The laser tracker is composed of an interferometric distance measurement system and an angular measurement system. The precision of distance measurement is far higher than that of angular measurement, thus the angular measurement precision directly dominates the overall precision performance of the laser tracker. This paper proposes a calibration scheme for the positional precision of the horizontal azimuth angle of the laser tracker using a vertical 552-tooth multi-tooth indexing table, and develops a harmonic analysis method based on the high-precision multi-tooth indexing table. Fourier series modeling is adopted to conduct harmonic analysis of angular measurement errors. Experimental results show that the forward horizontal angle measurement precision is $3.94^{\prime \prime}$ before compensation and $1.26^{\prime \prime}$ after compensation, while the reverse horizontal angle measurement precision is $3.89^{\prime \prime}$ before compensation and $1.18^{\prime \prime}$ after compensation, representing a 69 % precision improvement. The results verify that the proposed model can effectively compensate angular measurement errors, and provides a valuable reference for error compensation of similar angular measurement systems.

Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values.

The multi-vision defect sensing system, lining composed primarily of IRT and RGB cameras, allows for automatic identification and extraction of small surface ailments, greatly enhancing detection efficiency. However, the presence of various issues like train vibration, inconsistent lighting, fluctuations in temperature and humidity leads to the images showing inadequate uniformity in illumination, blurriness, and a decrease in the level of detail. The above issues have led to unsatisfactory fusion processing results for multiple visual images and increased missed detection rates. To address the above-mentioned issue, multi visual images fusion approach for subway tunnel defects based on saliency optimization of pixel level defect image features is proposed. The approach initially analyses the train's motion status and image blurring conditions. It then eliminates the dynamic blurring in the image. Secondly, Image weights are allocated based on the uniformity of visible light image illumination in the tunnel, as well as real-time temperature and humidity. Finally, image feature extraction and fusion are performed by a U-Net network that integrates channel attention mechanisms. The entire experiment was carried out on a dataset consisting of leakage data from the tunnel lining of Shanghai Metro and tunnel defect data from Beijing Metro. The experimental results demonstrate that this approach improves the image pixel value variation rate by 39.7%, enhances the edge quality by 23%, and outperforms similar approach in terms of average gradient, gradient quality, and sum of difference correlation with improvements of 15.9%, 7.3%, and 26.6% respectively.

Objective In high-energy physics research and industrial applications,measurement tasks in particle accelerator radiation zones are rendered extremely difficult due to radiation hazards,making direct human measurement infeasible.Additionally,since accelerators are typically installed in tunnels,space constraints and obstructions often cause many measurement target points to be hidden from view.To overcome these challenges,a rod-waving measurement technique based on laser trackers has been proposed to accurately measure the position of hidden points. Methods The rod-waving measurement technique utilizes a laser tracker and a specially designed fiducial bar for hidden point measurement to perform spatial coordinate measurements.The fiducial bar for hidden point measurement is equipped with a target sphere.By waving the rod,the laser tracker acquires spatial coordinate data of the target sphere at different positions.Through spherical fitting of these data points,the three-dimensional coordinates of the hidden point can be determined.To address the issue of prolonged computation time associated with traditional spherical fitting algorithms,the algebraic spherical fitting algorithm was proposed.This new algorithm maintains accuracy while significantly reducing measurement time,thereby reducing the radiation exposure to measurement personnel.Subsequently,simulation experiments were conducted to test the impact of changing the length of the fiducial bar for hidden point measurement on fitting results,as well as the effects of varying zenith angles and spherical coverage ranges on the fitting results. Results and Discussions The proposed algebraic spherical fitting algorithm requires only 18.85％of the computation time compared to the Gauss-Newton fitting method(Tab.l).In the simulation experiments,it was found that increasing the reference rod length from 0.5 meters to 3 meters resulted in a root mean square error(RMSE)of the fitting deviation changing by only 0.42 micrometers,indicating almost no impact on the fitting results(Tab.2).Additionally,it was discovered that under both global coverage(the horizontal angle is 0°to 360°)and hemispherical coverage(the horizontal angle is 180°to 360°)conditions,the RMSE of the fitting deviation remained consistently below 30 micrometers when the zenith angle was varied(Tab.4).Finally,practical experiments were conducted at the Beijing High Energy Photo Source laboratory using the AT960 laser tracker to measure four hidden points.The RMS of the fitting deviation for the measured data was 25.53 micrometers,meeting the precision requirement of being within 30 micrometers(Tab.6). Conclusions The combination of laser trackers with the rod-waving method has demonstrated significant advantages in measuring hidden points within the radiation areas of particle accelerators.This approach overcomes spatial constraints and line-of-sight obstructions,offering extremely high measurement accuracy and reliability.The proposed algebraic spherical fitting algorithm significantly outperforms the commonly used Gauss-Newton method in terms of computation time,requiring only 18.85％of the latter's time.This not only enhances measurement efficiency but also substantially reduces the duration of radiation exposure for operators.Simulation experiments revealed that the algorithm is highly stable against changes in the fiducial bar length,with the root mean square error(RMSE)of the fitting deviation changing by only 0.42 micrometers.The experiments also showed that whether under global or hemispherical coverage,the RMS of the fitting deviation remained consistently below 30 micrometers when the zenith angle was varied,demonstrating the algorithm's reliability.Ultimately,through field measurements,an RMS of the fitting deviation of 25.53 micrometers was achieved,successfully meeting the precision requirement of being within 30 micrometers.This provides an efficient and reliable solution for precise hidden point measurement in special environments such as particle accelerator radiation areas.

