检索结果-内蒙古大学图书馆

IEEE International Conference on Robotics and Automation (ICRA)

作者： Lin, Qinjie Ye, Guo Liu, Han Northwestern Univ Dept Comp Sci Evanston IL 60208 USA

ISBN: (纸本)9798350323658

We propose EMS (R), a cloud-enabled massive computational experiment management system supporting high-throughput computational robotics research. Compared to existing systems, EMS (R) features a sky-based pipeline orchestrator which allows us to exploit heterogeneous computing environments painlessly (e.g., on-premise clusters, public clouds, edge devices) to optimally deploy large-scale computational jobs (e.g., with more than millions of computational hours) in an integrated fashion. Cornerstoned on this sky-based pipeline orchestrator, this paper introduces three abstraction layers of the EMS (R) software architecture: (i) Configuration management layer focusing on automatically enumerating experimental configurations;(ii) Dependency management layer focusing on managing the complex task dependencies within each experimental configuration;(iii) Computation management layer focusing on optimally executing the computational tasks using the given computing resource. Such an architectural design greatly increases the scalability and reproducibility of data-driven robotics research leading to much-improved productivity. To demonstrate this point, we compare EMS (R) with more traditional approaches on an offline reinforcement learning problem for training mobile robots. Our results show that EMS (R) outperforms more traditional approaches in two magnitudes of orders (in terms of experimental high throughput and cost) with only several lines of code change. We also exploit EMS (R) to develop mobile robot, robot arm, and bipedal applications, demonstrating its applicability to numerous robot applications.

关键词： computational robotics cloud robotics massive computational experiments sky computing

来源：评论

学校读者我要写书评

暂无评论

Closed-loop field development with multipoint geostatistics and statistical performance assessment

引用

JOURNAL OF computational PHYSICS 2019年第0期390卷 249-264页

作者： Shirangi, Mehrdad G. Stanford Univ Stanford CA 94305 USA

Closed-loop field development (CLFD) optimization is a comprehensive framework for optimal development of subsurface resources. CLFD involves three major steps: 1) optimization of full development plan based on current set of models, 2) drilling new wells and collecting new spatial and temporal (production) data, 3) model calibration based on all data. This process is repeated until the optimal number of wells is drilled. This work introduces a new CLFD implementation for complex systems described by multipoint geostatistics (MPS). Model calibration is accomplished in two steps: conditioning to spatial data by a geostatistical simulation method, and conditioning to production data by optimization-based PCA. A statistical procedure (TruMAP) is presented to assess the performance of CLFD. For performance assessment by TruMAP, the methodology is applied to an oil reservoir example for 25 different true-model cases. Application of a single-step of CLFD, improved the true NPV in 64%-80% of cases. The full CLFD procedure (with three steps) improved the true NPV in 96% of cases, with an average improvement of 37%. These results indicate that probability of improving true NPV increases with closed-loop step. This massive computational experiment involved about 9.5 million reservoir simulation runs that took about 320,000 CPU hours. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Optimization under uncertainty Closed-loop optimization massive computational experiments Statistical performance assessment Data assimilation Spatial statistics

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：