一种基于多电压采样的密立根油滴实验控制方法及系统
The Millikan oil drop experiment control method, which utilizes multi-voltage sampling and dual-output neural network mapping, solves the problem that traditional experiments rely on human experience for candidate oil drop selection and balance voltage setting, achieving more efficient and stable oil drop measurement and control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NAT UNIV OF DEFENSE TECH
- Filing Date
- 2026-05-07
- Publication Date
- 2026-07-17
AI Technical Summary
In traditional Millikan oil drop experiments, candidate oil drop selection relies on human experience, the initial setting of the equilibrium voltage depends on repeated trials, it takes a long time for a single high-quality oil drop to enter a stable and measurable state, and repeatability is significantly affected by operator differences.
Multi-voltage sampling is used to obtain the velocity characteristics of the oil droplet trajectory. A mapping relationship between the feature vector and the predicted equilibrium voltage and the predicted free fall velocity is established through a dual-output neural network. Gating screening is performed by combining the prediction results and their uncertainty statistics. The average equilibrium voltage is calculated for the initial setting of the subsequent voltage regulation stage.
Reduce ineffective voltage regulation and ineffective measurements, improve the quality and efficiency of Millikan oil drop experiments, shorten the time for high-quality oil droplets to enter a stable and measurable state, and improve the repeatability of the balance voltage and the accuracy of single charge measurements.
Smart Images
Figure CN122131637B_ABST