DFG Priority Programme 1305: Control Theory of Digitally Networked Dynamical Systems
Bibliographic record
Abstract
Abstract The application of modern means of communication, like wireless LAN or Ethernet, enables novel concepts and structures for control engineering. Data can be transmitted between sensors, actuators, and controllers over long distances. Information can even be exchanged between moving objects which was impossible in conventional point to point wired realizations of control loops. However, besides these advantages the use of networks within a control loop also brings up new challenges due to nondeterministic effects like delays and dropouts of data packets. But for yet, there does not exist a theory for the modeling, analysis and design of such complex systems. The connection of several components of a control loop over a communication network represents a digitally networked dynamical system, which is subject of the Priority Programme 1305 “Control Theory of Digitally Networked Dynamical Systems”. The goal of this priority programme, which is founded by the German Research Foundation (DFG) over a period of six years, is to develop a comprehensive theory for this new class of systems. Here, three defined cross-sectional issues are worked out by 13 research groups and their solutions are experimentally proved in demonstration and benchmark processes. Within this priority programme several research groups from the disciplines of control engineering, mathematics and information technology cooperate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".