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

• URL: http://arxiv.org/abs/2306.04919v4
• Date: Sun, 9 Jul 2023 03:04:53 GMT
• Title: Unsupervised Cross-Domain Soft Sensor Modelling via Deep Physics-Inspired Particle Flow Bayes
• Authors: Junn Yong Loo, Ze Yang Ding, Surya G. Nurzaman, Chee-Ming Ting, Vishnu Monn Baskaran and Chee Pin Tan
• Abstract summary: We propose a deep Particle Flow Bayes framework for cross-domain soft sensor modeling. In particular, a sequential Bayes objective is first formulated to perform the maximum likelihood estimation. We validate the framework on a complex industrial multiphase flow process system.
• Score: 3.2307729081989334
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 coherence in data. To address these challenges, we propose a deep Particle Flow Bayes (DPFB) framework for cross-domain soft sensor modeling in the absence of target state labels. In particular, a sequential Bayes objective is first formulated to perform the maximum likelihood estimation underlying the cross-domain soft sensing problem. At the core of the framework, we incorporate a physics-inspired particle flow that optimizes the sequential Bayes objective to perform an exact Bayes update of the model extracted latent and hidden features. As a result, these contributions enable the proposed framework to learn a rich approximate posterior feature representation capable of characterizing complex cross-domain system dynamics and performing effective time series unsupervised domain adaptation (UDA). Finally, we validate the framework on a complex industrial multiphase flow process system with complex dynamics and multiple operating conditions. The results demonstrate that the DPFB framework achieves superior cross-domain soft sensing performance, outperforming state-of-the-art deep UDA and normalizing flow approaches.

Related papers

• Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss [9.287932323337163]
  3D particle tracking velocimetry (PTV) is a key technique for analyzing turbulent flow. Deep learning-based methods have achieved impressive accuracy in dual-frame fluid motion estimation. We introduce a new method that is completely self-supervised and notably outperforms its fully-supervised counterparts.
  arXiv  Detail & Related papers  (2024-10-15T18:00:00Z)
• FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation [65.01601309903971]
  We introduce FAFA, a Frequency-Aware Flow-Aided self-supervised framework for 6D pose estimation of unmanned underwater vehicles (UUVs) Our framework relies solely on the 3D model and RGB images, alleviating the need for any real pose annotations or other-modality data like depths. We evaluate the effectiveness of FAFA on common underwater object pose benchmarks and showcase significant performance improvements compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-25T03:54:01Z)
• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology [0.5461938536945721]
  Quantum sensors offer control flexibility during estimation by allowing manipulation by the experimenter across various parameters. We introduce a versatile procedure capable of optimizing a wide range of problems in quantum metrology, estimation, and hypothesis testing. We combine model-aware reinforcement learning (RL) with Bayesian estimation based on particle filtering.
  arXiv  Detail & Related papers  (2023-12-28T12:04:15Z)
• Sensitivity-Aware Amortized Bayesian Inference [8.753065246797561]
  Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. We propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to integrate sensitivity analyses into simulation-based inference with neural networks. We demonstrate the effectiveness of our method in applied modeling problems, ranging from disease outbreak dynamics and global warming thresholds to human decision-making.
  arXiv  Detail & Related papers  (2023-10-17T10:14:10Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• GAFlow: Incorporating Gaussian Attention into Optical Flow [62.646389181507764]
  We push Gaussian Attention (GA) into the optical flow models to accentuate local properties during representation learning. We introduce a novel Gaussian-Constrained Layer (GCL) which can be easily plugged into existing Transformer blocks. For reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM)
  arXiv  Detail & Related papers  (2023-09-28T07:46:01Z)
• Information-Based Sensor Placement for Data-Driven Estimation of Unsteady Flows [0.0]
  This paper presents a sensor-selection framework for the intended application of data-driven, flow-field estimation. This framework combines data-driven modeling, steady-state Kalman Filter design, and a sparsification technique for sequential selection of sensors.
  arXiv  Detail & Related papers  (2023-03-22T02:00:51Z)
• Deep Measurement Updates for Bayes Filters [5.059735037931382]
  We propose the novel approach Deep Measurement Update (DMU) as a general update rule for a wide range of systems. DMU has a conditional encoder-decoder neural network structure to process depth images as raw inputs. We demonstrate how the DMU models can be trained efficiently to be sensitive to condition variables without having to rely on an information bottleneck.
  arXiv  Detail & Related papers  (2021-12-01T10:00:37Z)
• Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection [86.69077525494106]
  Unsupervised domain adaptation (UDA) has achieved unprecedented success in improving the cross-domain robustness of object detection models. Existing UDA methods largely ignore the instantaneous data distribution during model learning, which could deteriorate the feature representation given large domain shift. We propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains.
  arXiv  Detail & Related papers  (2020-03-19T13:30:45Z)
• A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network [55.852401381113786]
  This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive-temporal quality framework for wireless access latency of connected vehicles. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Varienational Autocoder (VAE)
  arXiv  Detail & Related papers  (2020-03-16T03:43:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.