In this paper, we analyze the effectiveness of transfer learning on
classifying electronic components. Transfer learning reuses pre-trained models
to save time and resources in building a robust classifier rather than learning
from scratch. Our work introduces a lightweight CNN, coined as VoltaVision, and
compares its performance against more complex models. We test the hypothesis
that transferring knowledge from a similar task to our target domain yields
better results than state-of-the-art models trained on general datasets. Our
dataset and code for this work are available at
this https URL

本文分析了在对电子元件进行分类时，迁移学习的有效性。我们介绍了一种轻量级卷积神经网络模型 VoltaVision，并将其性能与更复杂的模型进行了比较。我们测试了从类似任务中迁移知识到目标领域是否比在一般数据集上训练的最先进模型获得更好的结果。

VoltaVision: 电子元件分类的迁移学习模型

VoltaVision: A Transfer Learning model for electronic component  classification

In small satellites there is less room for heat control equipment, scientific
instruments, and electronic components. Furthermore, the near proximity of the
electronics makes power dissipation difficult, with the risk of not being able
to control the temperature appropriately, reducing component lifetime and
mission performance. To address this challenge, taking advantage of the advent
of increasing intelligence on board satellites, a deep reinforcement learning
based framework that uses Soft Actor-Critic algorithm is proposed for learning
the thermal control policy onboard. The framework is evaluated both in a naive
simulated environment and in a real space edge processing computer that will be
shipped in the future IMAGIN-e mission and hosted in the ISS. The experiment
results show that the proposed framework is able to learn to control the
payload processing power to maintain the temperature under operational ranges,
complementing traditional thermal control systems.

通过利用卫星间越来越智能化的优势，提出了一种基于深度强化学习和软优势演员 - 评论家算法的框架来学习卫星上的热控策略，实验证明该框架能够学习控制处理功率以维持温度在操作范围内，并补充传统的热控系统。