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    2023,15(4):379-392, DOI: 10.13878/j.cnki.jnuist.20220426002
    In order to reduce the sulfur and olefin and the loss of octane number so as to promote the clean production of gasoline,an octane number loss prediction model is established based on data accumulated by the S Zorb device.First,the Lasso is used to screen out the modeling variables,then the index factor contributions are calculated by the BP neural network,based on which 15 main variables are screened out to build the model.Second,four modeling approaches are compared and analyzed,which shows that the BP neural network has better prediction accuracy thus is more suitable to model the octane number loss.The ten-fold cross-validation produces the average MSE value of 0.027 193 and the average R2 value of 0.904 87,verifying the reliability of the model.Furthermore,the main variables are optimized and adjusted by multiple linear regression under the premise that the sulfur content is not greater than 5 μg/g.The results show that multiple variables need to be adjusted simultaneously to reduce the octane number loss by more than 30%.The multiple linear regression model has good prediction accuracy and can adjust main variables positively or negatively according to a certain proportion.The trajectories of octane number and sulfur content are also visualized in the paper.
    2023,15(4):393-402, DOI: 10.13878/j.cnki.jnuist.20220505003
    In order to improve the classification performance of hyperspectral images with limited training samples,a hyperspectral image classification Network based on Double Pooling Attention Mechanism (DPAMN) is proposed in this paper.First,the DPAMN uses three-dimensional convolution to extract the spatial and spectral shallow information of hyperspectral images.Second,the double pooling attention mechanism is introduced into DPAMN to enhance the feature extraction ability of the network.Finally,the three-dimensional convolution dense connection module is introduced into the deep layer of the network,which can not only fully extract the spatial and spectral features of hyperspectral images,but also improve the ability of feature discrimination.Experiments show that the overall average accuracy of 95.45%,97.11%,95.30% and 93.71% can be achieved on datasets of Indian Pines,University of Pavia,Salinas and Houston 2013,respectively.Compared with the current mainstream advanced methods,the proposed method greatly improves classification performance on four datasets,indicating its strong generalization capacity.
    2023,15(4):403-411, DOI: 10.13878/j.cnki.jnuist.20220617003
    In order to prevent the mosaic line from passing through the visually significant features of orthophoto map thus damaging its integrity,a mosaic line extraction approach for orthophoto images is proposed,which combines edge information with optimized Linear Spectral Clustering (LSC) and improved A* algorithm.First,the super-pixel segmentation theory of LSC is introduced for the mosaic line extraction,of which the classical LSC is optimized by edge intensity factor to effectively use the spectral information and edge information in orthophoto images.Second,the improved LSC is applied to the overlapping areas of two orthophoto images to obtain the boundary feature maps with various ground objects,and then the edge irregularity and isolated noise in the boundary feature maps are removed by mathematical morphology.Finally,the A* algorithm is improved by replacing the original heuristic function based on Euclidean distance measure with Manhattan distance function,and then used to search the shortest path in the boundary map to quickly obtain the optimal image mosaic line.This approach is compared with related methods by using aerial orthophoto image taken by a UAV.The results show that the proposed approach can extract mosaic lines efficiently and with high quality,during which it effectively bypasses the visual salient features and meets the application requirements of orthophoto mapping.
    2023,15(4):412-418, DOI: 10.13878/j.cnki.jnuist.20220623001
    Electroencephalogram (EEG) signals are easy to record and difficult to camouflage,so EEG-based emotion recognition has attracted more and more attention.However,the diversity and individual variability of human emotion make the EEG-based emotion recognition still a difficult problem in the field of affective computing.To solve this problem,a multi-source domain adaptive dictionary learning and sparse representation approach is proposed in this study.To reduce the difference of data distribution between the source domain and the target domain,the data of all domains are projected into a shared subspace,where a common dictionary is learned.The sparse representation has the ability of class recognition according to the criteria of minimizing intra-class error and maximizing inter-class error of sparse reconstruction.In addition,each source domain adapts its domain weight to avoid negative migration.The model parameters are solved by parameter alternating optimization,and all parameters can reach the optimal solution simultaneously.The experimental results on DEAP dataset show that the proposed approach is the best among all the compared methods.
