The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreach...The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering.展开更多
We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs bas...We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions,including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.展开更多
Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential ro...Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(DBSCAN) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.展开更多
High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,wh...High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.展开更多
Local geometric information and discontinuity features are key aspects of the analysis of the evolution and failure mechanisms of unstable rock blocks in rock tunnels.This study demonstrates the integration of terrest...Local geometric information and discontinuity features are key aspects of the analysis of the evolution and failure mechanisms of unstable rock blocks in rock tunnels.This study demonstrates the integration of terrestrial laser scanning(TLS)with distinct element method for rock mass characterization and stability analysis in tunnels.TLS records detailed geometric information of the surrounding rock mass by scanning and collecting the positions of millions of rock surface points without contact.By conducting a fuzzy K-means method,a discontinuity automatic identification algorithm was developed,and a method for obtaining the geometric parameters of discontinuities was proposed.This method permits the user to visually identify each discontinuity and acquire its spatial distribution features(e.g.occurrences,spac-ings,trace lengths)in great detail.Compared with hand mapping in conventional geotechnical surveys,the geometric information of discontinuities obtained by this approach is more accurate and the iden-tification is more efficient.Then,a discrete fracture network with the same statistical characteristics as the actual discontinuities was generated with the distinct element method,and a representative nu-merical model of the jointed surrounding rock mass was established.By means of numerical simulation,potential unstable rock blocks were assessed,and failure mechanisms were analyzed.This method was applied to detection and assessment of unstable rock blocks in the spillway and sand flushing tunnel of the Hongshiyan hydropower project after a collapse.The results show that the noncontact detection of blocks was more labor-saving with lower safety risks compared with manual surveys,and the stability assessment was more reliable since the numerical model built by this method was more consistent with the distribution characteristics of actual joints.This study can provide a reference for geological survey and unstable rock block hazard mitigation in tunnels subjected to complex geology and active rockfalls.展开更多
In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of...In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.展开更多
To relieve traffic congestion in urban rail transit stations,a new identification method of crowded passenger flow based on automatic fare collection data is proposed.First,passenger travel characteristics are analyze...To relieve traffic congestion in urban rail transit stations,a new identification method of crowded passenger flow based on automatic fare collection data is proposed.First,passenger travel characteristics are analyzed by observing the temporal distribution of inflow passengers each hour and the spatial distribution concerning cross-section passenger flow.Secondly,the identification method of crowded passenger flow is proposed to calculate the threshold via the probability density function fitted by Matlab and classify the early-warning situation based on the threshold obtained.Finally,a case study of Xinjiekou station is conducted to prove the validity and practicability of the proposed method.Compared to the traditional methods,the proposed comprehensive method can remove defects such as efficiency and delay.Furthermore,the proposed method is suitable for other rail transit companies equipped with automatic fare collection systems.展开更多
The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship i...The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.展开更多
Electric vehicle charging identification and positioning is critically important to achieving automatic charging.In terms of the problem of automatic charging for electric vehicles,a dual recognition and positioning m...Electric vehicle charging identification and positioning is critically important to achieving automatic charging.In terms of the problem of automatic charging for electric vehicles,a dual recognition and positioning method based on deep learning is proposed.The method is divided into two parts:global recognition and localization and local recognition and localization.In the specific implementation process,the collected pictures of electric vehicle charging attitude are classified and labeled.It is trained with the improved YOLOv4 networkmodel and the corresponding detectionmodel is obtained.The contour of the electric vehicle is extracted by the BiSeNet semantic segmentation algorithm.The minimum external rectangle is used for positioning of the electric vehicle.Based on the location relationship between the charging port and the electric vehicle,the rough location information of the charging port is obtained.The automatic charging equipment moves to the vicinity of the charging port,and the camera near the charging gun collects pictures of the charging port.The model is detected by the Hough circle,the KM algorithmis used for featurematching,and the homography matrix is used to solve the attitude.The results show that the dual identification and location method based on the improved YOLOv4 algorithm proposed in this paper can accurately locate the charging port.The accuracy of the charging connection can reach 80%.It provides an effective way to solve the problems of automatic charging identification and positioning of electric vehicles and has strong engineering practical value.展开更多
