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Extract features from lithium-ion battery electrochemical impedance spectra and estimate state of health based on improved convolutional autoencoder-temporal convolutional network

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Ionics 2025年 1-19页

作者： Lou, Cheng Wang, Shi Li, Zhaoting Wang, Kai School of Electrical Engineering Weihai Innovation Research Institute Qingdao University Qingdao266000 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Engineering Brown University ProvidenceRI02912 United States Shandong Suoxiang Intelligent Technology Co. Ltd Weifang261101 China

Predicting the state of health (SOH) of batteries is crucial for understanding their remaining lifespan and formulating more effective maintenance and management strategies. Utilizing electrochemical impedance spectroscopy (EIS) to assess the health status of batteries offers advantages such as high precision, rapid response, non-invasiveness, and reliability for accurately forecasting the remaining lifespan of batteries. In this paper, we propose an innovative method combining electrochemical impedance spectroscopy data to efficiently recognize and process complex patterns using deep neural networks by converting EIS into a two-dimensional image format. We have developed an improved convolutional autoencoder (ICAE) optimized to extract key features directly related to battery capacity in 2D EIS images and significantly improve feature characterization. The optimized features are further fed into the temporal convolutional network (TCN) to perform SOH prediction tasks. TCN utilizes its powerful time-dependent capture capability and long sequence memory mechanism to demonstrate superior performance in the field of SOH estimation. Compared with traditional methods, the proposed strategy not only significantly increases the prediction accuracy, but also opens up a new way to understand and analyze the internal relationship between complex time series and image data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep neural networks

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Spectrum fragmentation-aware dynamic network slicing deployment in computing power networks based on elastic optical networks

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Journal of Optical Communications 2025年第2期46卷 335-346页

作者： Wang, Laiming Zhang, Haojie Li, Lei Ren, Danping Hu, Jinhua Zhao, Jijun School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China Beijing KELIBANG Information Technology Corporation Limited Beijing10081 China

The widespread application of AI with high computing requirements has driven the rapid development of the computing field. Computing Power Networks (CPNs) have been recognized as solutions to providing on-demand computing services, and its service provisioning can be modeled as a network slicing deployment problem. Elastic Optical Networks (EONs) offer the flexibility to allocate spectrum resources, making them well-suited for network slicing technology. Consequently, EON-based CPNs have attracted considerable attention. However, the unbalanced distribution of computing resources leads to inefficient computing resource utilization. Meanwhile, spectrum resources may be isolated and difficult for other services. This phenomenon is known as spectrum fragmentation, leading to inefficient spectrum resource utilization. To achieve balanced and efficient resource utilization, this paper first analyzes the main reasons for load unbalance and spectrum fragmentation in CPNs: mismatched slicing deployment and inappropriate resource scheduling. Therefore, a dynamic network slicing scheme based on traffic prediction (DNS-TP) is designed. Its core highlight is cooperative optimization slicing deployment and resource scheduling based on spectrum fragmentation awareness. Simulation results show that the proposed scheme enhances the network slicing acceptance ratio, computing and spectrum resource utilization while exhibiting strong performance in resource balancing. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fiber optic networks

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Research on Active Noise Control Algorithm for Stamping Workshops Based on SAS-FxLMS 24

Research on Active Noise Control Algorithm for Stamping Work...

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2024 International Conference on Virtual Reality, Image and Signal processing, ICVISP 2024

作者： Zhiyuan, Zhang Wang, Dongxia Zhao, Jintao He, Yuqi Wang, Dongsheng Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798400710926

To enhance noise protection for frontline workers in high-noise environments like stamping workshops, this paper proposes an SAS-FxLMS algorithm to address active noise control challenges in complex settings. The algorithm effectively overcomes the shortcomings of the classical FxLMS algorithm in terms of convergence speed and steady-state error by employing a scaled weight update and an exponentially varying step-size adjustment strategy. Experimental results demonstrate that the SAS-FxLMS algorithm achieves a significant noise reduction effect, particularly at a center frequency of 100Hz in the low-frequency range, reaching 46.72dB. This performance surpasses that of other algorithms, fully validating its effectiveness in practical noise control applications. © 2024 Copyright held by the owner/author(s).

