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Bin Jiang

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15 papers

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EAAI Journal 2026 Journal Article

Degradation-induced fault identification for component-stacked systems: A mechanism-informed, distribution-aware perspective

Leiming Ma
Bin Jiang
Rui He
Ningyun Lu

Component-stacked rotor systems are coupled through shared load paths and vibration transmission. Degradation in any component can change system-level structural parameters and introduce uncertainty into their evolution, thereby reshaping the measured features and apparent fault patterns. Fault identification should therefore account for degradation effects. In this study, we refer to this objective as degradation-induced fault identification. A key challenge is that failure histories capturing progressive degradation are often scarce. Purely data-driven models trained on such samples may learn feature distributions that do not adequately characterize degradation evolution and its associated fault modes. To address this issue, we develop a mechanism-informed generative modeling framework. In the physical model, structural parameters are modeled as stochastic variables following specified probability distributions, enabling the augmented data to better cover the underlying distribution of degradation-induced fault states. Additionally, we develop an uncertainty-guided attention mechanism that concentrates on long-term dependencies in high-uncertainty feature regions. It quantifies the uncertainty propagation from structural parameters to the learned feature space, and provides interpretable insights into degradation-induced fault manifestations. By integrating distributional parameter modeling with physical knowledge, the framework characterizes degradation variability and evolution within a unified model, demonstrating promising applicability in multi-component systems.

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EAAI Journal 2026 Journal Article

Dual-task collaborative network for camouflaged object detection via edge-coarse segmentation map fusion

Bin Jiang
Jinlan Li
Tingting Zhou
Kun Zuo
Shidong Xiong
Hanguang Xiao
Guibin Bian

Camouflaged objects exhibit high similarity to their background, low contrast, and blurry boundaries, resulting in the underutilization of edge information. Accurately identifying their weak boundaries and positional information is a key yet highly challenging aspect of camouflaged object detection. Meanwhile, the performance of current models that leverage edge information still has significant room for improvement. To address these issues and achieve more accurate detection of camouflaged objects, we propose a dual-task collaborative network based on edge information maps and coarse segmentation maps. Specifically, the method involves four key steps: first, a Feature Enhancement Module is designed to enhance important details in images, providing more accurate information for boundary regions; second, an Edge Perception Module is developed to effectively extract edge information of camouflaged objects; third, a Coarse Segmentation Module is proposed to capture multi-scale features, guiding the subsequent refinement process; finally, a Semantic Integration Module is constructed to fuse coarse segmentation information and edge information, enabling the transformation from initial segmentation to high-quality segmentation results. Experimental results demonstrate that our method shows significant advantages in edge clarity and detail preservation. Notably, on the COD10K dataset, the F β score of our model is improved by approximately 7 percentage points compared with the previous state-of-the-art result. In conclusion, this dual-task collaborative network effectively solves problems such as insufficient utilization of edge information in camouflaged object detection, and provides a new solution for improving the performance of camouflaged object detection.

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AAAI Conference 2025 Conference Paper

Dynamic Spectral Graph Anomaly Detection

Jianbo Zheng
Chao Yang
Tairui Zhang
Longbing Cao
Bin Jiang
Xuhui Fan
Xiao-ming Wu
Xianxun Zhu

Graph anomaly detection is crucial for identifying anomalous nodes within graphs and addressing applications like financial fraud detection and social spam detection. Recent spectral graph neural network methods advance graph anomaly detection by focusing on anomalies that notably affect the distribution of graph spectral energy. Such spectrum-based methods rely on two steps: graph wavelet extraction and feature fusion. However, both steps are hand-designed, capturing incomprehensive anomaly information of wavelet-specific features and resulting in their inconsistent feature fusion. To address these problems, we propose a dynamic spectral graph anomaly detection framework DSGAD to adaptively capture comprehensive anomaly information and perform consistent feature fusion. DSGAD introduces dynamic wavelets, consisting of trainable wavelets to adaptively learn anomalous patterns and capture wavelet-specific features with comprehensive anomaly information. Furthermore, the consistent fusion of wavelet-specific features achieves dynamic fusion by combining wavelet-specific feature extraction with energy difference and channel convolution fusion using location correlation. Experimental results on four datasets substantiate the efficacy of our DSGAD method, surpassing state-of-the-art methods in both homogeneous and heterogeneous graphs.

