检索结果-内蒙古大学图书馆

4th International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2024

作者： Amahrouch, Abdelhadi Bouhamidi, Mehdi Saadi, Youssef El Kafhali, Said Sultan Moulay Slimane University Faculty Of Sciences And Techniques Data Science For Sustainable Earth Laboratory B.P. 523 Beni Mellal23000 Morocco Polytechnic University Laboratory Of Industrial And Human Automation Mechanics And Computer Science Hauts-de-France France Hassan First University Of Settat Faculty Of Sciences And Techniques Computer Networks Modeling And Mobility Laboratory B.P. 539 Settat26000 Morocco

ISBN: (纸本)9798350309508

In cloud computing, virtual machines consolidation (VMC) techniques are commonly used to improve resource utilization and reduce energy consumption. Task scheduling in cloud systems is a crucial aspect of VMC as it involves mapping clients' requirements to the appropriate computing resources such as Virtual Machines (VMs) or Physical Machines (PMs). The cloud provider must ensure that tasks are executed efficiently using the available shared computing resources while maintaining the quality of service (QoS) and minimizing the carbon footprint. Therefore, a good VM migration based on the customer's needs and IT resource capacity is required to maintain the best performance of the cloud system. In this work, we introduce an approach that leverage machine learning-based algorithms for VMs classification based on their latency sensitivity to facilitate subsequent migration into a suitable PM for better VMC. These algorithms categorize VMs into two groups: potentially inter-active machines (exhibiting periodic behavior on a daily scale) and latency-insensitive machines (for example, batch workloads, development, and test workloads). Our model demonstrated robust performance, achieving an accuracy of around 83%, thus establishing itself as the most proficient classifier in this study. © 2024 IEEE.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

An Efficient Model based on Machine Learning Algorithms for Virtual Machines Classification in Cloud Computing Environment

An Efficient Model based on Machine Learning Algorithms for ...

引用

Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Abdelhadi Amahrouch Mehdi Bouhamidi Youssef Saadi Said El Kafhali Faculty of Sciences and Techniques Data Science for Sustainable Earth Laboratory Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Industrial and Human Automation Mechanics and Computer Science Polytechnic University Hauts-de-France France Faculty of Sciences and Techniques Computer Networks Modeling and Mobility Laboratory Hassan First University of Settat Settat Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

关键词： Cloud computing Machine learning algorithms Sensitivity Processor scheduling Computational modeling Quality of service Virtual machining

来源：评论

学校读者我要写书评

暂无评论

A comparative study of machine learning models for predicting length of stay in hospitals

引用

Journal of Information science and Engineering 2021年第5期37卷 1025-1038页

作者： MEKHALDI, RACHDA NAILA CAULIER, PATRICE CHAABANE, SONDES CHRAIBI, ABDELAHAD PIECHOWIAK, SYLVAIN Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science Polytechnic University of Hauts-de-France Valenciennes CEDEX9 59313 France Alicante Company Seclin59113 France

There has been a growing interest in recent years in correctly predicting the Length of Stay (LoS) in a hospital setting. Estimating the LoS on patient' admission helps hospitals in planning, controlling costs and, providing better services. In this paper, we consider predicting the LoS as a regression problem for which we implement and compare different Machine Learning (ML) algorithms. Multiple Linear Regression (MLR), Support Vector Machines (SVM), Random Forests (RF), and Gradient Boosting model (GBM) are implemented using an open-source dataset. The methodological process involves a preprocessing step combining data transformation, data standardization, and categorical data encoding. Moreover, the Synthetic Minority Over Sampling Technique for Regression (SMOTER) is applied to handle unbalanced data. Then, ML algorithms are employed, with a hyperparameter tuning phase to obtain optimal coefficients. Finally, Mean Absolute Error (MAE), R-squared (R2), and Adjusted R-squared (Adjusted R2) metrics are selected to evaluate the model with parameters. © 2021 Institute of Information science. All rights reserved.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

ADAS Evolution: LSTM-Driven Autonomous Adaptation to Individual Driver Preferences

引用

IFAC-PapersOnLine 2024年第30期58卷 230-235页

作者： Y. Wang N. Ota S. Debernard J-C. Popieul Univ. Polytechnique Hauts-de-France CNRS UMR 8201 – LAMIH - Laboratory of Industrial and Human Automation Control Mechanical engineering and Computer science F-59313 Valenciennes France University of Tsukuba Tsukuba Japon

The ongoing advancement of advanced driver assistance systems (ADAS) has led to notable improvements in vehicle safety and efficiency within the automotive industry (Kala, 2016). Nevertheless, these systems also present new challenges and demands for developers, especially concerning driver trust and acceptance, which are vital for the safety and efficiency of collaborative human-machine driving. In response to these problems, the CoCoVéIA project proposes an innovative approach in which the driver assistance system (ADAS) integrates continuous learning of individual driving preferences, to boost driver confidence and acceptance. At the core of this method is the use of autonomous learning algorithms, particularly long short-term memory (LSTM) networks, which are adept at learning and adjusting to different driving preferences in real-time. This paper confirms the efficacy of LSTM algorithms in precisely learning and predicting driving preferences through experimental data, showcasing their ability to accommodate a variety of driver behaviors and preferences.

关键词： ADAS Intelligent Vehicles Machine Learning LSTM human-Machine Cooperation

来源：评论

学校读者我要写书评

暂无评论

Empowering ADAS With Driver-Supervised Learning Of Preferences: Parameterization And human-Machine Interaction 49

Empowering ADAS With Driver-Supervised Learning Of Preferenc...

