检索结果-内蒙古大学图书馆

Automation and Computation (AUTOCOM), International Conference on

作者： Gunveen Ahluwalia Vaibhav Goutham Suresh M D Anto Praveena Kalyan Acharjya Trushna Paridaname Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models.

关键词： Deep learning Cloud computing Automation Machine learning algorithms Heuristic algorithms Estimation Dynamic scheduling Prediction algorithms Real-time systems Resource management

来源：评论

学校读者我要写书评

暂无评论

Event Prediction for Network Edge Devices using Log Analysis 5

Event Prediction for Network Edge Devices using Log Analysis

引用

5th International Conference on computer, Communication, and Signal Processing, ICCCSP 2021

作者： Rm, Pitchappan P Suraj Subramanian, A. Sabari Nathan, M. Harishkumar, R. Sadasivam, G. Sudha Thomas, Manoj PSG College of Technology Dept. of Computer Science and Engineering Coimbatore India Samsung Research Institute Bangalore Bangalore India

ISBN: (纸本)9781665432771

Devices that control data flow at the boundary between two networks are known as Network Edge Devices and they are typically used as network entry or exit points. It is not possible to check and monitor these devices manually. Further monitoring these devices using a separate application creates unnecessary overhead. An effective approach is to automate monitoring of these devices by building intelligence into these devices to perform such tasks using Machine Learning. This paper proposes the usage of Elasticstack to ship all logs to a central server where they are analyzed to predict failures of the network edge devices. The research yielded good results while using application logs to predict failure of small cells. © 2021 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Integrated Control and Modeling of a PV-Wind Hybrid System for DC Electric Micro-Networks

Integrated Control and Modeling of a PV-Wind Hybrid System f...

引用

IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Santhosha B M Harendra Singh Negi V. Balaraju Daxa Vekariya Harshal Patil Ramya Maranan Department of Electrical and Electronics Engineering Ballari Institute of Technology and Management Ballari Visvesvaraya Technological University Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Basic Engineering and Applied Sciences Dr. NTR College of Agricultural Engineering Bapatla Andhra Pradesh India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

The main objective of this work is to model, control and simulate an electrical microgrid. To achieve this objective, models of the subsystems or associated elements (generators, power interfaces, electrical loads, etc.) are built, the control strategies necessary for the integration of each subsystem in the microgrid are implemented, and it is proposed an optimization algorithm that allows the economic dispatch of the system to be carried out under different generation and demand conditions. Finally, after simulating the model, its behavior is validated in a considered operating scenario. The work environment selected to develop this project is Simulink, a tool integrated into MATLAB, due to the ease it offers when modeling and simulating dynamic systems, allowing you to customize the included libraries and export data to MATLAB for subsequent analysis. At the end of this work, it is intended to have a simulated environment to validate the behavior of the proposed microgrid, which allows considering different generation and demand scenarios, and an optimization algorithm that allows carrying out the economic dispatch of the system under the operating conditions. considered, without taking into account the randomness of generation sources and energy demand.

关键词： Economics Wind energy generation Analytical models Wind energy Microgrids Mathematical models Generators

来源：评论

学校读者我要写书评

暂无评论

Experimental design method for air defense and anti-missile weapons based on uniform design

Experimental design method for air defense and anti-missile ...

引用

computer, Internet of Things and Smart City (CIoTSC), International Conference on

作者： Shunmin Li Ruihan Luo Peng Zeng Yidan Zhang Zhili Wang System Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (数字)9798350306569

ISBN: (纸本)9798350306576

Due to the rapid development of modern military technology, aircraft have become one of the main threats to opponents in modern warfare during military operations. Air missile defense has also become a focus. This article demonstrates an effective performance optimization method. The study of this experimental design method is of great significance to the development of air defense and antimissile weapons and equipment systems. At present, experimental research on air defense and anti-missile weapons and equipment systems at home and abroad rarely adopts uniform design methods. This paper uses the uniform design method to optimize the testing of air defense and antimissile weapons and equipment systems. In the parameter space including 6 factors and 30 levels, the uniform design method was used to determine the effective combination of factor levels and the factors that have the greatest impact on system performance. Experiments have proven that this method can improve the interception accuracy of incoming aircraft. The proposed method is proven to be an effective interception accuracy optimization method and is applicable to other combat systems

关键词： Accuracy Uncertainty Smart cities Design methodology System performance Optimization methods Military aircraft Aircraft Optimization Testing

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Techniques for IoT Data Sharing

Privacy-Preserving Techniques for IoT Data Sharing

引用

International Conference on science technology engineering and Management (ICONSTEM)

