检索结果-内蒙古大学图书馆

2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Jindal, Mayank Mimani, Sushant Pillai, Saniaikanth E Vadakkethil Somanathan Jiwani, Nasmin Kiruthiga, T. San Jose CA United States GoDaddy WA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Server less cloud computing has won vast recognition in current years because of its capability to provide green and price-effective solutions for numerous computing needs. On this model, cloud carriers manage the scaling and provisioning of sources, permitting users to consciousness totally on their application code. As an end result, server less computing gives an appealing opportunity to standard server-primarily based computing, in which customers needed to control the whole infrastructure themselves. However, with the rise of server less computing, there was a growing challenge about the effectiveness of algorithms on these surroundings. Traditional algorithms are designed to run on a set of resources, which may not translate properly to the dynamic and elastic nature of server less structures. This has caused a want for studies in developing algorithms mainly tailor-made for server less computing. One fundamental assignment on this place is the optimization of resources and price inside the server less environment. Since customers simplest pay for the resources they use, it's far critical to layout algorithms that minimize useful resource consumption and value. Previous research has already indicated that conventional algorithms that rely upon pre-determined aid allocation won't carry out nicely within the server less model. Moreover, server less systems additionally introduce new challenges in phrases of useful resource availability, network latency, and processing delays due to the pay-in keeping with-use model. © 2024 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Traffic Signal Management System Incorporating Reinforcement Learning

An Adaptive Traffic Signal Management System Incorporating R...

引用

2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Nobel, S.M. Nuruzzaman Islam, Md Babul Shahiduzzaman, Md. Bangladesh University of Business and Technology Dept of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Dehradun India University of Petroleum and Energy Studies Dehradun India Unical Dept. of Computer Modeling Electronic and System Engineering Rende Italy

ISBN: (纸本)9798350303452

Although there has been a lot of study done on traffic monitoring, control, modeling, and the use of artificial intelligence to handle these problems, there are still a lot of critical problems that need to be solved. In existing traffic signal research, a major problem is longer delays and queues, causing significant traffic congestion. Our research aims to minimize these issues, successfully reducing delays and queues. This has led to improved traffic flow and a significant decrease in congestion. Reinforcement learning for traffic signal control uses RL techniques to create intelligent agents that make real-time decisions about changing traffic signals at intersections. Its goal is to enhance traffic flow, reduce congestion, and improve overall transportation efficiency by optimizing signal phases dynamically. Reinforcement property state contain the current circumstances at an intersection, actions correspond to the choices made by the traffic signal controller, and rewards reflect the controller's performance and are in line with traffic objectives like minimizing delays, reducing congestion, cutting down on travel times, and improving safety. We scrutinize Deep Q learning by reason of Deep Q Learning gives Q value, By estimating and updating Q-values, the agent can learn which actions are more beneficial in different traffic conditions. We are able to cut the queue length by 9.7% to shorten wait times and relieve traffic. Get a happy outcome of traffic signal control and improve rewards by 9.7%. This research study showcases the promising capabilities of reinforcement learning in enhancing the synchronization of traffic light controllers, effectively mitigating the adverse consequences of traffic congestion within urban environments. The adoption of this innovative approach holds the promise of fostering a more sustainable and streamlined transportation system for the future. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Developing a Face Aging Model Using Face Synthesis Techniques

Developing a Face Aging Model Using Face Synthesis Technique...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Priyanka Kaushik Zakiya Manzoor Khan Muntaha Manzoor Khan Beenu Mago K. Jeevanadan Shivansh Dwivedi Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science Lovely Professional University Punjab India Dept. of Computer Science NIILM University Haryana India Dept. of Computer Science Skyline University College Sharjah UAE Dept. of Civil Engineering Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Regarding the realm of advancing technology, the subsequent development of face aging applications has garnered substantial interest. This research examines face synthesis techniques, with specific emphasis for the various practical usage of computational adversarial networks (GANs). The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age-progressed face images. The project assimilates Python programming, convolutional neural networks (CNNs), and several other machine learning and image processing proceeds, advancing in qualitative data from Kaggle. The goal of our project is to advancement of face-aging technology over the use of this multifaceted method, with the possible uses in amusement, forensic science, and technological process.

