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Hua Chen

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

A Temporal-spatial Causal Variational Network for accurate sintering temperature forecasting in rotary kilns

Kai Wang
Hua Chen
Xiaogang Zhang
Qianyu Chen
Yuqi Cai
Lei Zhang

Accurate forecasting of sintering temperatures (ST) is pivotal to the high-efficiency, low-energy operation of rotary kilns. The complexity of coupled multivariable process data in industrial environments makes it difficult to uncover patterns and structures in the data, leading to unsatisfactory predictive performance. To accurately analyze temporal–spatial relationships among thermal process variables in rotary kilns, we analyze the causal association among variables and construct a causal graph of the sintering process according to the physicochemical mechanism of sintering. An autoregressive Causal Hidden Markov Model is introduced to model the causal relationships of variables and propagates to generate ST forecasting. In implementation, a generative recurrent neural network, Temporal–spatial Causal Variational Network (TCVN) is designed to generate the representation of hidden variables and extract ST-related features robustly. Each time step in TCVN is composed of a Causal Variational Module (CVM) that integrates a Graph Convolutional Network (GCN) with a Variational Autoencoder (VAE) based on the constructed causal graph. The experiments on real-world data demonstrate that the proposed approach effectively improves the forecasting accuracy of ST with horizons of 1, 3, 6, and 12 steps, confirming the superiority of the proposed model. • A TCVN is proposed for accurate sintering temperature forecasting in rotary kilns. • A causal graph according to the mechanism of sintering is designed. • A CVM is designed to learn the hidden variables in the causal graph. • Detailed experiments are conducted to validate the performance of the TCVN.

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ICML Conference 2025 Conference Paper

DiffAdvMAP: Flexible Diffusion-Based Framework for Generating Natural Unrestricted Adversarial Examples

Zhengzhao Pan
Hua Chen
Xiaogang Zhang

Unrestricted adversarial examples(UAEs) have posed greater threats to deep neural networks(DNNs) than perturbation-based adversarial examples(AEs) because they can make extensive changes to images without being restricted in a fixed norm perturbation budget. Although current diffusion-based methods can generate more natural UAEs than other unrestricted attack methods, the overall effectiveness of such methods is restricted since they are designed for specific attack conditions. Additionally, the naturalness of UAEs still has room for improvement, as these methods primarily focus on leveraging diffusion models as strong priors to enhance the generation process. This paper proposes a flexible framework named Diffusion-based Adversarial Maximum a Posterior(DiffAdvMAP) to generate more natural UAEs for various scenarios. DiffAdvMAP approaches the generation of UAEs by sampling images from posterior distributions, which is achieved by approximating the posterior distribution of UAEs using the prior distribution of real data learned by the diffusion model. This process enhances the naturalness of the UAEs. By incorporating an adversarial constraint to ensure the effectiveness of the attack, DiffAdvMAP exhibits excellent attack ability and defense robustness. A reconstruction constraint is designed to enhance its flexibility, which allows DiffAdvMAP to be tailored to various attack scenarios. Experimental results on Imagenet show that we achieve a better trade-off between image quality, flexibility, and transferability than baseline unrestricted adversarial attack methods.

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EAAI Journal 2024 Journal Article

Interpretable detector for cervical cytology using self-attention and cell origin group guidance

Peng Jiang
Juan Liu
Jing Feng
Hua Chen
Yuqi Chen
Cheng Li
Baochuan Pang
Dehua Cao

