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Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method.

We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method.

To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors.

The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light.

The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

Laser absolute distance measurement has the characteristics of high precision, wide range and non-contact. In laser ranging system, tracking and aiming measurement point is the precondition of automatic measurement. To solve this problem, this paper aims to propose a novel method.

For the central point of the hollow angle coupled mirror, this paper proposes a method based on correlation filtering and ellipse fitting. For non-cooperative target points, this paper proposes an extraction method based on correlation filtering and feature matching. Finally, a visual tracking and aiming system was constructed by combining the two-axis turntable, and experiments were carried out.

The target tracking algorithm has an accuracy of 91.15% and a speed of 19.5 frames per second. The algorithm can adapt to the change of target scale and short-term occlusion. The mean error and standard deviation of the center point extraction of the hollow Angle coupling mirror are 0.20 and 0.09 mm. The mean error and standard deviation of feature points matching for non-cooperative target were 0.06 mm and 0.16 mm. The visual tracking and aiming system can track a target running at a speed of 0.7 m/s, aiming error mean is 1.74 pixels and standard deviation is 0.67 pixel.

The results show that this method can achieve fast and high precision target tracking and aiming and has great application value in laser ranging.

The purpose of this paper is to properly calibrate the laser tracking system (LTS) prior to using it for metrology and improving the measuring accuracy of LTS.

A kinematics model that describes not only the motion but also geometric variations of LTS is developed. Effects of tracking mirror mechanism dimension errors on measured coordinates and target reflector alignment errors on sensor reading are investigated.

Through error analysis of the proposed model, it is claimed that gimbals axis misalignments and tracking mirror center offset are the key contributors to measure errors of LTS. Intensive simulation studies are conducted to check the validity of the theoretical results and various practical issues are also explored in the simulations. The simulation results demonstrate that under realistic conditions the 10‐parameter model is the minimal and complete model.

This model, together with its error model which is also presented in this paper, can be used for design, calibration, and control of LTS.

This paper develops a kinematics model that describes not only the motion but also geometric variations of LTS, and demonstrates that gimbals axis misalignments and tracking mirror center offset is the key contributor to measuring errors of LTS.

This article gives a brief introduction to computer applications in information retrieval, library management and Chinese character processing in some information organisations in China. It outlines the history, present status and future trends of these aspects.

Reductions in emissions intensity have been expressed in commitments of many countries’ intended nationally determined contribution. Energy structure adjustment is one of the main approaches to reduce carbon emissions. This paper aims to study the causal relationship between carbon emission intensity and energy consumption structure in China based on path analysis.

After data collection, this paper performs correlation analysis, regression and path analysis.

Correlation results display clear collinearity among energy structure variables. Regression finds that coal, oil, natural gas and technology can be used as indicators for carbon intensity while primary electricity has been excluded. Path analysis shows that coal had the largest direct and positive impact on emission intensity. Natural gas had a positive direct and negative indirect effect through its negative relationship with coal on emission intensity. Technology has the largest negative elasticity while all fossil energies are positive. Results indicate a negative effect of energy structure adjustment on China’s national carbon intensity.

Given the major role of China in global climate change mitigation, significant future reductions in China’s CO₂ emissions will require transformation toward low-carbon energy systems. Considering the important role in mitigating global climate change, China needs to transition toward a low-carbon energy system to significantly reduce its carbon intensity in the future.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.