关键词： Ethics Technological innovation Forensics Aging Generative adversarial networks Generators Convolutional neural networks Faces Stress Python

来源：评论

学校读者我要写书评

暂无评论

FasNet: An Advanced Deep Learning Model for Liver Tumor Segmentation

FasNet: An Advanced Deep Learning Model for Liver Tumor Segm...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Al Amin Md. Sanaul Haque Shakib Imtiaz Muhammad Nazrul Islam Faiz Al Faisal Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

A critical focus area in medical imaging is liver tumors, which present significant challenges due to their high fatality rate, rapid progression, and severe impact on public health. Accurate detection and segmentation of liver tumors play an essential role in deciding on the proper treatment options and assessing the risk of liver resection. This study introduce a hybrid deep learning model named FasNet, designed to enhance the accuracy of liver tumor segmentation. FasNet combines the strengths of the ResNet-50 and VGG-16 architectures, incorporating 3D convolutional layers to process volumetric data from liver CT scans more effectively than traditional 2D methods. This 3D approach enables the model to capture complex spatial relationships and contextual information within liver images. FasNet leverages VGG-16's capabilities for fine-grained feature extraction and integrates the residual learning approach from ResNet-50 to address the vanishing gradient problem. Our extensive evaluation of FasNet using a large liver imaging dataset demonstrates its high accuracy and effectiveness, highlighting its potential for application in clinical settings where precise tumor segmentation is crucial for optimal patient care. FasNet represents a significant advancement in medical imaging with dice score of 0.854, precision as 85.1%, recall as 81% and Fl-score as 99.5%, offering new opportunities for improving diagnostic methods and ultimately enhancing patient outcomes in the fight against liver disease.

关键词： Deep learning Image segmentation computer vision Accuracy Three-dimensional displays Liver computer architecture Feature extraction Tumors Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

引用

2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Gaines, Timothy B. Hurt, J. Alex Keller, James M. Scott, Grant J. Boyle, Camden I. Maschmann, Matthew R. Price, Stanton R. University of Missouri Dept. of Electrical Engineering and Computer Science Columbia United States University of Missouri Dept. of Mechanical and Aerospace Engineering Columbia United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9798350359527

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre-and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-The-Art deep learning models. Additionally, we experiment with various pre-Training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification. © 2023 IEEE.

关键词： Aluminum

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review for Optimizing the Design of Cluster Head Selection to Enhance the Network Lifetime in WSN

A Comprehensive Review for Optimizing the Design of Cluster ...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： S Bhuvaneswari A Abirami R P Suvetha Sri R Srinithy R Swathi Faculty of dept. of Computer Science and Engineering Easwari Engineering College Chennai India Student of dept. of Computer Science and Engineering Easwari Engineering College Chennai India

Large numbers of cooperative sensor nodes make up wireless sensor networks (WSNs). Sensors are the integral component in WSN IoT. Sensors separate and organize in the clusters by similar characteristics. A cluster head is in charge of gathering information from the nodes in every cluster and transmitting it to its base station (BS). Because every node are powered by batteries, lessens energy usage and elongating lifetime of network are crucial concerns for WSN. The primary goals of this analysis are to address issues that previous writers have not addressed and to provide beginning researchers with a fundamental understanding of how to choose cluster head (CH) based on various methodologies. To designate a node as CH, some enhancing parameters are chosen, such as residual energy, packet transmission rate, throughput, distance between BS and Cluster Node (CN), and betweenness centrality. Data packets are transferred using the optimized protocol to reduce delay time and increase transmission rate after many CH are chosen. Analysis of the many literatures was done. An Optimization Algorithm is needed to enhance network lifetime from this analysis. A comparison of various algorithm based on the features and difficulties from the analysis is done. There are many drawbacks, such as a cluster head is chosen at random, data transmission rate is not considered, no proper formation of clusters, importance for load and temperature is not given, stability is not taken into account. Thus, there is a need for optimized algorithm to overcome all the mentioned issues.

关键词： Wireless communication Wireless sensor networks Temperature distribution Clustering algorithms Throughput Data models Delays

来源：评论

学校读者我要写书评

暂无评论

Thermal Error Modelling using Features selected from Particle Swarm Optimization 5

Thermal Error Modelling using Features selected from Particl...

