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Laser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.

First, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.

The experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.

A linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

External institutional policy and its impact on corporate social responsibility (CSR) have been widely discussed by researchers, but its effect still remains controversial. This study aims to use the minimum wage policy as an illustrative example to analyze its impact on the corporate social responsibility (CSR) of tourist enterprises. Furthermore, the research seeks to examine the boundary conditions that influence the minimum wage’s effect on CSR.

This paper takes the data of 42 listed tourism companies from 2010 to 2020 in China as samples and uses the mixed OLS regression method and the fixed effects panel model to examine the effect of the minimum wage on CSR.

Findings show that increasing wages has a significantly negative impact on their total CSR investment. Also, low-operating-capacity enterprises and private enterprises will react more adversely when faced with increasing minimum wages. And found that the increase of minimum wage has no significant negative impact on the strategic social responsibility of tourism enterprises; however, it has a significantly negative impact on their tactical social responsibility. In addition, as far as employees’ rights and interests are concerned, the minimum wage increase has effectively increased employee salaries, but the nonsalary benefits of the employees have significantly decreased.

The contribution of this paper not only expands the research on the antecedents and boundary mechanisms of CSR but also clarifies the specific effect of the rise of the minimum wage on corporate social responsibility; it further deepens the impact of institutional policy factors on CSR, which also opens new perspectives for policy evaluation and provides a theoretical basis for government policymakers.

Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.

This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.

Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.

The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

The rapid development of Web 2.0 and social media enables the rise of crowdsourcing. Crowdsourcing contest is a typical case of crowdsourcing and has been adopted by many organisations for business solution and decision making. From a participant's perspective, it is interesting to explore what motivates people to participate in crowdsourcing contest. The purpose of this paper is to investigate the category of motivation based on self-determination theory and synthesises various motivation factors in crowdsourcing contest. Meanwhile, perceived motivational affordances and task granularity are also examined as the moderate constructs.

The paper builds a conceptual model to illustrate the relationships between various motivations (extrinsic and intrinsic) and participation effort under the moderating of perceived motivational affordances and task granularity. An empirical study is conducted to test the research model by surveying the Chinese participants of crowdsourcing contest.

The results show that various motivations might play different roles in relating to participation effort expended in the crowdsourcing contest. Moreover, task granularity may positively moderate the relationship between external motivation and participation effort. The results also show that supporting of a participant's perceived motivational affordances might strengthen the relationship between the individual's motivation with an internal focus (intrinsic, integrated, identified and introjected motivation) and participation effort.

Overall, the research has some conceptual and theoretical implications to the literature. This study synthesises various motivation factors identified by previous studies in crowdsourcing projects or communities as a form of motivation spectrum, namely external, introjected, identified, integrated and intrinsic motivation, which contributes to the motivation literatures. Meanwhile, the findings indicate that various motivations might play different roles in relating to participation effort expended in the crowdsourcing contest. Also, the study theoretically extends the crowdsourcing participation research to incorporate the effects of perceived motivational affordances in crowdsourcing contest. In addition, the study may yield some practical implications for sponsors, managers and designers in crowdsourcing contest.

The purpose of this paper is to overcome the shortcomings of existing multivariate grey incidence models that cannot analyze the similarity of behavior matrixes.

First, the feasibility of using gradient to measure the similarity of continuous functions is analyzed theoretically and intuitively. Then, a grey incidence degree is constructed for multivariable continuous functions. The model employs the gradient to measure the local similarity, as incidence coefficient function, of two functions, and combines local similarity into global similarity, as grey incidence degree by double integral. Third, the gradient incidence degree model for behavior matrix is proposed by discretizing the continuous models. Furthermore, the properties and satisfaction of grey incidence atom of the proposed model are research, respectively. Finally, a financial case is studied to examine the validity of the model.

The proposed model satisfies properties of invariance under mean value transformation, multiple transformation and linear transformation, which proves it is a model constructed from similarity perspective. Meanwhile, the case study shows that proposed model performs effectively.

The method proposed in the paper could be used in financial multivariable time series clustering, personalized recommendation in e-commerce, etc., when the behavior matrixes need to be analyzed from trend similarity perspective.

It will promote the accuracy of multivariate grey incidence model.

The study aims to understand how cultural route heritage is conceptualized and managed in China by systematically reviewing the research literature on Chinese cultural route heritage (CRH). The study intends to inspire further discussion on the theoretical and practical development of cultural routes since the development is still at a liminal stage in China.

A total of 253 research articles related to Chinese cultural rote heritage from major Chinese and English research databases China National Knowledge Infrastructure (CNKI), Web of Science (WOS) and Scopus have been comprehensively identified and reviewed for the purpose of the study.

Four major themes of research on Chinese CRH have been identified: conceptual evaluation, list of the routes and characteristics of the routes, conservation and utilization. The results revealed that China has very rich resources in CRH, many of which were formed a long time ago, which exist across vast geographic regions and have assumed multiple functions and undergone dynamic reciprocal exchanges among diverse cultures and ethnicities.

The paper summarizes some major obstacles faced by CRH in China and proposes a strategic model to address the need for a more sustainable development of CRH in the Chinese context.

The paper offers a comprehensive overview of CRH in China and discusses practical issues in management and development of heritage great in size, number and complexity.

Academic social (question and answer) Q&A sites are now utilised by millions of scholars and researchers for seeking and sharing discipline-specific information. However, little is known about the factors that can affect their votes on the quality of an answer, nor how the discipline might influence these factors. The paper aims to discuss this issue.

Using 1,021 answers collected over three disciplines (library and information services, history of art, and astrophysics) in ResearchGate, statistical analysis is performed to identify the characteristics of high-quality academic answers, and comparisons were made across the three disciplines. In particular, two major categories of characteristics of the answer provider and answer content were extracted and examined.

The results reveal that high-quality answers on academic social Q&A sites tend to possess two characteristics: first, they are provided by scholars with higher academic reputations (e.g. more followers, etc.); and second, they provide objective information (e.g. longer answer with fewer subjective opinions). However, the impact of these factors varies across disciplines, e.g., objectivity is more favourable in physics than in other disciplines.

The study is envisioned to help academic Q&A sites to select and recommend high-quality answers across different disciplines, especially in a cold-start scenario where the answer has not received enough judgements from peers.

Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media.

Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately.

Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity.

Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

In general, the existing compressor design methods require abundant knowledge and inspiration. The purpose of this study is to identify an intellectual design optimization method that enables a machine to learn how to design it.

The airfoil design process was solved using the reinforcement learning (RL) method. An intellectual method based on a modified deep deterministic policy gradient (DDPG) algorithm was implemented. The new method was applied to agents to learn the design policy under dynamic constraints. The agents explored the design space with the help of a surrogate model and airfoil parameterization.

The agents successfully learned to design the airfoils. The loss coefficients of a controlled diffusion airfoil improved by 1.25% and 3.23% in the two- and four-dimensional design spaces, respectively. The agents successfully learned to design under various constraints. Additionally, the modified DDPG method was compared with a genetic algorithm optimizer, verifying that the former was one to two orders of magnitude faster in policy searching. The NACA65 airfoil was redesigned to verify the generalization.

It is feasible to consider the compressor design as an RL problem. Trained agents can determine and record the design policy and adapt it to different initiations and dynamic constraints. More intelligence is demonstrated than when traditional optimization methods are used. This methodology represents a new, small step toward the intelligent design of compressors.