• Current Issue
  • Online First
  • Archive
  • Most Downloaded
  • Special Issue
  • Special Column
    Select AllDeselectExport15
    Display Method:
    2025,17(2):151-164, DOI: 10.13878/j.cnki.jnuist.20241009001
    Abstract:
    To accurately detect road damages with large size differences and small scales in vehicle-mounted images,this paper presents a real-time road damage detection model based on improved YOLOv5s,termed as VRD-YOLO (Vehicle-mounted image Road Damage Detection-YOLO).Firstly,a Channel Mix Slide Transformer (CMST) module is proposed to enhance the model's global context modeling capability and strengthen the extraction of fine-grained road damage semantic feature information.Secondly,a generalized feature pyramid with cross-layer fusion and cross-scale fusion is introduced to expand the network receptive field and strengthen the fusion of multi-scale damage features.Thirdly,to optimize the model's feature response and further improve detection performance,a dynamic detection head is designed to achieve scale perception,spatial perception,and task perception.Finally,a Vehicle-mounted Image Road Damage Dataset (VIRDD) is constructed to expand the quantity and types of existing road damage datasets,and ablation and comparative experiments are conducted based on this dataset.Experimental results show that the VRD-YOLO achieves a mean Average Precision (mAP@0.5) of 74.45% on the VIRDD dataset,with a detection speed reaching 28.56 frames per second.Compared to the YOLOv5s model,VRD-YOLO improves the precision,recall,F1 score,and mAP by 2.79,2.32,2.54,and 3.19 percentage points,respectively.Additionally,compared with six other classical and mainstream object detection models,the proposed VRD-YOLO attains the highest detection accuracy with the smallest model parameter count of 9.68 million,verifying its effectiveness and superiority.
    2025,17(2):165-171, DOI: 10.13878/j.cnki.jnuist.20241024001
    Abstract:
    To address the deficiencies of traditional graph neural network methods in point cloud semantic segmentation,such as high requirements for supervision accuracy,one-way only node label propagation,and neglect of global information,this paper proposes a point cloud semantic segmentation method based on bidirectional attention mechanism.Firstly,the point cloud is over-segmented into superpoints and a superpoint graph is constructed,thus introducing the point cloud classification problem into the superpoint graph network framework.Subsequently,the two-way attention module is utilized to alternately focus on superpoints and update their features according to the weights of neighboring superpoints,enabling the two-way information propagation.Unlike previous graph pooling methods,this study applies both maximum pooling and average pooling,and combines their pooled features.Finally,the public dataset Semantic 3D is used for training and experiments.The results show that the proposed method can effectively correct labelling errors while coupling local features with long-range information,and the mean Intersection over Union (mIoU) and overall Accuracy (oAcc) of the dataset are 75.4% and 95.1%,respectively,exhibiting a better label delivery mechanism and higher classification accuracy compared with existing methods.
    2025,17(2):172-180, DOI: 10.13878/j.cnki.jnuist.20240927001
    Abstract:
    To tackle the current challenges of low efficiency,poor performance,and inadequate real-time capabilities in bridge crack detection,this paper introduces a drone-based image detection method for bridge cracks using an improved YOLOv8 model.Firstly,the dynamic snake convolution kernel is integrated into the C2f module in the backbone of YOLOv8 to enhance the crack feature extraction.Then,the Context Augmentation Module (CAM) is introduced to improve the detection capability for small targets.Finally,the influence of low-quality datasets on detection results is reduced via optimizing the prediction box loss function.Experimental results show that the improved model achieves a GFLOPs of 14.4 and a mean Average Precision (mAP@50) of 94%,exhibiting a significant accuracy improvement compared to the baseline models.The detection speed reaches 147 frames per second,satisfying the requirements for real-time crack detection by UAVs.
    2025,17(2):181-190, DOI: 10.13878/j.cnki.jnuist.20240513002
    Abstract:
    Aiming at the extraction of cavern surface deformation from three-dimensional laser scanning dense point clouds,we propose a method integrating the Multiscale Model-to-Model Cloud Comparison (M3C2) with an improved Alpha Shapes algorithm.First,the two-phase surface point cloud data are registered,and the improved Alpha Shapes algorithm is used to identify the outer contour point clouds.After the fine registration of these two-phase outer contour point clouds,the M3C2 algorithm calculates the deformation value of each point,and finally the continuous deformation regions are extracted through distance clustering.Experimental results show that the proposed method effectively eliminates the points at small furrows as well as those affected by mixed pixels.Specifically,the removal rates of point clouds in the two phases within 10 m from the scanner to the cavern section are 14.17% and 13.52%,respectively,which are 6.25% and 6.42% within 70 m.This method accurately and efficiently extracts the cavern surface deformation regions with more than twice the registration error.
    2025,17(2):191-202, DOI: 10.13878/j.cnki.jnuist.20240229001
    Abstract:
    To address the low visual quality of stego-image in existing color image Reversible Data Hiding (RDH) algorithms,a novel RDH scheme utilizing multi-level interpolation prediction and global sorting is proposed.Firstly,to fully exploit the features of different texture regions in the image,a multi-level interpolation prediction method is designed to significantly improve the prediction accuracy of pixels.Then,a complexity-based global sorting strategy is designed to sort the prediction errors in the three channels of color images respectively,thereby fully utilizing the global characteristics of the prediction errors in each channel to generate a more concentrated Three-Dimensional Prediction Error Histogram (3D PEH).Finally,an adaptive 3D mapping strategy is used to modify the error histogram and embed secret data.Experimental results show that the proposed approach outperforms some of the latest schemes in embedding performance.
    2025,17(2):203-214, DOI: 10.13878/j.cnki.jnuist.20230927001
    Abstract:
    Here,we propose a pruning and optimization approach based on Gradient Weight Pursuit (GWP) to address the overfitting in unsupervised domain,which manifests as significantly lower accuracy on downstream tasks compared to that on training sets.To tackle the overfitting challenge in unsupervised domain,we employ the dense-sparse-dense strategy,focusing on both difference-based and adversarial adaptive methods.First,the network is pretrained intensively to identify crucial connections.Second,during the pruning stage,the optimization algorithm in this paper distinguishes itself from original dense-sparse-dense strategy by jointly considering both weight and gradient information.Specifically,it leverages both weight (i.e.zero-order information) and gradient (i.e.first-order information) to influence pruning process.In the final dense phase,the pruned connections are restored and the dense network is retrained with a reduced learning rate.Finally,the obtained network achieves desirable outcomes in downstream tasks.The experimental results show that the proposed GWP approach can effectively improve the accuracy of downstream tasks,offering a plug-and-play capability compared with original difference-based and adversarial domain adaptation methods.
