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Dan Li

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7 papers

1 author row

EAAI Journal 2026 Journal Article

Consistency and consensus-based decision-making for probabilistic linguistic information reliability

Yan Chen
Dan Li
Lin Liu
Xiao Wang
Lei Xu

Probabilistic linguistic information is a new decision-making tool that can effectively handle uncertain information in the decision-making process. In the group decision-making process based on probabilistic linguistic information, the PageRank algorithm based on social network expert weight calculation, the reliability of individual experts and the reliability of the group decision-making are rarely considered. This research mainly addresses this series of questions. Four processes are included: the operation laws for linguistic term sets, the consistency analysis process of the probabilistic linguistic preference matrix, expert weights based on the improved PageRank algorithm and the process of expert reliability analysis. In the first process, a new algorithm is proposed in this paper, which solves the problem of closure and transferability of operations between linguistic term sets. In the second process, the concept of the derived matrix is defined, and combined with the concept of the reachability matrix, the method of judging the complete consistency and satisfactory consistency of the probabilistic linguistic preference matrix is given. In the third process, the concept of dual trust propagation is defined and combined with the trust relationship between experts and the PageRank algorithm to calculate the expert weights. The concept of two-level consensus and a consensus threshold are introduced in the meanwhile, which ensures the quality of decision-making. According to the properties of the derived matrix, a ranking method of alternatives is given by solving the eigenvector of the maximum eigenvalue. In the last process, the concept of decision-making reliability is presented, and the calculation method of expert reliability and group decision-making reliability are given. The effectiveness and practicality of the method given in this paper are illustrated by numerical examples.

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EAAI Journal 2026 Journal Article

Large language models for explainable fault diagnosis of machines

Hamzah A.A.M. Qaid
Bo Zhang
Shuai Su
Dan Li
See-Kiong Ng
Wei Li

Large Language Models (LLMs) have demonstrated remarkable capabilities in capturing complex conceptual representations from textual data for a wide range of real-world applications. However, in Intelligent Fault Diagnosis (IFD), leveraging sensor data such as vibration signals is essential but remains a challenge due to the modality gap between time series and LLMs’ inputs. Existing efforts to bridge this gap often treat LLMs merely as classifiers, overlooking their potential for understanding and reasoning over vibration-based data. In this paper, we propose a novel LLM-based fault diagnosis framework (FD-LLM) that aligns vibration signals with LLMs by encoding the signals into textual representations. FD-LLM introduces a classification-oriented approach, which formulates fault diagnosis as a multi-class classification task for benchmarking LLMs’ performance, and a context-aware spectrum language modeling approach that enables explainable, reasoning-driven fault analysis. We evaluate four open-source LLMs using FD-LLM across multiple datasets and noise conditions, assessing their validity, adaptability, and robustness. The results demonstrate that models such as LLaMA models achieve robust diagnostic performance, strong zero-shot adaptability across operating conditions, and effective generalization in cross-dataset scenarios with few-shot learning. The results further indicate that explainable fault diagnosis can be achieved in LLMs.

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NeurIPS Conference 2025 Conference Paper

Transcending Cost-Quality Tradeoff in Agent Serving via Session-Awareness

Yanyu Ren
Li Chen
Dan Li
Xizheng Wang
Zhiyuan Wu
Yukai Miao
Yu Bai

Large Language Model (LLM) agents are capable of task execution across various domains by autonomously interacting with environments and refining LLM responses based on feedback. However, existing model serving systems are not optimized for the unique demands of serving agents. Compared to classic model serving, agent serving has different characteristics: predictable request pattern, increasing quality requirement, and unique prompt formatting. We identify a key problem for agent serving: LLM serving systems lack session-awareness. They neither perform effective KV cache management nor precisely select the cheapest yet competent model in each round. This leads to a cost-quality tradeoff, and we identify an opportunity to surpass it in an agent serving system. To this end, we introduce AgServe for AGile AGent SERVing. AgServe features a session-aware server that boosts KV cache reuse via Estimated-Time-of-Arrival-based eviction and in-place positional embedding calibration, a quality-aware client that performs session-aware model cascading through real-time quality assessment, and a dynamic resource scheduler that maximizes GPU utilization. With AgServe, we allow agents to select and upgrade models during the session lifetime, and to achieve similar quality at much lower costs, effectively transcending the tradeoff. Extensive experiments on real testbeds demonstrate that AgServe (1) achieves comparable response quality to GPT-4o at a 16. 5\% cost. (2) delivers 1. 8$\times$ improvement in quality relative to the tradeoff curve.