The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detecti...The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.展开更多
Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross...Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross-correlation demodulation scheme,referred to as CICCD,yielded a set of single short signals based on the prior information of AIS,after the frequency,code rate and modulation index were estimated.It demodulates the corresponding short codes according to the maximum peak of cross-correlation,which is simple and easy to implement.Numerical simulations show that the bit error rate of proposed algorithm improves by about 40% compared with existing ones,and about 3 dB beyond the standard AIS receiver.In addition,the proposed demodulation scheme shows the satisfying performance and engineering value in mixing AIS environment and can also perform well in low signal-to-noise conditions.展开更多
There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters ...There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.展开更多
Earthquake prediction practice and a large number of earthquake cases show that anomalous images of small earthquake belts may appear near the epicenter before strong earthquakes.Through the research of earthquake cas...Earthquake prediction practice and a large number of earthquake cases show that anomalous images of small earthquake belts may appear near the epicenter before strong earthquakes.Through the research of earthquake cases,researchers have a relatively consistent method to determine the clarity of an identified seismic belt,but there is still a lack of method on seismic belt identification from the distribution of scattered points.Due to the complexity of exhaustive algorithm,the rapid automatic identification technique of seismic belts has been progressing slowly.Visual recognition is still the basic method of seismic belt identification.Based on the algorithm of distance correlation,this paper presents a fast automatic identification method of seismic belts.The effectiveness of this method was proved by 100 random earthquakes and an example of seismic belts of magnitude 4.0 before the 2005 Jiujiang M5.7 earthquake.The results show that:①the automatic identification of seismic belts should first identify the"relational earthquake",then identify the"suspected seismic belt",and finally use the criterion of seismic belt clarity to determine;②random earthquakes and real earthquakes identification results show that the distance correlation method can realize the fast automatic identification of seismic belts by computer.展开更多
Laparoscopic surgery is a difficult surgical procedure compared with laparotomy. In particular, considerable skills and care are required for thread knotting in laparoscopic surgery. In this paper, a method for automa...Laparoscopic surgery is a difficult surgical procedure compared with laparotomy. In particular, considerable skills and care are required for thread knotting in laparoscopic surgery. In this paper, a method for automatic identification of a laparoscopic surgical procedure for ligation and online distinction of an abnormality, defined as any unusual manipulation, in the identified surgical procedure is proposed. Ligation is divided into several individual surgical procedures, and on the basis of the threshold criteria, each surgical procedure is identified. Next, the identified surgical procedure, thread knotting, is classified as either normal or abnormal using a self-organizing map. Finally, to reduce surgical error, an abnormality warning system which warns detection of an unusual manipulation in the surgical procedure to the operator is constructed.展开更多
Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy...Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.展开更多
Identification and counting of rice light-trap pests are important to monitor rice pest population dynamics and make pest forecast. Identification and counting of rice light-trap pests manually is time-consuming, and ...Identification and counting of rice light-trap pests are important to monitor rice pest population dynamics and make pest forecast. Identification and counting of rice light-trap pests manually is time-consuming, and leads to fatigue and an increase in the error rate. A rice light-trap insect imaging system is developed to automate rice pest identification. This system can capture the top and bottom images of each insect by two cameras to obtain more image features. A method is proposed for removing the background by color difference of two images with pests and non-pests. 156 features including color, shape and texture features of each pest are extracted into an support vector machine （SVM） classifier with radial basis kernel function. The seven-fold cross-validation is used to improve the accurate rate of pest identification. Four species of Lepidoptera rice pests are tested and achieved 97.5% average accurate rate.展开更多
Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed ...Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.展开更多
Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoo...Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.展开更多
Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)informati...Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.展开更多
