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Dimensional-noise-aware battery lifetime prediction via an EM-TLS framework

Progress in Natural science:Materials International 2025年第1期35卷 146-155页

作者： Chenlong Yu Ting Lu Guohua Liu Xiaoang Zhai Wuyan Deng Jiayu Wan Yang Liu Xin Li School of Computer Science and Technology Donghua University Future Battery Research Center Global Institute of Future Technology Shanghai Jiaotong University Data Science Research Center Duke Kunshan University

Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.

关键词： Battery lifetime prediction Dimension-variate measurement error Energy storage Expectation-maximization Total-least-squares regression

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FedLRes: enhancing lung cancer detection using federated learning with convolution neural network (ResNet50)

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Neural Computing and Applications 2025年第14期37卷 8273-8284页

作者： Usharani, C. Selvapandian, A. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering Gnanamani College of Technology Namakkal India

Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innovative (FedLRes) approach proposes a comprehensive system for automated diagnoses of lung cancer using federated learning with ResNet50. The lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases was gathered by the IQ-OTH/NCCD. The dataset consists of 1097 images, split into training (822 images) and validation (275 images) sets. The training set includes 312 normal, 420 malignant, and 90 benign cases. The training set's data are further enhanced by using data augmentation techniques, which are then applied to normalise images differences before being fed into the federated learning with ResNet50 architecture. This approach combines deep learning models trained on different datasets allowing for improvising accuracy and generalisation. The federated learning approach enables the use of distributed data while ensuring data privacy and security. The proposed approach compared with different state of art algorithms. Through rigorous experimentation, our system showcases remarkable advancements a classification accuracy of 99.40%. This innovative approach, utilising 3D input CT scan data, offers a potent and precise tool for early detection and effective treatment strategies against the scourge of lung cancer. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Triple Sensors Integrated Deep Learning-Based Network for Detection of Defects in Medicines

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IEEE Sensors Journal 2025年第10期25卷 18171-18179页

作者： Ganesh, Karthikeyan Prakash, M. SRM Institute of Science and Technology School of Computing Department of Research Scholar Data Science and Business Systems Tamil Nadu Kattankulathur603203 India SRM Institute of Science and Technology Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603203 India

The accurate medicine dispensing is extremely important in all hospitals, as medical errors can cause serious health issues, underscoring the need for improved protocols. Numerous research has been conducted on developing detection techniques for unpackaged pills or drugs with similar packages. However, the existing frameworks face issues, such as dispensing errors and accurate defect prediction. To overcome these challenges, a novel Tri-SID Medi technique has been proposed for detecting the defects in medicines and delivering defect-free medicines using tri sensors, such as ultrasonic sensor, weight sensor, and global positioning system (GPS). Initially, the collected drug images from the hospital/pharmacy are preprocessed using the adaptive unsharp mask-guided filter to enhance the image quality and reduce noise artifacts. Then, the preprocessed images are fed into APM-YOLO to detect the images as defect or nondefect. The nondefect medicines are given into the first similarity group (SG), in which the squeeze and excitation-based bidirectional long short-term memory (SE-BiLSTM) framework is trained for grouping the medicinal products. Finally, the defect-free group medicines are directly delivery to the customers in the delivery phase by using tri sensors and global system for mobile communications (GSMs). The proposed Tri-SID Medi method achieves 99.03% accuracy, which is 9.11%, 2.45%, and 5.07% better than the existing techniques, such as YOLO, deep convolutional neural network (DCNN), and bidirectional encoder representations from transformer (BERT). The experimental results of the proposed techniques highlight the potential for real-time drug defect detection and similar drug groupings with diverse packaging, which can reduce medical errors and are key to ensure patient safety. © 2001-2012 IEEE.

