Ding-jian Huang, Li-gang Yao, Wen-jian Li and Jun Zhang
The purpose of this research is to achieve a novel magnetic nutation drive for an industry robotic wrist reducer.
Abstract
Purpose
The purpose of this research is to achieve a novel magnetic nutation drive for an industry robotic wrist reducer.
Design/methodology/approach
A novel magnetic nutation drive is proposed, and the structure and principle of the designed magnetic nutation drive are described in this study. Three-dimensional finite element analysis is used to compute the magnetic and torque of the magnetic nutation drive. Furthermore, a prototype of this novel magnetic nutation drive device is developed with 3D printing technology and tested to verify the feasibility of the proposed structure and principle.
Findings
The simulation and experimental results indicated that the proposed magnetic nutation drive device could meet the desired specifications, and that this novel magnetic nutation drive device successfully realized the non-contact transmission ratio of 105:1 required for a robotic wrist reducer.
Practical implications
This novel magnetic nutation drive is low-cost and easy to make and use, and which provides the non-contact transmission ratio of 105:1 required for a robotic wrist reducer.
Originality/value
For the first time, this research applies the permanent magnet drive technology to nutation drive and puts forward a new non-contact nutation drive mode. The novel drive mode can solve some problems of the traditional mechanical contact nutation drive, such as vibration, friction loss, mechanical fatigue and necessity of lubrication. The proposed non-contact nutation drive device can achieve a high reduction ratio with compact structure and can be suitable for industry application.
Details
Keywords
Chuanming Yu, Zhengang Zhang, Lu An and Gang Li
In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of…
Abstract
Purpose
In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.
Design/methodology/approach
The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.
Findings
The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.
Originality/value
The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.
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Keywords
Xinyue Zhou, Zhilin Yang, Michael R. Hyman, Gang Li and Ziaul Haque Munim
Miaomiao Chen, Lu An, Gang Li and Chuanming Yu
The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public…
Abstract
Purpose
The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public events.
Design/methodology/approach
This study constructed the severity assessment system of public events from the dimensions of the netizens' role, the Internet media's role, the spread of public events and the attitudes and feelings of netizens. The method of analyzing the influence tendency of the public event severity indicators was proposed. A total of 1,107,308 microblogging entries regarding four public events were investigated. The severity of public events was divided into four levels.
Findings
It is found that serious public events have higher indicator values than medium level events on the microblogging platform. A quantitative severity classification standard for public events was established and the early warning mechanism of public events was built.
Research limitations/implications
Microblogging and other social media platforms provide rich clues for the real-time study and judgment of public events. This study only investigated the Weibo platform as the data source. Other social media platforms can also be considered in future.
Originality/value
Different from the ex-post evaluation method of judging the severity of public events based on their physical loss, this study constructed a quantitative method to dynamically determine the severity of public events according to the clues reflected by social media. The results can help the emergency management departments judge the severity of public events objectively and reduce the subjective negligence and misjudgment.
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Keywords
Lu An, Yan Shen, Gang Li and Chuanming Yu
Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can…
Abstract
Purpose
Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.
Design/methodology/approach
This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.
Findings
The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.
Originality/value
The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.
Details
Keywords
Qiujun Lan, Haojie Ma and Gang Li
Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot…
Abstract
Purpose
Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot of informal expressions, which lead to high computational complexity.
Design/methodology/approach
A method based on Chinese characters instead of words is proposed. This method represents the text into a fixed length vector and introduces the chi-square statistic to measure the categorical sentiment score of a Chinese character. Based on these, the sentiment identification could be accomplished through four main steps.
Findings
Experiments on corpus with various themes indicate that the performance of proposed method is a little bit worse than existing Chinese words-based methods on most texts, but with improved performance on short and informal texts. Especially, the computation complexity of the proposed method is far better than words-based methods.
Originality/value
The proposed method exploits the property of Chinese characters being a linguistic unit with semantic information. Contrasting to word-based methods, the computational efficiency of this method is significantly improved at slight loss of accuracy. It is more sententious and cuts off the problems resulted from preparing predefined dictionaries and various data preprocessing.
Details
Keywords
Ruilian Han, Lu An, Wei Zhou and Gang Li
Social media platforms (SMPs) are pivotal in information dissemination and molding public opinion. Various platforms exhibit differences and characteristics. It is necessary to…
Abstract
Purpose
Social media platforms (SMPs) are pivotal in information dissemination and molding public opinion. Various platforms exhibit differences and characteristics. It is necessary to compare and analyze the roles played by different platforms in the evolution of public events.
Design/methodology/approach
This study develops a framework to evaluate the role of SMPs at different stages of public events. To calculate some of these indicators, the GPT-AP-TextRank topic model is constructed. The study further analyzes the correlation between indicators at different stages and SMP’s role and compares SMP’s different roles among the four stages.
Findings
The results reveal significant disparities in the role of different SMPs during public events. Weibo demonstrates notable performance during the outbreak, spread and recession stages of the event, exhibiting a strong influence on public event evolution. Bilibili, Douban, Zhihu and Baidu Tieba show relatively ordinary roles. In addition, compared to the spread stage, SMPs exhibit a stronger ability to influence event redirection in the initial stage, which is different from the original assumption of the study.
Practical implications
The findings expose the powerful roles of SMPs in event evolution, providing valuable insights for enhancing public event governance.
Originality/value
This study proposes an evaluation method for SMPs’ role and introduces a novel GPT-AP-TextRank topic generation model for the indicator calculation.