Mesoscale eddies are an important component of oceanic features.How to automatically identify these mesoscale eddies from available data has become an important research topic.Through careful examination of existing m...Mesoscale eddies are an important component of oceanic features.How to automatically identify these mesoscale eddies from available data has become an important research topic.Through careful examination of existing methods,we propose an improved,SSH-based automatic identification method.Using the inclusion relation of enclosed SSH contours,the mesoscale eddy boundary and core(s) can be automatically identified.The time evolution of eddies can be examined by a threshold search algorithm and a tracking algorithm based on similarity.Sea-surface height(SSH) data from Naval Research Laboratory Layered Ocean Model(NLOM) and sea-level anomaly(SLA) data from altimeter are used in the many experiments,in which different automatic identification methods are compared.Our results indicate that the improved method is able to extract the mesoscale eddy boundary more precisely,retaining the multiple-core structure.In combination with the tracking algorithm,this method can capture complete mesoscale eddy processes.It can thus provide reliable information for further study of reconstructing eddy dynamics,merging,splitting,and evolution of a multi-core structure.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41941017 and 42177139)Graduate Innovation Fund of Jilin University(Grant No.2024CX099)。
文摘The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment.Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes.In contrast,unmanned aerial vehicle(UAV)photogrammetry is not limited by terrain conditions,and can efficiently collect high-precision three-dimensional(3D)point clouds of rock masses through all-round and multiangle photography for rock mass characterization.In this paper,a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling.The method is based on four steps:(1)Establish a point cloud spatial topology,and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms;(2)Extract discontinuities using the density-based spatial clustering of applications with noise(DBSCAN)algorithm and fit the discontinuity plane by combining principal component analysis(PCA)with the natural breaks(NB)method;(3)Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud;and(4)Adopt a Poisson reconstruction method for refined rock block modeling.The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys.The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks.The calculation results are accurate and reliable,which can meet the practical requirements of engineering.
基金granted access to projects supported by the National Major Fundamental Research Program of China ‘‘On basic research problems in applied geophysics for deep oil and gas fields’’(Grant Number 2013CB228605)CNPC Science and Technology Project(Grant Number 2016A-3303)and CNPC Logging Project(Grant Number 2017E-15)
文摘We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions,including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.
基金National Natural Science Foundation of China under Grant Nos. 51725901 and 51639006。
文摘Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(DBSCAN) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.
基金The National Natural Science Foundation of China under contract No.61362002the Marine Scientific Research Special Funds for Public Welfare of China under contract No.201505002
文摘High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.
基金support of the National Natural Science Foundation of China(Grant No.42102316)the Open Project of the Technology Innovation Center for Geological Environment Monitoring of Ministry of Natural Resources of China(Grant No.2022KFK1212005).
文摘Local geometric information and discontinuity features are key aspects of the analysis of the evolution and failure mechanisms of unstable rock blocks in rock tunnels.This study demonstrates the integration of terrestrial laser scanning(TLS)with distinct element method for rock mass characterization and stability analysis in tunnels.TLS records detailed geometric information of the surrounding rock mass by scanning and collecting the positions of millions of rock surface points without contact.By conducting a fuzzy K-means method,a discontinuity automatic identification algorithm was developed,and a method for obtaining the geometric parameters of discontinuities was proposed.This method permits the user to visually identify each discontinuity and acquire its spatial distribution features(e.g.occurrences,spac-ings,trace lengths)in great detail.Compared with hand mapping in conventional geotechnical surveys,the geometric information of discontinuities obtained by this approach is more accurate and the iden-tification is more efficient.Then,a discrete fracture network with the same statistical characteristics as the actual discontinuities was generated with the distinct element method,and a representative nu-merical model of the jointed surrounding rock mass was established.By means of numerical simulation,potential unstable rock blocks were assessed,and failure mechanisms were analyzed.This method was applied to detection and assessment of unstable rock blocks in the spillway and sand flushing tunnel of the Hongshiyan hydropower project after a collapse.The results show that the noncontact detection of blocks was more labor-saving with lower safety risks compared with manual surveys,and the stability assessment was more reliable since the numerical model built by this method was more consistent with the distribution characteristics of actual joints.This study can provide a reference for geological survey and unstable rock block hazard mitigation in tunnels subjected to complex geology and active rockfalls.
基金the Yunnan Applied Basic Research Projects(No.2016FD039)the Talent Cultivation Project in Yunnan Province(No.KKSY201503063)
文摘In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.