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TIST Journal 2025 Journal Article

Multi-Autonomous Underwater Vehicle Trajectory Planning in Ocean Current Based on Hierarchical Hunting and Evolutionary Learning

Bin Jiang
Yining Wang
Fanhui Kong
Jian Wang

In the context of rising demands for marine resource exploitation and scientific research, collaborative trajectory planning for multiple Autonomous Underwater Vehicles (AUVs) in complex underwater environments—marked by obstacles, ocean currents, and low visibility—remains a critical challenge. Although the Gray Wolf Optimization (GWO) algorithm has advanced multi-objective trajectory planning, it faces issues such as poor high-dimensional space adaptability, susceptibility to local optima, and insufficient constraint handling. To address these, this article proposes a multi-AUV trajectory planning algorithm (EA-GWO) based on evolutionary learning to improve GWO. The method optimizes multi-AUV trajectory planning by leveraging hierarchical population hunting behavior, integrating position update equations to prioritize population bootstrapping, and balancing exploration and exploitation via fitness-based population distribution. Experimental validation across general, ocean current, and threat environments compares EA-GWO with the traditional GWO and multiple population GWO (MP-GWO). For sailing time: in the general environment, EA-GWO reduces total time by 90.6% compared to GWO and 90.6% compared to MP-GWO; in the ocean current environment, it cuts time by 0.9% versus GWO and 2.4% versus MP-GWO; in the threat environment, it cuts time by 13.6% versus GWO and 14.9% versus MP-GWO. For sailing distance: in the general environment, EA-GWO shortens total distance by 9.8% compared to GWO and 3.4% compared to MP-GWO; in the ocean current environment, it reduces distance by 2.3% versus GWO and 4.9% versus MP-GWO; in the threat environment, it shortens distance by 5.5% versus GWO and 1.0% versus MP-GWO. In terms of convergence performance reflected by the fitness curve: across the three environments, EA-GWO demonstrates faster convergence speed. These results highlight that EA-GWO outperforms the other two algorithms in sailing time, distance, and convergence efficiency, verifying its effectiveness in real-time dynamic coordination and constraint handling for multi-AUV missions.

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EAAI Journal 2025 Journal Article

Point cloud semantic segmentation network based on graph convolution and attention mechanism

Nan Yang
Yong Wang
Lei Zhang
Bin Jiang

Point cloud data provides rich three-dimensional spatial information. Accurate three-dimensional point cloud semantic segmentation algorithms enhance environmental understanding and perception, with wide-ranging applications in autonomous driving and scene analysis. However, Graph Neural Networks often struggle to retain semantic relationships among neighboring points during feature extraction, potentially leading to the omission of critical features during aggregation. To address these challenges, we propose a novel network, the Feature-Enhanced Residual Attention Network. This network includes an innovative graph convolution module, the Neighborhood-Enhanced Convolutional Aggregation Module, which utilizes K-Nearest Neighbor and Dilated K-Nearest Neighbor techniques to construct diverse dynamic graphs and aggregate features, thereby prioritizing essential information. This approach significantly enhances the expressiveness and generalization capabilities of the network. Additionally, we introduce a new spatial attention module designed to capture semantic relationships among points. Experimental results demonstrate that the Feature-Enhanced Residual Attention Network outperforms benchmark models, achieving an average intersection ratio of 61. 3% and an overall accuracy of 86. 7% on the Stanford Large-Scale Three-dimensional Indoor Spaces dataset, thereby significantly improving semantic segmentation performance.

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EAAI Journal 2024 Journal Article

Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network

Li Guo
Hongwei He
Yiran Ren
Runze Li
Bin Jiang
Jianye Gong

In the field of battery health state prognostics, the inaccurate lithium-ion battery's health status prediction is usually caused by the capacity regeneration (CR) phenomenon triggered by relaxation effects during the degradation process. To address this issue, we pay more attention to the main rapid degradation trend of battery capacity instead of many researches focusing on only CR phenomenon. This paper presents a prognostic framework called CEEMDAN-LSTM to decouple the normal capacity degradation process while eliminating local regenerative capacity, capturing degradation characteristics for battery state-of-health (SOH) prognostics. It introduces the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original battery capacity degradation curve into principal trend sequence and other high-frequency subsequences, and Pearson correlation coefficient (PCC) is calculated to remove irrelevant high-frequency subsequences. Predictions task is completed by bidirectional long short-term memory network (Bi-LSTM) and long short-term memory network (LSTM) groups. Experimental validations are conducted on two lithium-ion battery datasets from NASA Ames Research Center and the Advanced Life-Cycle Engineering Center of the University of Maryland. The results demonstrate that the proposed framework achieves more accurate SOH prediction than many previous mainstream methods.

