Author name cluster

Nan Liu

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9 papers

1 author row

EAAI Journal 2026 Journal Article

Meta-learning with variational inference for few-shot faults diagnosis of automotive transmission under variable operating conditions

Bin Sun
Hongkun Li
Nan Liu
Feifei Li
Zhenhui Ma

The automotive transmission is a critical component for regulating vehicle speed. However, in real industrial settings, the complexity and variability of operating conditions, along with a limited number of fault samples, make traditional deep learning methods inadequate for practical applications. To address these challenges, this paper presents a few-shot fault diagnosis method for automotive transmissions under variable conditions, based on Variational Agnostic Meta-Learning for Robust Inference (VAMPIRE). First, the vibration data collected from sensors is sliced and converted into two-dimensional grayscale images to create the dataset. Next, by integrating Bayesian theory with a meta-learning framework, we use variational inference to approximate the posterior distribution. This allows the learned meta-parameters to coherently explain the variability of the data, thereby enhancing the model's generalization ability across different operating conditions. Finally, this study utilized data from an industrial-grade gearbox test bench and real-road test data of an industrial truck gearbox to conduct comparative experiments under multiple variable working conditions, and compared the results with various methods. The experimental results show that regardless of the sample size or the complexity of working conditions, the proposed method performs excellently in terms of accuracy, stability, and generalizability. For example, in test scenarios involving multiple unknown working conditions, the proposed method achieved an average diagnostic accuracy of 96. 52 % for test bench data and 97. 54 % for real-vehicle data in 5-shot learning tasks. Even in the most challenging 1-shot learning tasks, its average accuracy remained at 93. 88 % and 94. 82 %, respectively, significantly outperforming the comparative methods.

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EAAI Journal 2025 Journal Article

A hyperparameter-fusion neural networks for deposition prediction

Li Ding
Kun Pang
Junjie Li
Hua Shao
Nan Liu
Rui Chen
Zhiqiang Li
Zhenjie Yao

As integrated circuit manufacturing processes develop into the nanometer scale, precise control and prediction of the deposition process have become crucial. Nanoscale manufacturing imposes unprecedentedly high demands on film quality, uniformity, and consistency, presenting significant challenges to traditional control and prediction methodologies. This study proposes a novel approach that, for the first time, formulates the thin-film deposition process as a video prediction task, enabling the use of deep learning for morphological forecasting under varying process conditions, and introduces a novel hyperparameter-fusion neural network, referred to as DepositionNet (DepoNet). Unlike conventional video prediction models, DepoNet specifically accounts for the influence of deposition parameters on the entire simulation process. We have incorporated a novel Hyper Projector that allows the model to flexibly adapt to varying deposition conditions and material characteristics. Through comprehensive comparative experimental analyses, we demonstrate that DepoNet significantly outperforms existing deep-learning models and achieves a mean squared error of 17. 34, representing a 3. 67% improvement over the second best model and a 1, 435 × speedup over physics-based methods, thereby validating its exceptional generalization capability. Extensive experiments reveal that the model maintains high performance even under conditions of limited training data, for instance, achieving a peak signal-to-noise ratio (PSNR) of 41. 516 decibels (dB) when trained with only 20% of the available data.

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JBHI Journal 2025 Journal Article

Benchmarking Foundation Models with Multimodal Public Electronic Health Records

Kunyu Yu
Rui Yang
Jingchi Liao
Siqi Li
Huitao Li
Irene Li
Yifan Peng
Rishikesan Kamaleswaran

Foundation models have emerged as a powerful approach for processing electronic health records (EHRs), offering flexibility to handle diverse medical data modalities. In this study, we present a comprehensive benchmark that evaluates the performance, fairness, and interpretability of foundation models, both as unimodal encoders and as multimodal learners, using the publicly available MIMIC-IV database. To support consistent and reproducible evaluation, we developed a standardized data processing pipeline that harmonizes heterogeneous clinical records into an analysis-ready format. We systematically compared twelve foundation models, encompassing both unimodal and multimodal models, as well as domain-specific and general-purpose variants. Our findings demonstrate that incorporating multiple data modalities generally improves predictive performance without introducing additional bias. While domain-specific fine-tuning offers a cost-effective solution for unimodal foundation models, this effectiveness does not translate well to multimodal scenarios. Additionally, our experiments reveal limited task generalizability in current large vision-language models (LVLMs), emphasizing the need for more versatile and robust medical LVLMs. Through this benchmark, we aim to support the development of effective and trustworthy multimodal artificial intelligence (AI) systems for real-world clinical applications.

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AIIM Journal 2025 Journal Article

Reporting guideline for chatbot health advice studies: The CHART statement

Bright Huo
Gary Collins
David Chartash
Arun Thirunavukarasu
Annette Flanagin
Alfonso Iorio
Giovanni Cacciamani
Xi Chen

The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as Chatbot Health Advice (CHA) studies. CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of CHA studies. These include Title (subitem 1a), Abstract/Summary (subitem 1b), Background (subitems 2ab), Model Identifiers (subitem 3ab), Model Details (subitems 4abc), Prompt Engineering (subitems 5ab), Query Strategy (subitems 6abcd), Performance Evaluation (subitems 7ab), Sample Size (subitem 8), Data Analysis (subitem 9a), Results (subitems 10abc), Discussion (subitems 11abc), Disclosures (subitem 12a), Funding (subitem 12b), Ethics (subitem 12c), Protocol (subitem 12d), and Data Availability (subitem 12e). The CHART checklist and corresponding methodological diagram were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of CHA studies.

