Deep Learning Based Automatic Charging Identification and Positioning Method for Electric Vehicle

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.

Automatic identification of rock discontinuity and stability analysis of tunnel rock blocks using terrestrial laser scanning

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.

悬空波导环境下AIS信号超视距传播特性分析

大气波导可陷获无线电波信号,使其发生超视距传播现象,影响无线电系统信号传播预测和性能评估应用。本文针对由探空数据诊断的悬空波导个例,利用对流层电波传播确定性方法模拟岸-船自动识别系统(automatic identification system,AIS)传播链路,定量研究了不同悬空波导环境影响下AIS信号的超视距传播特性。结果表明:波导强度50 M单位且底高约7 m的悬空波导环境下,AIS信号发生明显超视距传播现象;波导底高约700 m且强度较大的悬空波导环境也会对AIS信号传播产生影响;相比于单一悬空波导模型,复合悬空波导(文中指两个悬空波导层结构组成)环境对AIS信号传播影响更大。本文研究成果可为大气波导监测反演、无线电系统设计、装备效应评估等应用提供重要理论参考。

基于联邦学习的船舶AIS轨迹预测算法

联邦学习是一种可以在弱通信环境下有效解决数据孤岛问题的分布式机器学习方法。针对海上船舶轨迹实时预测问题,提出基于Fedves联邦学习框架与卷积神经网络-门控循环单元(CNN-GRU)模型的船舶轨迹预测算法(E-FVTP)。根据Fedves联邦学习框架,通过规范客户端数据集规模以及客户端正则项,在保持原有客户端数据特征的前提下,减小数据非独立同分布特征对全局模型的影响,加快收敛速度。面向海洋通信资源短缺场景,建立基于船舶自动识别系统(AIS)数据的CNN-GRU船舶轨迹预测模型,解决了船舶终端设备计算能力不足的问题。在MarineCadastre开源和舟山海洋船舶航行AIS数据集上的实验结果表明,E-FVTP在预测误差比集中式训练降低40%的情况下,收敛速度提升67%、通信代价降低76.32%,可实现复杂海洋环境中船舶轨迹的精确预测,保障海上交通安全。

一种基于AIS的大气波导实时探测方法

在海域进行大气波导传输特性试验的过程中,发现是否能够通过船舶自动识别系统(Automatic Identification System,AIS)探测到超视距船舶与大气波导强弱具有很大相关性,据此本文提出一种基于AIS的大气波导实时探测方法。该方法能够实时探测超视距两点间是否存在大气波导,有效地弥补了当前大气波导探测方法仅能对当前区域进行探测的缺陷,并给出了基于AIS的大气波导传输损耗计算方法,以及一种大气波导探测结果的可视化方法。

海上目标雷达与AIS航迹时空匹配方法

针对海上目标的雷达与船舶自动识别系统(Automatic Identification System,AIS)中,航迹之间存在的时空不匹配现象,提出了在空间对准之后结合海上航迹运动特点,对AIS航迹数据进行插值对准雷达目标航迹的方法,在解决雷达与AIS航迹之间时空不匹配问题的同时,最大程度减小插值误差。该方法根据船舶航向变化率,结合航速航向法和内插外推法的优势,针对不同航迹特点自动选择最佳的插值配准方法,实现海上目标的雷达与AIS航迹点的自动插值和时空对齐。仿真实验结果表明,所提方法针对海上目标复杂运动,可以自动匹配选择最佳插值方法,有效降低误差,实现雷达与AIS航迹之间的时空匹配。

AIS航标在遥测遥控系统中的定位跳变问题探究

文章以汕头港5号船舶自动识别系统(Automatic Identification System,AIS)航标为例,对AIS航标在遥测遥控系统中发生定位跳变的现象进行探讨,并简单阐述规范AIS航标管理和避免类似情况再次发生的一些工作思路。

船舶虚假AIS信号识别分析研究

为提升海上监管效率,打击海上违法行为,通过分析两起真实案例,结合日常监管提出“真假船台切换”“航路、航速异常”等各类虚假船舶自动识别系统(AIS)信号的违法、违规行为,重点介绍虚假AIS信号的识别方法,并从设备管理、人员、资源、监管等方面提出完善AIS管理的建议,为后期进行技术手段筛查、打击违法行为积累经验。

Automatic fracture–vug identification and extraction from electric imaging logging data based on path morphology

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.