作者： P Karthikeyan Shashiraj Teotia Toralkar Pawan B Ardly Melba Reena Chirag Vibhakar Kalai Priya V Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu Department of Computer Application Swami Vivekanand Subharti University Meerut Department of Artificial Intelligence Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electrical Engineering Gujarat Power Engineering &. Research Institute Mevad Mehsana Gujarat Technological University Ahmedabad Gujarat India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In aperiod dominated by the Internet of Effects (IoT), the sharing of data has become an integral aspect of technological advancements. still, the ubiquitous nature of IoT raises significant enterprises regarding stonerse questration. Thisexploration paper delves into innovatives equestration conservingwaysacclimatized for securing the sharing of IoT-generated data. The explores cryptographic protocols, anonymisation strategies, and decentralized fabrics toguard sensitive information while easing meaningful data exchange. Addressing the contradiction between datamileage and individuals equestration, the paper proposes new methodologies that intricately balance the need for dataparticipation with the imperative tocoverstoner confidentiality. By introducing a comprehensive analysis of sequestration-conserving mechanisms with in the IoT eco system, this exploration contribute sprecious perceptivity to the ongoingconverse on securing the digitalgeography. The findings offer a foundation for developing robustfabrics that harmonize the imperatives of dataparticipating with the consummatesignificance of securing stoner sequestration in the expanding realm of Io'Toperations.

关键词： Mathematics Fabrics Internet of Things STEM Cryptographic protocols

来源：评论

学校读者我要写书评

暂无评论

Statistical Spectrum Prediction for Cognitive Radio Networks

Statistical Spectrum Prediction for Cognitive Radio Networks

引用

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： J. P. D Manoj Sithara MWP Maduranga Valmik Tilwari Nilotpal Chakraborty Debashree Sharma Department of Electrical Technology University Collage of Ratmalana Ratmalana Sri Lanka Centre for Telecommunication Research Sri Lanka Technology Campys Padukka Sri Lanka Electronic Communication Engineering Indian Institute of Information Technology Guwahati India Computer Science Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Cognitive radio (CR) technology offers a promising solution to address the issue of bandwidth inefficiency. The CR system identifies vacant slots or underutilized channels in a primary user communication system and efficiently allocates them to secondary users, resulting in improved channel utilization. To achieve optimal spectrum utilization with CR, dynamic handling of spectrum sensing, decision-making, sharing, and mobility is essential. Furthermore, CR systems employ spectrum prediction techniques to estimate the availability of future channels for secondary user communication. Among the various spectrum prediction methods, one commonly used in practice is spectrum prediction using a probabilistic assertion during spectrum sensing. This paper introduces a novel spectrum prediction technique that seeks to enhance existing methods, particularly in realistic channel conditions. The proposed method introduces a new type of primary user activity model into the multichannel system, thereby improving the accuracy and effectiveness of spectrum prediction. The performance evaluation of this new concept utilizes Binary Phase Shift Keying signaling within a theoretical CR model. By presenting this innovative spectrum prediction technique, the research contributes to the advancement of CR technology and its practical implementation in real-world scenarios. The results of the evaluation showcase the potential of the proposed method to optimize spectrum utilization and enhance the overall performance of cognitive radio systems.

关键词： Accuracy Computational modeling Simulation Channel estimation Predictive models Reliability theory Sensors

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

引用

Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization

arXiv

引用

arXiv 2024年

作者： Zheng, Lihao Wang, Hongxuan Li, Xiaocong Ma, Jun Vadakkepat, Prahlad The School of Data Science The Chinese University of Hong Kong Shenzhen China Research Intern The Singapore Institute of Manufacturing Technology A*STAR Singapore The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Singapore Institute of Manufacturing Technology Agency for Science Technology and Research A*STAR 138634 Singapore Guangzhou China

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimal control systems

来源：评论

学校读者我要写书评

暂无评论

A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

引用

60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of Serverless Architecture for Cloud Automation

Exploring the Potential of Serverless Architecture for Cloud...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Refonaa Dhananjay Kumar Yadav Gourav Sood Sanjay Kumar Sonbhadra Sampangi Rama Reddy. B.R Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Server less architecture has become increasingly used in cloud computers since it is highly scalable and affordable and allows for the rapid deployment of services. However, no one has abused server less architecture yet, especially from a cloud operations and management perspective. This paper will explore the benefits of cloud automation using server less architecture. Cloud Automation: techniques that replace manual, repetitive activities in the cloud, such as provisioning resources, configuring applications, and monitoring performance. Server less architecture allows organizations to automate more processes and become more operationally efficient. The server less architecture frees developers from the responsibility of maintaining and managing servers. This directly reduces the overhead on IT teams. It allows them to spend time on more important things like creating and deploying applications. Moreover, server less architecture automatically scales resources based on demand, facilitating optimal utilization and cost-effectiveness. In addition, cloud automation using server less architecture allows you to combine different tools and services, one example being event-driven services that play nicely with databases or third-party services. This, in turn, enables a more holistic and cohesive solution for cloud operations automation.

关键词： Automation Costs Databases Scalability computer architecture Organizations Manuals Servers Reliability 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：