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Liming Zhang

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4 papers

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EAAI Journal 2025 Journal Article

A novel approach to model-based diagnosis with multiple observations

Ran Tai
Dantong Ouyang
Weiting Liu
Luyu Jiang
Liming Zhang

Model-based diagnosis (MBD) with multiple abnormal observations utilizes inconsistencies between actual and expected observations of systems to localize system faults. Current state-of-the-art algorithms still require solvers to consider a substantial number of suspected faulty components. To address this challenge, we introduce the Dual Principles with Decision Node (DPDN) algorithm. The first part of DPDN consists of two innovative principles: the Output Dependency Judgment Principle (ODJP) and the Deep Propagation Dependency Principle (DPDP). These principles are designed to identify and categorize a larger portion of components as ‘normal’, thereby broadening the idea of filtered nodes. By increasing the number of components classified as ‘normal’, the diagnostic process becomes more efficient as fewer components need to be diagnosed. The second part of DPDN integrates a newly defined decision node guided by its Decision Node Principle (DNP). This decision node, along with its corresponding principle, further bolsters the diagnostic process by classifying additional components as normal. With DPDN, complex real-world systems can reduce the number of components considered during the diagnostic process by eliminating those that are functioning normally, thereby decreasing the time required to obtain diagnoses. We conduct comparative experiments to evaluate the efficiency of our algorithm against other prevalent methods. Empirical results distinctly underscore DPDN’s superior performance in relation to other state-of-the-art algorithms.

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EAAI Journal 2025 Journal Article

Use deep transfer learning for efficient time-series updating of subsurface flow surrogate model

Wenhao Fu
Piyang Liu
Kai Zhang
Jinding Zhang
Xu Chen
Liming Zhang
Xia Yan

In subsurface flow, history matching is a complex high-dimensional sampling problem demanding numerous computationally intensive numerical simulations, which result in high costs. To mitigate this, various deep learning-based surrogate-assisted techniques are employed to approximate the original numerical simulation processes for more efficient computation. Despite the widespread application of deep learning-based surrogate models, significant limitations remain, particularly the need for the frequent reconstruction of surrogate models when new observed data become available. This process requires additional high-fidelity simulation data, further increasing computational burden of history matching. To tackle this problem, we introduce a transfer learning-based framework within an image-to-sequence surrogate model to enable efficient updates as new data become available. This surrogate model consists of a geological feature extraction module and a sequence data regression module. To facilitate rapid learning of new tasks while retaining previously learned knowledge, we employ a decoupled learning strategy where the backbone structure remains fixed, and only the sequence data regression module is fine-tuned. The surrogate model is subsequently used for posterior sampling of the geological model using the Randomized Maximum Likelihood (RML) method. The proposed workflow is evaluated on both two-dimensional and three-dimensional oil-water flow models. Experiments indicate that our surrogate model achieves a Root Mean Square Error (RMSE) below 0. 08 and a Coefficient of Determination (R2) above 0. 97. Moreover, by leveraging transfer learning, the model attains comparable predictive accuracy with only 40 % of the original training data. The results demonstrate the surrogate model's capability to reduce computational costs while preserving accuracy.

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AAAI Conference 2022 Conference Paper

Two Compacted Models for Efficient Model-Based Diagnosis

Huisi Zhou
Dantong Ouyang
Xiangfu Zhao
Liming Zhang

Model-based diagnosis (MBD) with multiple observations is complicated and difficult to manage over. In this paper, we propose two new diagnosis models, namely, the Compacted Model with Multiple Observations (CMMO) and the Dominated-based Compacted Model with Multiple Observations (D-CMMO), to solve the problem in which a considerable amount of time is needed when multiple observations are given and more than one fault is injected. Three ideas are presented in this paper. First, we propose to encode MB- D with each observation as a subsystem and share as many system variables as possible to compress the size of encoded clauses. Second, we utilize the notion of gate dominance in the CMMO approach to compute Top-Level Diagnosis with Compacted Model (CM-TLD) to reduce the solution space. Finally, we explore the performance of our model using three fault models. Experimental results on the ISCAS-85 benchmarks show that CMMO and D-CMMO perform better than the state-of-the-art algorithms.

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JBHI Journal 2019 Journal Article

A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based Signal Compression Algorithm With Application on ECG Signals

Chunyu Tan
Liming Zhang
Hau-Tieng Wu

This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieves a faster convergence rate with higher fidelity. The proposed compression algorithm is applied to the electrocardiogram signal. To assess the performance of the proposed compression algorithm, in addition to the generic assessment criteria, we consider the less discussed criteria related to the clinical needs—for the heart rate variability analysis purpose, how accurate the R-peak information is preserved is evaluated. The experiments are conducted on the MIT-BIH arrhythmia benchmark database. The results show that the proposed algorithm performs better than other state-of-the-art approaches. Meanwhile, it also well preserves the R-peak information.

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