基于机器学习技术的膨松控制系统

Desilda Toska, Alfredo Pulla, Stefano Robustelli, Gianmarco Fiamma
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摘要

我们的研究工作描述了如何成功地应用深度学习技术来创建一种非侵入式方法来控制发酵过程中面团上升的动态密度。本文详细解释了在现场训练和应用卷积神经网络(CNN)的步骤,以监测米兰制造的传统圣诞意大利甜面包的发酵过程,称为Panettone,通常需要一个准确和监督的发酵过程,大约需要三天。我们的主要目标之一是证明这些cnn及其学习的内部表征如何容易地成为开发能够监控发酵过程的远程监督框架的基础。由于这个关键阶段的持续时间无法完全预测,因为它取决于许多外部因素,并且通常发生在夜间,因此采用一种无监督的方法是有意义的,这种方法能够自动检测并通知面包房人员(发送短信,电子邮件,whatsapp等)当面团密度被认为是最佳的,合适的并且准备开始烘焙阶段。结果表明,基于CNN的范式比目前使用的经验方法更有效,更准确。即使用于训练和测试分类器的图像和样例集合有限,模型也会收敛,平均损失值接近于零。虽然应用于烘焙产品环境,但设计的方法可以很容易地适应其他监测任务或行业领域,并且它独立于用户专家知识和特定的手工技能可以被认为是主要优势之一。
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Leavening control system based on machine learning techniques
Our research work describes how deep learning techniques can be applied with success to create a non-invasive method to control the dynamic density of dough rising during the fermentation process. This paper explains in detail the steps performed to train and apply on the field a Convolutional Neural Network (CNN) to monitor the leavening of a traditional Christmas Italian type of sweet bread made in Milan, called Panettone, that usually needs an accurate and supervised leavening process of around three days. One of our main goals was to prove how these CNNs and their learned inner representations could easily become the foundation for developing a remote-supervision framework capable to monitor the leavening process. Since the duration of this crucial phase is not exactly predictable, as it depends on many external factors, and usually takes place during the night, it makes sense to adopt a not supervised approach that is able to autonomously detect and notify to the bakery personnel (sending sms, email, whatsapp, etc.) when the density of dough is considered optimal, appropriate and ready to start the baking phases.Results demonstrated that a CNN based paradigm is more effective and more accurate than the current used empirical methods. The model converged and the average loss value was near to zero, even if the set of images and examples adopted to train and test the classifier was limited. Though applied in the bakery products context, the designed approach can be easily adapted to other monitoring tasks or industry domains, and its independence from User expert knowledge and specific artisanal skills can be considered one of the major advantages.
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