检索结果-内蒙古大学图书馆

20th CIrP Conference on Modeling of Machining Operations in Mons, CIrP CMMO 2025

作者： Lorenzoni, Margaux Spitaels, Laurent rivière-Lorphèvre, Edouard Odent, Jérémy M'Saoubi, rachid Cloëz, Liam Fontaine, Michaël ducobu, François Machine Design and Production Engineering Lab Research Institute for Materials Science and Engineering University of Mons Place du Parc 20 Mons7000 Belgium University of Mons Place du Parc 20 Mons7000 Belgium R&D Material and Technology Development Seco Tools AB Björnbacksvägen 2 FagerstaSE-737 82 Sweden Department of Mechanical Engineering Sciences Division of Production and Materials Engineering Lund University Ole Römers väg 1 LundSE-221 00 Sweden FEMTO-ST Institute-Applied Mechanics Dept. Université de Franche-Comté-SUPMICROTECH-CNRS Rue de l'épitaphe 24 BesançonF-25000 France

Fused Filament Fabrication (FFF) is an additive manufacturing process based on Material Extrusion (MEX) of polymeric filament. This manufacturing technology is commonly used for personalized production applications and prototyping, allowing to obtain customized parts with complex geometries at a low cost. However, this production technique has its limitations regarding dimensional accuracy and surface roughness of the final parts. To overcome these limitations, finish milling is considered to be a promising technique as it is widely used for metallic parts. Yet, the specific thermal properties of polymers such as low thermal conductivity and low melting temperature are adding a challenge in the determination of optimal cutting conditions. In this context, the use of cutting fluid could be the key to keep the material from melting during milling. Therefore, this paper proposes to compare the impact of using a compressed air flow to dry conditions on the cutting forces and the surface quality obtained on 3d-printed polylactide (PLA) parts. The qualification test of the tool-material couple standard (NF E 66-520-6) will be used as a guideline to determine the relevance of cutting fluid use for cutting conditions varying around an operating point. Moreover, simulated cutting forces using a mechanistic model will be compared to experimental data to evaluate the applicability of the model. © 2025 The Authors.

关键词： Compressed air

来源：评论

学校读者我要写书评

暂无评论

digital Evidence Management: Modern Techniques in digital Forensics

Digital Evidence Management: Modern Techniques in Digital Fo...

引用

Electronics and renewable Systems (ICEArS), International Conference on

作者： N. Venkataramanan Jayalakshmi V S. Anitha r. Ganesan V. Jenifer Department of Computer Applications DRBCCC Hindu College Chennai Department of Computer Science and Applications SRM Institute of Science and Technology (FSH) Ramapuram Department of Artificial Intelligence and Machine Learning St. Joseph’s College of Engineering Anna University Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Applications SRM Institute of Science and Technology (FSH)

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This research study reviews the significance of digital forensic analysis in modern criminal investigations, highlighting the way it complements conventional methods of inquiry and the difficulties it encounters in the information era. To solve crimes effectively, investigators must combine digital knowledge with traditional forensic techniques. This will enable them to use evidence from many different types of digital and physical sources. The specific intricacies of digital crime scenes need specialized methods for the proper storage and analysis of digital evidence to identify criminal intent and patterns of conduct. To propose Modern Techniques for digital Evidence Management [MT-dEM] based digital Forensics Service. Multidisciplinary teams and cutting-edge technology are becoming necessities in digital forensic investigation as the field adjusts to new threats like data encryption and massive amounts of data. The admissibility of evidence in the legal world depends on digital forensic procedures being transparent and adhering to set criteria. digital forensic analysis has become an indispensable tool for contemporary investigators with the rise of digital evidence.

关键词： renewable energy sources Analytical models Systematics Law enforcement reviews digital forensics Companies Encryption Internet of Things Best practices

来源：评论

学校读者我要写书评

暂无评论

Adaptive Electric Vehicle Charge Controllers Using deep reinforcement learning for real-Time Optimization

