Browsing by Author "Yang, C"

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    Online quantitative partial discharge monitor based on interferometry
    (2020) Xue, L; Zhu, Y; Yang, C; Kumarawadu, S
    Interferometry-based online partial discharge (PD) monitor presented in this paper can detect the occurrence of PD sensitively, evaluate the peak value of the discharge inception voltage with random waveform and the damage extent relatively cost effectively. The interferograms affected by the PD are collected online. By extracting the phase information of the interference fringes quantitatively, the peak value of the discharge inception voltage with random waveform can be retrieved real-time. Merits of the proposed method as an online quantitative PD monitor are validated via theoretical analysis as well as experimentations by the use of an artificially localized PD source. Furthermore, the proposed method can capture the light signal emitted by the discharge. Quite in contrast to many commonly used sensor-based methods, our approach avoids the need of amplifying the light signal strength making its practical implantation much convenient. The proposed method promises strong potential for field application.
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    A Profile boosted RAF to Recommend Journals for Manuscript
    (2014-08-14) Silva, ATP; Ma, J; Yang, C; Liang, H
    With the increasing pressure on researchers to produce scientifically rigorous and relevant research, researchers need to find suitable publication outlets with the highest value and visibility for their manuscripts. Traditional approaches for discovering publication outlets mainly focus on manually matching research relevance in terms of keywords as well as comparing journal qualities, but other research-relevant information such as social connections, publication rewards, and productivity of authors are largely ignored. To assist in identifying effective publication outlets and to support effective journal recommendations for manuscripts, a three-dimensional profile-boosted research analytics framework (RAF) that holistically considers relevance, connectivity, and productivity is proposed. To demonstrate the usability of the proposed framework, a prototype system was implemented using the ScholarMate research social network platform. Evaluation results show that the proposed RAF-based approach outperforms traditional recommendation techniques that can be applied to journal recommendations in terms of quality and performance. This research is the first attempt to provide an integrated framework for effective recommendation in the context of scientific item recommendation.
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    Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach
    (Macmillan Ltd, 2019) Kueffner, R; Zach, N; Bronfeld, M; Norel, R; Atassi, N; Balagurusamy, V; Di Camillo, B; Chio, A; Cudkowicz, M; Dillenberger, D; Garcia-Garcia, J; Hardiman, O; Hoff, B; Knight, J; Leitner, LM; Li, G; Mangravite, L; Norman, T; Wang, L; Xiao, J; Fang, WC; Peng, J; Yang, C; Chang, H-J; Stolovitzky, G
    Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.