检索结果-内蒙古大学图书馆

Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks

Journal of Energy Chemistry 2025年第5期104卷 618-627页

作者： Rui Xiong Yinghao He Yue Sun Yanbo Jia Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical Engineering Beijing Institute of Technology School of Science Computing and Engineering Technologies Swinburne University of Technology

For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven models and physical models, each offering unique advantages but also facing ***-informed neural networks(PINNs) provide a robust framework to integrate data-driven models with physical principles, ensuring consistency with underlying physics while enabling generalization across diverse operational conditions. This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV) curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity, electrode capacities, and lithium inventory capacity. The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs) and is validated using a public dataset. The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests, with errors in reconstructed OCV curves remaining within 15 mV. This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level, advancing the potential for precise and efficient battery health management.

关键词： Ageing diagnosis Convolutional neural networks Electrode level Lithium-ion batteries Physics-informed neural network

来源：评论

学校读者我要写书评

暂无评论

When Software Security Meets Large Language Models:A Survey

引用

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 317-334页

作者： Xiaogang Zhu Wei Zhou Qing-Long Han Wanlun Ma Sheng Wen Yang Xiang IEEE the School of Computer and Mathematical Sciences the University of Adelaide the School of Science Computing and Engineering Technologies Swinburne University of Technology

Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.

关键词： Large language models (LLMs) software analysis software security software testing

来源：评论

学校读者我要写书评

暂无评论

Early prediction of diabetics using three-layer classifier model and improved dimensionality reduction

引用

Multimedia Tools and Applications 2025年 1-27页

作者： G, Keerthana R, Mohanasundaram School of Computing Science and Engineering Vellore Institute of Technology TN Vellore India

Diabetes is a long-term illness that results in a variety of chronic body damage, such as kidney failure, heart problems, eye damage, depression, and nerve damage. This disease is caused by several risk factors, the most prevalent of which are age, obesity, hypertension, and insulin resistance. As a result, early detection of these chronic diseases is critical in assisting patients in reversing diabetes and living healthy lives. Moreover, the main motive of this research is to enhance the prediction and classification score of the existing models by improved dimensionality reduction. In this study, a new early diabetic prediction model is developed using a new Three-Layer Classifier model. The proposed model is designed by using the Support Vector Machine (SVM), Random Forest (RF), and Improved Restricted Boltzmann machine (I-RBM). At first, the raw data is pre-processed using a data cleaning method, where the Missing Value handling, Unwanted data removal, and noise and Outliers handling are accomplished. The noisy images are filtered/denoised by median filters. Then, from this pre-processed data, features like improved Independent Component Analysis (i-ICA), information gain, joint mutual information (JMI), conditional mutual information (CMI), Correlation, and Joint entropy are extracted. As a result, the performance of the proposed model can be validated with different existing methods such as IRBM, RF-SVM, RBM, RF, and SVM. The developed system provides better accuracy about 98.7% of disease classification accuracy and it is high when compared with the existing methodologies such as SVM, RF, RBM, RF-SVM, and IRBM. The accuracy of SVM is about 87%, the accuracy of RF is 91%, RBM and RF-SVM models have 95% accuracy, and the IRBM model has 96% accuracy. Moreover, the higher accuracy of the proposed model indicates that the system can able to reduce the high dimensionality data to avoid overfitting and thus it can enhance the perfromance. © The Author(s), under excl

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

引用

science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

来源：评论

学校读者我要写书评

暂无评论

The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

来源：评论

学校读者我要写书评

暂无评论

Robust federated contrastive recommender system against targeted model poisoning attack

引用

science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

来源：评论

学校读者我要写书评

暂无评论

Research Progress in Solar Flare Prediction Methods

引用

Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

来源：评论

学校读者我要写书评

暂无评论

Ensemble Multimodal Disease Risk Prediction: Integrating Chest X-Ray Images and Medical Data with ERSGB-RSW Method

