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3,392 results on '"Li, Qingqing"'

24. A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

Author

Li, Li, Li, Qingqing, Xu, Guozheng, Zhou, Pengwei, Tu, Jingmin, Li, Jie, Li, Mingming, and Yao, Jian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Roof plane segmentation from airborne LiDAR point clouds is an important technology for 3D building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can exactly distinguish adjacent planar patches. The quality of point feature directly determines the accuracy of roof plane segmentation. Most of existing approaches use handcrafted features to extract roof planes. However, the abilities of these features are relatively low, especially in boundary area. To solve this problem, we propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation. We design a three-branch network to predict semantic labels, point offsets and extract deep embedding features. In the first branch, we classify the input data as non-roof, boundary and plane points. In the second branch, we predict point offsets for shifting each point toward its respective instance center. In the third branch, we constrain that points of the same plane instance should have the similar embeddings. We aim to ensure that points of the same plane instance are close as much as possible in both Euclidean and embedding spaces. However, although deep network has strong feature representative ability, it is still hard to accurately distinguish points near plane instance boundary. Therefore, we first group plane points into many clusters in the two spaces, and then we assign the rest boundary points to their closest clusters to generate final complete roof planes. In this way, we can effectively reduce the influence of unreliable boundary points. In addition, we prepare a synthetic dataset and two real datasets to train and evaluate our approach. The experiments results show that the proposed approach significantly outperforms the existing state-of-the-art approaches.

Published
2023

31. ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data

Author

Luo, Zhirui, Qi, Ruobin, Li, Qingqing, Zheng, Jun, and Shao, Sihua

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important features for occupancy detection. We adopt two smart meter datasets widely used for building occupancy detection in our performance evaluation. A set of state-of-the-art shallow machine learning and deep learning models are included for performance comparison. The results show that ABODE-Net significantly outperforms other models in all experimental cases, which proves its validity as a solution for non-intrusive building occupancy detection., Comment: To be published in The 7th International Conference on Smart Computing and Communication (SmartCom 2022)

Published
2022

37. Responses of nitrobenzene removal performance and microbial community by modified biochar supported zerovalent iron in anaerobic soil

Author

Lu Hainan, Li Peng, Li Qingqing, Liu Fang, Zhou Dong, Huang Shenfa, Yang Jie, and Li Zhiheng

Subjects
Modified biochar, ZVI, Microbial community, Nitrobenzene, Anaerobic soil, Medicine, Science
Abstract

Abstract Biochar-supported ZVI have received increasing attention for their potential to remove nitrobenzene in groundwater and soil. However, the capacity of this material to enhance the biological reduction of nitrobenzene and alter microbial communities in anaerobic groundwater have not been explored. In this study, the nitrobenzene removal performance and mechanism of modified biochar-supported zerovalent iron (ZVI) composites were explored in anaerobic soil. The results showed that the 700 °C biochar composite enhanced the removal of nitrobenzene and inhibited its release from soil to the aqueous phase. NaOH-700-Fe50 had the highest removal rate of nitrobenzene, reaching 64.4%. However, the 300 °C biochar composite inhibited the removal of nitrobenzene. Microbial degradation rather than ZVI-mediated reduction was the main nitrobenzene removal pathway. The biochar composites changed the richness and diversity of microbial communities. ZVI enhanced the symbiotic relationship between microbial genera and weakened competition between soil microbial genera. In summary, the 700 °C modified biochar composite enhanced the removal of nitrobenzene by increasing microbial community richness and diversity, by upregulating functional genes, and by promoting electron transfer. Overall, the modified biochar-supported ZVI composites could be used for soil remediation, and NaOH-700-Fe50 is a promising composite material for the on-site remediation of nitrobenzene-contaminated groundwater.

Published
2024
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38. Nursing interventions based on the knowledge-attitude-practice model on anxiety and treatment compliance in an elderly patient with Ramsay-Hunt Syndrome and vestibular neuritis (知信行理论模式干预1例耳带状疱疹合并前庭神经炎老年患者焦虑情绪和治疗依从性的护理体会)

Author

CAO Haiyan (曹海燕), XU Ling (徐玲), YANG Hui (杨惠), and LI Qingqing (李青青)

Subjects
knowledge-attitude-practice model, anxiety, medication adherence, vertigo ramsay-hunt syndrome, vestibular neuritis, wind-yang disturbance syndrome, kap模式, 焦虑情绪, 依从性, 耳带状疱疹, 前庭神经炎, 眩晕, 风阳上扰证, Nursing, RT1-120
Abstract

This paper summarized the nursing interventions based on the knowledge-attitude-practice model on anxiety and treatment compliance in an elderly patient with Ramsay-Hunt Syndrome and vestibular neuritis. This elderly female patient was diagnosed as vertigo of wind-Yang disturbance syndrome by Traditional Chinese Medicine syndrome differentiation, and she was suffered from anxiety and low treatment compliance. Based on the Traditional Chinese Medicine syndrome differentiation, nursing interventions based on the knowledge-attitude-practice model were carried out to relieve the anxiety and improve the treatment compliance. The Self-rating Anxiety Scale score dropped form 60 to 25 before discharge form hospital. The Morisky medication adherence scale score increased from 2. 5 to 8, indicating a improved level of medication adherence of the patient after intervention. (本文总结基于知信行理论(KAP)模式干预1例耳带状疱疹合并前庭神经炎老年患者焦虑情绪和治疗依从性的护理体会。本例高龄女性患者中医诊断为眩晕(风阳上扰证), 且存在焦虑情绪和治疗依从性差的问题。基于中医辨证分型, 运用KAP模式进行护理干预。患者焦虑自评量表(SAS)评分从60分(轻度焦虑)降低至25分(无焦虑), 焦虑情绪得到改善; Morisky服药依从性量表评分从2. 5分上升至8分, 治疗依从性得到提高。)

Published
2024
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