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- 저자 : Linkun Wang (검색결과 2 건)
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Wang, Jiangwei,Yang, Yingying,Wang, Yingxue,Zhang, Lu,Ji, Wei,Hong, Zheng,Zhang, Linkun The Korean Association Of Orthodontists 2022 대한치과교정학회지 Vol.52 No.5
Objective: This study aimed to estimate the clinical effects of different types of bone-anchored maxillary protraction devices by using a network meta-analysis. Methods: We searched seven databases for randomized and controlled clinical trials that compared bone-anchored maxillary protraction with tooth-anchored maxillary protraction interventions or untreated groups up to May 2021. After literature selection, data extraction, and quality assessment, we calculated the mean differences, 95% confidence intervals, and surface under the cumulative ranking scores of eleven indicators. Statistical analysis was performed using R statistical software with the GeMTC package based on the Bayesian framework. Results: Six interventions and 667 patients were involved in 18 studies. In comparison with the tooth-anchored groups, the bone-anchored groups showed significantly more increases in Sella-Nasion-Subspinale (°), Subspinale-Nasion-Supramentale(°) and significantly fewer increases in mandibular plane angle and the labial proclination angle of upper incisors. In comparison with the control group, Sella-Nasion-Supramentale(°) decreased without any statistical significance in all treated groups. IMPA (angle of lower incisors and mandibular plane) decreased in groups with facemasks and increased in other groups. Conclusions: Bone-anchored maxillary protraction can promote greater maxillary forward movement and correct the Class III intermaxillary relationship better, in addition to showing less clockwise rotation of mandible and labial proclination of upper incisors. However, strengthening anchorage could not inhibit mandibular growth better and the lingual inclination of lower incisors caused by the treatment is related to the use of a facemask.
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Yong-Peng Luo,Linkun Wang,Xu Guo,Jinlin Zheng,Fei-Yu Liao,Zixiong Guo 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.8
This article proposes a structural damage identification method based on one-dimensional convolutional neural network group considering sensor faults. The method aims to reduce the damage misjudgment caused by sensor faults. In the proposed method, according to the sensor layout, some convolutional neural network sub-models are established to extract the features from raw vibration data for sensor fault diagnosis and structural damage identification; then two convolutional neural networks groups, namely the sensor fault diagnosis group and the damage identification group are designed on the basis of the functions of each sub-model. The sensor fault diagnosis group determines whether the sensor data is abnormal and truncates the abnormal signal. The remaining normal signal are entered into the damage identification group and the final damage identification results are calculated according to the statistical decision module. The effectiveness of the devised method is verified by the IASC–ASCE benchmark structure and laboratory experiments. The results demonstrate that the sensor fault diagnosis and damage identification accuracy of each sub-model ranges from 98.54% to 99.77% and from 87.21% to 91.74% respectively at different noise levels; the damage identification group can reduce the impact of sub-model misjudgment on the structural damage identification. The accuracy of the final damage identification results is 100%. The identification time of all samples in the test set is 53.09 s and 22.93 s, respectively, for SHM benchmark and Laboratory experiment cases. And the average judgment time of each submodel in the sensor fault diagnosis group was 278 and 94 ms, and that of each submodel in the damage identification group was 294 and 105 ms, respectively, for a single test sample, which fulfills the requirements of online damage identification for structural health monitoring.
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