基金The National Key Research and Development Program of China(No.2016YFE0206800)
文摘To relieve traffic congestion in urban rail transit stations,a new identification method of crowded passenger flow based on automatic fare collection data is proposed.First,passenger travel characteristics are analyzed by observing the temporal distribution of inflow passengers each hour and the spatial distribution concerning cross-section passenger flow.Secondly,the identification method of crowded passenger flow is proposed to calculate the threshold via the probability density function fitted by Matlab and classify the early-warning situation based on the threshold obtained.Finally,a case study of Xinjiekou station is conducted to prove the validity and practicability of the proposed method.Compared to the traditional methods,the proposed comprehensive method can remove defects such as efficiency and delay.Furthermore,the proposed method is suitable for other rail transit companies equipped with automatic fare collection systems.
文摘The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.
基金supported by Guangdong Province Key Research and Development Project(2019B090909001)National Natural Science Foundation of China(52175236)+1 种基金the Natural Science Foundation of China(Grant 51705268)China Postdoctoral Science Foundation Funded Project(Grant 2017M612191).
文摘Electric vehicle charging identification and positioning is critically important to achieving automatic charging.In terms of the problem of automatic charging for electric vehicles,a dual recognition and positioning method based on deep learning is proposed.The method is divided into two parts:global recognition and localization and local recognition and localization.In the specific implementation process,the collected pictures of electric vehicle charging attitude are classified and labeled.It is trained with the improved YOLOv4 networkmodel and the corresponding detectionmodel is obtained.The contour of the electric vehicle is extracted by the BiSeNet semantic segmentation algorithm.The minimum external rectangle is used for positioning of the electric vehicle.Based on the location relationship between the charging port and the electric vehicle,the rough location information of the charging port is obtained.The automatic charging equipment moves to the vicinity of the charging port,and the camera near the charging gun collects pictures of the charging port.The model is detected by the Hough circle,the KM algorithmis used for featurematching,and the homography matrix is used to solve the attitude.The results show that the dual identification and location method based on the improved YOLOv4 algorithm proposed in this paper can accurately locate the charging port.The accuracy of the charging connection can reach 80%.It provides an effective way to solve the problems of automatic charging identification and positioning of electric vehicles and has strong engineering practical value.
文摘The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.
基金Project(9140C860304) supported by the National Defense Key Laboratory Foundation of China
文摘Aiming at the potential presence of mixing automatic identification system(AIS) signals,a new demodulation scheme was proposed for separating other interfering signals in satellite systems.The combined iterative cross-correlation demodulation scheme,referred to as CICCD,yielded a set of single short signals based on the prior information of AIS,after the frequency,code rate and modulation index were estimated.It demodulates the corresponding short codes according to the maximum peak of cross-correlation,which is simple and easy to implement.Numerical simulations show that the bit error rate of proposed algorithm improves by about 40% compared with existing ones,and about 3 dB beyond the standard AIS receiver.In addition,the proposed demodulation scheme shows the satisfying performance and engineering value in mixing AIS environment and can also perform well in low signal-to-noise conditions.
基金Key research project "Research of Shanghai City and Costal Heavy Fog Remote Sensing Detecting and Warning System" of Science and Technology Commission of Shanghai Municipality (075115011)
文摘There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.
基金the Major State Basic Research Development Program of China(NO.2017YFC 1500502-05)the National Natural Science Foundation of China(No.11672258)We would like to thank Mingxiao Li,Zhiping Song,Gang Li and Yang Zang for the valuable discussions.
文摘Earthquake prediction practice and a large number of earthquake cases show that anomalous images of small earthquake belts may appear near the epicenter before strong earthquakes.Through the research of earthquake cases,researchers have a relatively consistent method to determine the clarity of an identified seismic belt,but there is still a lack of method on seismic belt identification from the distribution of scattered points.Due to the complexity of exhaustive algorithm,the rapid automatic identification technique of seismic belts has been progressing slowly.Visual recognition is still the basic method of seismic belt identification.Based on the algorithm of distance correlation,this paper presents a fast automatic identification method of seismic belts.The effectiveness of this method was proved by 100 random earthquakes and an example of seismic belts of magnitude 4.0 before the 2005 Jiujiang M5.7 earthquake.The results show that:①the automatic identification of seismic belts should first identify the"relational earthquake",then identify the"suspected seismic belt",and finally use the criterion of seismic belt clarity to determine;②random earthquakes and real earthquakes identification results show that the distance correlation method can realize the fast automatic identification of seismic belts by computer.
