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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Gao, Yijie Zhang, Xuan Yu, Yaowen Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan China Hitachi Abb Power Grid San JoseCA United States

ISBN: (纸本)9781665414395

Unit commitment (UC) is a central market clearing process in wholesale electricity markets. With the increasing size and complexity of market-clearing models, UC problems become more and more complicated. To improve the computation efficiency, this paper tightens UC formulations with the general start-up ramp rate. Through analyzing their parameters over three periods, units are categorized into several categories for constructing corresponding tight formulations, and three of the categories are beyond existing works. By applying a recent systematic formulation tightening approach on top of these categories, new tight constraints of the general start-up ramp rate have been obtained. Numeral testing results illustrate the tightening process and demonstrate the improvement of computational efficiency when using our tightened formulations. © 2021 IEEE.

关键词： Computational efficiency

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CLIP-guided Prototype Modulating for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Cen, Jun Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China The Hong Kong University of Science and Technology Hong Kong

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to alleviate the inaccurate prototype estimation issue due to data scarcity, which is a critical problem in low-shot regimes. To this end, we present a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation. Specifically, the former bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions. The latter leverages the transferable textual concepts from CLIP to adaptively refine visual prototypes with a temporal Transformer. By this means, CLIP-FSAR can take full advantage of the rich semantic priors in CLIP to obtain reliable prototypes and achieve accurate few-shot classification. Extensive experiments on five commonly used benchmarks demonstrate the effectiveness of our proposed method, and CLIP-FSAR significantly outperforms existing state-of-the-art methods under various settings. The source code and Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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A Method for Detecting the Reducers’ Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers’ Grease Failure Based o...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Ditong Wang Hong Zhang Ying Zheng Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multispectral Information Processing and School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Real time detection of the lubricating grease’s effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task.

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Prescribed Performance control of Mobile Wheeled Inverted Pendulum Systems Under Arbitrary Initial Conditions

Prescribed Performance Control of Mobile Wheeled Inverted Pe...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Mengshi Zhang Yu Cao Bo Yang Jian Huang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen China

Mobile wheeled inverted pendulum (MWIP) is a typical naturally unstable underactuated system and it is very necessary to constrain its transient response to improve the overall safety. Prescribed performance control (PPC) is an effective method, however the system initial conditions are usually required to be known and constraint, otherwise it can lead to singularity problems. In practical systems, it is often difficult to obtain the exact initial state leading to limitations in the application of PPC. To overcome this issue, this paper presented a composite error transformation function to address the problem of initial condition dependence. A continuous bounded function was designed to compress the arbitrary initial error into the predefined bound to avoid singularities. After a set time, it was degraded to an original system error. Based on this composite transformation, a state feedback controller was proposed for the underactuated MWIP system to achieve the constraint outputs. Then, by employing the Lyapunov theorem, it was guaranteed that the closed-loop system would remain stable. Various numerical simulations under different initial conditions were carried out to prove the validity of the proposed controller.

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Unsupervised Time Series Anomaly Detection Based on Adversarial Interpolation and Pseudo-anomaly Calibration

Unsupervised Time Series Anomaly Detection Based on Adversar...

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International Conference on control, Automation and Robotics (ICCAR)

作者： Xinwei Chen Xiaohui Lin Zuoyong Li Haoyi Fan Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly detection method based on adversarial interpolation and pseudo-anomaly calibration for training an anomaly detector under data con-tamination. Specifically, the normality description learned from the model is improved by using the following two proposed calibration methods: (1) by enforcing adversarial interpolation to learn an effective normality description from the typical samples rather than the edge or abnormal samples, and (2) by discriminating the original samples from the simulated pseudo-anomaly samples to learn the exact normality description boundary. These two calibration ways result in the normality description that prevents data contamination. Experimental results compared with baselines on public time series datasets demonstrate the effectiveness of our proposed method.

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Pre-trained Models Succeed in Medical imaging with Representation Similarity Degradation

arXiv

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arXiv 2025年

作者： Zu, Wenqiang Xie, Shenghao Chen, Hao Ma, Lei Institute of Automation Chinese Academy of Sciences China Academy for Advanced Interdisciplinary Studies National Biomedical Imaging Center National Key Laboratory for Multimedia Information Processing Peking University China School of Chemical Sciences University of Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China

This paper investigates the critical problem of representation similarity evolution during cross-domain transfer learning, with particular focus on understanding why pre-trained models maintain effectiveness when adapted to medical imaging tasks despite significant domain gaps. The study establishes a rigorous problem definition centered on quantifying and analyzing representation similarity trajectories throughout the fine-tuning process, while carefully delineating the scope to encompass both medical image analysis and broader cross-domain adaptation scenarios. Our empirical findings reveal three critical discoveries: the potential existence of high-performance models that preserve both task accuracy and representation similarity to their pre-trained origins;a robust linear correlation between layer-wise similarity metrics and representation quality indicators;and distinct adaptation patterns that differentiate supervised versus self-supervised pre-training paradigms. The proposed similarity space framework not only provides mechanistic insights into knowledge transfer dynamics but also raises fundamental questions about optimal utilization of pre-trained models. These results advance our understanding of neural network adaptation processes while offering practical implications for transfer learning strategies that extend beyond medical imaging applications. The code will be available once accepted. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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An Integrated Intelligent Autonomous Driving Bulldozer System: Pose Estimation, Object Detection, and Work Planning for Dumping Operations