The Ground-Based SAR Dynamic Measurement System is capable of acquiring the time-displacement sequence of ancient pagodas, enabling effective assessment of their structural health. To extract the instantaneous resonance frequency of the pagodas from signals containing noise, the conventional Hilbert-Huang Transform often encounters mode mixing issues, leading to the influence of false components on the measurement accuracy. Therefore, this study employs a non-interference method based on normal time-frequency transform Theory to monitor the pagodas.The essence of the normal time-frequency transform Theory lies in a kind of linear filter, which exhibits strong anti-interference capability in extracting periodic signal components. This approach yields time-varying, unbiased harmonic instantaneous amplitude, frequency, and phase. The monitoring and analysis of the ground-based SAR dynamic detection signals from the Rangdeng Pagoda in Tongzhou District, Beijing, were conducted in this study. Experimental results indicate that the amplitude and frequency derived from the normal time-frequency transform Theory better match the actual vibration characteristics of the pagoda. Additionally, this method involves lower computational complexity compared to conventional approaches and places greater emphasis on continuous time-frequency analysis. These findings provide scientifically reasonable foundational data for analyzing the architectural characteristics and safety assessment of ancient pagodas.

In urban pavement crack detection, the single detection platform with visible light sensors is mostly used for detection. Visible light sensors are highly susceptible to lighting conditions. In low-light environments or areas with shadows over the cracks, this may result in false positives or missed detections of pavement cracks. The paper utilizes an image acquisition system that integrates multiple detection platforms, including visible light and infrared sensors, to enhance both the efficiency and accuracy of pavement crack detection. We propose an automatic pavement crack detection approach that uses the similarities and differences between visible light images and infrared image features (MSFSD) to achieve all-weather, real-time detection of pavement cracks. MSFSD mainly includes multi-source image fusion and pixel-level crack detection. In multi-source image fusion, a generative adversarial network (C-GAN) with skip connections and dilated convolutional blocks (CRes2Net blocks) is constructed. The structural similarity index (SSIM) function and the sum of the correlations of differences (SCD) function are used as the loss function of the generator, allowing the fusion image to retain more details and edge information in the source image. In pixel-level crack detection, a crack detection model is constructed, and CRes2Net is used as the core network to obtain pixel-level labels and perform semantic segmentation. It is verified by experiments that MSFSD achieves 85.1% precision and 88.4% recall in different types of crack detection. The MIoU and detection efficiency of MSFSD are 9% and 48% higher than other methods, respectively.