关键词： Adaptive filters

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GUEST EDITORIAL Efficient Cooperative Transmission over Satellite Internet for 6G

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China communications 2025年第2期22卷 6-8页

作者： Zhu Lidong Michele Luglio Sun Zhili Zhang Gengxin Yang Mingchuan University of Electronic Science and Technology of China University of Rome “Tor Vergata” the Institute for Communication Systems (ICS) Nanjing University of Posts and Telecommunications Communication Research Center Harbin Institute of Technology

Satellite Internet, as a strategic public information infrastructure, can effectively bridge the limitations of traditional terrestrial network coverage, support global coverage and deep space exploration, and greatly... 详细信息

关键词： Special issues and sections Satellite communications Internet Deep-space communications Public infrastructure 5G mobile communication 6G mobile communication Information services Global communication Human factors Terrestrial communications communication systems

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Blockchain-based trust management for data exchange in internet of vehicle network

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Multimedia Tools and Applications 2025年第8期84卷 4837-4855页

作者： Srivastava, Sandeep Agarwal, Deepshikha Chaurasia, Brijesh Kumar Adhikari, Mainak Computer Science Indian Institute of Information Technology UP Lucknow226002 India Information Technology Indian Institute of Information Technology UP Lucknow226002 India Computer Science Pranveer Singh Institute of Technology UP Kanpur208029 India School of Data Science Indian Institute of Science Education & Research Thiruvananthapuram India

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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Wav2Lip Bridges communication Gap: Automating Lip Sync and Language Translation for Indian Languages

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IEEE Access 2025年 13卷 72906-72917页

作者： Venkataraghavan, Vaishnavi Sivapatham, Shoba Kar, Asutosh Vellore Institute of Technology School of Electronics Engineering Chennai600127 India Vellore Institute of Technology Centre for Advanced Data Science Chennai600127 India Dr B R Ambedkar National Institute of Technology Electronics and Communication Engineering Punjab Jalandhar144011 India

Translating spoken speech in videos from one language to another is known as audio-visual translation (AVT). This paper describes the implementation of an automated AVT and lip-synced dubbing application. It addresses the difficulty of synchronizing mouth movements with translated speech by building a web application that synthesizes the speaker’s lip movements to match translated audio. Using ASR models, the speech from the source video is converted to text, translated into several languages, and then automatically synthesized into speech in the target language. A lip synchronization model, Wav2Lip, is used to alter the mouth movements in the video to correspond to the target language. We compare our work with two wellknown ASR systems: Wav2vec 2.0 and Google Speech Recognition. Wav2vec 2.0 performs better with the lesser average WER% of 15.38 and is used in our final web application. The performance of the video dubbing component is discussed with the generated speech in Tamil, Telugu, Hindi, and English, and we determine that our generated videos outperform the existing ones. Our proposed AVT application is user-friendly for a wide variety of speakers, utilizing readily available TTS systems instead of training on an individual speaker’s voice. © 2013 IEEE.

关键词： Speech recognition

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Partial Label Learning with Noisy Labels

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Annals of data science 2025年第1期12卷 199-212页

作者： Zhao, Pan Tang, Long Pan, Zhigeng School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China Research Institute of Talent Big Data Nanjing University of Information Science & Technology Nanjing210044 China

Partial label learning (PLL) is a particular problem setting within weakly supervised learning. In PLL, each sample corresponds to a candidate label set in which only one label is true. However, in some practical application scenarios, the emergence of label noise can make some candidate sets lose their true labels, leading to a decline in model performance. In this work, a robust training strategy for PLL, derived from the joint training with co-regularization (JoCoR), is proposed to address this issue in PLL. Specifically, the proposed approach constructs two separate PLL models and a joint loss. The joint loss consists of not only two PLL losses but also a co-regularization term measuring the disagreement of the two models. By automatically selecting samples with small joint loss and using them to update the two models, our proposed approach is able to filter more and more suspected samples with noise candidate label sets. Gradually, the robustness of the PLL models to label noise strengthens due to the reduced disagreement of the two models. Experiments are conducted on two state-of-the-art PLL models using benchmark datasets under various noise levels. The results show that the proposed method can effectively stabilize the training process and reduce the model's overfitting to noisy candidate label sets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Phase locked loops

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年 1-22页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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