Adaptive Electric Vehicle Charge Controllers Using Deep Rein...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： N. Kavitha Mohammad Bdair P. Chandrasekar Veerakumar S M. Hema Kumar Vinay Kumar Department of EEE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India School of Architecture Computing & Engineering University of East London London Uk Department of EEE Bannari Amman Institute of Technology Erode Tamil Nadu India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Artificial Intelligence and Machine Learning B N M Institute of Technology Bengaluru India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The increasing adoption of electric vehicles necessitates intelligent charging systems to address challenges such as grid overload, renewable energy integration, and user prioritization. This paper presents a new framework in optimizing the efficiency of electric car charging: Hierarchical Multi-Agent deep reinforcement learning. In this architecture, a global coordinator relies on Proximal Policy Optimization to coordinate across stations, and the local agents follow deep Q-Networks to manage their resources at the station level. Test results: A comparison of the test results with regular methods shows an average waiting time reduced by 30%, a 15% increase in the usage of renewable energy, and a reduction of 22% in the system latency. For grid loads, consistent results from the operation of this architecture ensure that minimal unserved energy demand leads to the balanced serving of loads. This method, therefore, aligns well with sustainable energy goals through simultaneous improvement in effectiveness of charging infrastructure and on the flexible scale of solution.

关键词： renewable energy sources Vehicle-to-grid deep reinforcement learning Electric vehicle charging real-time systems Automobiles resource management Optimization Standards Testing

来源：评论

学校读者我要写书评

暂无评论

Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting

引用

INTErNATIONAL JOUrNAL OF dATA SCIENCE ANd ANALYTICS 2019年第4期7卷 311-329页

作者： de Stefani, Jacopo Le Borgne, Yann-Ael Caelen, Olivier Hattab, dalila Bontempi, Gianluca Univ Libre Bruxelles ULB Dept Comp Sci Fac Sci Machine Learning Grp Brussels Belgium Worldline SA NV R&D Brussels Belgium Equens Worldline R&D Lille Seclin France

Most multivariate forecasting methods in the literature are restricted to vector time series of low dimension, linear methods and short horizons. Big data revolution is instead shifting the focus to problems (e.g., issued from the IoT technology) characterized by very large dimension, nonlinearity and long forecasting horizons. This paper discusses and compares a set of state-of-the-art methods which could be promising in tackling such challenges. Also, it proposes dFML, a machine-learning version of the dynamic factor model, a successful forecasting methodology well-known in econometrics. The dFML strategy is based on a out-of-sample selection of the nonlinear forecaster, the number of latent components and the multi-step-ahead strategy. We will discuss both a batch and an incremental version of dFML, and we will show that it can consistently outperform state-of-the-art methods in a number of Synthetic and real forecasting tasks.

关键词： Multivariate forecasting Multi-step-ahead forecasting dynamic factor models Nonlinear forecasting

来源：评论

学校读者我要写书评

暂无评论

Streamlining Attendance with Fast Face recognition Model

Streamlining Attendance with Fast Face Recognition Model

引用

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

作者： Koushik Sundar ravikumar S Jeyalakshmi J Eugene Berna I Prithi Samuel Citibank NA Jersey City NJ USA Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment.

关键词： Training Accuracy databases Face recognition Manuals Feature extraction User experience Security Usability System implementation

来源：评论

学校读者我要写书评

暂无评论

Early diagnosis of diabetes and Prediction Using machine learning Algorithm

Early Diagnosis of Diabetes and Prediction Using Machine Lea...

引用

Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Pavithra S ravikumar S Sreesubha S Babu r M. Kalaiselvi School of Computer Science and Engineering Vellore Institute of Technology Chennai Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Alagappa chettiar government college of engineering and technology

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This study utilizes machine learning techniques to predict the presence of diabetes at an early stage. diabetic complications, including kidney and heart disease, can worsen over time if not detected at an earlier stage. Making the correct changes to one's lifestyle at the right time is crucial for preventing diabetes and its complications. So, there is an increasing need for a tool that can help doctors to detect this potentially deadly disease. This study has proposed machine learning based early stage diabetes detection model. The proposed model's performance is evaluated by analyzing how well the random forest classifier, a model known for its high accuracy, and resistance to overfitting performs. random forest is a great option for feature selection because they deal with feature-intensive datasets, even in high-dimensional settings, and give useful information regarding the importance of features.

关键词： resistance Analytical models Accuracy machine learning algorithms Medical services robustness diabetes random forests Tuning diseases

来源：评论

学校读者我要写书评

暂无评论

Hyper Spectral Spatial Image Segmentation Using machine learning Algorithms

Hyper Spectral Spatial Image Segmentation Using Machine Lear...

