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23 results on '"Yang, Yujun"'

14. Equivalent Modeling of Microbump Layer in Microsystem for Thermal Analysis Based on Differential Idea.

Author

Kui, Li, Wang, Xin, Zhang, Zhixiang, Kuang, Nailiang, Yang, Yujun, Jing, Li, and Gao, Wu

Subjects
DIFFERENTIAL thermal analysis, THERMAL conductivity, HEAT transfer, MICROELECTROMECHANICAL systems
Abstract

The through-silicon-vias (TSV) packaging structure in the microsystem is complex, especially there are a large number of microbumps. Meanwhile, the microsystem structure size has a cross-scale problem. Then, detailed modeling will bring difficulties in meshing and simulation calculation efficiency. Based on the actual bump structure in the microbump layer, a calculating method for the anisotropy equivalent thermal conductivity of the microbump layer is proposed by using differential ideas combined with the heat transfer theory. The thermal simulation analysis of the detailed model and the equivalent model is completed. The comparison simulation results show that the equivalent modeling method has high accuracy and greatly calculation efficiency. By comparing the thermal test result, the simulation result is basically consistent with the test result, which verified the accuracy of the equivalent modeling method again. Later, the influence of the bump radius, the bump pitch, and the microbump layer height on the equivalent thermal conductivity of the microbump layer is mainly studied, which provides theoretical support on thermal design for the microbump layer in the microsystem. [ABSTRACT FROM AUTHOR]

Published
2022
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15. Research on Data Storage and Mining of Early Warning of Crop Diseases and Insect Pests

Author

Yang Yujun, Guo Longyuan, and Yang Yimei

Subjects
050101 languages & linguistics, Warning system, Distributed database, Computer science, Relational database, business.industry, 05 social sciences, Big data, 02 engineering and technology, Agricultural engineering, Storage efficiency, Agriculture, Computer data storage, Scalability, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, business
Abstract

The data of crop diseases and insect pests have the characteristics of massive, heterogeneous and multi-source. The traditional data storage methods and systems have the problems of low storage efficiency and weak scalability. To solve these problems, this paper introduces a HBase based data storage system of crop diseases and insect pests and a data mining technology of crop diseases and insect pests early warning based on Hadoop. The system can store and manage pest data persistently, make up for the deficiency of traditional relational database, improve the speed of query and early warning, and provide a new method for the data storage and mining of crop pest in big data environment.

Published
2019
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17. Research on financial time series forecasting based on SVM

Author

Yang Yujun, Li Jianping, and Yang Yimei

Subjects
Series (mathematics), Structured support vector machine, Computer science, business.industry, 0206 medical engineering, Online machine learning, Approximation algorithm, 02 engineering and technology, Machine learning, computer.software_genre, Support vector machine, Relevance vector machine, Statistical learning theory, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, Artificial intelligence, Time series, business, computer, 020602 bioinformatics
Abstract

The support vector machine (SVM) is a machine learning method developed based on statistical learning theory. The SVM is widely used in classification and prediction. Since the financial time series is complex, the traditional forecasting methods are less reliable. In this paper, we research on financial time series forecasting based on the support vector machine. Although the speed of prediction process is slow, it can improve the prediction accuracy of the financial time series. The experimental results show the prediction accuracy of this approach based on the support vector machine.

Published
2016
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