Objective China is currently planning on building several 4th-generation light source facilities that are larger in scale and have highly complex equipment. Therefore, higher alignment and global control point accuracies are required to ensure the robust and stable operation of the facility. The accuracy requirement has reached the measurement accuracy limit of laser trackers. It is of great significance for the construction and development of 4th-generation light source facilities to explore high-precision and stable data processing methods for laser trackers. In this study, an improvement on the existing adjustment method for the control network,which can retain the extremely high accuracy of laser tracking data while avoid some small and unidentifiable gross errors that will affect the overall processing results, is proposed.Methods Based on robust estimations, an adaptive weighting strategy for rank-defect weighted 3D bundle adjustment that can adaptively adjust the weight matrix of the observed values and weight matrix of the datum equation to achieve robust estimation is employed in this study. Simultaneously, due to data processing using routine robust estimation without any gross error, gross error misjudgment can also be avoided. First, after the first iteration of adjustment, the sum of the residuals less than or equal to the product of the mean value of the two selected weight thresholds and mean square error is calculated. If this value is less than half of the total numbers of observations, the thresholds are considered to be set too low, and vice versa. The two thresholds are corrected proportionally by multiplying them with a specific correction factor when the threshold is high and dividing them by the same factor when the threshold is low. Along with the iteration process, the selected weight thresholds are dynamically and adaptively adjusted to allow the dynamic and adaptive adjustment of the weight of the observed values. This process ends when the changes in the threshold are less than 0. 001. Simultaneously, according to the corresponding relationship between the parameters of the datum equation and observed values, the sum of the weight values of all the observed values corresponding to each point in the parameters is calculated and divided by the numbers of respective observed values to obtain the weight matrix of the parameters of the datum equation. Thus,the adaptive adjustment of the weight matrix of the reference equation is realized（Fig. 1） owing to the progress of adjustment iterations and the adaptive adjustment of the weight matrix of the observed values.Results and Discussions The robustness and stability of the self-adaptive weighted bundle adjustment are verified using simulation data. Based on the accelerator alignment measurement scene and characteristics of the laser tracker, the alignment control point measurement data are simulated（Figs. 2-4）, and the minimum norm adjustment method, the unified spatial metrology network（USMN） of SpatialAnalyzer software, and the method used in this study are used to adjust the simulated data. The error range and change trend of the treatment results are close, which conforms to the actual measurement experience（Fig. 5）. The accuracy of the proposed method is basically equivalent to that of the USMN, with a deviation of about 0. 002 mm（Table 1）. To verify the ability to handle gross errors, a gross error of 1 mm is added to a point observed at the fifth station to simulate the gross error observation in the measurement process. This method maintains the processing stability in all three directions, avoids the influence of gross error observation, and has an overall accuracy equal to that of the USMN. When conducting data processing for two groups, the weight selection thresholds decrease proportionally with increased numbers of iterations and tend to be stable after eight iterations.Moreover, after the addition of gross error observations, the threshold must be decreased to ensure the stability of the selected observation range（Fig. 7）. The measured data are processed in the Hefei Advanced Light Facility（HALF）, and the results obtained are similar to those of mature commercial software（Figs. 9 and 10）. The deviation is approximately 0. 02 mm, which is within the accuracy requirements of the 4th-generation light facilities.Conclusions In this study, an adaptive weighting method for adjusting the weighted rank-defect bundle is proposed. Based on the weight selection and actual accuracy of the observed values, the weight selection thresholds are dynamically and adaptively adjusted to realize the adaptive adjustment of the weight matrix of the observed values. Simultaneously, the parameters of the weight matrix of the datum equation are adaptively adjusted according to the weight matrix of the observed values. In the adjustment process of the high-precision data of laser tracker and data with a slight gross error, the observed values are not misjudged as gross error during processing and the influence of the gross error can be simultaneously avoided. The simulation data and measured data show that the proposed method has a stability and robustness of adjustment similar to those of commercial software processing and can be a basic reference for studies on accelerator alignment.

