39,654 research outputs found
4D Continuous Descent Operations Supported by an Electronic Flight Bag
This paper describes a set of flight simulation
experiments carried out with the DLR’s Generic Cockpit
Simulator (GECO). A new concept named time and energy
managed operations (TEMO), which aims to enable advanced
four dimensional (4D) continuous descent operations (CDO), was
evaluated after three full days of experiments with qualified
pilots. The experiment focused to investigate the possibility of
using a 4D-controller on a modern aircraft with unmodified or
only slightly modified avionic systems. This was achieved by
executing the controller in an Electronic Flight Bag (EFB) and
using the pilot to “close the loop” by entering speed and other
advisories into the autopilot Flight Control Unit (FCU). The
outcome of the experiments include subjective (questionnaires
answered by pilots) and objective (trajectory logs) data. Data
analysis showed a very good acceptance (both in terms of safety
and operability of the procedure) from the participating crews,
only with minor suggestions to be improved in future versions of
the controller and the speed advisories update rates. Good time
accuracy all along the descent trajectory was also observed.Peer ReviewedPostprint (published version
Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry
Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas
D-SPACE4Cloud: A Design Tool for Big Data Applications
The last years have seen a steep rise in data generation worldwide, with the
development and widespread adoption of several software projects targeting the
Big Data paradigm. Many companies currently engage in Big Data analytics as
part of their core business activities, nonetheless there are no tools and
techniques to support the design of the underlying hardware configuration
backing such systems. In particular, the focus in this report is set on Cloud
deployed clusters, which represent a cost-effective alternative to on premises
installations. We propose a novel tool implementing a battery of optimization
and prediction techniques integrated so as to efficiently assess several
alternative resource configurations, in order to determine the minimum cost
cluster deployment satisfying QoS constraints. Further, the experimental
campaign conducted on real systems shows the validity and relevance of the
proposed method
Git workflow for active learning - a development methodology proposal for data-centric AI projects
As soon as Artificial Intelligence (AI) projects grow from small feasibility studies to mature projects, developers and data scientists face new challenges, such as collaboration with other developers, versioning data, or traceability of model metrics and other resulting artifacts. This paper suggests a data-centric AI project with an Active Learning (AL) loop from a developer perspective and presents ”Git Workflow for AL”: A methodology proposal to guide teams on how to structure a project and solve implementation challenges. We introduce principles for data, code, as well as automation, and present a new branching workflow. The evaluation shows that the proposed method is an enabler for fulfilling established best practices
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
- …