to predict dissolved oxygen,chlorophyll A,turbidity,and blue-green algae.The results show that the prediction accuracy of R-OSELM model is better than that of the comparison model.It is verified that the proposed R-OSELM model is capable of online prediction of marine environment data,which can provide support for early warning of marine eutrophication and other marine environmental pollution.
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    2014,6(5):405-419, DOI:
    [Abstract] (1719) [HTML] (0) [PDF 1.98 M] (21401)
    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] (1475) [HTML] (0) [PDF 1.40 M] (7682)
    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] (2313) [HTML] (0) [PDF 960.26 K] (7431)
    2017,9(2):159-167, DOI: 10.13878/j.cnki.jnuist.2017.02.006
    [Abstract] (1085) [HTML] (0) [PDF 1.56 M] (5923)
    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] (1350) [HTML] (0) [PDF 1.22 M] (5743)
    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
    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] (1604) [HTML] (0) [PDF 1.04 M] (4241)
    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] (920) [HTML] (0) [PDF 1.56 M] (4187)
    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] (873) [HTML] (0) [PDF 1.33 M] (4127)
    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] (1818) [HTML] (0) [PDF 1.29 M] (3984)
    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):414-420, DOI:
    [Abstract] (942) [HTML] (0) [PDF 1.04 M] (3584)
    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.
    2017,9(6):575-582, DOI: 10.13878/j.cnki.jnuist.2017.06.002
    [Abstract] (1330) [HTML] (0) [PDF 1.18 M] (3572)
    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:
    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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