Gesture recognition has always been one of the important research directions in the field of computer vision. The dynamic gesture has the problems of complex backgrounds and many interference factors. The gesture recognition model based on deep learning usually has high computational cost and poor real-time performance. In addition, deep learning models are limited to recognizing existing categories in the training set and their performance largely depends on the amount of labeled data. To address the above problems, this paper presents a dynamic gesture recognition method named 3SCKI based on a three-stream coordinate attention (CA) network, knowledge distillation, and image-text contrastive learning. Specifically, 1) CA is utilized for feature fusion to make the model focus more on target gestures and reduce background interference, 2) traditional knowledge distillation loss is improved to reduce the amount of calculation and improve the real-time performance. Specifically, the guidance function is added to make the student network only learn the classification probability correctly identified by the teacher network, and 3) multi-granularity context prompt template integration method is proposed to construct an improved CLIP visual language model MG-CLIP. It aligns text and visual concepts from the image level to the object level to the part level. Through comparative learning of image features and text features, gesture classification is performed, enabling the model to identify image categories that have not appeared during the training phase. The proposed method is evaluated on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD). The results show that our proposed method can obtain recognition rates of 65.87% on the validation set of IsoGD. For single mode data, 3SCKI can obtain the state-of-the-art recognition accuracy on RGB, Depth, and Optical Flow data (61.22%, 58.84%, and 50.30% of the validation set of IsoGD, respectively).

With the acceleration of urbanization, the Chinese government has placed increasing importance on the preservation of historical and cultural sites. Recently, the Beijing municipal government has been working on the World Heritage application for the Beijing Central Axis for several years and has set a clear goal to formally apply for World Heritage status before 2025. Currently, the project has entered the final sprint stage (Zhang Yongjun, 2018(12):105-107).The Beijing Central Axis, as an important carrier of natural and cultural heritage, embodies a unique cultural heritage. As "Beijing's unique and magnificent order," the Central Axis is not just a single line but a corridor. However, during the preparation for the World Heritage application, there have been different understandings of the concept of the Central Axis, and the selection of heritage sites has not been comprehensive and systematic. Therefore, in studying the issues related to the Central Axis, utilizing POI data to conduct kernel density analysis and linear regression analysis on the cultural landmarks within Beijing can help infer the location of the Central Axis in different periods. By employing spatial analysis methods using ArcGIS, a systematic study of the characteristics of the Beijing Central Axis can be conducted, summarizing its distribution and spatial form characteristics across different dynasties. This research can provide valuable reference for future in-depth studies, conservation and utilization efforts, and the World Heritage application of the Central Axis.

In practical engineering, precise triangulation elevation measurements are required for the construction of large-span bridges across rivers. During this process, the atmospheric conditions above the water area exhibit complex and varying states in different times and spatial locations. The collection of environmental parameters such as temperature and pressure in the airspace above the water area is extremely difficult, making it impossible to establish a stable spatial atmospheric refraction coefficient model that satisfies high-precision triangulation elevation measurements. In this paper, based on the method of mathematical statistics, ignoring the atmospheric environment parameters, a non-uniform atmospheric refraction correction model is constructed according to the variation law of the atmospheric refraction coefficient in a certain time and space. Through combinatorial optimization and iterative calculation, the interference of atmospheric refraction coefficient in unstable space is solved to a certain extent. This method does not need to solve the problem of collecting atmospheric environmental parameters, and continuously adjusts the atmospheric refraction coefficient suitable for different time and space through program design. This method is effectively applied in a large-scale cross-river bridge under construction. At the same time, the algorithm design has strong practicability and flexibility, which greatly improves the accuracy of triangular elevation measurement in the unstable space of cross-river monitoring.

