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A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction

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Green Energy and Intelligent Transportation 2024年第2期3卷 50-59页

作者： Yuan Chen Wenxian Duan Yigang He Shunli Wang Carlos Fernandez School of Artificial Intelligence Anhui UniversityHefei 230009China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430000China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Pharmacy and Life Sciences Robert Gordon UniversityAberdeenAB10-7GJUK

Battery life prediction is of great significance to the safe operation,and reduces the maintenance *** paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the *** feature extraction,eight features are obtained to fed into the life prediction *** hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,*** parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search *** algorithm can improve the local escape ability and convergence performance and find the global *** comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction *** with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.

关键词： State of heath Improved sparrow search algorithm Remaining useful life Variational mode decomposition Multi-kernel support vector regression Feature extraction

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Accuracy characterization of Shack-Hartmann sensor with residual error removal in spherical wavefront calibration

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Light(Advanced Manufacturing) 2023年第4期4卷 55-65页

作者： Yi He Mingdi Bao Yiwei Chen Hong Ye Jinyu Fan Guohua Shi School of Biomedical Engineering(Suzhou) Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China Department of Ophthalmology and Vision Science Eye and ENT HospitalFudan UniversityShanghai 200031China Key Laboratory of Myopia of State Health Ministry and Key Laboratory of Visual Impairment and Restoration of ShanghaiShanghai 200031China

The widely used Shack-Hartmann wavefront sensor(SHWFS)is a wavefront measurement *** measurement accuracy is limited by the reference wavefront used for calibration and also by various residual errors of the sensor *** this study,based on the principle of spherical wavefront calibration,a pinhole with a diameter of 1μm was used to generate spherical wavefronts with extremely small wavefront errors,with residual aberrations of 1.0×10^(−4)λRMS,providing a high-accuracy reference *** the first step of SHWFS calibration,we demonstrated a modified method to solve for three important parameters(f,the focal length of the microlens array(MLA),p,the sub-aperture size of the MLA,and s,the pixel size of the photodetector)to scale the measured SHWFS *** only three iterations in the calculation,these parameters can be determined as exact values,with convergence to an acceptable *** a simple SHWFS with an MLA of 128×128 sub-apertures in a square configuration and a focal length of 2.8 mm,a measurement accuracy of 5.0×10^(−3)λRMS was achieved across the full pupil diameter of 13.8 mm with the proposed spherical wavefront *** accuracy was dependent on the residual errors induced in manufacturing and assembly of the *** removing these residual errors in the measured wavefront results,the accuracy of the SHWFS increased to 1.0×10^(−3)λRMS,with measured wavefronts in the range ofλ/***-term stability of wavefront measurements was confirmed,with residual deviations of 8.04×10^(−5)λPV and 7.94×10^(−5)λ*** study demonstrates that the modified calibration method for a high-accuracy spherical wavefront generated from a micrometer-scale pinhole can effectively improve the accuracy of an *** accuracy improvement was verified with correction of residual errors,making the method suitable for challenging wavefront measurements such as in lithography lenses,astronomical telescope systems,and adaptive optics.

关键词： Shack-Hartmann wavefront sensor Spherical wavefront calibration Residual aberration correction High-accuracy measurement of wavefronts

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Research on virtual human swarm football collaboration technology based on reinforcement learning

Research on virtual human swarm football collaboration techn...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiaoshan Mo Bin Hu Caixu Xu Yuling Yang Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Faculty of Arts and Humanities Macao University of Science and Technology Macao China

Reinforcement learning is used in many applications in artificial intelligence fields such as computer vision and environmental contextualized decision-making scenarios; virtual human swarms provide research directions for multi-intelligent body collaboration and environmental field detection in multi-intelligent body swarms. Football is a group sport, which is characterized by its holistic nature, confrontation, versatility, and ease of implementation, as well as the characteristics of both individual intelligence and group intelligence, and is a typical application scenario for multi-intelligence collaboration. This paper takes football as the research object to study the team collaboration problem and team gaming problem of the virtual human swarm in a specific environment and solves the cooperation and competition relationship between intelligence with reinforcement learning of multi-intelligence training.

