Use Case. The purpose of tokens in 'read' and 'bring online' primarily is to steer data streams or better to steer the location of files. The space reservation aspect of tokens is of minor interest. An example is that the same dataset may be needed by the reprocessing system as well as for FTS export or user analysis. It would be envisioned that this file is served to the various competing processes by different locations in the system mainly not to interfere or slowdown expensive reprocessing.
Use Case. Autopilot Drone (AD) Overview Commercial and industrial UAVs use autopilots, which are typically powered by microcontrollers. When the drone application requires more on-board processing power, it is usually solved by adding a microprocessor-based companion computer. Good examples of such application are real-time computer vision or large-scale data gathering from external sources. It is important to integrate their capabilities without interfering with the UAV autopilot main task, the flight control. A wide variety of sensing devices can be integrated into such setup, but the power efficiency and cost of the platform with all the components is a major factor. Adding new computing and sensing capabilities with minimal increase in battery consumption is especially important for UAVs. This is where micro-ROS is very useful, allowing to easily connect inexpensive and power-effective microcontroller-based external sensors to the autopilot. Additional benefits come from seamless integration with existing ROS 2 packages and systems that can interact with drone thanks to micro-ROS. This use-case is based on the data gathering and processing scenario commonly used in the commercial and industrial UAV markets. This scenario appears in different applications such as crop management (Precision agriculture), environment measure and observation, search and rescue and similar applications. In this case, we use the environment measure and observation scenario, providing measures taken on the fly by the UAV and measures taken from an external sensor placed on a UAV reachable place. We will focus on environmental metrics like sound, humidity, pressure and/or temperature. External sensors measures will be used on the central computer, in this case, they will be incorporated into a historical of the measures in the gathering spot. Apart from environmental measures, Autopilot will use data gathered from sensors placed on board for enhancing flight capabilities. These on-board sensors can improve flight control capabilities and precision. Adding external inertial measurement unit, IMU, or as in this case, using an external height/altitude sensor, e.g. barometer, rangefinder, can improve the flight control capabilities of the Autopilot.
Use Case. Modular Arm (MA)
Use Case. Smart warehouse (SW) Overview There is an increase in deployment of mobile robots in warehouses, where substantial area can be covered by fleets of logistic platforms, used for autonomous or semi-autonomous transportation of goods. Environment and software often need tailoring for the specifics of particular warehouse, but more adaptable platforms emerge. Robots can communicate with external systems to acquire tasks, trigger certain behaviors, notify of errors and to receive or store important data. In realization of this scenario through the use-case, a robot is operating in a smart warehouse, where challenges are resolved by local communication with various devices. The robot moves through the area, interacting with other devices and gathering data. Devices include a central system that provides the context information as well as sensors and effectors, further detailed in this document. Purpose To test, demonstrate and validate: • micro-ROS communication in a distributed system of heterogeneous devices, • resilience to dynamic changes in the communication network, • micro-ROS Bridge in a realistic environment, facilitating communication between low- power, microcontroller-based hardware and a regular robot, • to showcase micro-ROS deployment with several different devices, • and to show that micro-ROS enables enough diagnostics to empower safe and robust behavior in case of faults.
Use Case. In this section, we develop a realistic use case to illustrate the application of the energy aware SLA and the VNFD extension.
Use Case. The Contextualising Tool Xxxxx is an editor at Deutsche Welle’s (DW) Online News Desk. In general, she is reporting about energy-related topics and events. Today she has to report about “fracking”, also known as shale gas extraction. After the nuclear accident in Fukushima, the German Parliament decided to reverse its nuclear power facilities. Since then, energy transformation is an ever-growing topic on the political, economical and cultural media agenda. Fracking is a controversial topic around the world, especially in Germany. Therefore, Xxxxx has to consider all perspectives, opinions and involved entities since the debate began. First she has to get a general idea about all media items (articles, videos clips, audio clips) Deutsche Welle produced in the past. For this purpose, Xxxxx uses EUMSSI. EUMSSI indexes, analyses, links, enriches and contextualises all these items based on the latest research algorithms combining knowledge from three major research areas (audio, text and video analysis).
Use Case. Second Screen The following text is partially extracted from the DoW. Key statements derived from the guided interviews supplement our existing assumptions. There is an increasing popularity of video on-demand. On the one hand, users want to choose the programs they like. On the other hand, they also like some guidance to find out “what’s on, what’s new, what’s cool, what’s for me”. “The perfect second-screen service supplies me with entertaining and informative content.”
Use Case. MTQIP shall share MTQIP aggregated data set data with the Anesthesiology Performance Improvement and Reporting Exchange (ASPIRE) for the purpose of perioperative care quality improvement. ASPIRE may use MTQIP data for identification of patient outcomes. In exchange for sharing MTQIP data for the purpose(s) articulated ASPIRE will in turn share ASPIRE data with MTQIP to assist MTQIP to understand the relationship between anesthesia variables and outcome(s) to improve care and will be used in accordance with all uses enumerated in the Agreement. This Amendment, coupled with the underlying terms and conditions of the Agreement, contains and merges all of the terms and conditions between the parties with respect to the subject matter hereof without modifications. Participant: By: Print name: Title: Date of Signature: Address for Notice: Regents of the University of Michigan: By: Print name: _Jeanne Xxxxxxxxxx Title: Chief Compliance Officer Date of Signature: Address for Notice: University of Michigan NCRC MTQIP Building 16, room 139E 0000 Xxxxxxxx Xxxx Xxx Xxxxx, XX 00000-0000
Use Case. MTQIP shall share MTQIP aggregated data set data with MSQC for quality improvement. MSQC shall share MSQC aggregated data set data with MTQIP for quality improvement. The MSQC data set contains a comprehensive cadre of perioperative variables for patients who undergo an operative intervention. The MTQIP data set contains perioperative variables for patients who may or may not undergo an operative intervention. MSQC-MTQIP data aggregation allows for identification of potential care pathways for patients at high risk of mortality and/or complications in both operative and non-operative instances. This information can be used to aid clinicians in providing the safest care at the safest time. This Amendment, coupled with the underlying terms and conditions of the Agreement, contains and merges all of the terms and conditions between the parties with respect to the subject matter hereof without modifications.
Use Case. 3 – Heat transfer from three particles This use case has been selected to test the solver’s ability to model multiple particles that are separated by a comparably large distance (see Figure 3). Also, for this specific flow configuration, reference data from literature was available. As can be seen from Figure 4, the agreement with both sources of literature data agree very well with our predictions for the force and heat flux experienced by the individual particles. Figure 3: Streamlines predicted using the new HFD-IBM for flow around three spheres aligned with the main flow direction. Force (compared to Xxxxxxxxxx et al.) Heat Flux, d = 2.dp Figure 4: Relative deviations of the predicted force (left panel) and heat flux (right panel) when using the HFD-IBM solver from literature data.
