检索结果-内蒙古大学图书馆

Optimal-rate error estimates and a twice decoupled solver for a backward Euler finite element scheme of the Doyle-Fuller-Newman model of lithium-ion batteries

arXiv

引用

arXiv 2024年

作者： Xu, Shu Cao, Liqun School of Mathematical Sciences Peking University Beijing100871 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Scientific and Engineering Computing Beijing100190 China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing100190 China

We investigate the convergence of a backward Euler finite element discretization applied to a multi-domain and multi-scale elliptic-parabolic system, derived from the Doyle-Fuller-Newman model for lithium-ion batteries. We establish optimal-order error estimates for the solution in the norms l2(H1) and l2(L2(Hrq)), q = 0,1. To improve computational efficiency, we propose a novel solver that accelerates the solution process and controls memory usage. Numerical experiments with realistic battery parameters validate the theoretical error rates and demonstrate the significantly superior performance of the proposed solver over existing solvers. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

来源：评论

学校读者我要写书评

暂无评论

A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilizing EEG Signals

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4938-4947页

作者： Anik, Israq Ahmed Kamal, A.H.M. Kabir, Muhammad Ashad Uddin, Shahadat Moni, Mohammad Ali Jatiya Kabi Kazi Nazrul Islam University Department of Computer Science and Engineering Trishal2224 Bangladesh Jatiya Kabi Kazi Nazrul Islam University Trishal2224 Bangladesh Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia The University of Sydney School of Project Management Faculty of Engineering SydneyNSW2037 Australia Charles Sturt University Institute of Ai and Cyber Future BathurstNSW2795 Australia Charles Sturt University Rural Health Research Institute OrangeNSW2800 Australia

Major depressive disorder (MDD), commonly called depression, is a prevalent psychiatric condition diagnosed via questionnaire-based mental status assessments. However, this method often yields inconsistent and inaccurate results. Furthermore, there is currently a lack of a comprehensive diagnostic framework for MDD that assesses various brainwaves (alpha, theta, gamma, etc.) of electroencephalogram (EEG) signals as potential biomarkers, aiming to identify the most effective one for achieving accurate and robust diagnostic outcomes. To address this issue, we propose an innovative approach employing a deep convolutional neural network (DCNN) for MDD diagnosis utilizing the brainwaves present in EEG signals. Our proposed model, an extended 11-layer 1-D convolutional neural network (Ex-1DCNN), is designed to automatically learn from input EEG signals, foregoing the need for manual feature selection. By harnessing intrinsic brainwave patterns, our model demonstrates adaptability in classifying EEG signals into depressive and healthy categories. We have conducted an extensive analysis to identify optimal brainwave features and epoch duration for accurate MDD diagnosis. Leveraging EEG data from 34 MDD patients and 30 healthy subjects, we have identified that the Gamma brainwave at a 15-s epoch duration is the most effective configuration, achieving an accuracy of 99.60%, sensitivity of 100%, specificity of 99.21%, and an F1 score of 99.60%. This study highlights the potential of deep-learning techniques in streamlining the diagnostic process for MDD and offering a reliable aid to clinicians in MDD diagnosis. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks

arXiv

引用

arXiv 2025年

作者： Buehler, Markus J. Laboratory for Atomistic and Molecular Mechanics Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology CambridgeMA02139 United States

We present an agentic, autonomous graph expansion framework that iteratively structures and refines knowledge in situ. Unlike conventional knowledge graph construction methods relying on static extraction or single-pass learning, our approach couples a reasoning-native large language model with a continually updated graph representation. At each step, the system actively generates new concepts and relationships, merges them into a global graph, and formulates subsequent prompts based on its evolving structure. Through this feedback-driven loop, the model organizes information into a scale-free network characterized by hub formation, stable modularity, and bridging nodes that link disparate knowledge clusters. Over hundreds of iterations, new nodes and edges continue to appear without saturating, while centrality measures and shortest path distributions evolve to yield increasingly distributed connectivity. Our analysis reveals emergent patterns—such as the rise of highly connected "hub" concepts and the shifting influence of "bridge" nodes—indicating that agentic, self-reinforcing graph construction can yield open-ended, coherent knowledge structures. Applied to materials design problems, we present compositional reasoning experiments by extracting node-specific and synergy-level principles to foster genuinely novel knowledge synthesis, yielding cross-domain ideas that transcend rote summarization and strengthen the framework’s potential for open-ended scientific discovery. We discuss other applications in scientific discovery and outline future directions for enhancing scalability and interpretability. © 2025, CC BY-NC-ND.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Prediction of Rental Demands using different Machine Learning Algorithms

Prediction of Rental Demands using different Machine Learnin...