    2025,17(2):215-226, DOI: 10.13878/j.cnki.jnuist.20230810003
    Abstract:
    As a crucial component of weather systems,3D cloud simulation plays a significant role in various fields such as military and aviation.However,the current mainstream Bounding Volume Hierarchy (BVH) algorithm exhibits inefficient rendering performance when dealing with non-uniform and large-volume clouds.Here,a cloud rendering approach based on optimized BVH algorithm is proposed.The data points from the WRF(Weather Research and Forecasting) grids are used as cloud primitives,and a Z-order Hilbert curve is employed for spatial sorting.The BVH algorithm based on the Surface Area Heuristic (SAH) is optimized by locally optimizing the cloud primitive density,aiming to enhance computational efficiency.To tackle the data access overhead of overlapping BVH nodes,a novel storage structure called Overlapping Node Sets (ONS) is introduced,which reduces the time complexity.The optimized BVH algorithm reduces unnecessary intersection tests between rays and triangle surfaces,and resolves issues related to invalid boundary volume overlaps.Simulation experiments demonstrate that the proposed method achieves a 15.6% improvement in SAH cost compared to similar state-of-the-art algorithms,a 10% improvement in EPO(Effective Partial Overlap),and a reduction of over 100% in construction time.The computational efficiency of the optimized BVH algorithm outperforms similar algorithms in any WRF cloud scenario,indicating its capability for rapid rendering of WRF cloud products.
    2025,17(2):227-234, DOI: 10.13878/j.cnki.jnuist.20230921003
    Abstract:
    Texture extraction,a pivotal task in computer vision,significantly influences the accuracy of texture classification.Traditional single-texture extraction methods often fail to accurately describe the characteristics of various textures.To address this issue,this paper proposes a texture extraction approach based on an Improved Position Local Binary Pattern (IPLBP) and Gabor filters.The proposed IPLBP enhances texture description capability by integrating texture position information into the LBP framework.Specifically,the IPLBP algorithm captures local texture nuances,while Gabor filters extract global texture attributes.Subsequently,these two complementary feature sets are fused and classified using Support Vector Machine (SVM).Experimental results demonstrate that the proposed approach exhibits excellent performance in texture material classification tasks.Notably,compared to traditional LBP algorithms,the IPLBP-Gabor filter approach more accurately discerns the subtle differences between diverse texture features,thereby enhancing texture classification accuracy.
    2025,17(2):235-244, DOI: 10.13878/j.cnki.jnuist.20240617001
    Abstract:
    Here,a bearing fault diagnosis method based on recurrence analysis and Stacking ensemble learning is proposed to effectively extract nonlinear information from rolling bearing signals and improve diagnostic accuracy.Firstly,the nonlinear information in bearing signals is mapped to a two-dimensional recurrence plot through the application of recurrence analysis theory.Convolutional Neural Network (CNN) and Support Vector Machine (SVM) models are established from the perspectives of image recognition and recurrence quantification analysis,respectively.Finally,the Stacking method is employed to integrate these two models,leveraging their respective strengths.Experimental results demonstrate that the proposed method significantly improves the classification accuracy of bearing vibration signals and exhibits excellent stability under varying load conditions,providing a reliable solution for bearing fault diagnosis.
    2025,17(2):245-255, DOI: 10.13878/j.cnki.jnuist.20240512002
    Abstract:
    In the field of Brain-Computer Interface (BCI),the recognition of natural hand movements through electroencephalography (EEG) is crucial for achieving natural and precise human-machine interaction.However,attempts to enhance model generalization ability across different subjects using transfer learning are still rare in studies focusing on natural hand movement paradigms.Here,we investigate three natural hand movement paradigms of grasping,pinching and twisting through EEG experiments,and validate the effectiveness of two transfer learning algorithms,namely CA-MDM(Covariance matrix centroid Alignment-Minimum Distance to Riemannian Mean) and CA-JDA(Covariance matrix centroid Alignment-Joint Distribution Adaptation),on our experimental dataset.The results show that CA-JDA achieves average accuracies of 60.51%±5.78% and 34.89%±4.42% in binary and quadruple classification tasks,respectively,while CA-MDM performs at 63.88%±4.59% and 35.71%±4.84% in the same tasks,highlighting the advantages of Riemannian space-based classifiers in handling covariance features.This study not only confirms the feasibility of transfer learning in natural hand movement paradigms but also aids in reducing calibration time for BCI systems and implementing natural human-machine interaction strategies.
    2025,17(2):256-264, DOI: 10.13878/j.cnki.jnuist.20240506001
    Abstract:
    To address the issues of low search efficiency,long distance,and non-smooth paths in traditional path planning algorithms for autonomous vehicles,this study proposes an improvement by using key nodes of the optimized ant colony algorithm to replace the local target points in the dynamic window approach.Additionally,a target distance evaluation sub-function is incorporated into the dynamic window approach's evaluation function to enhance the efficiency and smoothness of path planning.Furthermore,a path decision-making method is employed to solve the problem of global path failure,enabling the vehicle to avoid obstacles and meet safety requirements of path planning.The improved ant colony algorithm utilizes the positional information of the start and end points to create an uneven initial pheromone distribution,thereby reducing time consumption during the initial search phase.By maintaining the global optimal paths and enhancing the pheromone concentration of excellent local paths,the pheromone update mechanism is optimized to speed up path exploration efficiency.The planned path is further optimized to reduce redundancy in nodes and turning points,thereby shortening path length.Simulation results show that compared to traditional path planning algorithms,the proposed integrated algorithm achieves better performance in terms of distance,smoothness,and convergence,aligning with the safety requirements for autonomous vehicle operation.
    Select AllDeselectExport
    Display Method:

