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An autonomic prediction suite for cloud resource provisioning

Ali Yadavar Nikravesh, Samuel A. Ajila, Chung-Horng Lung
2017 Journal of Cloud Computing: Advances, Systems and Applications  
This paper proposes an autonomic prediction suite to improve the prediction accuracy of the auto-scaling system in the cloud computing environment.  ...  Towards this end, this paper proposes that the prediction accuracy of the predictive auto-scaling systems will increase if an appropriate time-series prediction algorithm based on the incoming workload  ...  All authors contributed to the technical aspects and the writing of the paper. AYN designed and implemented the experiments based on guidance from SAA and CL.  ... 
doi:10.1186/s13677-017-0073-4 fatcat:7i77bjt22nfdfd6fj3fsltrhna

Towards an Autonomic Auto-scaling Prediction System for Cloud Resource Provisioning

Ali Yadavar Nikravesh, Samuel A. Ajila, Chung-Horng Lung
2015 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems  
• Hypothesis: Research Goal 5 Prediction accuracy of predictive auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the incoming workload  ...  systems can be increased by choosing an appropriate time-series prediction algorithm based on the incoming workload pattern •• Largely used for prediction purposes in auto-scaling • Performance highly  ...  On the webserver machine, count total number of user requests per minute and store results in a trace file 3.  ... 
doi:10.1109/seams.2015.22 dblp:conf/icse/NikraveshAL15 fatcat:c44ml5v3wvbsxox4q54yvvtae4

Cooperative Learning for Distributed In-Network Traffic Classification

S.B. Joseph, H.R. Loo, I. Ismail, T. Andromeda, M.N. Marsono
2017 IOP Conference Series: Materials Science and Engineering  
Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote  ...  The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative  ...  Acknowledgment This work is supported in part by CREST grant (UTM Vote No. 4B176) and Universiti Teknologi Malaysia Grant matching (UTM Vote No. 00M75)  ... 
doi:10.1088/1757-899x/190/1/012010 fatcat:3xwtkp55q5fwve2podtwq4po2e

Temporal Convolutions for Multi-Step Quadrotor Motion Prediction [article]

Samuel Looper, Steven L. Waslander
2021 arXiv   pre-print
Model-based control methods for robotic systems such as quadrotors, autonomous driving vehicles and flexible manipulators require motion models that generate accurate predictions of complex nonlinear system  ...  We demonstrate the approach with a thorough analysis of TCN performance for the quadrotor modeling task, which includes an investigation of scaling effects and ablation studies.  ...  Physics-based Model A key part of the study of TCNs for quadrotor modeling is ascertaining whether prior knowledge of the system's dynamics is required to improve prediction accuracy.  ... 
arXiv:2110.04182v1 fatcat:k2brtj467zfldeswgjjbrxydje

Cooperative Learning for Distributed In-Network Traffic Classification

S.B. Joseph, H.R. Loo, I. Ismail, T. Andromeda, M.N. Marsono
2016 Proceeding of the Electrical Engineering Computer Science and Informatics  
Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote  ...  The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative  ...  Acknowledgment This work is supported in part by CREST grant (UTM Vote No. 4B176) and Universiti Teknologi Malaysia Grant matching (UTM Vote No. 00M75)  ... 
doi:10.11591/eecsi.v3i1.1144 fatcat:xaa2oacxbngcbj57btln6hqjmq

Autonomous Trajectory Tracking and Contouring Control of Three Dimensional CNC

Henna A
2018 International Journal of Trend in Scientific Research and Development  
In this study we analyzed a three-axes computer numeric control (CNC) machine. Here improve the trajectory tracking and contouring performance of both linear and circular trajectories of a CNC.  ...  This method is best solution for improve the trajectory tracking ability and precision of a CNC machine for both linear and circular trajectories.  ...  The proposed system improve trajectory tracking control and contour performance.  ... 
doi:10.31142/ijtsrd9419 fatcat:miynyvhanfgi5d5uu5z2oldwim

Boat Detection in Marina using Time-Delay Analysis and Deep Learning

2022 International Journal of Data Warehousing and Mining  
An autonomous acoustic system based on two bottom-moored hydrophones, a two-input audio board and a small single-board computer was installed at the entrance of a marina to detect entering/exiting boat  ...  Since its installation, the single-board computer performs online prediction with a signal processing-based algorithm which achieved an accuracy of 80 %.  ...  ACKNoWLeDGMeNT The authors would like to thank Sodemo which supported this work and allowed the installation of the acoustic system at the entrance of Port Brunelet.  ... 
doi:10.4018/ijdwm.298006 fatcat:dyq37zcn7retrp76al6vyb457a

Computer Vision Positioning and Local Obstacle Avoidance Optimization Based on Neural Network Algorithm

