Search

Your search keyword '"Di, Chong-an"' showing total 684 results
Start Over Author "Di, Chong-an"
684 results on '"Di, Chong-an"'

1. Multi-heterojunctioned plastics with high thermoelectric figure of merit

Author

Wang, Dongyang, Ding, Jiamin, Ma, Yingqiao, Xu, Chunlin, Li, Zhiyi, Zhang, Xiao, Zhao, Yao, Zhao, Yue, Di, Yuqiu, Liu, Liyao, Dai, Xiaojuan, Zou, Ye, Kim, BongSoo, Zhang, Fengjiao, Liu, Zitong, McCulloch, Iain, Lee, Myeongjae, Chang, Cheng, Yang, Xiao, Wang, Dong, Zhang, Deqing, Zhao, Li-Dong, Di, Chong-an, and Zhu, Daoben

Published
2024
Full Text
View/download PDF

4. Technology Roadmap for Flexible Sensors

Author

Luo, Yifei, Abidian, Mohammad Reza, Ahn, Jong-Hyun, Akinwande, Deji, Andrews, Anne M, Antonietti, Markus, Bao, Zhenan, Berggren, Magnus, Berkey, Christopher A, Bettinger, Christopher John, Chen, Jun, Chen, Peng, Cheng, Wenlong, Cheng, Xu, Choi, Seon-Jin, Chortos, Alex, Dagdeviren, Canan, Dauskardt, Reinhold H, Di, Chong-an, Dickey, Michael D, Duan, Xiangfeng, Facchetti, Antonio, Fan, Zhiyong, Fang, Yin, Feng, Jianyou, Feng, Xue, Gao, Huajian, Gao, Wei, Gong, Xiwen, Guo, Chuan Fei, Guo, Xiaojun, Hartel, Martin C, He, Zihan, Ho, John S, Hu, Youfan, Huang, Qiyao, Huang, Yu, Huo, Fengwei, Hussain, Muhammad M, Javey, Ali, Jeong, Unyong, Jiang, Chen, Jiang, Xingyu, Kang, Jiheong, Karnaushenko, Daniil, Khademhosseini, Ali, Kim, Dae-Hyeong, Kim, Il-Doo, Kireev, Dmitry, Kong, Lingxuan, Lee, Chengkuo, Lee, Nae-Eung, Lee, Pooi See, Lee, Tae-Woo, Li, Fengyu, Li, Jinxing, Liang, Cuiyuan, Lim, Chwee Teck, Lin, Yuanjing, Lipomi, Darren J, Liu, Jia, Liu, Kai, Liu, Nan, Liu, Ren, Liu, Yuxin, Liu, Yuxuan, Liu, Zhiyuan, Liu, Zhuangjian, Loh, Xian Jun, Lu, Nanshu, Lv, Zhisheng, Magdassi, Shlomo, Malliaras, George G, Matsuhisa, Naoji, Nathan, Arokia, Niu, Simiao, Pan, Jieming, Pang, Changhyun, Pei, Qibing, Peng, Huisheng, Qi, Dianpeng, Ren, Huaying, Rogers, John A, Rowe, Aaron, Schmidt, Oliver G, Sekitani, Tsuyoshi, Seo, Dae-Gyo, Shen, Guozhen, Sheng, Xing, Shi, Qiongfeng, Someya, Takao, Song, Yanlin, Stavrinidou, Eleni, Su, Meng, Sun, Xuemei, Takei, Kuniharu, Tao, Xiao-Ming, Tee, Benjamin CK, Thean, Aaron Voon-Yew, and Trung, Tran Quang

Subjects
Data Management and Data Science, Information and Computing Sciences, Humans, Wearable Electronic Devices, Quality of Life, soft materials, mechanics engineering, flexible electronics, conformable sensors, bioelectronics, human-machine interfaces, body area sensor networks, technology translation, sustainable electronics, Nanoscience & Nanotechnology
Abstract

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Published
2023

7. An Auxiliary Modality Based Text-Image Matching Methodology for Fake News Detection

Author

Guo, Ying, Li, Bingxin, Ge, Hong, Di, Chong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
Full Text
View/download PDF

9. Influence Maximization on Dynamic Social Networks with Conjugate Learning Automata

Author

Li, Fangqi, Di, Chong, and Li, Shenghong

Subjects
Computer Science - Social and Information Networks
Abstract

Selecting the optimal subset from all vertices as seeds to maximize the influence in a social network has been a task of interest. Various methods have been proposed to select the optimal vertices in a static network, however, they are challenged by the dynamics, i.e. the time-dependent variation of the social network structure. Such dynamics hinder the paradigm for static networks and leaves a seemingly unbridgeable gap between algorithms of influence maximization on static networks and those on dynamic ones. In this paper, we extend our previous work and demonstrate that conjugate learning automata (an elementary variant of reinforcement learning) that have been successfully applied to maximize influence on static networks can be applied to dynamic networks as well. The network dynamics is measured by the variation of the influence range and absorbed into the learning procedure. Our proposal delicately formulates the effect of network dynamics: the more the influence range varies, the more likely the seeds are to be learned from scratch. Under this assumption, the continuity of the network variation is fully taken advantage of. Experimental results on both synthetic and real-world networks verify the privileges of our proposal against alternative methods., Comment: 6 pages

