论文标题
从有限的注释原材料数据到质量生产数据：牛奶行业的案例研究（技术报告）
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)
论文作者
论文摘要
Industry 4.0提供了使用物联网技术结合多个传感器数据源的机会，以更好地利用生产线中的原材料。普遍认为数据容易获得的信念（大数据现象）通常是由于需要在严重约束下有效获取质量数据的挑战。在本文中，我们提出了一种设计方法，利用主动学习来增强学习能力，以使用约束数量的原材料培训数据来建立生产结果模型。所提出的方法将现有的主动学习方法扩展到有效解决基于回归的学习问题，并可能在数据获取需要过多资源的情况下服务于物理世界中。我们进一步提出了一系列定性措施来分析学习者的绩效。提出的方法是使用牛奶行业中的实际应用来证明的，那里的牛奶是从多个小型牛奶农场收集的，并带到乳制品生产厂，以加工成干酪。
Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that data is readily available (the big data phenomenon), is oftentimes challenged by the need to effectively acquire quality data under severe constraints. In this paper we propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data. The proposed methodology extends existing active learning methods to effectively solve regression-based learning problems and may serve settings where data acquisition requires excessive resources in the physical world. We further suggest a set of qualitative measures to analyze learners performance. The proposed methodology is demonstrated using an actual application in the milk industry, where milk is gathered from multiple small milk farms and brought to a dairy production plant to be processed into cottage cheese.
