Non-tidal ocean loading (NTOL) is a significant environmental factor affecting gravity observations especially in coastal areas. Its non-periodic characteristics and regional dependence increase the difficulty of data analysis and signal separation. The Haikou superconducting gravimetry station, located in the northern part of Hainan Island, is situated close to the South China Sea. It is influenced by complex regional oceanic dynamic processes, and the NTOL signal in its gravity observations is particularly prominent. Based on continuous superconducting gravimeter (SG) observations from the iGrav-048 at the Haikou station and high-resolution global sea surface height data from the Copernicus Marine Environment Monitoring Service (CMEMS), we calculate the gravity variations and vertical deformations caused by NTOL in the South China Sea using the mass-loading Green’s function convolution method. The estimates are validated by comparison with products from the MPIOM model provided by the GFZ Helmholtz Centre for Geosciences. The results show that NTOL signals in the South China Sea should be the primary source of gravity residuals at the Haikou station. The CMEMS model shows a correlation coefficient of 0.83 with the observations. The maximum gravity change due to NTOL is estimated to be 2.6 μGal based on the CMEMS model. While the CMEMS and MPIOM models show good agreement in overall trend (R = 0.87), CMEMS performs better in simulating high-frequency signals due to its higher spatio-temporal resolution. This highlights the importance of high-precision regional ocean models for improving the modeling and correction of NTOL signals. This study systematically evaluates the impact of South China Sea NTOL on SG observations at the Haikou station, providing a scientific basis for the processing of coastal gravity data in the region. The findings suggest that incorporating NTOL corrections into coastal superconducting gravity data processing can effectively improve data reliability and geophysical interpretation, offering valuable insights for advancing regional ocean loading effect research and related geodetic applications.

The Eastern Himalayan Syntaxis (EHS), which is located at the southeastern edge of the Qinghai-Xizang Plateau, is a key region for understanding mountain-building and subduction processes. Bouguer gravity anomalies derived from the Earth Gravitational Model 2008 free-air anomaly data following topographic corrections, were analyzed. Wavelet multiscale decomposition and normalized full gradient methods were employed to analyze the data, revealing complex crustal density structures and fault depth characteristics in both the lateral and vertical dimensions. The results indicate significant lateral density variations within the crust, attributed to the wedging of the Indian Plate and partial melting of deep materials. The article also revealed several major faults that extend to 40 km in depth that potentially play crucial roles in regional stress release and material migration between the crust and mantle. Additionally, smaller faults at middle to shallow depths may influence the near-surface stress distribution and the evolution of shallow crustal structures. The analysis of Bouguer gravity anomalies in the EHS provides new insights into the complex crustal density architecture of the EHS and improves our understanding of mountain-building processes and subduction dynamics in this geologically significant region.

Coseismic gravity changes provide significant information for the study of the mechanisms of large earthquakes and for developing fault models (Sun, 2012). In this research, coseismic gravity changes of the 2008 Ms8.0 Wenchuan earthquake in China were studied by using gravity observation data and simulation based on a fault model.Firstly, a fine processing of relative and absolute gravity data from the Longmenshan Gravimetric Network was carried out and observed gravity change of 22 stations near this earthquake were obtained; Secondly ,simulation of coseismic gravity changes was conducted based on half-space dislocation theory using the fault model obtained by Wang et al(2008) through inversion with multiple types of geodetic survey data, including GPS, INSAR, and leveling, and the results were compared with the observations..It was found that the observed and simulated results are basically consistent, showing that the significant changes are mainly concentrated in the near-rupture zone in the hanging wall of the Yingxiu–Beichuan fault and that the changes decrease rapidly away from the rupture zone. The changes exhibit a positive to negative trend from east to west in the footwall of the Yingxiu–Beichuan fault and have a distribution characterized by alternate positive and negative changes in the hanging wall of the fault. This demonstrates the reliability of the observed results and the reasonableness of the fault model used in this paper.In the near-rupture zone on the west and east sides of the Yingxiu–Beichuan fault, there are still some differences between the observed and simulated results. The trends in the spatial distribution of these differences exhibit a deviation similar to “phase delay”; in other words, an observed result deviates from the corresponding simulated result in terms of spatial position, which is speculated to be caused by errors in the geometric parameters and in the slip distribution of the fault model. After the slip distribution of the Pengguan fault model was modified based on the actual surface rupture distribution, the simulated result at the Hongjiawan station near the eastern boundary of the fault model showed greater consistency with the observed result. This indicates that the observed gravity change results in this paper can provide an important reference for further detailed study of the fault model. Fig1.Schematic of the Chengdu Gravimetric Network Fig2.Spatial distribution of observed gravity changes and simulated results

