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Adaptive Wavelet Convolution Stochastic Analysis Graph Neural Network for Remaining Useful Life Prediction

IEEE Sensors Journal 2025年第11期25卷 19428-19441页

作者： Hu, Qidi Su, Binyi Kong, Jinzhen Liu, Weipeng Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin300130 China Hebei University of Technology School of Mechanical Engineering Tianjin300130 China

The prediction of the remaining useful life (RUL) of stochastically degrading devices is of critical importance for prognostics and health management (PHM). However, most of the existing approaches overlook the inherent coupling of trends and fluctuations in the degradation process. Meanwhile, these methods are struggling to effectively model the random fluctuations component in the degradation process and may lead to inaccurate prediction. To address these limitations, we propose a modified RUL prediction framework WTSAGNN. The framework extends the spatiotemporal dependencies of multisensor signals into the frequency domain, by leveraging the proposed adaptive orthogonal wavelet convolution to more effectively decouple the trend and fluctuation components of the degradation process. Furthermore, we introduce a stochastic differential network (SDN) to replace the traditional multilayer perceptron (MLP). The proposed method enhances the robustness of neural networks against stochastic variations in degradation processes through explicit stochastic modeling. Experimental results demonstrate that WTSAGNN outperforms existing state-of-the-art (SOTA) methods on benchmark datasets, achieving up to a 9.1% improvement in prediction accuracy with only a limited increase in computational overhead. In addition, the proposed modules are plug-and-play, allowing seamless integration with existing data-driven prediction frameworks. © 2001-2012 IEEE.

关键词： Graph neural networks

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Disturbance observer-based fuzzy fault-tolerant control for high-speed trains with multiple disturbances

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Chinese Physics B 2023年第10期32卷 383-391页

作者：王千龄马彩青林雪 School of Artificial Intelligence Hebei University of TechnologyTianjin 300131China College of Automation and Electronic Engineering Qingdao University of Science and TechnologyQingdao 260061China

The fault-tolerant control problem is investigated for high-speed trains with actuator faults and multiple *** on the novel train model resulting from the Takagi–Sugeno fuzzy theory, a sliding-mode fault-tolerant control strategy is proposed. The norm bounded disturbances which are composed of interactive forces among adjacent carriages and basis running resistances are rearranged by the fuzzy linearity technique. The modeled disturbances described as an exogenous system are compensated for by a disturbance observer. Moreover, a sliding mode surface is constructed, which can transform the stabilization problem of position and velocity into the stabilization problem of position errors and velocity errors, i.e., the tracking problem of position and velocity. Based on the parallel distributed compensation method and the disturbance observer, the fault-tolerant controller is solved. The Lyapunov theory is used to prove the stability of the closed-loop system. The feasibility and effectiveness of the proposed fault-tolerant control strategy are illustrated by simulation results.

关键词： fault-tolerant control high-speed trains disturbance observer fuzzy logic

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Semantic-Aware Late-Stage Supervised Contrastive Learning for Fine-Grained Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5912-5924页

作者： Pan, Yijun Zhao, Quan Zhang, Yueyi Wang, Zilei Sun, Xiaoyan Wu, Feng University of Science and Technology of China School of Artificial Intelligence and Data Science Hefei230026 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China University of Science and Technology of China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application Hefei230026 China

Fine-grained action recognition typically faces challenges with lower inter-class variances and higher intra-class variances. Supervised contrastive learning is inherently suitable for this task, as it can decrease intra-class feature distances while increasing inter-class ones. However, directly applying it into fine-grained action recognition encounters two main problems. The first problem stems from the heavy training cost associated with supervised contrastive learning, which requires numerous training epochs, each involving double augmentation views per instance. To address this issue, we propose the late-stage supervised contrastive learning (late-SC) strategy, which effectively reduces the number of training epochs needed for the contrastive learning process. The second problem is that supervised contrastive loss does not explicitly consider the semantic distances between fine-grained actions when adjusting representation distances. This results in less reasonable and efficient adjustments to the representation space. To overcome this limitation, we introduce the semantic-aware temperature adaptation (STA) mechanism, enhancing the suitability of the supervised contrastive loss for fine-grained action recognition. We conduct experiments on several benchmark datasets for fine-grained action recognition, including Epic-Kitchens-55/100, SomethingSomething-V1, and Diving48-V2. The results demonstrate that our proposed method (referred to as LSC-STA) consistently enhances performance across various base feature extractors, without introducing additional inference overhead and incurring only a marginal increase in training expenses. © 1991-2012 IEEE.

