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Interval prediction strategy for the remaining useful life of lithium-ion battery pack considering cell inconsistency

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Journal of Power Electronics 2025年 1-13页

作者： Pang, Xiaoqiong Li, Xiao Kim, Jonghoon Lee, Miyoung Zhao, Zhen Wen, Jie Zeng, Jianchao School of Computer Science and Technology North University of China Taiyuan China Department of Electrical Engineering Chungnam National University Daejeon Republic of Korea Institute of Network Technology Yantai China School of Electrical and Control Engineering North University of China Taiyuan China

Accurate and robust remaining useful life (RUL) prediction of lithium-ion battery packs is critical for ensuring system operation reliability and safety. However, the inconsistency accelerates battery pack degradation, making RUL prediction challenging. Furthermore, traditional prediction methods offering specific values cannot meet varying safety and urgency requirements. Aiming at the above problems, this study proposed an RUL interval prediction scheme considering cell inconsistency for lithium-ion battery packs. First, the degradation and inconsistency information of the battery pack is captured by extracting health indicators from the measured data. Second, fuzzy information granulation is introduced to transform the time series data into fuzzy granules, providing upper and lower bounds for interval prediction modeling. Then, long short-term memory is employed for modeling, generating interval prediction results that support decision adjustment for different safety and emergency levels. Finally, the experiment results show that the proposed strategy can effectively cover the real value and achieve reliable interval prediction. It also indicates that the impact of inconsistency on the prediction results becomes more remarkable as the starting point moves back. With 60% training data, the interval prediction evaluation criterion P of the model with inconsistency information is approximately 43% higher than the model without.

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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Long baseline underwater source localization based on deep K-Means++ clustering in complex underwater environments

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Digital Signal Processing: A Review Journal 2025年 164卷

作者： Dai, Yawen Yang, Lei Cao, Yifei Reconfigurable and Intelligent Computing Laboratory School of Computer and Information Anhui Polytechnic University Wuhu241000 China School of Network Engineering Wuhu Institute of Technology Wuhu241000 China School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

Long baseline localization relies on trilateration, with the least squares method being utilized to determine the unique position of underwater sources. However, it is highly sensitive to distance measurements from any of the reference beacons to the source. Furthermore, achieving continuous and stable high-precision distance measurements presents a significant challenge in complex marine environments. To address this practical problem, this paper proposes a long baseline underwater source localization method based on deep K-Means++ clustering. A three-layer stack denoising autoencoder (SDA) was utilized to extract the features of the trilateration results. Subsequently, the K-Means++ algorithm was utilized to conduct multi-source location cluster analysis and fine-tuning. The experimental results demonstrate that, compared to the existing Kmeans-based long baseline localization method, the proposed approach demonstrates a modest enhancement in localization accuracy. Concurrently, it has led to an average increase of 7 percentage points in normalized mutual information (NMI), a rise of 8 percentage points in the adjusted Rand index (ARI), and an improvement of 2.5 percentage points in clustering accuracy (ACC). This not only ensures the stability and robustness of accuracy against ocean noise in long baseline mode but also enhances the operational efficiency and versatility of clustering methods applied within this field. © 2025 Elsevier Inc.

关键词： Least squares approximations

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Satellite-UAV networks for 6G Control: A Sensing-Communication-Computing-Control Closed Loop Perspective

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IEEE network 2025年

作者： Lei, Chengleyang Fang, Xinran Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Mao, Shiwen The Department of Electronic Engineering State Key Laboratory of Space Network and Communications Tsinghua University Beijing100084 China The Department of Engineering University of Durham DurhamDH1 3LE United Kingdom The Department of Information Science and Engineering School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The future sixth-generation (6G) networks are envisioned to support machines and robots besides human beings. In order to efficiently support machines and robots working in remote and post-disaster areas, satellites and unmanned aerial vehicles (UAVs) can be utilized. In this article, we investigate how satellite-UAV networks can serve robots for their control tasks. We introduce the typical use cases of the satellite-UAV networks for control and outline the relevant challenges. The control process usually involves sensing, communication, computing, and control components, which forms a sensing-communication-computing-control (SC3) closed loop. In analogy with the reflex arc structure of humans, we reveal the indivisibility of the SC3 closed loops and propose a closed-loop-oriented system design framework. Within this framework, we take the closed-loop control performance as the design target, and highlight the holistic consideration of sensing, communication, computing, and control components in the SC3 closed loop. Furthermore, we discuss the extended SC3 closed loop with multiple sensors and robots to decompose complex control systems. A case study is provided to show the performance gain of the proposed closed-loop-oriented system design. Finally, we outline open issues in designing more efficient satellite-UAV networks for control. ©1986-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Cost-efficient FEC Scheme for Time-sensitive Multi-hop Transmissions in Overlay networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Chao Wang, Jessie Hui Li, Rui Wu, Hao Wang, Jilong Zhang, Jun Zheng, Kai Tsinghua University Institute for Network Sciences and Cyberspace BNRist Beijing China Huawei Technologies Co. Ltd. China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

In pursuit of low latency, real-time communication (RTC) service providers usually use multi-hop overlay links worldwide to bypass congested links, especially for medium- and long-distance transmissions. In such multi-hop long-distance transmission scenarios, utilizing retransmission to recover lost packets can result in increased end-to-end latency. Therefore, Forward Error Correction (FEC) is viewed as a promising way to solve the loss problem. However, for multi-hop overlay transmission, existing FEC schemes either introduce a non-negligible processing delay at each hop or reduce the processing delay at the cost of a high coefficient overhead. In this work, we propose a multi-hop FEC scheme, i.e., FEC-OEM, which considers both processing delay and coefficient overhead. FEC-OEM is designed based on two observations we obtained from measurements. First, coefficient overhead can only be reduced through an implicit transmission way. Therefore, we design a modulation-based recoding module that enables implicit coefficient transmission and hop-by-hop recoding at the same time. Second, using on-the-fly computation is a promising way to reduce processing delay. Accordingly, we design an elimination method to make the modulation-based recoding can be carried out on-the-fly. Real-world experiments demonstrate that FEC-OEM can reduce the processing delay by up to 88% without increasing the coefficient overhead compared to state-of-the-art schemes. We also use FEC-OEM to transmit packets for applications with different loss tolerances, and the results show that FEC-OEM can improve the QoE more effectively than state-of-the-art coding schemes. © 2002-2012 IEEE.