引用

Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), International Conference on

作者： Saravanan G Suresh A Jagannathan J Thanga Mariappan L Najeem dheen Abdul Majeeth Nithish Kumar Department of Computer Science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Tamilnadu India

In recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KdB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KdB model. By implementing the KdB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real rOSIS Hyper Spectral dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

real-time Human Action recognition using deep Convolutional Neural Networks

Real-time Human Action Recognition using Deep Convolutional ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： r. Vignesh J. Balaraju P. Poongothai Vaishali rajput N. rajkumar V. G. Pratheep Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore Department of CSE Anuraag University Hyderabad Department of Computer Science KristuJayanti College(Autonomous) Bangalore Computer engineering Vishwakarma Institute o Technology Pune CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Thiruvalluvar Tamil Nadu Department of Mechatronics Engineering Kongu Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

current advances in deep gaining knowledge of have enabled automated systems to understand human moves from movies in actual-time. deep convolutional neural networks (dCNNs) had been used to allow accurate and efficient real-time movement reputation. with the aid of leveraging the statistics-driven illustration mastering capability of dCNNs, movement popularity has become a powerful device for video analysis applications including robotics, surveillance, entertainment, augmented truth, and healthcare. dCNNs use an aggregate of densely connected convolutional layers and in addition completely linked layers for function extraction from films. The proposed architectures are usually skilled using supervised learning. Convolutional layers are used to understand nearby abstractions in motion pictures, which in turn are used to research worldwide features. diverse techniques together with temporal segmentation, movement-based characteristic extraction, and feature switch gaining knowledge of are also hired by means of dCNNs for achieving higher performance. recent works have investigated exceptional techniques to enhance the accuracy and speed of action reputation the use of dCNNs. This consists of techniques which includes model compression, decoupled inference networks, and efficient convolutional networks. By using combining those strategies, it’s far viable to acquire higher popularity accuracy at the same time as at the equal time preserving an excessive computational speed.

关键词： Accuracy Surveillance Supervised learning Switches Streaming media Feature extraction Motion pictures real-time systems Convolutional neural networks Human activity recognition

来源：评论

学校读者我要写书评

暂无评论

revolutionizing Financial Cloud Services: AI and Blockchain-driven resource Allocation for Maximized Transaction Speed and Security

Revolutionizing Financial Cloud Services: AI and Blockchain-...

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： M. Lenin Kumar M. Amanullah revatthy Krishnamurthy N. Mohana Suganthi S.A. Kalaiselvan Department of Electronics Communication and Engineering Vignan Institute of Technology and Sciences Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In response to the evolving challenges of real-time monitoring in financial cloud environments, we propose a comprehensive solution integrating cutting-edge technologies. Our system employs AI-driven decision-making (AddM) with a dynamic Nested Neural Network (dNN) for adaptive resource allocation. This is coupled with a Markov decision Process (MdP) for probabilistic decision-making, optimizing computational power, storage, and network resources dynamically. Blockchain technology fortifies transaction verification through a decentralized consensus mechanism and cryptographic techniques like rSA. The collaborative synergy of optimized resource allocation and Blockchain security ensures near-instantaneous processing, meeting the demands of a dynamic financial landscape. Our proposed system transforms the financial cloud environment into an adaptive and intelligent system, enhancing efficiency, security, and responsiveness for seamless and reliable real-time transaction processing. The results demonstrate accelerated transaction speeds, resource efficiency, and a robust security framework, addressing critical concerns in the realm of financial transactions.

关键词： Adaptive systems Scalability Neural networks decision making Transforms dynamic scheduling Probabilistic logic real-time systems resource management reliability

来源：评论

学校读者我要写书评

暂无评论

AES Efficient Encryption of Multimedia data using Threads for 5G System

AES Efficient Encryption of Multimedia Data using Threads fo...

引用

machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： dr. Govinda rajulu. G dr. L. Sharmila Mr. d. Venkatesan Nathi Kranthi Kumar Jarabhala Venkateswaramma Chelumala Nirosha Department of Computer Science and Design Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineering College Hyderabad India Deptarment of CSE(AI & ML) St. Martin's Engineering College Hyderabad India Department of CSE(AI&ML) St. martin's engineering college Secunderabad Department of EEE St. Martin's Engineering College Dhulapally Secunderabad Telangana

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method.

关键词： Protocols 5G mobile communication Multithreading Multimedia systems Instruction sets Organizations Streaming media Encryption Protection Videos

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：