引用

Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 697-711页

作者： Amirtharaj, Shiny Irene David Marudappa, Pushpalatha Sachin, Lade Khalid, Mohammad Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific diseases. Traditional methods face several challenges such as limited capacity to handle heterogeneous datasets, high computational power, and less efficiency. To solve the drawbacks a novel Ensemble Random Support vector Gradient Bayes Random Seal Whiskers method is proposed. In this study, the Random Forest is utilized to extract chest X-ray images from the dataset, the Support Vector Machine (SVM) is employed to categorize images by integrating various medical data modalities, the gradient boosting is implemented to capture complex relationships in chest X-ray images and Naive Bayes is used to classify image features. Also, a weighted average ensemble is employed to integrate the outcomes of the methods namely Random Forest, SVM, Gradient boosting, and Naive Bayes by assigning different weights based on its predictions in this work, the Harbor seal whiskers optimization algorithm with a random update is employed to optimize the tuned parameters based on the datasets. The experimental results revealed that the proposed method improved the efficiency in the process of multimodal disease risk prediction. © The Institution of Engineers (India) 2024.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

EHRT-RWB: A Novel Ensemble Hybrid Recurrent Transformer for Multimodal Heart Disease Risk Prediction

引用

Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 475-486页

作者： Irene, D. Shiny Peter, J. Selvin Paul Sankarasubramanian, Nivetha Krishnakanth, S. Praveen Department of Computing Technologies School of Computing Colllege of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chennai India

The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various diagnostic measures for the prediction of heart diseases. However, these techniques consist of some errors, delays, and high-cost consumption. These limitations affect the patients in many ways such as inadequate medication during the right time, affecting their mental health, affecting their physical health, and sometimes death. Hence, there is a need for an effective automatic multimodal disease risk prediction mechanism related to heart diseases. Therefore, this paper proposes the Ensemble hybrid recurrent transformer-based random wolf bird (EHRT-RWB) algorithm. The details of the patient acquired as input by the heart disease dataset are preprocessed initially and classification is performed with the EHRT-RWB methodology. The RNN method extracts reliable features and helps to study the temporal sequence data. Time series classifications are maintained with the help of the transformer method and the ensemble method integrates advanced cutting-edge technologies for performance augmentation. Finally, the RWB algorithm selects and tunes the hyperparameters of the developed classification method. Experiments conducted using certain performance assessment measures and the comparison with existing strategies show the method achieves superior cardiac disease detection performance with the highest accuracy score of 98.7%. © The Institution of Engineers (India) 2024.

关键词： Birds

来源：评论

学校读者我要写书评

暂无评论

Multimodal Cross-Attention Mechanism-Based Algorithm for Elderly Behavior Monitoring and Recognition

引用

Chinese Journal of Electronics 2025年第1期34卷 309-321页

作者： Hao Liu Zhiquan Feng Qingbei Guo School of Information Science and Engineering Unicersity of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Unicersity of Jinan

In contrast to the general population, behavior recognition among the elderly poses increased specificity and difficulty, rendering the reliability and usability aspects of safety monitoring systems for the elderly more challenging. Hence, this study proposes a multi-modal perception-based solution for an elderly safety monitoring recognition system. The proposed approach introduces a recognition algorithm based on multi-modal cross-attention mechanism, innovatively incorporating complex information such as scene context and voice to achieve more accurate behavior recognition. By fusing four modalities, namely image, skeleton, sensor data, and audio, we further enhance the accuracy of recognition. Additionally, we introduce a novel human-robot interaction mode, where the system associates directly recognized intentions with robotic actions without explicit commands, delivering a more natural and efficient elderly assistance paradigm. This mode not only elevates the level of safety monitoring for the elderly but also facilitates a more natural and efficient caregiving approach. Experimental results demonstrate significant improvement in recognition accuracy for 11 typical elderly behaviors compared to existing methods.

关键词： Privacy Accuracy Image recognition Speech recognition Robot sensing systems Skeleton Safety Reliability Older adults Monitoring

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：