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YNIMG Journal 2024 Journal Article

Refining hemodynamic correction in in vivo wide-field fluorescent imaging through linear regression analysis

Jing Li
Fan Yang
Kathleen Zhang
Shiqiang Wu
James Niemeyer
Mingrui Zhao
Peijuan Luo
Nan Li

Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

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JBHI Journal 2021 Journal Article

Boundary Aware U-Net for Retinal Layers Segmentation in Optical Coherence Tomography Images

Bo Wang
Wei Wei
Shuang Qiu
Shengpei Wang
Dan Li
Huiguang He

Retinal layers segmentation in optical coherence tomography (OCT) images is a critical step in the diagnosis of numerous ocular diseases. Automatic layers segmentation requires separating each individual layer instance with accurate boundary detection, but remains a challenging task since it suffers from speckle noise, intensity inhomogeneity, and the low contrast around boundary. In this work, we proposed a boundary aware U-Net (BAU-Net) for retinal layers segmentation by detecting accurate boundary. Based on encoder-decoder architecture, we design a dual tasks framework with low-level outputs for boundary detection and high-level outputs for layers segmentation. Specifically, we first use the multi-scale input strategy to enrich the spatial information in the deep features of encoder. For low-level features from encoder, we design an edge aware (EA) module in skip connection to extract the pure edge features. Then, a U-structure feature enhanced (UFE) module is designed in all skip connections to enlarge the features receptive fields from the encoder. Besides, a canny edge fusion (CEF) module is introduced to aforementioned architecture, which can fuse the priory edge information from segmentation task to boundary detection branch for a better predication. Furthermore, we model each boundary as a vertical coordinates distribution for boundary detection. Based on this distribution, a topology guarantee loss with combined A-scan regression loss and structure loss is proposed to make an accurate and guaranteed topological boundary set. The method is evaluated on two public datasets and the results demonstrate that the BAU-Net achieves promising performance than other state-of-the-art methods.

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JBHI Journal 2021 Journal Article

Sleep Staging Using Plausibility Score: A Novel Feature Selection Method Based on Metric Learning

Tao Zhang
Zhonghui Jiang
Dan Li
Xiao Wei
Bing Guo
Wu Huang
Guobiao Xu

As an effective method, feature selection can reduce computational complexity and improve classification performance. A number of criteria exist for feature selection using labeled data, unlabeled data and pairwise constraints, most of which are based on the Euclidean distance. In this paper, we propose a filter method for feature selection with pairwise constraints, aiming to jointly evaluate a feature subset based on metric learning. Two criteria are designed based on the well-known Kullback-Leibler divergence for measuring the difference between must-link constraints and cannot-link constraints that can indicate the feature subset discrimination based on Keep It Simple and Straightforward (KISS) metric learning and Cross-view Quadratic Discriminant Analysis (XQDA) metric learning. To address the challenging feature selection problem, we formulate a sequential search algorithm guided by indicators that are simplified from the proposed criteria. Furthermore, we conducted several experiments on sleep staging based on electroencephalogram (EEG) recordings from the Sleep-EDF Database Expanded. The experimental results demonstrate the effectiveness of the proposed method compared with nine representative feature selection methods. On the data set from healthy volunteers and the data set from volunteers that had mild difficulty falling asleep, the classification average accuracies achieve 97. 66% and 93. 57% by using the proposed method, respectively.

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NeurIPS Conference 2018 Conference Paper

BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training

Songtao Wang
Dan Li
Yang Cheng
Jinkun Geng
Yanshu Wang
Shuai Wang
Shu-Tao Xia
Jianping Wu

In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training. In this paper we propose BML, a new gradient synchronization algorithm with higher network performance and lower network cost than the current practice. BML runs on BCube network, instead of using the traditional Fat-Tree topology. BML algorithm is designed in such a way that, compared to the parameter server (PS) algorithm on a Fat-Tree network connecting the same number of server machines, BML achieves theoretically 1/k of the gradient synchronization time, with k/5 of switches (the typical number of k is 2∼4). Experiments of LeNet-5 and VGG-19 benchmarks on a testbed with 9 dual-GPU servers show that, BML reduces the job completion time of DML training by up to 56. 4%.

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