    2023,15(4):419-428, DOI: 10.13878/j.cnki.jnuist.20220919001
    To address the low efficiency and high false alarm rate in detection of DDoS (Distributed Denial of Service) flood attacks,this paper proposes a DWT (Discrete Wavelet Transform) and AKD (Adaptive Knowledge Distillation) self-encoder neural network based approach to detect DDoS attacks.The approach uses the DWT to extract frequency features,the auto-encoder neural network to encode and classify the features,and the AKD to compress the model in order to achieve efficient detection of DDoS attacks.The results show that the approach has high detection efficiency for proxy server attacks,database vulnerabilities & TCP flood attacks,and UDP flood attacks,with low false alarm rate.
    2023,15(4):429-438, DOI: 10.13878/j.cnki.jnuist.20220302002
    It has always been a difficult subject for marine radar to detect small targets on sea surface.To overcome the low detection probability of traditional detectors,a Feature Detector via Relative Sample Entropy (denoted as FD-RSE) is proposed in this paper.First,the whitened spectrum is defined to suppress the main clutter region,thus enlarge the irregularity of the sea clutter sequence.Then,by introducing sample entropy to describe the complexity of sea clutter sequence,the relative sample entropy is extracted from whitened spectrum to serve as feature.Therefore,the difference between the geometric characteristic of sea clutter and that of target echo can be thoroughly exploited in the Doppler spectrum.Finally,the superiority of the proposed FD-RSE over traditional detectors in improving detection performance can be verified by the IPIX measured dataset.
    2023,15(4):439-447, DOI: 10.13878/j.cnki.jnuist.20220501001
    The ground temperature changes at a rate of 0.1 ℃ every 10 years,however,a solar radiation error of about 1 K can be produced by conventional radiation shields due to the influence of solar radiation.In order to improve the accuracy of surface temperature measurement and reduce working energy consumption,this paper designs a temperature sensor based on the accelerated diffusion of radiant heat via piezoelectric ceramic vibration.First,the Computational Fluid Dynamics (CFD) method is used to calculate the radiation error of the temperature sensor under multi physical factors,then the data is fitted and analyzed using the neural network algorithm,and finally the field experiment platform is built to place the temperature sensor in real environment to verify the feasibility of the scheme.The experimental results show that the absolute error and root mean square error between the corrected value and reference value of the surface temperature sensor are 0.041 ℃ and 0.055 ℃,respectively,which also verifies the superior correction performance of the neural network algorithm.
    2023,15(4):448-459, DOI: 10.13878/j.cnki.jnuist.20220703002
    The non-intrusive load decomposition is to decompose the power signal of a single load device according to the known total power signal.However,deep learning based models are perplexed by problems such as insufficient load feature extraction,low decomposition accuracy,large decomposition error for infrequently used load equipment.Here,we propose an Attention Recurrent Neural Network (ARNN) model,which combines regression network and classification network to realize the non-invasive load decomposition.The model extracts the features of sequence signals through RNN network,and uses the attention mechanism to locate the position of important information in the input sequence,so as to improve the representation ability of network.Experiments on public datasets of Wiki-Energy and UK-DALE show that the proposed deep neural network is superior to the most advanced neural network under all experimental conditions.Furthermore,the attention mechanism and auxiliary classification network can correctly detect the on or off of devices,and locate the high-power signal,which improves the accuracy of load decomposition.