引用

5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Ajith Kumar, B. Nayana, B.R. Shashank, P. M. S. Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Bengaluru India Ace Designers Ltd. Research and Technology Development Group Bengaluru India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528539

computer Numerical Control (CNC) machines are employed in various industrial manufacturing processes to produce precise components. The CNCs are prone to thermal deviations from the heat generated during machining. Axis motors, rotary bearings, and spindle motors are the main sources of heat. In this context, temperature sensitive areas (TSAs) apply to these locations. The accuracy of the machining process is directly affected by the heat-induced deviation between the tool and the workpiece. The objective is to minimize these deviations between the tool and the workpiece. The most challenging aspect of creating thermal error compensation models is figuring out which TSAs to use. This work proposes employing particle swarm optimization (PSO), a computational intelligence (CI) technique, to choose significant features. First, information about the machine's temperature and thermal deviation is collected. Second, the outliers are eliminated by cleaning the data. Following this, PSO optimizes the error band to identify which TSAs are significant. Advanced machine learning (ML) models like polynomial regression (PR) and random forest (RF) regression are trained utilizing these TSAs as features. The proposed approach is compared with matching models developed with features selected through the brute force (BF) approach. While PSO-RF outperformed BF-RF in providing 16.60% error reduction with different features, PSO-PR and BF-PR provided 47.12% error band reduction with similar features. Based on the findings, it can be concluded that PSO-PR can be used on the machine and that PSO outperforms BF in terms of identifying the ideal subset of influential features. © 2024 IEEE.

关键词： Error compensation

来源：评论

学校读者我要写书评

暂无评论

Design and Development of an IoT Based Smart Vaccine Carrier and Real-Time Monitoring System

Design and Development of an IoT Based Smart Vaccine Carrier...

引用

Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Raisa T. Hasanat Nafees Mansoor Nabeel Mohammed Mohammad S. Rahman Sifat Momen Mirza Rasheduzzaman EA Technology Capenhurst United Kingdom Dept. of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering United International University Dhaka Bangladesh Dept. of Electrical and Electronic Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Vaccination stands as the most reliable defense against a spectrum of life-threatening diseases. However, the eq-uitable distribution of vaccines in developing and least-developed nations face formidable challenges due to the absence of efficient and transparent cold chain management systems. This article introduces a real-time data-centric monitoring system designed to track the distribution and transportation of vaccines. Additionally, this research presents an energy-efficient vaccine carrier based on thermoelectric cooling, boasting minimal power consumption requirements. This innovative carrier ensures the safe transportation of vaccines even to the most remote regions. Moreover, the proposed system regulates the temperature and humidity of individual carriers by continuously monitoring sensor data. Furthermore, the system includes real-time tracking and reporting features. Anticipated outcomes include a significant increase in vaccination coverage in remote areas and enhanced transparency in the distribution process.

关键词： Temperature sensors Temperature distribution Power demand Cold chain Transportation Humidity Real-time systems Vaccines Reliability Monitoring

来源：评论

学校读者我要写书评

暂无评论

Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

arXiv

引用

arXiv 2023年

作者： Yoon, Yongho Lee, Woosuk Yi, Kwangkeun Seoul National University Dept. of Computer Science & Engineering Korea Republic of Hanyang University Dept. of Computer Science & Engineering Korea Republic of

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques. © 2023, CC BY.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Networks in Neuroimaging: A U-Net Model for Precise Brain Tumor Detection

Deep Neural Networks in Neuroimaging: A U-Net Model for Prec...

引用

Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This work holds the promise of being a considerable breakthrough in medical imaging diagnosis by studying advanced U-Net topologies for accurate brain tumour segmentation from magnetic resonance imaging (MRI) data. In this research, we present a more sophisticated pipeline that covers data preprocessing, robust model validation, and architecture tuning of the deep learning techniques. Performing accurately at 94.7% accuracy, the model showed a decent precision and recall rates with extremely promising results against traditional segmentation approaches as well as current deep learning frameworks. These results show how U-Net models could improve the diagnoses accuracy and guide personalized treatment tactics. Our research demonstrates the transformative ability of deep learning in medical diagnostics and opens a pathway for additional strides in individualized therapy, albeit diffuse tumour border segmentation barrier and MRI image heterogeneity limitations.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Brain modeling Robustness Topology Telecommunication computing Planning Tuning

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：