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197 results on '"Fan, Chunyang"'

1. A Fair Post-Processing Method based on the MADD Metric for Predictive Student Models

Author

Verger, Mélina, Fan, Chunyang, Lallé, Sébastien, Bouchet, François, and Luengo, Vanda

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Discrete Mathematics, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Predictive student models are increasingly used in learning environments. However, due to the rising social impact of their usage, it is now all the more important for these models to be both sufficiently accurate and fair in their predictions. To evaluate algorithmic fairness, a new metric has been developed in education, namely the Model Absolute Density Distance (MADD). This metric enables us to measure how different a predictive model behaves regarding two groups of students, in order to quantify its algorithmic unfairness. In this paper, we thus develop a post-processing method based on this metric, that aims at improving the fairness while preserving the accuracy of relevant predictive models' results. We experiment with our approach on the task of predicting student success in an online course, using both simulated and real-world educational data, and obtain successful results. Our source code and data are in open access at https://github.com/melinaverger/MADD ., Comment: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education (RKDE 2023) at ECML PKDD 2023, September 2023, Turino, Italy

Published
2024

24. Flexible Control Strategy of DC Bus for Hybrid AC/DC Microgrid with Electric Vehicle

Author

Wang, Jingsong, Li, Peng Yun, Gong, Chengming, Li, Donghui, Fan, Chunyang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Xue, Yusheng, editor, Zheng, Yuping, editor, and Bose, Anjan, editor

Published
2021
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40. Fault Feature Analysis and Diagnosis Method of Rolling Bearing based on Empirical Mode Decomposition and Deep Belief Network

Author

Yu Xiao, Fan Chunyang, Dong Fei, Ding Enjie, Wu Shoupeng, and Wang Xin

Subjects
Empirical modal decomposition, Extreme learning machine, Deep belief network, Rolling bearing, Fault diagnosis, Mechanical engineering and machinery, TJ1-1570
Abstract

In order to realize the intelligent diagnosis of rolling bearing failure,a fault diagnosis model of vibration signal based on empirical mode decomposition（ EMD） and deep belief network（ DBN） is proposed.Firstly,the vibration signal is processed by empirical mode decomposition,and the statistical parameters of the effective intrinsic modal function（ IMF） component and its Hilbert envelope are obtained as the original feature set. Then,the extreme learning suite（ ELM） classifier feature selection method is proposed to remove the redundancy and interference characteristics of the original feature set and to select the fault state sensitive features. Finally,by using the depth learning advantages in high-dimensional and non-linear processing,the DBN-based fault feature adaptive analysis and fault state intelligent identification is completed. The results show that the ELM method can select the sensitive statistical characteristics of the fault,and the adaptive characteristic of the DBN method can effectively improve the accuracy of fault state recognition.

Published
2018
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42. Manipulation of Cationic Group Density in Covalent Organic Framework Membranes for Efficient Anion Transport

Author

Kong, Yan, Lyu, Bohui, Fan, Chunyang, Yang, Yi, Wang, Xiaoyao, Shi, Benbing, Jiang, Jianwen, Wu, Hong, and Jiang, Zhongyi

Abstract

Anion exchange membranes with high anion conductivity are highly desired for electrochemical applications. Increasing ion exchange capacity is a straightforward approach to enhancing anion conductivity but faces a challenge in dimensional stability. Herein, we report the design and preparation of three kinds of isoreticular covalent organic framework (COF) membranes bearing tunable quaternary ammonium group densities as anion conductors. Therein, the cationic groups are integrated into the backbones by flexible ether-bonded alkyl side chains. The highly quaternary ammonium-group-functionalized building units endow COF membranes with abundant cationic groups homogeneously distributed in the ordered channels. The flexible side chains alleviate electrostatic repulsion and steric hindrance caused by large cationic groups, ensuring a tight interlayer stacking and multiple interactions. As a result, our COF membranes achieve a high ion exchange capacity and exceptional dimensional stability simultaneously. Furthermore, the effect of the ionic group density on the ion conductivity in rigid COF channels is systematically explored. Experiments and simulations reveal that the ionic group concentration and side chain mobility jointly determine the ion transport behavior, resulting in the abnormal phenomenon that the anion conductivity is not positively correlated to the ionic group density. The optimal COF membrane achieves the ever-reported highest hydroxide ion conductivity over 300 mS cm–1at 80 °C and 100% RH. This study offers insightful guidelines on the rational design and preparation of high-performance anion conductors.

Published
2023
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47. Charged Nanochannels in Covalent Organic Framework Membranes Enabling Efficient Ion Exclusion

Author

You, Xinda, primary, Cao, Li, additional, Liu, Yawei, additional, Wu, Hong, additional, Li, Runlai, additional, Xiao, Qianxiang, additional, Yuan, Jinqiu, additional, Zhang, Runnan, additional, Fan, Chunyang, additional, Wang, Xiaoyao, additional, Yang, Pengfei, additional, Yang, Xiaoyu, additional, Ma, Yu, additional, and Jiang, Zhongyi, additional

Published
2022
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