With the increasing complexity and digitization of industrial processes, the safety and reliability of process industrial components (PICs) are facing significant challenges. Due to the limitations of single-state signals in comprehensively reflecting the operating conditions of PICs, this paper considers the correlation and complementarity among multiple sensor signals, proposing a multi-sensor information interaction and multi-level feature adaptive fusion method (M2F2N) for PICs fault diagnosis. Firstly, considering the characteristics of faults at different stages, a shallow-middle-deep multi-level feature extraction network is designed to parallelly extract independent features of different sensors at multiple stages. Then, an information interaction network is developed to extract complementarity features between heterogeneous sensors, enhancing the fault representation capability of the network through mutual attention among signals. Lastly, the adaptive fusion network adaptively weights the features at all stages layer-by-layer to generate the final recognizable fusion features. The experimental results show that the average accuracy of the M2F2N method reaches 96.20% on the plunger pump dataset, and the convergence speed is 75% faster than that of the traditional method. On the control valve dataset, its robustness is reflected in that the error rate is the lowest compared with other methods under varying noise interference levels, which proves the superiority and robustness of the proposed M2F2N method.

通过回顾四川地区测网变迁和震例总结,客观阐述四川流动重力观测的发展历程及其在地震趋势判定中的应用效能.结果表明,利用流动重力资料观测到高梯度带、四象限分布、零值线拐弯等重力场时变异常信号,对川滇菱形块体内部及边界带发生的汶川地震、芦山地震、九寨沟地震、泸定地震等地震进行了准确的中期预测,尤其在强震地点判定上预测效果较好,准确率达60％.

Autonomous lane change technology of intelligent vehicle is one of the current research hotspots, and its development is very important to improve the driving safety of intelligent vehicle. In this paper, an autonomous lane change decision model based on GSCV-LightGBM and a multi-constraint autonomous lane change trajectory planning model based on quintic polynomial are proposed to optimize the safety, comfort and traffic efficiency of autonomous vehicle active lane change. The automatic lane change decision model uses simple moving average filtering technology to remove outliers from the public NGSIM data set, and uses grid search cross-validation algorithm to optimize the parameters of LightGBM model. Compared with other machine learning algorithms, GSCV-LightGBM model has the highest accuracy and the shortest decision time. The trajectory planning model based on quintic polynomial multi-boundary constraints divides vehicle free lane change behavior into barrier-free lane change and free lane change with obstacles based on safety distance threshold. The local path planning evaluation function is set respectively, and the lane change trajectory is evaluated twice by using the urgency degree to ensure the safety, comfort and traffic efficiency of the planned trajectory. Finally, the model predictive control method was used to control the trajectory of the simulation vehicle following the lane change planning through CarSim-Simulink co-simulation to verify the reliability of the lane change decision model and the trajectory planning model. The results show that the planned trajectory conforms to the multi-constraint conditions and can effectively improve the comfort, lane change efficiency and safety of the autonomous vehicle.

文中推导并给出了基于非格网分布的起伏面扰动重力或重力异常解算区域扰动重力梯度场模型的数值计算公式.基于澳大利亚 West Arnhem Land 地区的格网重力数据,以频谱域(二维快速傅里叶变换)解算的扰动重力梯度全张量作为"基准值",然后利用基于推导公式的最小二乘配置方法(LSC)对相同区域非规则范围的重力数据进行扰动重力梯度模型解算,将结果作为"评估值".对比"基准值"与"评估值"之差,研究发现:1)基于推导公式的最小二乘配置方法解算得到的扰动重力梯度值与频谱域方法得到扰动梯度"基准值"各分量在空间形变变化上是一致的;2)统计扰动重力梯度各分量的差值 ΔδΓfft-lscxx、ΔδΓfft-lscxy、ΔδΓfft-lscxz、ΔδΓfft-lscyy、ΔδΓfft-lscyz 和 ΔΓfft-lsczz,"基准值"与"评估值"差值的标准差分别为 5.54E、5.30E、1.85E、6.55E、2.09E 和 9.67E(1 E=1×10-9s-2),远低于国际上实测重力梯度与解算模型差值的研究结果.最后,基于云南地区实测地表差分重力值,文中首次给出了该区域半波长约 20km的重力梯度场年际变化模型.文中的思路和方法提高了广泛分布的重力数据(主要为重力异常和扰动重力)的使用效率,可为地球物理学、地质学研究更好地理解和解释重力数据、重力梯度数据及其与场源的关系提供数据基础.

