arXiv subjects

Jinghua Xu

Publications and source records attributed to Jinghua Xu.

3 recordsLinked to original sources

arXiv2023

Vibration-suppressed toolpath generation for kinematic and energy performance optimization in large dimension additive manufacturing

Product quality and sustainability in large dimension additive manufacturing (LDAM) highly rely on the kinematic and energy performance of LDAM systems. However, the mechanical vibration occurring in the fabrication process hinders the further development of AM technology in industrial manufacturing. To this end, a kinematic and energy performance optimization method for LDAM via vibration-suppressed toolpath generation (VTG) is put forward. Kinematic modeling of LDAM system and related energy consumption modeling are first constructed to reveal that the toolpath of servo system assumes the main responsibility for kinematic and energy performance in LDAM. The vibration-suppressed toolpath generation (VTG) method is thus proposed to customize servo trajectory for kinematic and energy performance optimization in LDAM. Extensive numerical and physical experiments are conducted on the W16 cylinder, including the measurement and analysis of the mechanical vibration amplitude, and assessment of the surface roughness before and after using the proposed VGT. These experimental results demonstrate that our VGT is able to simultaneously achieve kinematic and energy performance optimization in LDAM, even though the fabricated part owns aberrant and complex morphology.

cs.CE↗

arXiv2022

Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection

This paper describes my participation in the SemEval-2022 Task 4: Patronizing and Condescending Language Detection. I participate in both subtasks: Patronizing and Condescending Language (PCL) Identification and Patronizing and Condescending Language Categorization, with the main focus put on subtask 1. The experiments compare pre-BERT neural network (NN) based systems against post-BERT pretrained language model RoBERTa. This research finds NN-based systems in the experiments perform worse on the task compared to the pretrained language models. The top-performing RoBERTa system is ranked 26 out of 78 teams (F1-score: 54.64) in subtask 1, and 23 out of 49 teams (F1-score: 30.03) in subtask 2.

cs.CL↗

arXiv2022

How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began

Following the outbreak of a global pandemic, online content is filled with hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people, which triggered a new round of anti-China hate both online and offline. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic (2020 and 2021). Through Twitter's API, in total 2,172,333 tweets hashtagged #china posted during the time were collected. By employing multiple state-of-the-art pretrained language models for hate speech detection, we identify a wide range of hate of various types, resulting in an automatically labeled anti-China hate speech dataset. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the average rate of online hate speech on Twitter at 0.6% identified in Gao et al., 2017. We further analyzed the longitudinal development of #china tweets and those identified as hateful in 2020 and 2021 through visualizing the daily number and hate rate over the two years. Our keyword analysis of hate speech in #china tweets reveals the most frequently mentioned terms in the hateful #china tweets, which can be used for further social science studies.

cs.CL↗