The high-precision and high spatio-temporal resolution settlement time series data generated by integrating Global Navigation Satellite System (GNSS) and Interferometric Synthetic-Aperture Radar (InSAR) data are of great value for the safe operation of high-speed railways. The GNSS monitoring stations and InSAR monitoring area present zonal distribution along the high-speed railways, which causes the spatial matrix of the GNSS and InSAR data fusion model to be an ill-conditioned matrix. The inverse of the ill-conditioned space matrix is unstable, and it is difficult to estimate the optimal parameter value of the fusion model, which leads to low accuracy and large fluctuations in the fusion of GNSS and InSAR data. We propose a spatio-temporal filter fusion (STFF) method to solve the influence of the ill-conditioned space matrix on the fusion of GNSS and InSAR data, which realizes the high-precision fusion of GNSS and InSAR data along the high-speed railway. First, we propose the kriging model of the adaptive spectral correction method to construct the spatial model of GNSS and InSAR data, avoiding the generation of the ill-conditioned space matrix. Second, iterative almost unbiased estimation is used to obtain the weights of GNSS and InSAR data, avoiding the occurrence of negative variance components. The experimental data are fused using STFF, the spatio-temporal Kalman filter (STKF) method, and the spatio-temporal random effect (STRE) fusion method, respectively. The research results show that the comprehensive performance of STFF is significantly better than that of STKF and STRE. In the spatial domain, the root mean square error (RMSE) reduced by 47% and 46% compared with STKF and STRE, respectively, and the structural similarity index increased by 22% and 16% compared with STKF and STRE, respectively. In the time domain, the RMSE reduced by 29% and 34% compared with STKF and STRE, respectively.

In view of the low efficiency of manual translation of place names and the lack of research on automatic transliteration of Spanish proper names, this paper proposed an automatic transliteration method of Spanish proper names based on prior knowledge by analyzing the grammatical rules and pronunciation characteristics of Spanish. By constructing the priori knowledge based of Spanish place names, the priori knowledge was divided into instance priori knowledge and rule priori knowledge, and the transliteration of Spanish place names was divided into instance priori knowledge and rule priori knowledge. The method was applied to the transliteration of Spanish place names, and the results were compared with the translation software. The transliteration results of the method conformed to the transliteration rules, and were more standardized and accurate, which proved the feasibility of the method.

When designing a multi-vision stereo vision network, the camera’s positional information has a critical impact on the measurement accuracy. In order to solve the problem of optimizing the camera pose during network design, in this paper, we split the multi-vision stereo vision system into binocular stereo vision system. Based on the mathematical model of binocular stereo vision system, the error function of the positional parameters is constructed. By simulation analyzing the function models the field of view angle, the angle between the optical axis and the baseline, the baseline length, the law of the influence of each parameter on the measurement accuracy and the optimal range of values are obtained. Then we compare the actual measurement results with the simulation analysis results. The results show that as the field of view angle, optical axis and baseline angle, baseline length increases, the measurement error first decreases and then increases, and the increase is gradually accelerated. When each parameter is located in the optimal range, the measurement error meets the precision engineering measurement accuracy requirement of 0.05mm, and the minimum error is reduced by 0.114mm, 0.120mm and 0.061mm compared to the maximum error. In the network design, the optimization of the pose parameters can effectively obtain better pose information of the camera and thus improve the accuracy of the measurement.

鉴于传统的测绘工程专业工程思维教学体系不能满足创新型人才培养新要求,提出工程思维向工程及产品思维转变的思维培养模式,拓展专业内涵外延的测绘导航方向,围绕"课程体系、教材建设、实践教学、创新创业"改革人才培养方法,构建一种兼顾工程及产品思维的智能导航实验班创新型人才培养体系.近3年的教学实践表明:兼顾工程及产品思维的创新型人才培养体系在教学质量提升、教学与实践体系推广、教材建设应用、学科发展与专业建设的教学应用成效显著,为智能导航实验班创新型拔尖人才产出奠定坚实基础,具有重要的指导意义和推广价值.