引用

49th Annual Conference of the IEEE industrial Electronics Society, IECON 2023

作者： Wang, Yikai Debernard, Serge Popieul, Jean-Christophe Simon, Philippe Floris, Jerome Univ. Polytechnique Hauts-de-France Cnrs Umr 8201 - Lamih Automation Control Mechanical Engineering and Computer Science Laboratory of Industrial and Human ValenciennesF-59313 France Univ. Poly Technique Hauts-de-France Lamih Cnrs Umr 8201 ValenciennesF-59313 France Insa Hauts-de-France ValenciennesF-59313 France

ISBN: (纸本)9798350331820

The development and widespread use of advanced driver assistance systems (ADAS) has made considerable progress;however, in order for drivers to be confident in delegating driving tasks to these systems, there is a need to further improve their acceptance and trust. One approach to achieve this goal is to provide the driving system with the ability to autonomously learn a driver's preferences under the driver's supervision through a human-machine interface (HMI). This requires first of all identifying these driving preferences, which are derived from the driving situation to be managed, and from the driver's behavior. This paper presents the first results of an experiment conducted on a full-scale simulator in order to validate the parameterization of driving preferences and to identify future interactions through the HMI between drivers and the proposed new driving assistance system. © 2023 IEEE.

关键词： Advanced driver assistance systems

来源：评论

学校读者我要写书评

暂无评论

Platoon-Based Distributed Control for Automated Material Handling Systems

Platoon-Based Distributed Control for Automated Material Han...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empowering ADAS With Driver-Supervised Learning Of Preferences: Parameterization And human-Machine Interaction

Empowering ADAS With Driver-Supervised Learning Of Preferenc...

引用

Annual Conference of industrial Electronics Society

作者： Yikai Wang Serge Debernard Jean-Christophe Popieul Philippe Simon Jérôme Floris Laboratory of Industrial and Human Univ. Polytechnique Hauts-de-France CNRS UMR 8201 - LAMIH Automation control Mechanical engineering and Computer science Valenciennes France Univ. Poly technique Hauts-de-France LAMIH CNRS UMR 8201 Valenciennes France INSA Hauts-de-France Valenciennes France

The development and widespread use of advanced driver assistance systems (ADAS) has made considerable progress; however, in order for drivers to be confident in delegating driving tasks to these systems, there is a need to further improve their acceptance and trust. One approach to achieve this goal is to provide the driving system with the ability to autonomously learn a driver's preferences under the driver's supervision through a human-machine interface (HMI). This requires first of all identifying these driving preferences, which are derived from the driving situation to be managed, and from the driver's behavior. This paper presents the first results of an experiment conducted on a full-scale simulator in order to validate the parameterization of driving preferences and to identify future interactions through the HMI between drivers and the proposed new driving assistance system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Lifecycle traceability towards sustainable and circular value chains: analysis framework and state of the art in the fashion industry

引用

IFAC-PapersOnLine 2022年第10期55卷 1705-1710页

作者： Riemens Joséphine Lemieux Andrée-Anne Lamouri Samir IFM-KERING Sustainability Chair Institut Français de la Mode 34 Quai d'Austerlitz 75013 Paris Laboratory of Industrial and Human Automation control Mechanical engineering and Computer Science (LAMIH – UMR CNRS 8201) Arts et Métiers Institute of Technology 151 Boulevard de l'Hôpital 75013 Paris

Tremendous environmental and social challenges have been raising traceability as a critical priority in the fashion industry. However, this traditional quality concept inherited from the food sector is prompting major confusions when it comes to sustainability and circularity transition, hindering its implementation. Based on three interrelated dimensions ( Objectives, Scope, and Action levers ) the paper proposes a novel analysis framework to conduct a literature review on lifecycle traceability towards sustainable and circular value chains in the fashion industry. Given the early stage of the topic, the paper opens significant research avenues as well as potential support to practitioners to build-up appropriate traceability action plans.

关键词： traceability transparency sustainability lifecycle supply chain fashion apparel textile

来源：评论

学校读者我要写书评

暂无评论

The concept of "safety bubble" to build ethical reconfigurable assembly systems

引用

IFAC-PapersOnLine 2020年第2期53卷 17023-17028页

作者： Thierry Berger Thérèse Bonte Jean-Jacques Santin Yves Sallez Univ. Polytechnique Hauts-de-France LAMIH — Laboratory of Industrial and Human Automation control Mechanical engineering and Computer Science CNRS UMR 8201 F-59313 Valenciennes France

The concept of Reconfigurable Assembly System has been proposed in the last decade to deal with mass-customization problems and volatile market environment. If physical design or control issues of these systems have been studied intensively, very few works concern the inherent ethical risks and associated safety problems. However these issues are of first interest for reconfigurable robotized systems with frequent interventions of human operators. Indeed, the re-configurability features of these manufacturing systems induce new issues in safety. The manufacturing enterprises must pay attention to the possible consequence of the Reconfigurable Assembly System design on the safety of the humans and on their possible responsibility in case of an accident. The present paper proposes the concept of safety bubble aiming to insure human’s safety by cooperation among safe robotized units. After a presentation of the ethical considerations, a design methodology of such safety bubble is detailed. The on-going works in simulation and on a real demonstrator, including collaborative robots and mobile robots, are then described.

关键词： Industry 4.0 ethics safety reconfigurable manufacturing system robotics

来源：评论

学校读者我要写书评

暂无评论

Technology Roadmap for Flexible Sensors

引用

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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：