Dynamic coal quantity detection for conveyor belt is the foundation and key to achieving energy consumption optimization measures for multi-stage belt conveyors such as coal flow starting and automatic speed regulation. The existing coal quantity detection methods based on ultrasonic have low precision. Multiple ultrasonic sensors are susceptible to interference. In order to solve the above problems, a dynamic coal quantity detection system for conveyor belts based on ultrasonic array is designed. Using the principle of ultrasonic ranging, the coal material height corresponding to the detection points of each ultrasonic sensor array element is detected in real-time through an ultrasonic array. The cross-section slicing method is used to calculate the total volume of coal material passing through the conveyor belt per unit time. The real-time coal flow and total coal quantity of the conveyor belt are calculated based on the coal material stacking density. In order to reduce the crosstalk of the same frequency acoustic wave and the error caused by the attenuation of ultrasonic waves in harsh underground environments, 10 ultrasonic sensor arrays with different center frequencies are selected and arranged in a 2×5 linear array form. The collected coal height data is compensated through multiple rows of ultrasonic sensors to improve the accuracy of coal height data detection. The analysis results of real-time performance indicate that the ultrasonic array detection speed theoretically meets the coal quantity detection requirements of a belt conveyor with a belt speed of 5 m/s. The experimental results show that the average relative errors of regular material volume detection are 4.99% and 5.16% at belt speeds of 0.125 m/s and 0.170 m/s, respectively. Under simulated actual operating conditions, the average relative error of coal quantity detection is 5.56%. In the low belt speed state, the system has a measurement accuracy of over 94% for regular materials and coal. It basically achieves real-time and accurate detection of the dynamic coal quantity of the conveyor belt, meeting the coal quantity detection requirements of the belt conveyor.

Existing image fusion algorithms have difficulty in effectively preserving valuable target features in infrared and visible images, which easily introduces blurry edges and unremarkable notable targets during their fusion process. We propose the MGFuse algorithm as a solution to this problem, which is a novel fusion algorithm that utilizes multiscale decomposition optimization and gradient-weighted local energy. Initially, non-subsampled shearlet transform (NSST) is applied to partition both the infrared and visible images into several high-frequencies and low-frequencies components. Subsequently, the acquired low frequencies continue to be decomposed via the proposed optimization function to get base layers and texture layers, which can optimize the quality of image edges and preserve fine-grained details, respectively. In addition, we have formulated an intrinsic attribute-based energy (IAE) fusion scheme to merge the two base layers. The texture layers and high-frequencies are extracted by gradient-weighted local energy (GE) operator based on structure tensor, which is employed to construct the fusion strategy for these parts. At last, the acquired texture and base parts are linearly combined to get the integrated low-frequency layer on which the final image is acquired using inverse NSST. Numerous experimental observations demonstrate that our MGFuse algorithm achieves superior fusion capability than the reference nine advanced algorithms in both qualitative and quantitative assessment, and robustness to noisy images with different noise levels.

因托辊故障引发的远程带式输送机事故越来越多,而传统的人工巡检已不能满足需求,且现有接触式加速度信号检测方式存在传感器需求量大及数据收集难的问题,所以有必要通过智能巡检机器人搭载拾音器进行非接触式巡检.托辊运行环境嘈杂,为剔除信号中的噪声,提出基于完全噪声辅助集合经验模态分解(CEEMDAN)、主成分分析(PCA)和鲁棒性独立分量分析(RobustICA)的单通道盲源分离(SCBSS)去噪方法;托辊信号具有非平稳、非线性的特点,仅用梅尔倒谱系数(MFCC)不能完美刻画信号特征参数,提出基于CEEMDAN、PCA、MFCC、MFCC的1阶差分系数和Delta值的自适应特征参数提取方法;最后采用支持向量机(SVM)作为分类器进行故障识别,识别率达到97.2％.

Ideological and Political Education has become a necessary link and content requirement of classroom teaching in colleges and universities nowadays.For a long time,due to the professional characteristics of science and engineering and the export type of teachers’ own talents,there are many problems and difficulties in the mining of ideological and political elements in the curriculum of vehicle engineering.This paper focus on ideological and political elements in the curriculum of vehicle engineering as the object.By analyzing the special significance of classroom ideological and political to the specialty and the pain points in the mining process,combined with the characteristics of vehicle engineering specialty,this paper analyzes the path of classroom ideological and political element mining.Finally,taking the course of “Automotive Electrical and Electronic Technology” as an example,this paper carries out the practice of curriculum ideological and political element mining.

