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1,023 results on '"Yang, Chunsheng"'

101. Particle Filter-Based Model Fusion for Prognostics

Author

García, Claudia Maria, Zou, Yanni, Yang, Chunsheng, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Ali, Moonis, editor, Kwon, Young Sig, editor, Lee, Chang-Hwan, editor, Kim, Juntae, editor, and Kim, Yongdai, editor

Published
2015
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105. A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN

Author

Ferdousi, Rahatara, Laamarti, Fedwa, Yang, Chunsheng, Saddik, Abdulmotaleb El, Ferdousi, Rahatara, Laamarti, Fedwa, Yang, Chunsheng, and Saddik, Abdulmotaleb El

Abstract

Accurate Defect detection is crucial for ensuring the trustworthiness of intelligent railway systems. Current approaches rely on single deep-learning models, like CNNs, which employ a large amount of data to capture underlying patterns. Training a new defect classifier with limited samples often leads to overfitting and poor performance on unseen images. To address this, researchers have advocated transfer learning and fine-tuning the pre-trained models. However, using a single backbone network in transfer learning still may cause bottleneck issues and inconsistent performance if it is not suitable for a specific problem domain. To overcome these challenges, we propose a reusable AI-enabled defect detection approach. By combining ensemble learning with transfer learning models (VGG-19, MobileNetV3, and ResNet-50), we improved the classification accuracy and achieved consistent performance at a certain phase of training. Our empirical analysis demonstrates better and more consistent performance compared to other state-of-the-art approaches. The consistency substantiates the reusability of the defect detection system for newly evolved defected rail parts. Therefore we anticipate these findings to benefit further research and development of reusable AI-enabled solutions for railway systems., Comment: 28 pages, 13 Figures, Applied Intelligence Journal, Springer Nature

Published
2023

106. On the Perception of a Chinese American English Accent.

Author

Yang, Chunsheng, Niu, Gao, and Luo, Han

Subjects
*AMERICAN English language, *CHINESE Americans, *SECOND language acquisition, *LANGUAGE attrition, *LANGUAGE maintenance, CHINESE as a second language
Abstract

This article, titled "On the Perception of a Chinese American English Accent," is written by Chunsheng Yang, Gao Niu, and Han Luo. Chunsheng Yang is an associate professor of Chinese and applied linguistics at the University of Connecticut, with research areas including Chinese phonetics, phonology, second language acquisition, Chinese sociolinguistics, and Chinese pedagogy. Gao Niu is an assistant professor in actuarial science at Bryant University, specializing in big data analytics application in the insurance industry. Han Luo is an associate professor of Chinese at Lafayette College, focusing on Chinese linguistics, cognitive linguistics, second language acquisition, and foreign language pedagogy. [Extracted from the article]

Published
2024
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111. Chemical Reaction Metaphor in Distributed Learning Environments

Author

Lin, Hong, Yang, Chunsheng, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Orchard, Bob, editor, Yang, Chunsheng, editor, and Ali, Moonis, editor

Published
2004
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115. A Data Driven Approach for Smart Lighting

Author

Guo, Hongyu, Letourneau, Sylvain, Yang, Chunsheng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Ali, Moonis, editor, Pan, Jeng-Shyang, editor, Chen, Shyi-Ming, editor, and Horng, Mong-Fong, editor

Published
2014
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116. Particle Filter-Based Method for Prognostics with Application to Auxiliary Power Unit

Author

Yang, Chunsheng, Lou, Qingfeng, Liu, Jie, Yang, Yubin, Bai, Yun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Ali, Moonis, editor, Pan, Jeng-Shyang, editor, Chen, Shyi-Ming, editor, and Horng, Mong-Fong, editor

Published
2014
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117. Developing Data-driven Models to Predict BEMS Energy Consumption for Demand Response Systems

Author

Yang, Chunsheng, Létourneau, Sylvain, Guo, Hongyu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Ali, Moonis, editor, Pan, Jeng-Shyang, editor, Chen, Shyi-Ming, editor, and Horng, Mong-Fong, editor

Published
2014
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119. Model Fusion-Based Batch Learning with Application to Oil Spills Detection

Author

Yang, Chunsheng, Yang, Yubin, Liu, Jie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Jiang, He, editor, Ding, Wei, editor, Ali, Moonis, editor, and Wu, Xindong, editor