    Select AllDeselectExport
    Display Method:
    2014,6(5):405-419, DOI:
    [Abstract] (2296) [HTML] (0) [PDF 1.98 M] (26944)
    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] (2623) [HTML] (0) [PDF 1.11 M] (18558)
    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] (2206) [HTML] (0) [PDF 1.29 M] (15902)
    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] (2041) [HTML] (0) [PDF 1.40 M] (11908)
    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] (2567) [HTML] (0) [PDF 960.26 K] (11602)
    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。
    2012,4(4):351-361, DOI:
    [Abstract] (1863) [HTML] (0) [PDF 1.22 M] (9934)
    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):159-167, DOI: 10.13878/j.cnki.jnuist.2017.02.006
    [Abstract] (1453) [HTML] (0) [PDF 1.56 M] (9426)
    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.
    2017,9(2):174-178, DOI: 10.13878/j.cnki.jnuist.2017.02.008
    [Abstract] (1148) [HTML] (0) [PDF 830.02 K] (8225)
    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] (1710) [HTML] (0) [PDF 1.04 M] (7444)
    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] (1059) [HTML] (0) [PDF 1.56 M] (7378)
    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] (921) [HTML] (0) [PDF 1.33 M] (7205)
    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.
    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.
    2011(1):23-27, DOI:
    [Abstract] (3975) [HTML] (0) [PDF 1.11 M] (6883)
    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.
    2017,9(6):575-582, DOI: 10.13878/j.cnki.jnuist.2017.06.002
    [Abstract] (1561) [HTML] (0) [PDF 1.18 M] (6877)
    Abstract:
    Knowledge graph technology is widely concerned and studied during recent years,in this paper we introduce the construction methods,recent development of knowledge graph in details,we also summarize the interdisciplinary applications of knowledge graph and future directions of research.This paper details the key technologies of textual,visual and multi-modal knowledge graph,such as information extraction,knowledge fusion and knowledge representation.As an important part of the knowledge engineering,knowledge graph,especially the development of multi-modal knowledge graph,is of great significance for efficient knowledge management,knowledge acquisition and knowledge sharing in the era of big data.
    2013,5(5):414-420, DOI:
    [Abstract] (1373) [HTML] (0) [PDF 1.04 M] (6603)
    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.
    2015,7(1):86-91, DOI:
    [Abstract] (984) [HTML] (0) [PDF 4.24 M] (6099)
    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.

DownloadsMore+

    Authors CornerMore+

      Search

      • Search by:
      • Search term:
      • from to

      External Links

      Address:No. 219, Ningliu Road, Nanjing, Jiangsu Province

      Postcode:210044

      Phone:025-58731025