Research of the identification method on driver’s starting intention is very necessary. It can provide basis and support for subsequent vehicle clutch control, optimized shift curve and driving style recognition, and also contribute to vehicle assisted driving and intelligent driving. This paper presents a fuzzy inference-support vector machine (SVM) cascade algorithm to recognize the driver’s starting intentions, which can make up the low accuracy of fuzzy inference algorithm and overcome the difficulty to identify large samples of SVM algorithm. The proposed recognition method of driver’s starting intentions includes two-layer: the first layer is fuzzy inference layer while the second layer is SVM layer. At the same time, the fuzzy inference-SVM cascade algorithm is trained and tested with the sample data acquired from the actual vehicle. The experimental results show that the cascade algorithm has high recognition accuracy and moderate recognition time. Accordingly, the fuzzy inference-SVM cascade algorithm is an effective way to recognize driver’s starting intentions.

受外界环境噪声以及振噪耦合的影响,滚动轴承早期故障信号特征微弱,对其实现智能故障诊断具有挑战性.为了解决上述问题,提出一种基于改进最大相关峭度解卷积(improved maximum correlation kurtosis deconvolution,IMCKD)和多通道卷积神经网络(multi-channel convolution neural network,MCCNN)的智能故障诊断方法.首先利用萤火虫算法并行搜寻最大相关峭度解卷积的两个影响参数,对原始振动信号进行自适应滤波,得到诊断用的数据源;然后将其输入到MCCNN中进行特征学习,不断更新网络参数;最后将特征应用于分类器识别,从而实现滚动轴承的智能故障诊断.为了验证方法的可行性和有效性,利用滚动轴承故障模拟试验台采集的数据对该算法进行了验证.试验结果表明,该方法能准确、有效地对滚动轴承的故障类型进行分类,即使在强背景噪声下仍具有90％以上的故障识别率,并具有较好的稳定性和泛化能力.

文中基于滇西地震实验场1986—2014年间近30a的流动重力观测资料,研究了该地区重力场的长期变化背景.结果表明,重力场长期变化背景以负变化为主,年平均变化率约为-1.24×10-8 m/s2;空间分布上,重力场变化的剧烈程度与断裂带分布和历史强震活动存在密切关联,红河断裂北段、龙蟠-乔后断裂对本地区的重力场变化和地震活动分布具有明显的边界作用.结合地壳垂直形变、地壳结构和区域动力学背景对重力场变化机理进行分析,重力场整体负变化趋势可能反映下地壳物质流引起的地表隆升和地壳增厚,而重力场变化空间分布的细节则与区域动力学背景下具体断裂带的活动特性以及相关的局部性物质分布变化有关.

2019-12-26应城MS4.9地震前,襄阳重力台记录到第二类(double frequency,DF)地脉动信号持续增强异常.对比全球能量辐射模型(ASSM)及西太平洋台风数据发现,震前10 h左右的DF地脉动增强及优势频率增大的异常与西太平洋Phanfone台风靠近中国大陆这一远场信号源关系较弱,与本地未知近场同源同频信号有关.结合恩施重力台观测分析认为,该异常信号不属于应城地震前的慢地震事件.此外研究发现,长江中游巴东-秭归段和襄樊-广济断裂及其邻近区域5次MS 4.0以上地震中的4次都与七曜山-金佛山断裂附近发生的MS4.5～5.0地震呈成组活动,时间间隔为0.5～1 a,且发震时间都在DF地脉动信号的高噪声水平时段内(10月～次年3月).当恩施-襄阳DF地脉动信号基线的月中位数和众数值的差异持续4个月增加或处于高值时,研究区发生MS 4.0以上地震的可能性增加.对震前DF地脉动信号异常特征的总结可为预测未来长江中游巴东-秭归段和襄樊-广济断裂及其邻近区域MS 4.0以上地震的发生时段提供经验指标.

