Jingyi Wang

Shenzhen University

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i-10 index

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Mengmeng Dong

Mianyang City Center for Disease Control and Prevention

Chunyan Xu

Jiangsu Normal University

Xiaoying Cao

Jiangsu Normal University

Guoyin Kai

Zhejiang Chinese Medical University

Lingxia Xu

Jiangsu University

Wen Wan

Shanghai University

Jihong Jiang

Jiangsu Normal University

Yankai Cao

University of British Columbia

Biao Huang

University of Alberta

Jesús Moreira

Imperial Oil (Canada)

Cooperative Institutions

Chinese Academy of Sciences

Shenzhen University

Beijing Institute of Technology

University of Chinese Academy of Sciences

Huazhong University of Science and Technology

University of Alberta

National University of Defense Technology

Tongji University

Shanghai Jiao Tong University

Hubei University of Technology

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Data-driven deformation prediction and control for existing tunnels below shield tunneling

Engineering Applications of Artificial Intelligence (2024)

Zongbao Feng Jingyi Wang Wenzhao Liu Tiejun Li Xianguo Wu

10.1016/j.engappai.2024.109379

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Citations (5)

Neural network and Sparse identification of Nonlinear Dynamics Integrated Algorithm for Digital Twin identification

IFAC-PapersOnLine (2023)

Jingyi Wang Jesús Moreira Yankai Cao R. Bhushan Gopaluni

Digital twins play a critical role in simulating industrial manufacturing systems to increase productivity and reduce time spent on troubleshooting. Owing to the complexity of real-world industrial systems, automatic sparse identification has emerged as an attractive approach to perform digital twin modelling. The sparse identification of nonlinear dynamics (SINDy) is a machine learning algorithm that performs feature engineering by generating a model term library and then solves a sparse regression problem between the objective outputs and the generated features. By solving a linear-in-parameter sparse regression problem, SINDy provides automatic discovery of system governing equations. However, the performance of SINDy-based algorithms may decline dramatically when applied to identify complex nonlinear relationships, such as implicit relationships. The substantial number of input variables for a real industrial process may further complicate the modelling procedure. We therefore propose the neural network and SINDy integrated algorithm to automatically select the critical features from a model term library and utilize the neural network to capture the process nonlinearity that cannot be captured by a linear-in-parameter model. SINDy performs feature generation considering both numerical methods and first-principles knowledge, making the proposed algorithm a hybrid system identification approach. A diesel hydrotreating unit case study with 37 input variables is analyzed in this paper to demonstrate the advantages of the proposed algorithm for nonlinear digital twin identification. By combining the advantages from both SINDy and neural networks, the proposed algorithm is able to improve the output prediction accuracy for all the three objectives.

Identification

Trouble shooting

Feature (linguistics)

Nonlinear system identification

10.1016/j.ifacol.2023.10.503

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高排出されるエネルギーを持つポリ(ふっ化ビニリデン)ナノ複合材料のためのコア‐シェル構造化BaTiO_3@SiO_2ナノファイバー【Powered by NICT】

Materials Letters (2017)

Liu Shaohui Wang Jiao Jingyi Wang Bo Shen Zhai Jiwei

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Additional file 7 of Endophytic fungus Pseudodidymocyrtis lobariellae KL27 promotes taxol biosynthesis and accumulation in Taxus chinensis

Figshare (2022)

Xiaoying Cao Lingxia Xu Jingyi Wang Mengmeng Dong Chunyan Xu

Additional file 7: Table S3. GO and KEGG enrichment analysis. (XLS 20 kb)

10.6084/m9.figshare.17798649

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A Target Location Algorithm Based on Millimeter Wave Radar

2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC) (2022)

Wenhao Dong Tianren Sun Jiahao Tian Jingyi Wang Zhiyong Song

How to find the trapped rescue workers in the fire disaster is the first problem to be solved. This paper designs the signal waveform, receives the echo data based on the AWR1642 millimeter wave radar sensor produced by TI company. The radar has high speed resolution, is prone to Across Doppler Unit (ADU) phenomenon, and the target signal-to-noise ratio and signal-to-clutter ratio are small in the fire environment, so it is difficult to realize the focus detection of the target. In addition, the radar has low angular resolution and is difficult to achieve accurate target positioning. The range and velocity of the target are estimated through signal processing processes such as Moving Target Indication (MTI), Radon-Fourier Transform (RFT) focusing and cell average constant false alarm rate (CA-CFAR) detection, and then obtains the angle information through multiple signal classification (MUSIC) algorithm processing to complete the target positioning.

Moving target indication

Stationary target indication

Space-Time Adaptive Processing

SIGNAL (programming language)

10.1109/itoec53115.2022.9734670

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Citations (3)

Dual Neural Extended Kalman Filtering Approach for Multirate Sensor Data Fusion

IEEE Transactions on Instrumentation and Measurement (2020)

Jingyi Wang Yousef Alipouri Biao Huang

The dual neural extended Kalman filter (DNEKF) is proposed in this article to compensate for model inaccuracies and violations of noise assumption in the EKF-based multirate sensor fusion. The DNEKF employs two neural networks to improve process state and output predictions through simultaneous state and parameter estimations using state vector augmentation. Taking advantage of frequent but less accurate measurements and infrequent but more accurate measurements provides a multirate parameter update strategy for neural networks training in the proposed approach. Two numerical examples and one industrial application demonstrate the effectiveness of the proposed method to compensate for inadequate process knowledge to improve multirate sensor fusion performance.

Sensor Fusion

State vector

10.1109/tim.2020.3041825

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Citations (22)

Additional file 9 of Endophytic fungus Pseudodidymocyrtis lobariellae KL27 promotes taxol biosynthesis and accumulation in Taxus chinensis

Figshare (2022)

Xiaoying Cao Lingxia Xu Jingyi Wang Mengmeng Dong Chunyan Xu

Additional file 9: Table S5. Differential expression of all unigenes in phenylpropanoid biosynthesis pathway (ko00940). (XLS 81 kb)

10.6084/m9.figshare.17798661

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Citations (0)

Gamma Gaussian-mixture CPHD filter based on star-convex random hypersurface for extended targets

Kongzhi Lilun yu Yingyong (2019)

Cuiyun Li Jingyi Wang JI Hong-bing Yuan Liu

Star (game theory)

Hypersurface

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Additional file 8 of Endophytic fungus Pseudodidymocyrtis lobariellae KL27 promotes taxol biosynthesis and accumulation in Taxus chinensis

Figshare (2022)

Xiaoying Cao Lingxia Xu Jingyi Wang Mengmeng Dong Chunyan Xu

Additional file 8: Table S4. DEGs involving in KEGG pathways after KL27-FB treatment. (XLS 202 kb)

10.6084/m9.figshare.17798652

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Pondering over Tex-machinery Safety

Textile Accessories (2008)

Jingyi Wang

Analysis of the safety importance and the factors leading to insecurity finds that safety problems involving with tex-machinery amounts to 70%~80%.Inadequate importance on safety of the industry calls for legislation of safety of tex-machinery.The international standard should be introduced in equivalent completing China safety standard.Machinery safety authentication center should be established to control the products from tex-industry for easy access to the market.

Safety Equipment

Safety standards

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