Real-time Monitoring of Subsea Gas Pipelines,Offshore Platforms,and Ship Inspection Scores Using an Automatic Identification System

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.

Automatic modal parameter identification of high arch dams:feasibility verification

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.

Non-coherent sequence detection scheme for satellite-based automatic identification system

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.

基于AIS信号的低空大气波导监测试验分析

鉴于低空大气波导对海上工作的雷达、通信系统的显著影响,提出了基于实测船舶自动识别系统(automatic identification system, AIS)信号的海上低空大气波导遥感新方法.首先讨论了不同大气折射环境下的AIS信号传播特性,验证了低空大气波导尤其表面波导对海上实际VHF频段AIS信号的显著影响;然后给出了2018年4月20日以及22日岸基AIS接收机接收功率平面位置分布图结果,利用接收站附近探空站的修正大气折射率剖面验证了大气波导是形成AIS信号远距离传播的主要原因;最后给出了基于AIS信号反演低空大气波导的步骤,采用粒子群优化算法得到根据单一方位接收功率的反演结果.该方法具有被动接收、时空分辨率高的优点,是当前雷达海杂波、卫星导航信号遥感探测大气波导的有益补充.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

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.

A fast automatic identification method for seismic belts based on distance correlation and its earthquake case

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.

Combined iterative cross-correlation demodulation scheme for mixing space borne automatic identification system signals

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.

Automatic Identification for Laparoscopic Surgical Procedure for Ligation and Online Distinction of Abnormal Manipulation for Thread Knotting

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.

RESEARCH ON AUTOMATIC FOG IDENTIFICATION TECHNOLOGY BY METEOROLOGICAL SATELLITE REMOTE SENSING

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.

基于AIS数据的敏感水域船舶异常行为检测

为了检测敏感水域中船舶的异常行为,提出了基于船舶自动识别系统(AIS)数据的敏感水域船舶异常行为检测框架。首先,选定3个敏感水域,将进出次数、停留时间和速度比值3个指标作为异常因子并建立检测框架;然后,采用专家调查法构造判断矩阵,进而确定异常因子权重;接着,基于AIS数据确定异常因子值及其评分,综合异常因子权重并利用层次加权计算得到船舶异常得分;最后,利用南海、东京湾和琉球3个敏感水域近2年的AIS数据进行了试验分析。试验结果表明,该框架可有效检测出敏感水域中行为异常的船舶,具有可行性。

基于全球AIS的多源航迹关联数据集

数据、算法和算力是当前人工智能技术发展的3大推力,考虑到智能关联算法研究的迫切需求和多雷达协同观测航迹数据获取困难,针对航迹关联数据集缺失问题,该文公开了多源航迹关联数据集(MTAD),其由全球AIS航迹数据经栅格划分、自动中断和噪声添加处理步骤构建。该数据集包括训练集和测试集两大部分,共有航迹百万余条,其中训练集包含5000个场景样本,测试集包含1000个场景样本,每一个场景样本由几个到几百个数量不等的航迹构成,涵盖多种运动模式、多种目标类型和长度不等的持续时间。同时,进一步对构造的MTAD数据集进行可视化分析,详细研究了各个栅格内航迹的特点,证明了该数据集的丰富性、合理性和有效性。最后,作为参考,给出了关联评价指标和关联基线结果。该数据集目前已被用作海军“金海豚”杯竞赛科目专用数据集。