检索结果-内蒙古大学图书馆

IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Communication, Power, and Sla-Aware Virtual Machine Placement in Oversubscribed Cloud Data Center: A Monetary Approach

SSRN

引用

SSRN 2023年

作者： Kiani, Mohsen Khayyambashi, Mohammad Reza Department of Computer Science Khansar Campus University of Isfahan Isfahan Iran Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

In the present study, we propose an algorithm for mapping virtual machines (VMs) to physical machines (PMs) in cloud data centers. The proposed method models a dynamic system where VMs enter and terminate. The goal of the algorithm is to minimize the power consumption of the PMs and the network while preventing service level agreement violation (SLA). Moreover, oversubscription is leveraged to enhance PM utilization. The mapping problem is formulated as an optimization problem and solved using a heuristic and a meta-heuristic algorithm. For the latter, we used chemical reaction optimization. In addition, to convert various important metrics into one optimization goal, first, the raw revenue of executing the VMs is calculated. Then, all other measured parameters, including PM and network power consumption, migration cost, and SLA violation penalty, are converted to monetary measures to obtain a net revenue, which is considered as the optimization goal. The simulation results show that the implemented CRO algorithm outperforms heuristic methods by a significant margin in terms of SLA violation and power consumption. © 2023, The Authors. All rights reserved.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Unveiling Stress: A Comparative Analysis of Multimodal Sensor Fusion Techniques for Predictive Modeling

Unveiling Stress: A Comparative Analysis of Multimodal Senso...

引用

Intelligent Methods, Systems, and Applications (IMSA)

作者： Noha A. Saad Eldien Raghda Essam Ali Mustafa Ezzeldin Moamen Zaher Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt Software Engineering Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Stress in professional environments is a significant concern. Medical professionals are particularly vulnerable to stress, especially during emergencies. Nurses hold a vital position in delivering care within hospital settings. It's crucial to anticipate stress levels among nurses to help them perform their duties effectively and avoid the long-term impacts of stress. This study seeks to explore how body sensors and machine learning techniques can be employed to monitor physiological signs and identify stress levels among nurses. It utilizes a benchmark dataset collected from 15 different nurses, including signals such as heart rate (HR), Electrodermal Activity (EDA), and Skin Temperature alongside location data extracted by an accelerometer. This study explores several fusion strategies, such as data, model, and prediction fusion levels to improve the accuracy and reliability of stress prediction models. Through a comparative analysis, this paper highlights the strengths and limitations of diverse fusion techniques. shedding light on their efficacy in capturing the nuanced features of stress. The findings offer valuable insights into the optimization of multimodal sensor fusion for enhanced stress prediction, creating pathways for reliable frameworks in the healthcare domain. This research conducted a comparative study between 3 different levels: data-fusion, model-fusion, and prediction-fusion. Prediction-level fusion outperformed both model-level fusion by 1.97% and data-level fusion by 1.26%.

关键词： Temperature sensors Heart rate Temperature measurement Temperature distribution Multimodal sensors Predictive models Data models

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids

Reinforcement Learning based MIMO Controller for Virtual Ine...

引用

2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skiparev, Vjatseslav Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

引用

2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

引用

Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Systemic Big Data Framework for the Charging Pile Business 22

A Systemic Big Data Framework for the Charging Pile Business

引用

Proceedings of the 7th International Conference on Information Systems engineering

作者： Licheng Xu Shuai Xie Department of Software Engineering Universiti Malaya Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Malaysia

ISBN: (纸本)9781450397889

There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategies, which may elicit a negative response from EV users, cause colossal waste of resources and hinder the development of EVs. Based on the relevant charging pile data to be analyzed, this paper combines big data-related technologies to propose a big data framework for analyzing the charging pile data to solve the common charging service problems from a systemic perspective. The purpose of the framework is to provide decision-making reference information for charging pile operators, charging pile application operators and charging service marketers to improve the charging pile business more effectively. The framework is demonstrated by functional structure and technical structure.

关键词： Charging pile data Big Data framework Decision-making reference Charging business improvement

来源：评论

学校读者我要写书评

暂无评论

Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan

Diabetes Prediction Using Machine Learning Techniques: A Cas...

引用

Information Technology (ACIT)

作者： Raed Alazaidah Radwan Batyha Ghassan Samara Esraa Al Henawi Maram Y. Al-Safarini Ahmad A. Samhan Ali Elrashidi Mo’Ath Alluwaici Deana Al-Omari Department of Data Science and AI Faculty IT Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan

关键词： Measurement Machine learning Predictive models Diabetes Bayes methods Information technology Diseases

来源：评论

学校读者我要写书评

暂无评论

The Importance of Performance Feedback in Virtual Reality (VR) Training: A Table Tennis Coach Perspective

The Importance of Performance Feedback in Virtual Reality (V...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Sunday, Kissinger Anvari, Soraya S. Li, Yiwei Sun, Junwei Wehbe, Rina R. Neyedli, Heather Batmaz, Anil Ufuk Machuca, Mayra Donaji Barrera Faculty of Computer Science VR Lab. Dalhousie University Halifax NS Canada HCI4Good Dalhousie University Halifax NS Canada University of Western Ontario Toronto ON Canada School of Interactive Arts and Technology Simon Fraser University Vancouver BC Canada HCI4Good Faculty of Computer Science Dalhousie University Halifax NS Canada School Health & Human Performance Dalhousie University Halifax NS Canada Department of Computer Science & Software Engineering Concordia University Montreal QC Canada Faculty of Computer Science Dalhousie University Halifax NS Canada

ISBN: (纸本)9798400713958

Advances in Virtual Reality (VR) technology have redefined sports training, offering a new modality for athletes to prepare for competitions. This paper explores the coaching strategies used to train novice table tennis players in VR. Through interviews with the coach and participant feedback, we identified key strategies such as personalized feedback, table division, and a structured training regimen for skill development in VR. Challenges include limited facial awareness and the absence of virtual cheering, which impacted the coach’s ability to engage fully with participants. These findings offer valuable insights into coaching strategies in VR environments, contributing to developing more effective and engaging VR training systems. © 2025 Copyright held by the owner/author(s).

关键词： Feedback Sports Table Tennis Training Virtual Reality (VR)

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：