文摘Laparoscopic surgery is a difficult surgical procedure compared with laparotomy. In particular, considerable skills and care are required for thread knotting in laparoscopic surgery. In this paper, a method for automatic identification of a laparoscopic surgical procedure for ligation and online distinction of an abnormality, defined as any unusual manipulation, in the identified surgical procedure is proposed. Ligation is divided into several individual surgical procedures, and on the basis of the threshold criteria, each surgical procedure is identified. Next, the identified surgical procedure, thread knotting, is classified as either normal or abnormal using a self-organizing map. Finally, to reduce surgical error, an abnormality warning system which warns detection of an unusual manipulation in the surgical procedure to the operator is constructed.
基金supported by the National Key R&D Program of China“R&D of security technologies and equipment for national oil and gas reserves”(No.:2017YFC0805800)National Natural Science Foundation of China“Model study of hydrogen-damage induced failure and integration evaluation for X80 pipe steel”(No.:51874322).
文摘Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.
基金support of the National Natural Science Foundation of China (31071678)the Major Scientific and Technological Special of Zhejiang Province, China (2010C12026)+1 种基金the Ningbo Science and Technology Project, China (201002C1011001)Xiangshan Science and Technology Project, China(2010C0001)
文摘Identification and counting of rice light-trap pests are important to monitor rice pest population dynamics and make pest forecast. Identification and counting of rice light-trap pests manually is time-consuming, and leads to fatigue and an increase in the error rate. A rice light-trap insect imaging system is developed to automate rice pest identification. This system can capture the top and bottom images of each insect by two cameras to obtain more image features. A method is proposed for removing the background by color difference of two images with pests and non-pests. 156 features including color, shape and texture features of each pest are extracted into an support vector machine （SVM） classifier with radial basis kernel function. The seven-fold cross-validation is used to improve the accurate rate of pest identification. Four species of Lepidoptera rice pests are tested and achieved 97.5% average accurate rate.
文摘Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902)。
文摘Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.
基金supported by the National Natural Science Foundation of China(Nos.U2441250,62301380,and 62231027)Natural Science Basic Research Program of Shaanxi,China(2024JC-JCQN-63)+3 种基金the Key Research and Development Program of Shaanxi,China(No.2023-YBGY-249)the Guangxi Key Research and Development Program,China(No.2022AB46002)the China Postdoctoral Science Foundation(No.2022M722504 and 2024T170696)the Innovation Capability Support Program of Shaanxi,China(No.2024RS-CXTD-01).
文摘Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.
基金jointly supported by a grant from the National Natural Science Foundation of China(General Program)(41071250)Innovation Program of State Key Laboratory of Resources and Environmental Information System,Institute of Geographic SciencesNatural Resources Research,Chinese Academy of Sciences(088RA500KA)
文摘Mesoscale eddies are an important component of oceanic features.How to automatically identify these mesoscale eddies from available data has become an important research topic.Through careful examination of existing methods,we propose an improved,SSH-based automatic identification method.Using the inclusion relation of enclosed SSH contours,the mesoscale eddy boundary and core(s) can be automatically identified.The time evolution of eddies can be examined by a threshold search algorithm and a tracking algorithm based on similarity.Sea-surface height(SSH) data from Naval Research Laboratory Layered Ocean Model(NLOM) and sea-level anomaly(SLA) data from altimeter are used in the many experiments,in which different automatic identification methods are compared.Our results indicate that the improved method is able to extract the mesoscale eddy boundary more precisely,retaining the multiple-core structure.In combination with the tracking algorithm,this method can capture complete mesoscale eddy processes.It can thus provide reliable information for further study of reconstructing eddy dynamics,merging,splitting,and evolution of a multi-core structure.
