Soft sensors are mathematical models employed to estimate hard-to-measure variables from
available easy-to-measure variables. These sensors are typically developed using either …

Ensemble learning models have been widely used for wind power forecasting to facilitate
efficient dispatching of power systems. However, traditional ensemble methods cannot …

Data based approaches have recently gained extensive attention in modern process
industries. Accordingly, data based soft sensing technology used for making online …

Although recent transfer learning soft sensors show promising applications in multigrade
chemical processes, good prediction performance mainly relies on available target domain …

Data‐driven soft sensing approaches have been a hot research field for decades and are
increasingly used in industrial processes due to their advantages of easy implementation …

Without sufficient labeled data, the construction of accurate soft-sensor models for
multigrade chemical processes is challenging. To alleviate the dilemma, a transductive …

Soft sensor technology has become an effective tool to enable real-time estimations of key
quality variables in industrial rubber-mixing processes, which facilitates efficient monitoring …

鉴于流程工业过程的非线性, 多时段, 多模式, 时变性等复杂过程特性, 导致传统的全局和集成
学习软测量方法预测性能不佳, 提出一种基于多相似度局部状态辨识的集成学习自适应软测量建 …

Just-In-Time Learning (JITL) has proven to be an effective tool for developing virtual sensors
for complex industrial processes. However, most of the existing JITL-based virtual sensing …

集成软测量方法已被广泛应用于流程工业关键质量参数实时估计. 但是, 常规集成建模方法在基
模型构建过程中往往局限于挖掘样本之间的空间关系, 忽略了样本间的时序关系 …