Search

Your search keyword '"Li, Yahao"' showing total 212 results
Start Over Author "Li, Yahao"
212 results on '"Li, Yahao"'

3. Machine learning guided phase and hardness controlled AlCoCrCuFeNi high-entropy alloy design

Author

LI Yahao, YE Yicong, ZHAO Fengyuan, TANG Yu, ZHU Li'an, and BAI Shuxin

Subjects
machine learning, high entropy alloy, phase prediction, hardness prediction, composition design, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Machine learning(ML) assisted high-entropy alloys(HEA) design is dedicated to solving the problem of long period and high cost of designing by traditional trial and error experimental methods. The classic AlCoCrCuFeNi HEA was taken as the research object. The phase structure prediction model and hardness prediction model were established respectively. The support vector machine(SVM) models have the best training performance in both tasks. The best phase classification accuracy is 0.944, and the root mean square error(RMSE) of the hardness regression model is 56.065HV. The two ML models are further connected in series. Based on the upper and lower limits of the data set, the high-throughput prediction and selection of phases and hardness of AlCoCrCuFeNi HEA are carried out simultaneously, thus realizing the efficient composition design of the new alloy. The experimental results show that the five new alloys are in accord with the predicted results, and the RMSE is 12.58HV. It shows that the ML models can predict the phase and hardness of HEA efficiently and accurately.

Published
2024
Full Text
View/download PDF

25. Neural Network-Based Anomaly Data Classification and Localization in Bridge Structural Health Monitoring.

Author

Li, Yahao, Zhang, Nan, Sun, Qikan, Cai, Chaoxun, and Li, Kebing

Subjects
*CONVOLUTIONAL neural networks, *WORK environment, *DEEP learning, *ANOMALY detection (Computer security), *CABLE-stayed bridges, *STRUCTURAL health monitoring
Abstract

Due to the harsh working environments of certain bridges, the bridge structural health monitoring systems (SHMs) are prone to error warning because of anomaly data. Therefore, it is of great significance to accurately classify and locate the anomaly data for effectively addressing these issues. This paper proposes a method for classifying and locating anomaly data utilizing one-dimensional monitoring data based on convolutional neural networks. Compared to previous research reliant on visual features, the proposed method has lower computational costs. By incorporating the anomaly data localization network, manual localization operations for data restoration are replaced. The analysis in this paper is based on monitoring data from a large-span cable-stayed bridge, along with artificially generated anomaly data. The two neural network frameworks proposed in this paper are trained and validated, showcasing precise classification and localization of anomaly data. Furthermore, the paper discusses the impact of common errors in labeling data categories and locating training samples in practical operations. The results demonstrate that even in the presence of noticeable yet non-extreme labeling errors in the training set, the proposed method still achieves accurate classification and localization, highlighting its robustness. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

30. Hydrogeological characteristics and water loss factors of 'bottom aquifer' in Guotun Coal Mine

Author

ZHAO Renle, LI Hongyou, HOU Weihua, XU Guangquan, CHENG Hua, LI Yahao, PENG Shilong

Subjects
bottom aquifer, aquifer parameters, groundwater dynamics, mining crack, water pressure test, slug test, Mining engineering. Metallurgy, TN1-997
Abstract

The continuous decrease of the water level of the “bottom aquifer” in Cenozoic is the main cause of land subsidence or shaft deformation in the eastern mining areas of China. It is an important prerequisite to manage such disasters by identifying geological conditions and influencing factors. Taking Guotun Coal Mine as the object, on the basis of analyzing the “bottom aquifer” hydrogeological conditions, the water pressure test and the Slug test are used to carry out the test analysis of its permeability, and the factors affecting its water loss are analyzed. The results show that within the scope of the industrial square, there is a large difference in the spatial distribution of the “bottom aquifer” sediments, uneven thickness, the permeability and permeability coefficients in different areas also vary greatly, and the water loss of the “bottom aquifer” in different locations is different. The main factors that lead to “bottom aquifer” loss are thawing of wellbore in early stage, roadway driving and mining disturbance of working face near wellbore in late stage.

Published
2021
Full Text
View/download PDF

49. Genomic characterization of a new phage BUCT541 against Klebsiella pneumoniae K1-ST23 and efficacy assessment in mouse and Galleria mellonella larvae

Author

Pu, Mingfang, primary, Li, Yahao, additional, Han, Pengjun, additional, Lin, Wei, additional, Geng, Ronghua, additional, Qu, Fen, additional, An, Xiaoping, additional, Song, Lihua, additional, Tong, Yigang, additional, Zhang, Shuyan, additional, Cai, Zhen, additional, and Fan, Huahao, additional

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results