Deep learning has advanced the development of automated cervical cytology, yet limited studies have delved into methods for incorporating medical domain knowledge, and model interpretability has not been thoroughly investigated. To address this issue, this paper proposes a novel, explainable detection method for abnormal cervical cells, called dual-stream self-attention based feature fusion and origin grouping network (DSA-FFOGNet). To encourage the model to focus more on lesion cells and cell nuclei of diagnostic significance, the dual-stream self-attention (DSA) module is introduced to enhance the learning of lesion-specific features. In view of the complex background, cell dense distribution, cell overlap, or clumps existing in the actual cervical cytology images, multi-scale features are extracted and fused by using the path aggregation network (PAN) to enhance the feature representation ability. By integrating biomedical insights regarding cell provenance and formulating an origin grouping loss, DSA-FFOGNet adjusts the penalties for cervical cells originating from different groups, thereby enhancing the optimization of the model training process. To further improve the detection performance, the classification and localization tasks are decoupled via the use of double detection heads. Extensive experiments validate the robustness of the proposed DSA-FFOGNet. The visualization of class activation maps (CAMs) showcases the model’s interpretability. The proposed approach advances the application and development of explainable artificial intelligence (XAI) models in cervical cytology and inspires further research in automated cervical cytology.

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EAAI Journal 2024 Journal Article

PDSMNet: Parallel pyramid dual-stream modeling for automatic lung COVID-19 infection segmentations

Ichiro Nakamoto
Weiqing Zhuang
Hua Chen
Yan Guo

Artificial intelligence-based segmentation models can assist the early-stage detection of lung COVID-19 infections or lesions from medical images with higher efficiency versus traditional techniques, yet challenges remain attributable to factors such as heterogeneities in infection traits, small infection regions, blurred boundary context, mixtures of varying infection regions, and obscure intensity contrast between lesions and normal tissues. This study aims to improve the performance of automatic segmentation of lung COVID-19 infections by proposing a novel approach named parallel pyramid dual-stream modeling network (PDSMNet) with two major contributions: (1) refined design of the framework including (a) a transformer module termed parallel pyramid dual-stream module (PDSM) to effectively preserve channel, spatial, and other latent features; (b) fusion of a multi-scale pyramid parallel-pooling module (MPM) that extracts features in parallel at differentiated scales; (c) calibration of the skip-connection architectures to optimize prediction and preserve features; (d) calibration of attention mechanisms reflecting multiple sources of information including parallel-, serial-, and cross-attention contexts; and (e) improvement of the integral functionality of the framework in curtailing the burdens of parameter computations in a multi-modality scenario. (2) loss functions accounting for the training losses of normal tissues, diseases, and boundaries respectively to enhance the performance of the network. The calibrated loss design allowing for a margin improves the capacity of predictions. We conducted experiments using three different datasets with different modalities and compared the proposed framework PDSMNet with two other benchmarks and eight similar state-of-the-art (SOTA) networks. The experiments observed consistent performance improvements across all datasets. PDSMNet attained asymptotically a maximum increase of 16. 5%, 6. 2%, and 15. 5% for mean F1 score (mF1S), mean dice-score coefficient (mDSC), mean intersection over union (mIoU) versus SOTA PDEAtt-UNet, a maximum increase of 49. 6%, 25. 6%, and 38. 0% for mF1S, mDSC, and mIoU versus InfNet, a maximum increase of 26. 1%, 10. 8%, 21. 8% versus MiniSeg, a maximum increase of 14. 0%, 3. 0%, and 13. 4% versus TransUNet, and a maximum increase of 37. 9%, 16. 8%, 31. 2% versus Attention-UNet respectively. For other SOTA models such as MT-UNet, UCTransNet, and UTNetV2, a sizeable enhancement of performance was observed as well. PDSMNet also yielded asymptotically a maximum 30. 6%, 14. 8%, 26. 3% increase of mF1S, mDSC, mIoU versus benchmark UNet, and a maximum increase of 42. 8%, 21. 5%, 33. 3% versus benchmark UNet++ respectively. PDSMNet demonstrated reduced computational costs as well, yielding approximately 0. 53 M of parameters and 6. 55G of floating-point operations per second (FLOPs) respectively using different datasets, and the quantitative and qualitative ablation tests reinforced the effectiveness of the various components of the proposed framework.