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EAAI Journal 2023 Journal Article

Fault-tolerant control for second-order nonlinear systems with actuator faults via zero-sum differential game

Yajie Ma
Qingyuan Meng
Bin Jiang
Hao Ren

Stable operations of control systems play a vital role in mission accomplishments of the second-order nonlinear systems, such as six-axis robots used in intelligent production lines, industrial equipment and control systems. In this paper, a fault-tolerant control scheme is developed for a class of second-order nonlinear control system under actuator bias faults and loss of effectiveness faults via the zero-sum differential game method. Based on the backstepping method, a controller is designed to ensure system tracking performance. Then by the zero-sum differential game method, a fault-tolerant controller is designed for the equivalent error system. Simulation results show the validity of the designed fault-tolerant control scheme.

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AIIM Journal 2023 Journal Article

Reconstruction of central arterial pressure waveform based on CBi-SAN network from radial pressure waveform

Hanguang Xiao
Wangwang Song
Chang Liu
Bo Peng
Mi Zhu
Bin Jiang
Zhi Liu

The central arterial pressure (CAP) is an important physiological indicator of the human cardiovascular system which represents one of the greatest threats to human health. Accurate non-invasive detection and reconstruction of CAP waveforms are crucial for the reliable treatment of cardiovascular system diseases. However, the traditional methods are reconstructed with relatively low accuracy, and some deep learning neural network models also have difficulty in extracting features, as a result, these methods have potential for further advancement. In this study, we proposed a novel model (CBi-SAN) to implement an end-to-end relationship from radial artery pressure (RAP) waveform to CAP waveform, which consisted of the convolutional neural network (CNN), the bidirectional long-short-time memory network (BiLSTM), and the self-attention mechanism to improve the performance of CAP reconstruction. The data on invasive measurements of CAP and RAP waveform were used in 62 patients before and after medication to develop and validate the performance of CBi-SAN model for reconstructing CAP waveform. We compared it with traditional methods and deep learning models in mean absolute error (MAE), root mean square error (RMSE), and Spearman correlation coefficient (SCC). Study results indicated the CBi-SAN model performed great performance on CAP waveform reconstruction (MAE: 2. 23 ± 0. 11 mmHg, RMSE: 2. 21 ± 0. 07 mmHg), concurrently, the best reconstruction effect was obtained in the central artery systolic pressure (CASP) and the central artery diastolic pressure(CADP) (RMSE CASP: 2. 94 ± 0. 48 mmHg, RMSE CADP: 1. 96 ± 0. 06 mmHg). These results implied the performance of the CAP reconstruction based on CBi-SAN model was superior to the existing methods, hopped to be effectively applied to clinical practice in the future.

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JBHI Journal 2022 Journal Article

A Post-Hoc Interpretable Ensemble Model to Feature Effect Analysis in Warfarin Dose Prediction for Chinese Patients

Yuzhen Zhang
Cheng Xie
Ling Xue
Yanyun Tao
Guoqi Yue
Bin Jiang

To interprete the importance of clinical features and genotypes for warfarin daily dose prediction, we developed a post-hoc interpretable framework based on an ensemble predictive model. This framework includes permutation importance for global interpretation and local interpretable model-agnostic explanation (LIME) and shapley additive explanations (SHAP) for local explanation. The permutation importance globally ranks the importance of features on the whole data set. This can guide us to build a predictive model with less variables and the complexity of final predictive model can be reduced. LIME and SHAP together explain how the predictive model give the predicted dosage for specific samples. This help clinicians prescribe accurate doses to patients using more effective clinical variables. Results showed that both the permutation importance and SHAP demonstrated that VKORC1, age, serum creatinine (SCr), left atrium (LA) size, CYP2C9 and weight were the most important features on the whole data set. In specific samples, both SHAP and LIME discovered that in Chinese patients, wild-type VKORC1-AA, mutant-type CYP2C9*3, age over 60, abnormal LA size, SCr within the normal range, and using amiodarone definitely required dosage reduction, whereas mutant-type VKORC1-AG/GG, small age, SCr out of normal range, normal LA size, diabetes and heavy weight required dosage enhancementt.