引用

2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, S. Vijay, K. Pavithra, S. Prithi, S. Kannan, Sriram Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineeirng Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Chennai India SRM IST School of Computing Department of Computational Intelligence Kattankulathur India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348217

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Water Quality Analysis Using Machine Learning Techniques 2nd

Water Quality Analysis Using Machine Learning Techniques

引用

2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Kumar, M. Gobinath, T. Karthikeyan, M. Saravana Arun, C. Chettinad College of Engineering and Technology NH 67 Karur-Trichy Higways Puliyur CF Po Tamilnadu Karur639114 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9783031689048

The most crucial resource that must be preserved by humans is water. Due to numerous environmental and societal causes, the quality of the water is deteriorating day by day. In order to take remedial action in the best way possible, it is essential to regularly evaluate the quality of the water we consume. The purpose of this study is to use supervised machine learning techniques to examine the quality of water. Many supervised machine learning methods are employed in these applications. The relationship between the features and their related labels, the size of the datasets, and the applications all affect how well the machine learning technique performs. This study considers the K Nearest Neighbor method, Support Vector Machine algorithm, Logistic Regression technique, and Random Forest Classifier algorithm when evaluating the water quality. The Random Forest method performs better in terms of training and testing accuracy than other algorithms among the several supervised machine learning approaches used in our work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Scalable AI Solutions for IoT-based Healthcare Systems using Cloud Platforms 8

Scalable AI Solutions for IoT-based Healthcare Systems using...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Dankan Gowda, V. Chaithra, S.M. Gujar, Shantanu Sudhir Shaikh, Salman Firoz Ingole, Balaji Shesharao Reddy, N. Sudhakar Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Mathematics Karnataka India Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of CyberSecurity RochesterNY14623 United States Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Computer Science RochesterNY14623 United States India Mohan Babu University School of Engineering Department of Ece Tirupati India

ISBN: (纸本)9798350376425

This research offers a solution for AI in IoT-based health care systems that can help large scale implementation of AI-based cloud platforms for real-time data processing and analysis. The IoT sensors with the cloud-enabled environment supported by the CNNs and LSTM networks for health condition monitoring allows real-time management of patient health status. Based on the experimental context, the system was able to prove that the system can actually filter large data stream and work even through the fluctuating network environment. Specifically, the performance targets such as the time taken to process the data, AI output accuracy, the ability of the system to scale and the robustness of the system were assessed. The results demonstrate that the proposed solution has accuracy of up to 96% in health predictions and is easily scalable in terms of the number of IoT devices involved. Network latency was also well controlled, whereby the lowest network latency for networks was measured in 5G networks. Such conclusions prove that the application of cloud-based AI systems can enhance the conditions of the healthcare provisioning, in terms of timely identification of patients with certain health conditions. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine 8

Blood Cancer Detection and Classification using Auto Encoder...

引用

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kanimozhi, N. Nayak, Swikruti Keshav Kumar, K. Manjramkar, Vinda Kumar, Ranjeet Suganthi, D. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai Tamilnadu Kattankulathur India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India Department of Humanities and Mathematics Shaikpet Telangana Hyderabad India Zoology Maharashtra Thane India University College of Engineering & Technology Vinoba Bhave University Department of Electronics & Communication Jharkhand Hazaribagh India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350396638

Leukaemia is considered as the most common type of blood cancer. The length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. The proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Automated Decision-Making System for Decentralized Stock Trading using Blockchain Smart Contracts and Quaternion Generative Adversarial Networks 4

Automated Decision-Making System for Decentralized Stock Tra...