关键词： Training Computer vision Decision making Collaboration Reinforcement learning Ubiquitous computing Artificial intelligence

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Cooperative Classification and Rationalization for Graph Generalization

arXiv

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arXiv 2024年

作者： Yue, Linan Liu, Qi Liu, Ye Gao, Weibo Yao, Fangzhou Li, Wenfeng Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Douyin Vision Co. Ltd Beijing China

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module. Specifically, we first assume that multiple environments are available in the classification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, the rationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from the classification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into the classification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://***/yuelinan/Codes-of-C2R. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

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12th International Conference on CYBER technology in Automation, control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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Attention Mechanism and Detection Box Information Based Real-time Multi-Object Vehicle Detection

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Journal of Computing and Information technology 2022年第4期30卷 239-256页

作者： Wu, Hao Wu, Wei Sun, Xiaoyan Zhong, Jin Cao, Fengyun School of Computer Science and Technology Hefei Normal University Hefei China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Universities Joint Key Laboratory of Photoelectric Detection Science and Technology in Anhui Province Hefei China Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention Hefei China School of Economics and Trade Anhui Business and Technology College Hefei China

Ensuring both the accuracy of vehicle target detection and meeting real-time requirements is crucial in traffic videos. The YOLOv5s target detection frame-work, known for its accuracy and efficiency, has at-tracted attention in academic circles. However, there are still some features that can be optimized. First of all, the detection subnet of the YOLOv5s framework cannot smoothly convert complex feature maps into relatively sparse target prediction boxes. To solve this, we integrate a self-attention-based gating mechanism into the detection subnet, forming the YOLOv5s-SAG network. Secondly, the loss function of CIoU used by YOLOv5s pays insufficient attention to the overlap-ping area of the detection frame, which can be used as metric for measuring target detection effectiveness. We add the loss term of area ratio to CIoU to further improve the modeling ability. Finally, the current multi-class Non-Maximum Suppression algorithm can cause high overlap of multi-class detection frames. To improve it, we propose a multi-class CS-NMS algorithm based on category suppression. Experimental results show an approximately 8% improvement in the mAP50 index on the UA-DETRAC dataset compared with YOLOv5s. The proposed algorithm also achieves better detection results compared to mainstream target detection algorithms and meets the real-time requirements of traffic video analysis. © 2022, university of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.

关键词： Object detection

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

arXiv

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arXiv 2020年

作者： Hao, Shijie Zhou, Yuan Guo, Yanrong Hong, Richang Cheng, Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Chinese University of Hong Kong Hong Kong

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768×1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters. The code will be released at https://***/zhouyuan888888/SGCPNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Data protection and provenance in cloud of things environment: Research challenges

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International Journal of Information and Computer Security 2020年第4期12卷 416-435页

作者： Wang, Chundong Yang, Lei Guo, Hao Wan, Fujin Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin China Global Energy Internet Research Institute Beijing University of Technology Tianjin China Department of College of Computer and Control Engineering Nankai University Tianjin China

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks. © 2020 Inderscience Enterprises Ltd.

关键词： Data privacy

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Detail-recovery Image Deraining via Context Aggregation Networks

Detail-recovery Image Deraining via Context Aggregation Netw...

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Conference on Computer vision and Pattern Recognition (CVPR)

作者： Sen Deng Mingqiang Wei Jun Wang Yidan Feng Luming Liang Haoran Xie Fu Lee Wang Meng Wang Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Microsoft Applied Sciences Group Lingnan University The Open University of Hong Kong Hefei University of Technology

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

This paper looks at this intriguing question: are single images with their details lost during deraining, reversible to their artifact-free status? We propose an end-to-end detail-recovery image deraining network (termed a DRDNet) to solve the problem. Unlike existing image deraining approaches that attempt to meet the conflicting goal of simultaneously deraining and preserving details in a unified framework, we propose to view rain removal and detail recovery as two seperate tasks, so that each part could specialize rather than trade-off between two conflicting goals. Specifically, we introduce two parallel sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining. For complete rain removal, we present a rain residual network with the squeeze-and-excitation (SE) operation to remove rain streaks from the rainy images. For detail recovery, we construct a specialized detail repair network consisting of welldesigned blocks, named structure detail context aggregation block (SDCAB), to encourage the lost details to return for eliminating image degradations. Moreover, the detail recovery branch of our proposed detail repair framework is detachable and can be incorporated into existing deraining methods to boost their performances. DRD-Net has been validated on several well-known benchmark datasets in terms of deraining robustness and detail accuracy. Comparisons show clear visual and numerical improvements of our method over the state-of-the-arts.

关键词： Rain Maintenance engineering Task analysis Degradation Training Feature extraction Visualization

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