We applied a 2-dimensional, non-spectral technique to investigate the spatial variations of the lithospheric effective elastic thickness (T-e) in the eastern Bayan Har block and its adjacent areas. A program was designed to calculate the Moho flexure induced by topography loading, as well as the Bouguer gravity anomalies caused by that Moho flexure. The T-e values were determined by minimizing the RMS differences between the observed and calculated Bouguer gravity anomalies. The results suggested that T-e varies significantly from eastern Tibet to the Sichuan Basin. The eastern Bayan Har block and northern Sichuan-Yunnan block have low T-e values (0 < T-e < 20 km), indicating an easily deformable lithosphere. The Sichuan Basin has high T-e values (40 km < T-e < 100 km) acts as a rigid block that resists the eastward extrusion of plateau materials. The moderate T-e values (30 km < T-e < 40 km) found under the Longmen Shan fault belt indicate that the lithosphere of the Sichuan Basin plays an important role in supporting the Longmen Shan topography. The extremely low T-e (T-e < 10 km) observed in the seismogenic zone of the Ms7.0 Jiuzhaigou earthquake indicated that the topography is compensated locally, which is significantly different from the Longmen Shan topography. A banded region of relatively low T-e values (< 40 km) stretching from the northeastern Bayan Har block to the southwestern Ordos block sketches out an escape channel for plateau materials, and challenges the existing of crustal flow on the north side of the Sichuan Basin.

为研究重力时变因素对流动重力网数据处理结果的影响,利用动态和静态2种平差方法对南北地震带南段流动重力观测数据进行处理,并对2种方法的重力变化结果进行对比分析.结果表明:1)重力时变因素会导致静态平差方法计算的重力变化结果存在误差,对0.5a和1a时间尺度下的重力变化具有较为明显的影响,但对2a以上时间尺度下的重力变化影响较小,因此计算0.5a和1a时间尺度下的重力变化时宜采用动态平差方法;2)研究区内九寨沟7.0级、长宁6.0级和漾濞6.4级地震的发震地点与重力变化零值线具有较好的对应关系,重力变化图像可反映3次地震的发震背景.

为了在培养时代新人教学实践中讲好课程思政故事,文章分析了课程思政故事的使用特点,并针对故事效应多重性,展开分析了形成育人效果的故事激励效应及多重基础效应;探讨了表率效应对课程思政育人目标的作用,以及增强育人效果的故事效应,并针对故事效应的远期作用,调查分析了课程思政教学实践形成的育人印记;探讨了"自己人效应""拱道效应"的故事库闭环建设方法.

为评估全球潮汐模型在我国潮汐改正中的适用性,本文首先对10个重力站2016—2018年的观测数据进行了精度评定,而后基于均方根、和方根、纬度依赖关系以及重力残差等指标对7个全球潮汐模型进行了精度评定.结果表明:10个重力站的一些评价指标达到甚至超越了早期超导重力仪,例如M2波潮汐因子的中误差普遍小于0.00070,其中最高精度约为0.00014,5个主要潮波的稳定度均≤0.0015.在10个观测模型和7个全球潮汐模型中,DDW-NHi和M2001模型考虑了地球扁率的影响,基于这两个模型计算的和方根较其它模型所得的和方根均小,约为0.288×10-8 m/s2.基于最高精度的乌什站数据对Molodensky,DDW-NHi,M2001与观测模型的改正精度的对比显示,DDW-NHi模型改正计算的重力残差(±0.4×10-8—±1.0×10-8 m/s2)不及观测模型(±0.1×10-8—±0.5×10-8 m/s2),但依然优于M2001模型(±0.7×10-8—±1.4×10-8 m/s2),且DDW-NHi模型改正获得的残差比传统的Molodensky模型所得残差(±0.5×10-8—±1.5×10-8 m/s2)小1×10-8—2×10-8 m/s2.

文中利用小波多尺度分析方法对青藏高原东南缘WGM2012布格重力异常进行5阶分解,得到了该区域不同深度上的布格重力异常子集,并据此研究了该区域的地壳构造、物质运动及其孕震环境.结果表明:2、3阶小尺度重力异常反映了该地区的强震主要发生在高重力梯级带及活动地块边界上,对比分析各尺度重力异常,发现地震孕育不仅受控于中、上地壳的断裂地块构造,也与深部地壳的密度变化有关,这种地壳深、浅部相互作用的动力学过程可能是川滇地区地震孕育的重要条件;4阶中尺度重力异常显示松潘-甘孜地块的东南缘存在1个低布格重力异常圈闭,与巴颜喀拉地块地壳中存在着较厚的低速、低阻层的观测结果一致,推测可能与该地块东部岩石圈厚度大、下地壳温度较高、中下地壳部分岩体在高温下熔融有关.在攀枝花地区存在1个高布格重力异常圈闭,推测可能是在攀西古裂谷时期,深部高密度物质上涌过程中在中下地壳的物质残留所致;5阶大尺度重力异常显示在川滇菱形块体呈区域性负重力异常,为青藏高原东南缘"下地壳流"的存在提供支持证据.