引用

4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Mohideen AbdulKader, M. Nallusamy, Kalaichelvi Jebamalar Zulaiha Maryam, M. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamilnadu Chengalpattu India Tamilnadu Kalavakkam India Mohamed Sathak A. J. College of Engineering Department of Information Technology Tamilnadu Siruseri India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Mathematics & Actuarial Science GST Road Vandalur Tamilnadu Chennai India

ISBN: (纸本)9798331529635

In the field of financial technology, the creation of an automated decision system for the decentralized execution of stock market operations is a noteworthy development. The challenges of the generic approach, such as transparency, security, and efficiency in stock trading, are effectively solved with the help of predicting analytics combined with smart contracts based on blockchain technology. In this research, a Blockchain Smart Contract Adoption with Quaternion Generative Adversarial Networks, (BSCA-QGAN), is presented for decentralized stock market prediction over Yahoo Finance data. The first pre-processing is data cleaning and normalization of data collected and then feature extraction is done by the Spike Driven Transformer (SDT) which is the integration of Transformers and Spiking Neural Networks. Also, the Quaternion Generative Adversarial Networks (QGANs) forecast the stock prices whereby time series with built-in quaternion math is modelled. The QGAN's hyperparameters are optimized using the White Shark Optimizer (WSO). An automated decision system for decentralized stock markets is integrated with blockchain smart contracts. The system achieved 99.72% prediction accuracy and minimal memory usage of 10MB. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Based Ant Colony Optimization Scheduling in Cloud computing

引用

Computer Systems science & engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari M.Vinoth Kumar Garima Verma S.Balu Dilip Kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an Information Technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud computing scheduling ant colony optimization fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

State-of-the-art advances in vacancy defect engineering of graphitic carbon nitride for solar water splitting

引用

Journal of Semiconductors 2023年第8期44卷 16-34页

作者： Jie Li Kaige Huang Yanbin Huang Yumin Ye Marcin Ziółek Zhijie Wang Shizhong Yue Mengmeng Ma Jun Liu Kong Liu Shengchun Qu Zhi Zhao Yanjun Zhang Zhanguo Wang College of Mechanical and Electrical Engineering Handan UniversityHandan 056005China Hebei International Joint Research Center for Computational Optical Imaging and Intelligent Sensing Hebei International Joint Research Center for Computational Optical Imaging and Intelligent SensingSchool of Mathematics and Physics Science and EngineeringHebei University of EngineeringHandan 056038China Key Laboratory of Semiconductor Materials Science Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China Beijing Key Laboratory of Low Dimensional Semiconductor Materials and Devices Beijing 100083China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Materials Science and Engineering Faculty of Materials Science and Chemical EngineeringNingbo UniversityNingbo 315211China Faculty of Physics Adam Mickiewicz University Poznan61-614 PoznanPoland Guangdong-Hong Kong Joint Laboratory for Water Security Engineering Research Center of Ministry of Education on Groundwater Pollution Control and RemediationCenter for Water ResearchAdvanced Institute of Natural SciencesBeijing Normal University at ZhuhaiZhuhai 519087China

Developing low-cost,efficient,and stable photocatalysts is one of the most promising methods for large-scale solar water *** a metal-free semiconductor material with suitable band gap,graphitic carbon nitride(g-C_(3)N_(4))has attracted attention in the field of photocatalysis,which is mainly attributed to its fascinating physicochemical and photoelectronic ***,several inherent limitations and shortcomings—involving high recombination rate of photocarriers,insufficient reaction kinetics,and optical absorption—impede the practical applicability of g-C_(3)N_(4).As an effective strategy,vacancy defect engineering has been widely used for breaking through the current limitations,considering its ability to optimize the electronic structure and surface morphology of g-C_(3)N_(4) to obtain the desired photocatalytic *** review summarizes the recent progress of vacancy defect engineered g-C_(3)N_(4) for solar water *** fundamentals of solar water splitting with g-C_(3)N_(4) are discussed *** then focus on the fabrication strategies and effect of vacancy generated in g-C_(3)N_(4).The advances of vacancy-modified g-C_(3)N_(4) photocatalysts toward solar water splitting are discussed ***,the current challenges and future opportunities of vacancy-modified g-C_(3)N_(4) are *** review aims to provide a theoretical basis and guidance for future research on the design and development of highly efficient defective g-C_(3)N_(4).

关键词： g-C_(3)N_(4) vacancy defect water splitting photocatalyst charge carrier

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：