关键词： Contrastive Learning

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TransCrowd: weakly-supervised crowd counting with transformers

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science China(Information sciences) 2022年第6期65卷 48-61页

作者： Dingkang LIANG Xiwu CHEN Wei XU Yu ZHOU Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic Information and Communication Huazhong University of Science and Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

The mainstream crowd counting methods usually utilize the convolution neural network(CNN)to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and laborious process. During the testing phase, the point-level annotations are not considered to evaluate the counting accuracy, which means the point-level annotations are redundant. Hence,it is desirable to develop weakly-supervised counting methods that just rely on count-level annotations, a more economical way of labeling. Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm. However, having limited receptive fields for context modeling is an intrinsic limitation of these weakly-supervised CNN-based methods. These methods thus cannot achieve satisfactory performance, with limited applications in the real world. The transformer is a popular sequence-to-sequence prediction model in natural language processing(NLP), which contains a global receptive field. In this paper, we propose TransCrowd, which reformulates the weakly-supervised crowd counting problem from the perspective of sequence-to-count based on transformers. We observe that the proposed TransCrowd can effectively extract the semantic crowd information by using the self-attention mechanism of transformer. To the best of our knowledge, this is the first work to adopt a pure transformer for crowd counting research. Experiments on five benchmark datasets demonstrate that the proposed TransCrowd achieves superior performance compared with all the weakly-supervised CNN-based counting methods and gains highly competitive counting performance compared with some popular fully-supervised counting methods.

关键词： crowd counting visual transformer weakly supervised crowd analysis transformer

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Harnessing QGIS and Random Forest Analysis for Precision Storm Drain Reinforcement 2

Harnessing QGIS and Random Forest Analysis for Precision Sto...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramachandran, Ramya Arthi, D. Sudha, D. Anil, Abhirami B Babu, Aravind Md, Haripriya KCG College of Technology Artificial Intelligence and Data Science Chennai India KCG College of Technology Department of Computer Science and Engineering India KCG College of Technology Science Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331534950

Amidst the backdrop of increased urbanization and shifting climate patterns, this research delves into the growing vulnerability of landscapes to floods. The focus is on storm drain placement, emphasizing the identification of suitable locations and the need to reinforce drainage networks. The analysis takes into account parameters linked to flood sensitivity, including total rainfall, decadal growth rate, and soil types in each district within a state. Leveraging Geographic Information Systems (GIS) and relevant Coordinate Reference Systems (CRS), the study integrates a diverse array of datasets to inform precise cartographic projections. Employing a Random Forest Classifier, the study dissects the influence of these parameters on storm drain placement. The findings offer valuable insights for decision-makers to improve stormwater management systems, underscoring the importance of ongoing maintenance. Overcoming the challenge of obtaining official government data for recommendations, the research advocates the adoption of new technologies and data integration methods. By combining various data sources, such as topography, rainfall patterns, historical flood records, land use, and infrastructure data, it lays the groundwork for more advanced urban water management strategies, fortifying cities against flooding. © 2024 IEEE.

关键词： Rain

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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

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Journal of Computer science & technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor

Distributionally Robust Chance Constrained Trajectory Optimi...

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2024 IEEE International Conference on Robotics and automation, ICRA 2024

作者： Xu, Shaohang Ruan, Haolin Zhang, Wentao Wang, Yian Zhu, Lijun Ho, Chin Pang Huazhong University of Science and Technology School of Artificial Intelligence and Automation China City University of Hong Kong School of Data Science Hong Kong

ISBN: (纸本)9798350384574

Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, because its convex formulation can guarantee global optimality. The safe corridor is constructed based on the obstacle map, however, the non-ideal perception induces uncertainty, which is rarely considered in the context of trajectory generation. In this paper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) to consider the uncertainty of the safe corridor. Then, we integrate DRSCCs into the trajectory optimization framework using Bernstein basis polynomials. Theoretically, we rigorously prove that the proposed trajectory optimization problem is equivalent to a convex quadratic program, which is computationally efficient to deploy onto real robots. The simulation results show that our method enhances navigation safety by significantly reducing the infeasible motions compared to the baseline. Moreover, the proposed approach is validated through two robotic applications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robot Unitree A1. © 2024 IEEE.