Published
2012
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120. sj-docx-1-tan-10.1177_17562864231180734 – Supplemental material for Clinical use of dimethyl fumarate in multiple sclerosis treatment: an update to include China, using a modified Delphi method

Author

Gold, Ralf, Barnett, Michael, Chan, Andrew, Feng, Huiyu, Fujihara, Kazuo, Giovannoni, Gavin, Montalbán, Xavier, Shi, Fu-Dong, Tintoré, Mar, Xue, Qun, Yang, Chunsheng, and Zhou, Hongyu

Subjects
Neurology and Neuromuscular Diseases, FOS: Clinical medicine, Pharmacology and Pharmaceutical Sciences not elsewhere classified
Abstract

Supplemental material, sj-docx-1-tan-10.1177_17562864231180734 for Clinical use of dimethyl fumarate in multiple sclerosis treatment: an update to include China, using a modified Delphi method by Ralf Gold, Michael Barnett, Andrew Chan, Huiyu Feng, Kazuo Fujihara, Gavin Giovannoni, Xavier Montalbán, Fu-Dong Shi, Mar Tintoré, Qun Xue, Chunsheng Yang and Hongyu Zhou in Therapeutic Advances in Neurological Disorders

Published
2023
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121. Clinical use of dimethyl fumarate in multiple sclerosis treatment: an update to include China, using a modified Delphi method

Author

Gold, Ralf, Barnett, Michael, Chan, Andrew, Feng, Huiyu, Fujihara, Kazuo, Giovannoni, Gavin, Montalbán, Xavier, Shi, Fu-Dong, Tintoré, Mar, Xue, Qun, Yang, Chunsheng, and Zhou, Hongyu

Subjects
610 Medicine & health
Abstract

Dimethyl fumarate (DMF) is a widely used oral disease-modifying therapy for multiple sclerosis (MS). Its efficacy and safety profiles are supported by over a decade of experience. Differences exist between Asia and Europe/United States in the prevalence and characteristics of MS; most data for DMF are derived from populations outside Asia. DMF was recently (2021) approved for use in China. The objectives of this review were to evaluate the evidence for DMF's profile, to provide an update to healthcare providers on current knowledge surrounding its use and to assess the relevance of existing data to use in China. This study used a modified Delphi method based on the insights of a scientific Steering Committee (SC), with a structured literature review conducted to assess the data of DMF. The literature review covered all papers in English (from 01 January 2011 to 21 February 2022) that include 'dimethyl fumarate' and 'multiple sclerosis', and their MeSH terms, on PubMed, supplemented by EMBASE and Citeline searches. Papers were categorized by topic and assessed for relevance and quality, before being used to formulate statements summarizing the literature on each subject. SC members voted on/revised statements, requiring ⩾80% agreement and ⩽10% disagreement for inclusion. Statements not reaching this level were discussed further until agreement was reached or until there was agreement to remove the statement. A total of 1030 papers were retrieved and used to formulate the statements and evidence summaries considered by the SC members. A total of 45 statements were agreed by the SC members. The findings support the positive efficacy and safety profile of DMF in treating patients with MS. Limited Chinese patient data are an ongoing consideration; however, based on current evidence, the statements are considered applicable to both the global and Chinese populations. DMF is a valuable addition to address unmet MS treatment needs in China. Registration: Not applicable.

Published
2023
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122. Discovering Patterns for Prognostics: A Case Study in Prognostics of Train Wheels

Author

Yang, Chunsheng, Létourneau, Sylvain, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Mehrotra, Kishan G., editor, Mohan, Chilukuri K., editor, Oh, Jae C., editor, Varshney, Pramod K., editor, and Ali, Moonis, editor

Published
2011
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126. Clinical use of dimethyl fumarate in multiple sclerosis treatment: an update to include China, using a modified Delphi method

Author

Gold, Ralf, primary, Barnett, Michael, additional, Chan, Andrew, additional, Feng, Huiyu, additional, Fujihara, Kazuo, additional, Giovannoni, Gavin, additional, Montalbán, Xavier, additional, Shi, Fu-Dong, additional, Tintoré, Mar, additional, Xue, Qun, additional, Yang, Chunsheng, additional, and Zhou, Hongyu, additional