    2023,15(4):460-467, DOI: 10.13878/j.cnki.jnuist.20220913006
    It is difficult to successfully detect the false data injection attacks against the linear state estimation based on phasor measurement techniques in power systems.Here,we propose an intelligent method to detect false data injection attacks.First,the auto-encoder is used to extract the features of the power grid measurement data,which is done repeatedly to gradually reduce the feature dimension.Then the finally extracted feature is subjected to supervised learning through the Softmax layer,so as to obtain an attack detection algorithm based on stacked auto-encoders.Second,the attack detection approach is improved through noise reduction to solve the over fitting of auto-encoders.Finally,the proposed method is simulated and verified by IEEE-118 node test system,and the results show that the proposed attack detection method has high computational accuracy and efficiency.
    2023,15(4):468-477, DOI: 10.13878/j.cnki.jnuist.20220513001
    Aiming at the cyber physical system (CPS) subject to false data injection (FDI) attack,a control method based on sliding mode and extended observer is proposed.First,the system is dynamically linearized,an extended observer is constructed,and the convergence condition of the observation error is analyzed.Second,the integral sliding mode surface is designed,the asymptotic stability criterion of the sliding mode system is derived by using linear matrix inequality,and the sliding mode vector satisfying the gain performance of the system is obtained.Then,based on the exponential reaching law,an adaptive integral sliding mode controller is proposed to eliminate quantization errors and generalized disturbances,so that the system can reach the sliding surface.The advantages of this method include high estimation accuracy,fast response speed,and strong robustness to FDI attack and quantization parameter mismatch.Finally,numerical simulation verifies the effectiveness of the method.
    2023,15(4):478-487, DOI: 10.13878/j.cnki.jnuist.20220427001
    The output characteristics of photovoltaic (PV) array change with the environmental conditions and running state.In order to meet the control requirements of Maximum Power Point Tracking (MPPT) under different operation conditions,a segmented control method combining improved Quantum Particle Swarm Optimization (QPSO)and perturb and observe algorithm is proposed after analyzing the output characteristics of photovoltaic array under various working conditions.The inconsistent adaptive mutation DCWQPSO is used to search the maximum power point globally in the initial stage of tracking control to make the power point converge to the maximum power point quickly in order to improve the tracking speed,then the perturb and observe algorithm based on closed-loop fuzzy control is used to search the maximum power point locally to improve the tracking accuracy.The Matlab simulation results show that the segmented control method can complete MPPT in only 0.32 s under various working conditions of photovoltaic array and remains stable,which has faster tracking speed and higher tracking accuracy than others,indicating its capacity to improve the efficiency of PV generation effectively.
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    2014,6(5):405-419, DOI:
    [Abstract] (1754) [HTML] (0) [PDF 1.98 M] (21708)
    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] (2155) [HTML] (0) [PDF 1.11 M] (13202)
    2013,5(5):385-396, DOI:
    [Abstract] (1492) [HTML] (0) [PDF 1.40 M] (7935)
    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] (2336) [HTML] (0) [PDF 960.26 K] (7720)
    2017,9(2):159-167, DOI: 10.13878/j.cnki.jnuist.2017.02.006
    [Abstract] (1097) [HTML] (0) [PDF 1.56 M] (6139)
    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] (1409) [HTML] (0) [PDF 1.22 M] (5996)
    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.
    2011(1):1-22, DOI:
    [Abstract] (1861) [HTML] (0) [PDF 1.29 M] (5008)
    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(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] (1621) [HTML] (0) [PDF 1.04 M] (4457)
    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] (947) [HTML] (0) [PDF 1.56 M] (4420)
    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] (884) [HTML] (0) [PDF 1.33 M] (4351)
    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.
    2013,5(5):414-420, DOI:
    [Abstract] (1049) [HTML] (0) [PDF 1.04 M] (3808)
    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] (1346) [HTML] (0) [PDF 1.18 M] (3799)
    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.
    2010(3):211-215, DOI:
    [Abstract] (2698) [HTML] (0) [PDF 958.19 K] (3365)
    2010(3):280-283, DOI:
    [Abstract] (2819) [HTML] (0) [PDF 998.77 K] (3352)


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