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JBHI Journal 2022 Journal Article

Boundary Constraint Network With Cross Layer Feature Integration for Polyp Segmentation

Guanghui Yue
Wanwan Han
Bin Jiang
Tianwei Zhou
Runmin Cong
Tianfu Wang

Clinically, proper polyp localization in endoscopy images plays a vital role in the follow-up treatment (e. g. , surgical planning). Deep convolutional neural networks (CNNs) provide a favoured prospect for automatic polyp segmentation and evade the limitations of visual inspection, e. g. , subjectivity and overwork. However, most existing CNNs-based methods often provide unsatisfactory segmentation performance. In this paper, we propose a novel boundary constraint network, namely BCNet, for accurate polyp segmentation. The success of BCNet benefits from integrating cross-level context information and leveraging edge information. Specifically, to avoid the drawbacks caused by simple feature addition or concentration, BCNet applies a cross-layer feature integration strategy (CFIS) in fusing the features of the top-three highest layers, yielding a better performance. CFIS consists of three attention-driven cross-layer feature interaction modules (ACFIMs) and two global feature integration modules (GFIMs). ACFIM adaptively fuses the context information of the top-three highest layers via the self-attention mechanism instead of direct addition or concentration. GFIM integrates the fused information across layers with the guidance from global attention. To obtain accurate boundaries, BCNet introduces a bilateral boundary extraction module that explores the polyp and non-polyp information of the shallow layer collaboratively based on the high-level location information and boundary supervision. Through joint supervision of the polyp area and boundary, BCNet is able to get more accurate polyp masks. Experimental results on three public datasets show that the proposed BCNet outperforms seven state-of-the-art competing methods in terms of both effectiveness and generalization.

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YNICL Journal 2021 Journal Article

Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature

Aditi Deshpande
Nima Jamilpour
Bin Jiang
Patrik Michel
Ashraf Eskandari
Chelsea Kidwell
Max Wintermark
Kaveh Laksari

Accurate segmentation of cerebral vasculature and a quantitative assessment of its morphology is critical to various diagnostic and therapeutic purposes and is pertinent to studying brain health and disease. However, this is still a challenging task due to the complexity of the vascular imaging data. We propose an automated method for cerebral vascular segmentation without the need of any manual intervention as well as a method to skeletonize the binary segmented map to extract vascular geometric features and characterize vessel structure. We combine a Hessian-based probabilistic vessel-enhancing filtering with an active-contour-based technique to segment magnetic resonance and computed tomography angiograms (MRA and CTA) and subsequently extract the vessel centerlines and diameters to calculate the geometrical properties of the vasculature. Our method was validated using a 3D phantom of the Circle-of-Willis region, demonstrating 84% mean Dice similarity coefficient (DSC) and 85% mean Pearson's correlation coefficient (PCC) with minimal modified Hausdorff distance (MHD) error (3 surface pixels at most), and showed superior performance compared to existing segmentation algorithms upon quantitative comparison using DSC, PCC and MHD. We subsequently applied our algorithm to a dataset of 40 subjects, including 1) MRA scans of healthy subjects (n = 10, age = 30 ± 9), 2) MRA scans of stroke patients (n = 10, age = 51 ± 15), 3) CTA scans of healthy subjects (n = 10, age = 62 ± 12), and 4) CTA scans of stroke patients (n = 10, age = 68 ± 11), and obtained a quantitative comparison between the stroke and normal vasculature for both imaging modalities. The vascular network in stroke patients compared to age-adjusted healthy subjects was found to have a significantly (p < 0.05) higher tortuosity (3.24 ± 0.88 rad/cm vs. 7.17 ± 1.61 rad/cm for MRA, and 4.36 ± 1.32 rad/cm vs. 7.80 ± 0.92 rad/cm for CTA), higher fractal dimension (1.36 ± 0.28 vs. 1.71 ± 0.14 for MRA, and 1.56 ± 0.05 vs. 1.69 ± 0.20 for CTA), lower total length (3.46 ± 0.99 m vs. 2.20 ± 0.67 m for CTA), lower total volume (61.80 ± 18.79 ml vs. 34.43 ± 22.9 ml for CTA), lower average diameter (2.4 ± 0.21 mm vs. 2.18 ± 0.07 mm for CTA), and lower average branch length (4.81 ± 1.97 mm vs. 8.68 ± 2.03 mm for MRA), respectively. We additionally studied the change in vascular features with respect to aging and imaging modality. While we observed differences between features as a result of aging, statistical analysis did not show any significant differences, whereas we found that the number of branches were significantly different (p < 0.05) between the two imaging modalities (201 ± 73 for MRA vs. 189 ± 69 for CTA). Our segmentation and feature extraction algorithm can be applied on any imaging modality and can be used in the future to automatically obtain the 3D segmented vasculature for diagnosis and treatment planning as well as to study morphological changes due to stroke and other cerebrovascular diseases (CVD) in the clinic.