关键词： Forward error correction

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AI-enabled driver assistance: monitoring head and gaze movements for enhanced safety

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Complex & Intelligent Systems 2025年第7期11卷 1-28页

作者： Mudassar Shah, Sayyed Zengkang, Gan Sun, Zhaoyun Hussain, Tariq Zaman, Khalid Alwabli, Abdullah Jaffar, Amar Y. Ali, Farman School of Civil and Transportation Engineering Shenzhen University Guangdong China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China Department of Electrical Engineering College of Engineering and Computing in Alqunfudah Umm Al-Qura University Mecca Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science and Engineering School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

This paper introduces a real-time head-pose detection and eye-gaze estimation system for Automatic Driver Assistance Technology (ADAT) aimed at enhancing driver safety by accurately collecting and transmitting data on the driver’s head position and eye gaze to mitigate potential risks. Existing methods are constrained by significant limitations, including reduced accuracy under challenging conditions such as varying head orientations and lighting, higher latency in real-time applications (e.g., Faster-RCNN and TPH-YOLOv5), and computational inefficiency, which hinders their deployment in resource-constrained environments. To address these challenges, we propose a novel framework using the Transformer Detection of Gaze Head - YOLOv7 (TDGH-YOLOv7) object detector. The key contributions of this work include the development of a reference image dataset encompassing diverse vertical and horizontal gaze positions alongside the implementation of an optimized detection system that achieves state-of-the-art performance in terms of accuracy and latency. The proposed system achieves superior precision, with a weighted accuracy of 95.02% and Root Mean Square Errors of 2.23 and 1.68 for vertical and horizontal gaze estimation, respectively, validated on the MPII-Gaze and DG-Unicamp datasets. A comprehensive comparative analysis with existing models, such as CNN, SSD, Faster-RCNN, and YOLOv8, underscores the robustness and efficiency of the proposed approach. Finally, the implications of these findings are discussed, and potential avenues for future research are outlined.

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Lalitha, T. Szymański, Jerzy Ryszard Zurek-Mortka, Marta Sathiyanarayanan, Mithileysh Srilekha, K.R. Presidency University School of Information Science Karnataka Bengaluru India Casimir Pulaski University of Radom Faculty of Transport Electrical Engineering and Computer Sciences Department of Electrical Drives and Industrial Electronics Poland Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pulaskiego Radom Poland MIT Square London United Kingdom Vit University Tamilnadu Vellore India

ISBN: (纸本)9798331513894

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal's death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis. © 2025 IEEE.

关键词： Low emission

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ALANINE: A Novel Decentralized Personalized Federated Learning For Heterogeneous LEO Satellite Constellation

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IEEE Transactions on Mobile Computing 2025年

作者： Zhao, Liang Geng, Shenglin Tang, Xiongyan Hawbani, Ammar Sun, Yunhe Xu, Lexi Tarchi, Daniele School of Computer Science Shenyang Aerospace University Shenyang110136 China Research Institute China United Network Communications Corporation Beijing100048 China School of Electronic and Information Engineering University of Bologna Bologna40126 Italy

Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely, A Novel DecentraLized PersonAlized Federated Learning for HeterogeNeous LEO SatellIte CoNstEllation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions. © 2025 IEEE.

关键词： Federated learning

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More simplicity for trainers, more opportunity for attackers: black-box attacks on speaker recognition systems by inferring feature extractor 24

More simplicity for trainers, more opportunity for attackers...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yunjie Ge Pinji Chen Qian Wang Lingchen Zhao Ningping Mou Peipei Jiang Cong Wang Qi Li Chao Shen School of Cyber Science and Engineering Wuhan University School of Cyber Science and Engineering Wuhan University and Department of Computer Science City University of Hong Kong Department of Computer Science City University of Hong Kong Institute of Network Sciences and Cyberspace Tsinghua University School of Cyber Science and Engineering Xi'an Jiaotong University

ISBN: (纸本)9781939133441

Recent studies have revealed that deep learning-based speaker recognition systems (SRSs) are vulnerable to adversarial examples (AEs). However, the practicality of existing black-box AE attacks is restricted by the requirement for extensive querying of the target system or the limited attack success rates (ASR). In this paper, we introduce VoxCloak, a new targeted AE attack with superior performance in both these aspects. Distinct from existing methods that optimize AEs by querying the target model, VoxCloak initially employs a small number of queries (e.g., a few hundred) to infer the feature extractor used by the target system. It then utilizes this feature extractor to generate any number of AEs locally without the need for further queries. We evaluate Vox-Cloak on four commercial speaker recognition (SR) APIs and seven voice assistants. On the SR APIs, VoxCloak surpasses the existing transfer-based attacks, improving ASR by 76.25% and signal-to-noise ratio (SNR) by 13.46 dB, as well as the decision-based attacks, requiring 33 times fewer queries and improving SNR by 7.87 dB while achieving comparable ASRs. On the voice assistants, VoxCloak outperforms the existing methods with a 49.40% improvement in ASR and a 15.79 dB improvement in SNR.

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