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ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
Hwa Hui Tew;Fan Ding;Gaoxuan Li;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light of this, we propose a deep spatio-temporal hypergraph convolutional neural network for soft sensing (ST-HCSS). In particular, our proposed framework is able to construct and ...Show More
ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
Hwa Hui Tew;Fan Ding;Gaoxuan Li;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Cross-Domain Transfer Learning Using Attention Latent Features for Multi-Agent Trajectory Prediction
Jia Quan Loh;Xuewen Luo;Fan Ding;Hwa Hui Tew;Junn Yong Loo;Ze Yang Ding;Susilawati Susilawati;Chee Pin Tan
2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2024 | Conference Paper |
With the advancements of sensor hardware, traf-fic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailored to specific traffic networks at particular time periods. Consequently, deep learning models trained on one network may struggle to generalize effe...Show More
KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Hwa Hui Tew;Gaoxuan Li;Fan Ding;Xuewen Luo;Junn Yong Loo;Chee-Ming Ting;Ze Yang Ding;Chee Pin Tan
2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2024 | Conference Paper |
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledg...Show More
Sigma-Point Kalman Filter With Nonlinear Unknown Input Estimation via Optimization and Data-Driven Approach for Dynamic Systems
Junn Yong Loo;Ze Yang Ding;Vishnu Monn Baskaran;Surya Girinatha Nurzaman;Chee Pin Tan
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 need to linearize the system, we propose a derivative-free UI sigma-point Kalman filter (SPKF-nUI), where the SPKF is interconnected with a general nonl...Show More
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework
Shageenderan Sapai;Junn Yong Loo;Ze Yang Ding;Chee Pin Tan;Raphaël C.-W. Phan;Vishnu Monn Baskaran;Surya Girinatha Nurzaman
2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
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 exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged f...Show More
A Zero-Shot Soft Sensor Modeling Approach Using Adversarial Learning for Robustness Against Sensor Fault
Ze Yang Ding;Junn Yong Loo;Surya Girinatha Nurzaman;Chee Pin Tan;Vishnu Monn Baskaran
Soft sensors are widely used in many industrial systems to monitor key variables that are difficult to measure, using measurements from other available physical sensors. Because physical sensors are susceptible to faults, it is crucial for soft sensor models to be robust against them. Recently, deep learning has shown promising results in developing data-driven soft sensors for various application...Show More
Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing
Ze Yang Ding;Junn Yong Loo;Vishnu Monn Baskaran;Surya Girinatha Nurzaman;Chee Pin Tan
The mechanical compliance of soft robots comes at a cost of higher uncertainty in their sensing and perception, which deteriorates the accuracy of predictive models. Predictive uncertainty, which expresses the confidence behind model predictions, is necessary to compensate for the loss of accuracy in soft robot perceptive models. Nevertheless, developing a general framework to capture uncertaintie...Show More
Future trends in I&M: Indirect sensing in soft robots using observers/filters
A note from Melanie Ooi: Soft robotics is a very new and interesting research area with many applications, and sensing in soft robots remains a big challenge. Our guest columnists from Monash University Malaysia, Dr. Chee Pin Tan and Dr. Surya Nurzaman, have expertise in state estimation and soft robotics, respectively. Together with their graduate research students, they share with us how state e...Show More
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