Výsledky vyhledávání - "Ding, Ze Yang"

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Sigma-point Kalman Filter with Nonlinear Unknown Input Estimation via Optimization and Data-driven Approach for Dynamic Systems

Autor: Loo, Junn Yong, Ding, Ze Yang, Baskaran, Vishnu Monn, Nurzaman, Surya Girinatha, Tan, Chee Pin

Most works on joint state and unknown input (UI) estimation require the assumption that the UIs are linear; this is potentially restrictive as it does not hold in many intelligent autonomous systems. To overcome this restriction and circumvent the ne

Externí odkaz: http://arxiv.org/abs/2306.12361

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Unsupervised Cross-Domain Soft Sensor Modelling via Deep Physics-Inspired Particle Flow Bayes

Autor: Loo, Junn Yong, Ding, Ze Yang, Nurzaman, Surya G., Ting, Chee-Ming, Baskaran, Vishnu Monn, Tan, Chee Pin

Data-driven soft sensors are essential for achieving accurate perception through reliable state inference. However, developing representative soft sensor models is challenged by issues such as missing labels, domain adaptability, and temporal coheren

Externí odkaz: http://arxiv.org/abs/2306.04919

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Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework

Autor: Sapai, Shageenderan, Loo, Junn Yong, Ding, Ze Yang, Tan, Chee Pin, Phan, Raphael CW, Baskaran, Vishnu Monn, Nurzaman, Surya Girinatha

Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exha

Externí odkaz: http://arxiv.org/abs/2303.01693

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