Published
2020

10. On the Submodularity of Diffusion Models: Equivalent Conditions and Applications

Author

Li, Fangqi, Di, Chong, and Xia, Wenwen

Subjects
Computer Science - Social and Information Networks, Mathematics - Combinatorics
Abstract

The diffusion model has been a crucial component in studies about social networks. Many studies, especially these about influence maximization concern the proof of the submodularity of particular diffusion models. Such proofs have been model-dependent and are somewhat ad hoc. In this paper, we prove a theorem that provides a necessary and sufficient condition for a diffusion model to be submodular. This theorem can be used to justify the submodularity of an arbitrary diffusion model. We also apply this theorem to build a projection operator that maps an arbitrary diffusion model into a submodular one. Moreover, we use the established theorem to propose a diffusion model of multiple heterogeneous pieces of information that partially features submodularity., Comment: Conference pre-print

Published
2019

14. An Efficient Learning Automaton Scheme for Massive-Action Environments

Author

Zhu, Hongyu, Tian, Jianwei, Di, Chong, Tian, Zheng, Sun, Yizhen, Li, Qiyao, Li, Shenghong, 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, Li, Yong, 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, Oneto, Luca, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, and Zhang, Baoju, editor

Published
2022
Full Text
View/download PDF

19. Learning automata based SVM for intrusion detection

Author

Di, Chong

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of training the classifier for intrusion detection and increase the time of making predictions. To simplify the training process and improve the efficiency of the classifier, it is necessary to remove these dispensable features. in this paper, we propose a novel LA-SVM scheme to automatically remove redundant features focusing on intrusion detection. This is the first application of learning automata for solving dimension reduction problems. The simulation results indicate that the LA-SVM scheme achieves a higher accuracy and is more efficient in making predictions compared with traditional SVM.

Published
2018
Full Text
View/download PDF

20. A double competitive strategy based learning automata algorithm

Author

Di, Chong

Subjects
Computer Science - Artificial Intelligence
Abstract

Learning Automata (LA) are considered as one of the most powerful tools in the field of reinforcement learning. The family of estimator algorithms is proposed to improve the convergence rate of LA and has made great achievements. However, the estimators perform poorly on estimating the reward probabilities of actions in the initial stage of the learning process of LA. In this situation, a lot of rewards would be added to the probabilities of non-optimal actions. Thus, a large number of extra iterations are needed to compensate for these wrong rewards. In order to improve the speed of convergence, we propose a new P-model absorbing learning automaton by utilizing a double competitive strategy which is designed for updating the action probability vector. In this way, the wrong rewards can be corrected instantly. Hence, the proposed Double Competitive Algorithm overcomes the drawbacks of existing estimator algorithms. A refined analysis is presented to show the $\epsilon-optimality$ of the proposed scheme. The extensive experimental results in benchmark environments demonstrate that our proposed learning automata perform more efficiently than the most classic LA $SE_{RI}$ and the current fastest LA $DGCPA^{*}$.

Published
2017

28. Weight-Assignment Last-Position Elimination-Based Learning Automata

Author

An, Haiwei, Di, Chong, Li, Shenghong, 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, Wang, Wei, editor, Liu, Xin, editor, Na, Zhenyu, editor, Jia, Min, editor, and Zhang, Baoju, editor

Published
2020
Full Text
View/download PDF

29. Bayesian Method-Based Learning Automata for Two-Player Stochastic Games with Incomplete Information

Author

Ding, Hua, Di, Chong, Shenghong, Li, 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, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
Full Text
View/download PDF

30. A Double Competitive Strategy-Based Learning Automata Algorithm

Author

Di, Chong, Guo, Mingda, Huang, Jinchao, Li, Shenghong, 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, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
Full Text
View/download PDF

32. Learning Automata Based SVM for Intrusion Detection

Author

Di, Chong, Su, Yu, Han, Zhuoran, Li, Shenghong, 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, Ruediger, 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, 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, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mu, Jiasong, editor, Jia, Min, editor, Wang, Wei, editor, Feng, Xuhong, editor, and Zhang, Baoju, editor

Published
2019
Full Text
View/download PDF

40. Multilevel functional principal component analysis

Author

Di, Chong-Zhi, Crainiceanu, Ciprian M., Caffo, Brian S., and Punjabi, Naresh M.

Subjects
Statistics - Applications
Abstract

The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes., Comment: Published in at http://dx.doi.org/10.1214/08-AOAS206 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2009
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results