Lei Yang, Weimin Lei, Arpit Bhardwaj
2022 Computational Intelligence and Neuroscience  
The collection of robot obstacle path information improves the speed and accuracy of highly automated local obstacle avoidance.  ...  the autonomous navigation of mobile robots.  ...  At the same time, based on the improved speed obstacle method, the robot avoiding obstacles in the sidewalk is studied. e accuracy of motion planning is improved, and therefore, the speed and accuracy  ... 
doi:10.1155/2022/3061910 pmid:35401716 pmcid:PMC8993561 fatcat:lszqxf27czcy5n3twrbr5lzome

Sleep Apnea Detection Using Pulse Photoplethysmography

Margot Deviaene, Jesus Lazaro, Dorien Huysmans, Dries Testelmans, Bertien Buyse, Sabine Van Huffel, Carolina Varon
2018 2018 Computing in Cardiology Conference (CinC)  
PPG-time series known to be modulated by both respiration and the autonomous nervous system were derived: pulse rate, amplitude and width variability, slope transit time, maximal pulse upslope and the  ...  For all extracted time series, five features were computed over a 1 minute interval: the mean, minimum and maximum value, standard deviation and gradient.  ...  These time series were all resampled to 4 Hz. The autonomic nervous system can be assessed by studying the low frequency (0.04-0.15 Hz, LF) and high frequency power (0.15-0.4 Hz, HF) of the PRV.  ... 
doi:10.22489/cinc.2018.134 dblp:conf/cinc/Deviaene0HTBHV18 fatcat:bmny2s6drnhzpdd4n5pdg73nh4

An autonomic traffic analysis proposal using Machine Learning techniques

Fannia Pacheco, Ernesto Exposito, Mathieu Gineste, Cedric Budoin
2017 Proceedings of the 9th International Conference on Management of Digital EcoSystems - MEDES '17  
Autonomic Computing Autonomic computing principles are based on the human nervous system, which basically monitor, control and regulate automatically different parts of the human body.  ...  The feature extraction process performed is mainly statistical based, however, several approaches propose studying the time-series behavior and graph representation of the flows as well as the construction  ... 
doi:10.1145/3167020.3167061 dblp:conf/medes/PachecoEGB17 fatcat:ljul6uthmrfqhnqaagxcmx4qj4

When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey [article]

Chongzhen Zhang, Jianrui Wang, Gary G. Yen, Chaoqiang Zhao, Qiyu Sun, Yang Tang, Feng Qian, Jürgen Kurths
2020 arXiv   pre-print
Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability.  ...  Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and  ...  In order to improve the accuracy of the image rain removal results, consider the outstanding performance of GANs in the image inpainting or completion problems, a series of GANs-based methods have been  ... 
arXiv:2003.12948v3 fatcat:qtmjs74p2vh6thdotbhgebdvoi

Real-Time On-Board Deep Learning Fault Detection for Autonomous UAV Inspections

Naeem Ayoub, Peter Schneider-Kamp
2021 Electronics  
In the Drones4Energy project, we work toward building an autonomous vision-based beyond-visual-line-of-sight (BVLOS) power line inspection system.  ...  Our experimental results demonstrated that the proposed approach can be effective and efficient for fully automatic real-time on-board visual power line inspection.  ...  [19] introduced a new version of the YOLO series by adding some extra features to the YOLO models to further speed up detection and improve accuracy.  ... 
doi:10.3390/electronics10091091 fatcat:kcdiyl6ceje43gev6uynbbhhre

Geoinformation Support of Ground Vehicles' Autonomous Driving

S S Shadrin
2018 IOP Conference Series: Materials Science and Engineering  
efficiency, or safety; increased reliability, reduced requirements for on-board computing power, and improved performance.  ...  A "basetrack" data structure is presented with respect to the advantages of its implementation: the predetermined variability of control actions on the route, depending on the tasks of time saving, energy  ...  requirements for on-board computing power, and improves the system performance, allowing it to drive the vehicle at higher velocities.  ... 
doi:10.1088/1757-899x/386/1/012013 fatcat:7dtoohbf6zhtjcaclg77jc4jbm

When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey

Chongzhen Zhang, Jianrui Wang, Gary G. Yen, Chaoqiang Zhao, Qiyu Sun, Yang Tang, Feng Qian, Jürgen Kurths
2020 Patterns  
Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability.  ...  We furthermore review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation, and  ...  ACKNOWLEDGMENTS The authors would like to thank the Editor-in-Chief, Scientific Editor, and anonymous referees for their helpful comments and suggestions, which have greatly improved this paper.  ... 
doi:10.1016/j.patter.2020.100050 pmid:33205114 pmcid:PMC7660378 fatcat:vs7wm2yrwjamjbaml36663wvze

Multimodal Learning Models based on Data Fusion Analysis for Fully Autonomous Vehicle Navigation and Operation [article]

Siming Zheng
2020 Figshare  
The previous research based on the Multimodal Learning Models and Data Fusion techniques.  ...  How to improve the Mask R-CNN model execute quickly on the autonomous driving system, which is also one of the innovations of this research [12] .  ...  At the same time, the monitoring system is to make corrections or updates based on real-time road data.  ... 
doi:10.6084/m9.figshare.12001155 fatcat:u6iog636nbfbzaphiheikv2fqa
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