Published
2023
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129. Case Learning in CBR-Based Agent Systems for Ship Collision Avoidance

Author

Liu, Yuhong, Yang, Chunsheng, Yang, Yubin, Lin, Fuhua, Du, Xuanmin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Yang, Jung-Jin, editor, Yokoo, Makoto, editor, Ito, Takayuki, editor, Jin, Zhi, editor, and Scerri, Paul, editor

Published
2009
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130. 3D Scene Analysis Using UIMA Framework

Author

Yang, Yubin, Wei, Wei, Lu, Tong, Gao, Yang, Zhang, Yao, Yang, Chunsheng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Chien, Been-Chian, editor, Hong, Tzung-Pei, editor, Chen, Shyi-Ming, editor, and Ali, Moonis, editor

Published
2009
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133. Learning Chinese Idioms through iPads

Author

Yang, Chunsheng and Xie, Ying

Abstract

This paper reports on an action research study using iPads during the teaching of Chinese idioms to heritage learners. A class of 12 second-year Chinese learners were engaged in a self-generated learning process focused on learning abstract and concrete idioms using iPads. Students' short-term and long-term learning was measured; feedback from a questionnaire and artifacts created during the learning process are also reported here. The results show that the textual and visual illustrations of the idioms created by the heritage learners facilitated their learning of the idioms. The results also show that the learners enjoyed this mobile way of learning. This study identified three keys areas that will need to be addressed in the next cycle of action research: learner collaboration, peer learning and assessment, and technological issues.

Published
2013

135. A Multiagent-Based Simulation System for Ship Collision Avoidance

Author

Liu, Yuhong, Yang, Chunsheng, Du, Xuanmin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Heutte, Laurent, editor, and Loog, Marco, editor

Published
2007
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147. Lifetime Learning-enabled Modelling Framework for Digital Twin

Author

Yang, Chunsheng, Ferdou, Rahatara, El Saddik, Abdulmotaleb, Li, Yifeng, Liu, Zheng, and Liao, Min

Subjects
machine learning, digital twin, model behavior transfer, living models, distribution shift, transfer learning, proactive maintenance
Abstract

Recently Digital Twin (DT) has attracted much attention from researchers due to its capacity of system monitoring and health management to improve the reliability and availability of systems. This emerging technology has been considered a promising solution for various sectors to enhance the sustainability of business development. In general, DT relies on the living models for simulating system behaviors to monitor the systems or assets in production. Such models could be either mathematical/physics-based or data-driven, which are able to explain, predict, and describe system behaviors timely and accurately. Therefore, the machine learning-enabled modeling technology has become a powerful tool to develop such data-driven living models. However, data-driven models developed with supervised learning techniques carry a fatal deficiency: once the operational environments are changed, the model may hardly work well or even becomes useless due to the distribution shift between the training data and the new dataset. This paper attempts to address this issue by proposing to apply transfer learning techniques to develop lifetime robust living models for real-world DT systems. This paper presents a framework for developing lifetime data-driven living models. A case study, railway digital twin from our on-going research project along with the preliminary results, will be presented to demonstrate the feasibility and usefulness of the proposed modeling framework for digital twin., 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), August 20-24, 2022, Mexico City, Mexico

Published
2022
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148. RailTwin: A Digital Twin Framework For Railway

Author

Ferdousi, Rahatara, Laamarti, Fedwa, Yang, Chunsheng, and El Saddik, Abdulmotaleb

Subjects
reinforcement learning, deep learning, transfer learning, inspection, rail transportation, digital twins, automation
Abstract

This study aims at providing a conceptualized framework for railway to realize the Digital Twin (DT) beyond traditional structural modeling or information systems. First, we deduce a generic formula that shows that DT estimates the future states and decides actions beforehand. Then, based on this formula, we design a generic framework called RailTwin. The framework combines the insight of current states, the foresight representing the prediction of the future states, and the oversight based on the current and future state to enable automation and actuation. The key enabler of this framework to obtain these states is Artificial Intelligence (AI) technologies, including Deep Learning, Transfer Learning, Reinforcement Learning, and Explainable AI. We present a use case for asset health inspection and monitoring through the proposed framework., 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), August 20-24, 2022, Mexico City, Mexico

Published
2022
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