引用

Journal of Field Robotics 2025年第0期

作者： Peng, Gang Gao, Qiang Liu, Xingyu Zhou, Yicheng Duan, Hangqi Wu, ZhanGang Hu, Bin Zhu, Xukang Xu, Daosheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan China Shantui Construction Machinery Co. Ltd. Jining China

In recent years, the rapid advancement of automation control and intelligent sensing technologies has positioned autonomous driving as a focal point of interest for both academia and industry. As core equipment in modern construction and industrial production, engineering machinery urgently requires intelligent transformation. To promote the intelligent development of engineering machinery, we have designed an integrated intelligent autonomous driving bulldozer system, which can be extended to various types of engineering machinery. For the specific mine dumping operation environment, we propose a multisensor fusion pose estimation algorithm framework based on the Global Navigation Satellite System, inertial measurement unit, visual cameras, and light detection and ranging (LiDAR) to address issues arising from sensor failures under harsh construction conditions. Furthermore, to ensure safety during task execution, we design a three-dimensional object detection method based on the optimal observation plane from LiDAR data. By integrating pose estimation and environmental perception results, we develop a comprehensive work planning and path-tracking algorithm to maintain task efficiency. Experimental results demonstrate that our intelligent autonomous driving bulldozer system performs excellently under various working conditions. The accuracy of its pose estimation, object detection, and path tracking meets the requirements of actual construction environments, showcasing its significant potential in engineering applications. © 2025 Wiley Periodicals LLC.

关键词： Construction equipment

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Multi-modal Diffusion Network with controllable Variability for Medical Image Segmentation

Multi-modal Diffusion Network with Controllable Variability ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haoyu Wang Zheyu Zhang Yueyi Zhang Jing Zhang Yunwei Ou Xiaoyan Sun University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Tiantan Hospital Capital Medical University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In diffusion-based medical segmentation models, stochastic sampling is commonly used to generate multiple masks. However, the inherent variability in diffusion models can lead to significant biases in some masks, resulting in the fused mask deviating from the true mask. In this study, we propose a novel multi-modal diffusion segmentation network (MMDSN) with controllable variability, specifically designed to address the issue of variability in diffusion models. MMDSN achieves multi-modal conditional control through medical text annotations, thereby enhancing consistency of visual semantic representation and establishing a correspondence between vision and language for diffusion models. Additionally, MMDSN constrains the uncertainty distributions of multiple timesteps within the latent Gaussian space, controlling the variability at each denoising timestep. Extensive experiments on the Qata-Covid19 and MosMed datasets demonstrate that our proposed method surpasses existing state-of-the-art diffusion networks, producing a high-quality, controllable segmentation map with just a single reverse diffusion step and one sampling.

关键词： Image segmentation Visualization Uncertainty Semantics Noise reduction Stochastic processes Contrastive learning Gaussian distribution Diffusion models Biomedical imaging

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities

arXiv

引用

arXiv 2024年

作者： Zhang, Zheyu Liu, Xinzhao Chen, Zheng Zhang, Yueyi Yue, Huanjing Ou, Yunwei Sun, Xiaoyan MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Tianjin University Tianjin China Beijing Tiantan Hospital Capital Medical University Beijing China

Multi-modal Magnetic Resonance imaging (MRI) is imperative for accurate brain tumor segmentation, offering indispensable complementary information. Nonetheless, the absence of modalities poses significant challenges in achieving precise segmentation. Recognizing the shared anatomical structures between mono-modal and multi-modal representations, it is noteworthy that mono-modal images typically exhibit limited features in specific regions and tissues. In response to this, we present Anatomical Consistency Distillation and Inconsistency Synthesis (ACDIS), a novel framework designed to transfer anatomical structures from multimodal to mono-modal representations and synthesize modality-specific features. ACDIS consists of two main components: Anatomical Consistency Distillation (ACD) and Modality Feature Synthesis Block (MFSB). ACD incorporates the Anatomical Feature Enhancement Block (AFEB), meticulously mining anatomical information. Simultaneously, Anatomical Consistency ConsTraints (ACCT) are employed to facilitate the consistent knowledge transfer, i.e., the richness of information and the similarity in anatomical structure, ensuring precise alignment of structural features across mono-modality and multi-modality. Complementarily, MFSB produces modality-specific features to rectify anatomical inconsistencies, thereby compensating for missing information in the segmented features. Through validation on the BraTS2018 and BraTS2020 datasets, ACDIS substantiates its efficacy in the segmentation of brain tumors with missing MRI modalities. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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