Development of Novel Actuating Mechanisms for Smart Artificial Flowers
Bibliographic record
Abstract
We present novel actuating mechanisms for smart artificial flowers.Artificial flowers are static decorations, but if we make them movable, this will make them more lively and entertaining.The statement "smart" represents the flower that is able to interact with the surrounding and to show dynamic visual effects such as the blooming of flower, the swaying of stem just like light tracking of the sunflower.The flower can be executed with the pre-set program or be remote control by using smart phone or computer.The smart flower is consisted of actuating mechanisms, Arduino controllers, and sensors.In this paper, we focused on the movement of the flower.Instead of using conventional actuating mechanisms to actuate the flower, we use shape memory alloy (SMA) as actuators to avoid complication, large volume and noise generation of the components.Two types of the actuating mechanisms are designed, one is for onedirection bending, and the other is for multi-directions bending.The performances of the novel actuating mechanisms are evaluated through the experiment.The prototype of the smart flowers have been developed and can be realized in various application such as robotic flowers, flower lamps, fashion clothing accessories, and decorations in home or offices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".