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EAAI Journal 2025 Journal Article

Research on grinding force prediction of spiral bevel gear based on generalized regression neural network and undeformed grinding chips

Nan Liu
Jiang Han
Xiaoqing Tian
Lian Xia
Minglei Li
Rui Xue

This study is rooted in a generalized regression neural network (GRNN) and undeformed grinding chips to predict the grinding force of spiral bevel gears. Firstly, establish a grinding force model for an individual abrasive particle, and integrate it with the dynamic number of abrasive particles in the grinding wheel to obtain a spiral bevel gear grinding force model based on undeformed grinding chips. Secondly, using the duplex spread blade design technique, the spiral conical gear blank geometric dimensions, machine tool adjustment parameters, and tool specifications are calculated, and the simulation of milling and grinding is carried out in three-dimensional solid modeling software. The undeformed chips and related parameters are obtained through Boolean intersection. Due to the complexity of calculation and simulation in the development procedure of spiral bevel gears, the GRNN is established to predict the undeformed grinding chip parameters of non-simulated gears. Finally, each group's comprehensive coefficient of cutting and friction forces were calculated through grinding experiments at different generating motion speeds. Predict these coefficients through experimental data. The results showed that the maximum standard deviation between the expected grinding force and the experimental value was 3. 3260, which verifies the method's effectiveness. The GRNN algorithm can help select process parameters more reasonably, avoid tooth surface burns, and improve machining efficiency.

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TCS Journal 2024 Journal Article

New approximation algorithms for RNA secondary structures prediction problems by local search

Aizhong Zhou
Haodi Feng
Jiong Guo
Haitao Jiang
Nan Liu
Binhai Zhu
Daming Zhu

This paper investigates two combinatorial problems from RNA secondary structure prediction with arbitrary pseudoknots. Given a RNA sequence and a set of base pairs, two parallel and adjacent base pairs constitute a stacking. The Maximum Stacking Base Pairs problem (MSBP) aims at finding a maximum number of based pairs, all of which form stackings, while the Maximum Base Pair Stackings problem (MBPS) is to find a maximum number of stackings. Both problems are NP-hard. We present two new approximation algorithms for the two problems by local search methods. For MSBP, the approximation factor is improved from 5 2 to 7 3; as for the MBPS, the approximation factor is improved from 8 3 to 5 2.

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AIIM Journal 2023 Journal Article

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

Mingxuan Liu
Siqi Li
Han Yuan
Marcus Eng Hock Ong
Yilin Ning
Feng Xie
Seyed Ehsan Saffari
Yuqing Shang

Objective The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data. Materials and methods We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to February 8, 2023 that described the use of DL-based models for imputation. We examined selected articles from four perspectives: data types, model backbones (i. e. , main architectures), imputation strategies, and comparisons with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models. Results Out of 1822 articles, a total of 111 were included, of which tabular static data (29%, 32/111) and temporal data (40%, 44/111) were the most frequently investigated. Our findings revealed a discernible pattern in the choice of model backbones and data types, for example, the dominance of autoencoder and recurrent neural networks for tabular temporal data. The discrepancy in imputation strategy usage among data types was also observed. The “integrated” imputation strategy, which solves the imputation task simultaneously with downstream tasks, was most popular for tabular temporal data (52%, 23/44) and multi-modal data (56%, 5/9). Moreover, DL-based imputation methods yielded a higher level of imputation accuracy than non-DL methods in most studies. Conclusion The DL-based imputation models are a family of techniques, with diverse network structures. Their designation in healthcare is usually tailored to data types with different characteristics. Although DL-based imputation models may not be superior to conventional approaches across all datasets, it is highly possible for them to achieve satisfactory results for a particular data type or dataset. There are, however, still issues with regard to portability, interpretability, and fairness associated with current DL-based imputation models.

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NeurIPS Conference 2021 Conference Paper

Learning to Compose Visual Relations

Nan Liu
Shuang Li
Yilun Du
Josh Tenenbaum
Antonio Torralba

The visual world around us can be described as a structured set of objects and their associated relations. An image of a room may be conjured given only the description of the underlying objects and their associated relations. While there has been significant work on designing deep neural networks which may compose individual objects together, less work has been done on composing the individual relations between objects. A principal difficulty is that while the placement of objects is mutually independent, their relations are entangled and dependent on each other. To circumvent this issue, existing works primarily compose relations by utilizing a holistic encoder, in the form of text or graphs. In this work, we instead propose to represent each relation as an unnormalized density (an energy-based model), enabling us to compose separate relations in a factorized manner. We show that such a factorized decomposition allows the model to both generate and edit scenes that have multiple sets of relations more faithfully. We further show that decomposition enables our model to effectively understand the underlying relational scene structure.

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JBHI Journal 2014 Journal Article

Risk Scoring for Prediction of Acute Cardiac Complications from Imbalanced Clinical Data

Nan Liu
Zhi Xiong Koh
Eric Chern-Pin Chua
Licia Mei-Ling Tan
Zhiping Lin
Bilal Mirza
Marcus Eng Hock Ong

Fast and accurate risk stratification is essential in the emergency department (ED) as it allows clinicians to identify chest pain patients who are at high risk of cardiac complications and require intensive monitoring and early intervention. In this paper, we present a novel intelligent scoring system using heart rate variability, 12-lead electrocardiogram (ECG), and vital signs where a hybrid sampling-based ensemble learning strategy is proposed to handle data imbalance. The experiments were conducted on a dataset consisting of 564 chest pain patients recruited at the ED of a tertiary hospital. The proposed ensemble-based scoring system was compared with established scoring methods such as the modified early warning score and the thrombolysis in myocardial infarction score, and showed its effectiveness in predicting acute cardiac complications within 72 h in terms of the receiver operation characteristic analysis.

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