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YNIMG Journal 2021 Journal Article

The human posterior cingulate and the stress-response benefits of viewing green urban landscapes

Dorita H.F. Chang
Bin Jiang
Nicole H.L. Wong
Jing Jun Wong
Chris Webster
Tatia M.C. Lee

The mechanistic and neural bases of why green environments drive positive mental health outcomes remain poorly understood. We show that viewing green urban landscapes that vary in terms of green-space density elicits corresponding changes in the activity of the human ventral posterior cingulate cortex that is correlated to behavioural stress-related responses. We further show that cingulate responses are engaged early in the processing cascade, influencing attentional and executive regions in a predominantly feedforward manner. Our data suggest a key role for this region in regulating (nature) dose-dependent changes in stress responses, potentially through its extensive connections to the prefrontal and hippocampal regions which in turn project towards the neuroendocrine system. As the posterior cingulate cortex is implicated in a variety of neurological diseases and disorders, these findings raise a therapeutic potential for natural environmental exposure, highlighting green-cover as a modifiable element that links to changes in limbic responses, and has health consequences for practitioners and city-planners alike.

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JBHI Journal 2019 Journal Article

An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients

Yanyun Tao
Yenming J. Chen
Ling Xue
Cheng Xie
Bin Jiang
Yuzhen Zhang

The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and to build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) clustering algorithm with support vector regression (SVR). A dataset of 517 Han Chinese patients was collected from the data of The First Affiliated Hospital of Soochow University and dataset of International Warfarin Pharmacogenetics Consortium for training and testing. In EFCM+SVR, we adopted SVR to build a generalized base model (SVR model). To achieve an accurate prediction on patients with large dosage, we proposed an EFCM clustering algorithm that can be used to cluster the training set and designed a clustering model on clusters and centroids. The SVR and clustering models were integrated into an ensemble model by stepwise functions. In the experiment, three artificial neural networks, SVR, two ensemble models, and three regression models were used as comparators to the EFCM+SVR model, which obtained the smallest mean absolute error (0. 67 mg/d) in warfarin dose prediction and the largest R-squared (43. 9%). The model achieved satisfactory prediction in terms of the percentage of patients whose predicted dose of warfarin was within 15% and 20% of the actual stable therapeutic dose (15%-p of 36% and 20%-p of 46. 6%).

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EAAI Journal 2019 Journal Article

An RBMs-BN method to RUL prediction of traction converter of CRH2 trains

Chuanyu Zhang
Cunsong Wang
Ningyun Lu
Bin Jiang

Remaining useful life (RUL) prediction is essential to ensure safety and reliability of engineering systems. To achieve better prediction performance, causalities among the physical quantities are considered by applying Bayesian Network (BN) to RUL prediction. For this purpose, several improvements on BN modeling are made in this paper, to handle the closed-loop control structure of engineering systems, and to improve prediction performance with reduced complexity. Taking the traction converter of CRH2 trains as the object of the research, a closed-loop Bond Graph (BG) model is firstly developed to describe the causality of multi-domain physical quantities, which is then transformed to be a BN structure. Then, multi-dimensional features are extracted from the condition monitoring data and are used as the inputs to the nodes of BN model. Finally, Restricted Boltzmann Machines (RBMs) are used to further extract the latent features that cannot be directly observed or measured, but greatly improve the accuracy of the BN based RUL prediction. Case study is conducted using a hardware-in-loop simulation platform for traction system of China Railway High-speed (CRH2) trains, to predict RUL of the DC-link circuit with degradation of capacitance or resistance. The experimental results can show the validity and superiority of the proposed RBMs-BN based RUL prediction method.

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JBHI Journal 2019 Journal Article

Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese

Yanyun Tao
Yenming J. Chen
Xiangyu Fu
Bin Jiang
Yuzhen Zhang

An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and the genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared base models through a technique called “bagging. ” In the experiment, a dataset of 289 Chinese patients, which was provided by the First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with the M2+G group, namely age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R 2 ), the highest percentage of the predicted dose within 20% of the actual dose (20%-p), the smallest mean absolute error, mean squared error, and root-mean-squared error on the test set, and the least decrease in R 2 from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can, therefore, be a suitable prediction model of warfarin dose for clinical applications.

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