| 1. |
- Dillon, Tharam, et al.
(författare)
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Message from U-Science 2014 general chairs
- 2014
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Ingår i: 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing. - Piscataway : IEEE. - 9781479950799 - 9781479950782
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Konferensbidrag (populärvet., debatt m.m.)abstract
- Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.© 2014 IEEE
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| 2. |
- Heitz, Thomas, et al.
(författare)
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Investigation on eXtreme Gradient Boosting for cutting force prediction in milling
- 2023
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Ingår i: Journal of Intelligent Manufacturing. - : Springer. - 0956-5515 .- 1572-8145.
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Tidskriftsartikel (refereegranskat)abstract
- Accurate prediction of cutting forces is critical in milling operations, with implications for cost reduction and improved manufacturing efficiency. While traditional mechanistic models provide high accuracy, their reliance on extensive milling data for force coefficient fitting poses challenges. The eXtreme Gradient Boosting algorithm offers a potential solution with reduced data requirements, yet the optimal utilization of eXtreme Gradient Boosting remains unexplored. This study investigates its effectiveness in predicting cutting forces during down-milling of Al2024. A novel framework is proposed optimizing its precision, efficiency, and user-friendliness. The model training incorporates the mechanistic force model in both time and frequency domains as new features. Through rigorous experimentation, various aspects of the eXtreme Gradient Boosting configuration are explored, including identifying the optimal number of periods for the training dataset, determining the best normalization and scaling technique, and assessing the hyperparameters’ impact on model performance in terms of accuracy and computational time. The results show the remarkable effectiveness of the eXtreme Gradient Boosting model with an average normalized root mean square error of 14.7%, surpassing the 21.9% obtained by the mechanistic force model. Additionally, the machine learning model could capture the runout effect. These findings enable optimized milling operations regarding cost, accuracy and computation time.
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| 3. |
- Lin, Bing, et al.
(författare)
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A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multi-cloud Environments
- 2016
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Ingår i: IEEE Transactions on Network and Service Management. - 1932-4537. ; 13:3, s. 581-594
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Tidskriftsartikel (refereegranskat)abstract
- The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle Big Data problems. One of the biggest challenges in Multi-clouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for Multi-cloud environments. Moreover, the existing research works either fail to satisfy the Quality of Service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm which is called Multi-Clouds Partial Critical Paths with Pretreatment (MCPCPP) for Big Data workflows in Multi-clouds is presented. This algorithm incorporates the concept of Partial Critical Paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into considerations the essential characteristics of Multi-clouds such as the charge per time interval, various instance types from different cloud providers as well as homogeneous intra-bandwidth vs. heterogeneous inter-bandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.
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