关键词： Active noise control

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ODTr: Transformer Integrating OCR Auxiliary Map and Image Depth information for Document Image Unwarping 27th

ODTr: Transformer Integrating OCR Auxiliary Map and Image D...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Xiangyu Zhou, Yuxuan Gao, Liangcai Wangxuan Institute of Computer Technology State Key Laboratory of Multimedia Information Processing Peking University Beijing China

ISBN: (纸本)9783031781186

In this study, we introduce a novel application of transformer for document image unwarping, leveraging depth information and Optical Character Recognition (OCR) results. Our proposed model integrates two key modules: a depth module and an OCR module, into the transformer framework tailored for document image processing. The depth module predicts the relative depth of each pixel in the document image, thereby providing crucial spatial context for unwarping. Concurrently, the OCR module identifies regions suitable for OCR, acting as preferences for the unwarping process. This hybrid approach aims to mitigate text distortion inherent in document image unwarping, consequently enhancing OCR accuracy, although it may reduce our model’s capability in structural image unwarping. Experimental results showcase our model’s effectiveness, achieving a Character Error Rate (CER) of 24.81%, marking a significant 6.2% absolute enhancement compared to the baseline method, DocTr. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Depth information Document Image Unwarping OCR Transformer

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Predicting and preventing layer-wise surface defects: Towards proactive quality control in laser powder bed fusion

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Journal of Manufacturing Processes 2025年 145卷 630-641页

作者： Ma, Chenguang Zhang, Aoming Chen, Zhangdong Peng, Xiaojun Gao, Jiao Zhang, Yingjie Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou511442 China Guangdong Provincial Key Laboratory for Processing and Forming of Advanced Metallic Materials South China University of Technology Guangzhou China

The quality of as-built parts in laser powder bed fusion (LPBF) is significantly affected by the condition of each manufactured layer. This study introduces a proactive quality control approach that integrates predictive modeling and dynamic process control to improve layer-wise surface quality. Specifically, an encoder-convolutional long short-term memory (ConvLSTM)-decoder model is developed to predict the surface morphology of subsequent layers using sequential post-recoating images. These predictions enable a control strategy that dynamically adjusts laser power to maintain consistent surface quality. Experimental results demonstrate that this approach facilitates early detection of potential surface defects, allowing for timely process parameter adjustments and preventing defect progression. Parts manufactured with this proactive strategy exhibit significantly improved surface quality compared to those produced without such adjustments. This integration of predictive modeling and proactive control offers a promising solution to maintain high surface quality and enhance overall part quality in LPBF processes. © 2025

关键词： Model predictive control

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From First-Order to Second-Order Rationality: Advancing Game Convergence with Dynamic Weighted Fictitious Play 21st

From First-Order to Second-Order Rationality: Advancing Gam...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Ju, Qi Hei, Falin Liu, Yuxuan Fang, Zhemei Luo, Yunfeng Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multispectral Information Processing Wuhan China

ISBN: (纸本)9789819601271

Constructing effective algorithms to converge to Nash Equilibrium (NE) is a important problem in algorithmic game theory. Prior research generally posits that the upper bound on the convergence rate for games is OT-1/2. This paper introduces a novel perspective, positing that the key to accelerating convergence in game theory is "rationality". Based on this concept, we propose a Dynamic Weighted Fictitious Play (DW-FP) algorithm. We demonstrate that this algorithm can converge to a NE and exhibits a convergence rate of O(T-1) in experimental evaluations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consensus algorithm

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Bilateral Pricing for Dynamic Association in Federated Edge Learning

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4684-4697页

作者： Pan, Bangqi Lu, Jianfeng Cao, Shuqin Liu, Jing Tian, Wenlong Li, Minglu Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Dalian University of Technology Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence China University of South China School of Computer Science and Technology Hengyang421001 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and Parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. The proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. The source codes are available at https://***/kylechuuuuu/ICTSRN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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