arXiv subjects

Qingchen Zhang

Publications and source records attributed to Qingchen Zhang.

5 recordsLinked to original sources

arXiv2026

Dual-Learning based Penalized Multi-Align Clustering for Multi-View Incomplete and Disorderly Data

Multimodal feature fusion can effectively capture complex patterns in real-world data by integrating complementary information from different modalities. However, in many applications, such as boiler combustion monitoring, equipment failure, inconsistent sensor sampling frequencies, and network delays often cause missing modalities and temporal asynchrony. These issues lead to incomplete and disorderly multimodal data. To address them, previous studies have proposed several data fusion methods that align cluster centers before fusion. However, these methods have two key limitations. First, they cannot guarantee accurate sample-level alignment of data pairs. Second, they do not address significant discrepancies in data sizes across different classes, which may affect subsequent fusion performance. To address these problems, we propose a dual-learning based penalized multi-align clustering model, named DLPMAC. The dual-learning mechanism enables the model to learn prior knowledge from each modality, including semantic and structural information. This helps preserve semantic consistency and structural similarity across modalities at both local and global levels. In addition, the penalized multi-align module performs multi-to-multi data alignment through a penalty mechanism. It allows one sample to form data pairs with different samples from other modalities, thereby improving data-pair alignment accuracy. The penalty mechanism also prevents data aggregation, avoiding the case where excessive samples are linked to a single sample. Experimental results demonstrate the effectiveness of DLPMAC in addressing data alignment and fusion challenges from both sampling and clustering perspectives.

cs.LG↗

arXiv2026

Ensuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verification

Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventilator settings could cause ventilator-induced lung injury (VILI). Also, clinicians workload is shown to be directly linked to patient outcomes. Hence, MV should be personalized and automated to improve patient outcomes. Previous attempts to incorporate personalization and automation in MV include traditional supervised learning and offline reinforcement learning (RL) approaches, which often neglect temporal dependencies and rely excessively on mortality-based rewards. As a result, early stage physiological deterioration and the risk of VILI are not adequately captured. To address these limitations, we propose Transformer-based Conservative Q-Learning (T-CQL), a novel offline RL framework that integrates a Transformer encoder for effective temporal modeling of patient dynamics, conservative adaptive regularization based on uncertainty quantification to ensure safety, and consistency regularization for robust decision-making. We build a clinically informed reward function that incorporates indicators of VILI and a score for severity of patients illness. Also, previous work predominantly uses Fitted Q-Evaluation (FQE) for RL policy evaluation on static offline data, which is less responsive to dynamic environmental changes and susceptible to distribution shifts. To overcome these evaluation limitations, interactive digital twins of ARF patients were used for online "at the bedside" evaluation. Our results demonstrate that T-CQL consistently outperforms existing state-of-the-art offline RL methodologies, providing safer and more effective ventilatory adjustments. Our framework demonstrates the potential of Transformer-based models combined with conservative RL strategies as a decision support tool in critical care.

cs.LG↗

arXiv2025

Generating Reliable Initial Velocity Models for Full-waveform Inversion with Well and Structural Constraints

Full waveform inversion (FWI) plays an important role in velocity modeling due to its high-resolution advantages. However, its highly non-linear characteristic leads to numerous local minimums, which is known as the cycle-skipping problem. Therefore, effectively addressing the cycle-skipping issue is crucial to the success of FWI. Well-log data contain rich information about subsurface medium parameters, providing inherent advantages for velocity modeling. Traditional well-log data interpolation methods to build velocity models have limited accuracy and poor adaptability to complex geological structures. This study introduces a well interpolation algorithm based on a generative diffusion model (GDM) to generate initial models for FWI, addressing the cycle-skipping problem. Existing convolutional neural network (CNN)-based methods face difficulties in handling complex feature distributions and lack effective uncertainty quantification, limiting the reliability of their outputs. The proposed GDM-based approach overcomes these challenges by providing geologically consistent well interpolation while incorporating uncertainty assessment. Numerical experiments demonstrate that the method produces accurate and reliable initial models, enhancing FWI performance and mitigating cycle-skipping issues.

physics.geo-ph↗

arXiv2024

A self-supervised learning framework for seismic low-frequency extrapolation

Full waveform inversion (FWI) is capable of generating high-resolution subsurface parameter models, but it is susceptible to cycle-skipping when the data lack low-frequency. Unfortunately, the low-frequency components (< 5.0 Hz) are often tainted by noise in real seismic exploration, which hinders the application of FWI. To address this issue, we develop a novel self-supervised low-frequency extrapolation method that does not require labeled data, enabling neural networks to be trained directly on real data. This paradigm effectively addresses the significant generalization gap often encountered by supervised learning techniques, which are typically trained on synthetic data. We validate the effectiveness of our method on both synthetic and field data. The results demonstrate that our method effectively extrapolates low-frequency components, aiding in circumventing the challenges of cycle-skipping in FWI. Meanwhile, by integrating a self-supervised denoiser, our method effectively performs simultaneously denoising and low-frequency extrapolation on noisy data. Furthermore, we showcase the potential application of our method in extending the ultra-low frequency components of the large-scale collected earthquake seismogram.

physics.geo-ph↗

arXiv2023

An effective self-supervised learning method for various seismic noise attenuation

Faced with the scarcity of clean label data in real scenarios, seismic denoising methods based on supervised learning (SL) often encounter performance limitations. Specifically, when a model trained on synthetic data is directly applied to field data, its performance would drastically decline due to significant differences in feature distributions between the two. To address this challenge, we develop an effective self-supervised strategy. This strategy, while relying on a single denoising network model, adeptly attenuates various types of seismic noise. The strategy comprises two main phases: 1. The warm-up phase. By using prior knowledge or extracting information from real data, we introduce additional noise to the original noisy data, constructing a noisier data with intensified noise. This data serves as the input, with the original noisy data acting as pseudo-labels. This facilitates rapid pre-training of the network to capture a certain noise characteristics and boosts network stability, setting the stage for the subsequent phase. 2. Iterative data refinement (IDR) phase. During this phase, we use the predictions of the original noisy data from the network trained in the previous epoch as the pseudo-labels. We continue to add noise to the predictions, creating a new noisier-noisy dataset for the current epoch of network training. Through this iterative process, we progressively reduce the discrepancy between the original noisy data and the desired clean data. Ultimately, the network's predictions on the original noisy data become our denoised results. Validations under scenarios with random noise, backscattered noise, and blending noise reveal that our method not only matches the traditional SL techniques on synthetic data but significantly outperforms them on field data.

physics.geo-ph↗