结合某深基坑支护工程实例,为有效避开基坑临近地下管线,解决阴角处锚杆间的相交等问题,运用Sketchup软件建立等比例三维模型,并不断根据现场进度及施工情况微调模型,形成可用于指导现场施工的三维可视化控制技术.案例分析表明,三维可视化技术用于基坑支护工程实践指导,具有较强的可行性与先进性,对于提升施工进度,降低施工风险,节约财力、物力等具有重要促进作用.

In the precision engineering measurement, the total station is often used for free station measurement, and improving the accuracy of stitching the measurement data of each station is the premise of widely used total station measurement by free setting. This paper is based on the quadratic method to study the stitching method of data measured by total station with free set-up stations. The total station data is obtained by free setting based on the quaternion method, and the method of stitching the data is investigated. Taking the deformation monitoring of the wooden tower in Yingxian County as a case study, a total station was measured by free setting, and the quadratic algorithm was used to realize the coordinate conversion between stations by point name matching, which is comparable to the stitching results of Leica's Cyclone software. In this paper, the improved quaternion algorithm is applied to the free station splicing of total station with the features of fast matching, coarse difference detection and rejection, optimization of redundant observation of the same name point splicing algorithm, inclusion of splicing error correction, and high splicing accuracy.

利用2016年中国大陆构造环境监测网络的GNSS数据开展水汽短时频域特征研究,按气候类型将中国大陆地区划分为5个区域,并在每个区域中随机抽取若干个站点采用快速傅里叶变换方法进行分析,提取不同季节的GNSS水汽周期特征.结果表明,各类站点的水汽频域特征存在明显的区域性变化和季节性差异;高原山地气候、热带季风气候和亚热带季风气候类型的GNSS站点的周期性变化显著;热带季风地区、亚热带季风地区及沿海地区水汽振幅较大,高原山地和温带大陆地区水汽振幅较小.

为了提高超高精度、大尺寸三维控制网布设中点位交会的测量精度,利用激光跟踪仪测量系统建立高精度三维控制网,点位交会精度受测量点空间几何分布的影响,构建了基于激光跟踪仪测量系统的加权几何精度因子模型,根据测量点的点位分布和数量,从水平面、高程等方面对多方向多距离的空间点位交会精度进行了比较分析.实验结果表明:三点交会测量时,测量点距离越短,天顶距间差值越大,点位交会精度越高;在已知四面体的内部插入测量点,测量点距离越短,天顶距越大,点位交会精度越高;测量点为6个时,点位交会精度能很好地满足测量要求,且精度趋于稳定,随测量点的增加,精度提升缓慢.该方法可选取有利的点位位置,有效地提高点位交会精度,在实际工程应用中具有较高的应用价值.

对初步建设完成建筑物的墙面平整度的检测是后期进行墙面装饰必不可少的内容.由于建筑物的高度以及墙面面积,传统的检测法工作量变大、复杂以及成本较高.本文利用三维激光扫描技术采集墙面数据,采用最小二乘法进行墙面数据的拟合,通过计算点到拟合面的距离得出墙面的整体平整度,并且与全站仪的检测结果进行对比.结果 表明,利用三维激光扫描技术进行墙面的平整度检测精度可以达到要求,并且与传统方法相比速度快、成本低以及更加方便.

To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper. These problems are caused by low precision parameter design in various rainwater control measures such as the diameter of the rainwater pipe network and the green roof area ratio. This system is to be combined with the newly built rainwater pipe control optimization design project of China International Airport in Daxing District of Beijing, China. Through the optimization adjustment of the pipe network parameters such as the diameter of the rainwater pipe network, the slope of the pipeline, and the green infrastructure (GI) parameters such as the sinking green area and the green roof area, reasonable control of airport rainfall and the construction of sustainable drainage systems can be achieved. This research indicates that compared with the result of the drainage design under the initial value of the parameter, the green roof model and the conceptual model of the mesoscale sustainable drainage system, in the case of a hundred-year torrential rainstorm, the overflow rate of pipe network inspection wells has reduced by 36% to 67.5%, the efficiency of drainage has increased by 26.3% to 61.7%, which achieves the requirements for reasonable control of airport rainwater and building a sponge airport and a sustainable drainage system.