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EAAI Journal 2022 Journal Article

An efficient unsupervised image quality metric with application for condition recognition in kiln

Leyuan Wu
Xiaogang Zhang
Hua Chen
Yicong Zhou
Lianhong Wang
Dingxiang Wang

In this paper, we propose an unsupervised textural-intensity-based natural image quality evaluator (TI-NIQE) by modelling the texture, structure and naturalness of an image. In detail, an effective quality-aware feature named as textural intensity (TI) is proposed in this paper to detect image texture. The image structure is captured by the distribution of gradients and basis images. The naturalness is characterized through the distributions of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Furthermore, a new application pattern of image quality assessment (IQA) measures is proposed by taking the quality scores as the essential input of the recognition model. Using statistics of video quality scores computed by TI-NIQE as input features, an automatic IQA-based visual recognition model is proposed for the condition recognition in rotary kiln. Extensive experiments on benchmark datasets demonstrate that TI-NIQE shows better performance both in accuracy and computational complexity than other state-of-the-art unsupervised IQA methods, and experimental results on real-world data show that the recognition model has high prediction accuracy for condition recognition in rotary kiln.

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TCS Journal 2021 Journal Article

Scheduling with variable-length calibrations: Two agreeable variants

Hua Chen
Lin Chen
Guochuan Zhang
Vincent Chau

Machines usually require maintenance after running a fixed period. A calibration at a cost has to be performed during the process. Finding a feasible schedule minimizing the total cost of calibrations is of great importance. In this paper, we deal with a single machine scheduling model with K types of calibrations. A calibration of type k ∈ { 1, …, K } can be made instantaneously at any time point, which incurs a cost f k and can keep the machine active for a length T k. Given a set of n jobs with release times, deadlines, and processing times, the goal is to minimize the total cost of calibrations by assigning all jobs in the calibrated state, where job preemption is allowed. We investigate two agreeable settings. Regarding agreeable jobs, later release times imply later deadlines. We establish a pseudo-polynomial time optimal algorithm and a ( 3 + ε ) -approximation algorithm. Moreover, if the largest job processing time is no more than any calibration length, it admits a ( 2 + ε ) -approximation algorithm. As for agreeable calibrations, where the cost of each calibration is proportional to its length, a 2-approximation algorithm is presented.

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AAAI Conference 2019 Conference Paper

CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features

Yao Zhang
Wen-Ping Fan
Xuan Wu
Hua Chen
Bin-Yang Li
Min-Ling Zhang

Virtual desktop infrastructure (VDI) is a virtualization technology that hosts desktop operating system on centralized server in a data center of private or public cloud. Effective resource management is of crucial importance for VDI customers, where maintaining sufficient virtual machines helps guarantee satisfactory user experience while turning off spare virtual machines helps save running cost. Generally, existing techniques work in passive manner by either driving available capacity reactively or configuring management schedules manually. In this paper, a novel proactive resource management approach is proposed which aims to predict VDI pool workload adaptively by utilizing CoArse to Fine historical dEscriptive (CAFE) features. Specifically, aggregate session count from pool end users serves as the basis for workload measurement and predictive model induction. Extensive experiments on real VDI customers data sets clearly validate the effectiveness of multi-grained features for VDI workload prediction. Furthermore, practical insights identified in our VDI data analytics are also discussed.

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TIST Journal 2015 Journal Article

Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes

Hua Chen
Peng Ding
Zhi Geng
Xiao-Hua Zhou

Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable missing data mechanism may be violated in practice, because the missing data mechanism may depend directly on the missing outcome itself. Under noncompliance and an outcome-dependent nonignorable missing data mechanism, previous studies showed the identifiability of complier average causal effect for discrete outcomes. In this article, we study the semiparametric identifiability and estimation of complier average causal effect in randomized clinical trials with both all-or-none noncompliance and outcome-dependent nonignorable missing continuous outcomes, and propose a two-step maximum likelihood estimator in order to eliminate the infinite dimensional nuisance parameter. Our method does not need to specify a parametric form for the missing data mechanism. We also evaluate the finite sample property of our method via extensive simulation studies and sensitivity analysis, with an application to a double-blinded psychiatric clinical trial.

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