关键词： Microrobots

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Breast cancer classification using breast ultrasound images with a hybrid of transfer learning and Bayesian-optimized fast learning network

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Discover artificial intelligence 2025年第1期5卷 1-17页

作者： Ahishakiye, Emmanuel Kanobe, Fredrick Department of Networks Data Science and Artificial Intelligence Kyambogo University Kampala Uganda Department of Information Technology Uganda Management Institute Kampala Uganda

Breast cancer is the most prevalent cancer among women globally, with over 2.3 million new cases reported annually, hence, early and accurate diagnosis is crucial in minimizing mortality rates. In light of the limitation on the conventional interpretation of images, this study presents a novel hybrid model that integrates DenseNet201-based transfer learning with a Bayesian-Optimized Fast Learning Network (FLN) for breast cancer classification from ultrasound images. DenseNet201 was employed to obtain robust, high-quality features from pre-trained weights. FLN is finely tuned with Bayesian optimization to select optimal hyperparameters such as learning rate, hidden neurons, and dropout rate. The proposed model achieved an accuracy of 96.79%, an F1 score of 94.71%, a precision of 96.81%, and a recall of 93.48% with AUC scores of 0.96, 0.95, and 0.98 for benign, malignant, and normal classes. These results underscore the model’s balanced performance and its ability to minimize misclassifications, particularly false positives. The end-to-end hybrid approach not only outperforms several state-of-the-art models but also demonstrates improved generalization and stability, offering a promising, clinically viable tool for enhancing breast cancer diagnosis. © The Author(s) 2025.

关键词： Mammography

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Move Match: Live Dance Motion Monitoring and Feedback System

Move Match: Live Dance Motion Monitoring and Feedback System

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Saral, G. Baby Subasri, S. Ramya Shree, R. Bose, K. Regin Rajalakshmi Institute of Technology Dept. Artificial Intelligence & Data Science Chennai India Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Student Rajalakshmi Institute of Technology Chennai India Rajalakshmi Institute of Technology Dept. AI&DS & Cloud Computing Chennai India

ISBN: (纸本)9798331512965

Dance movement accuracy prediction is a critical component of modern dance training, yet traditional methods often lack objectivity due to the subjective nature of evaluations and the complexity of human movement. To address these challenges this paper introduces MoveMatch, which is a web-based platform built for real-time dance movement prediction. By leveraging deep learning techniques, the platform performs posture estimation in real-time and detects key points. The system compares the detected poses using Euclidean distance to calculate the alignment between corresponding key points which provides a quantitative measure of the user's accuracy. Based on this comparison, MoveMatch delivers real-time feedback, offering an accuracy score and personalized suggestions for improvement. This immediate feedback fosters a more efficient and engaging learning experience for dancers. Extensive testing of MoveMatch in various dance styles demonstrates its effectiveness in objectively assessing performance, enhancing both training quality and accessibility. The integration of pose estimation, deep learning and real-time comparison offers a novel, user-friendly solution for dancers seeking to improve their skills, transforming traditional dance education with AI-driven insights. © 2025 IEEE.

关键词： Deep learning

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Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

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Defence technology（防务技术） 2024年第4期34卷 19-28页

作者： Xiang-long Liang Zhi-kai Yao Yao-wen Ge Jian-yong Yao School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing210094China College of Automation&College of Artificial Intelligence Nanjing University of Post and TelecommunicationNanjing210023China

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange *** considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and *** these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become *** by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical *** approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control *** from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control *** developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output ***,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

关键词： Adaptive control Reinforcement learning Uncertain mechanical systems Asymptotic tracking

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