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SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control
Journal of Process Control ( IF 4.2 ) Pub Date : 2022-07-27 , DOI: 10.1016/j.jprocont.2022.07.006
Joshua L. Pulsipher, Luke D.J. Coutinho, Tyler A. Soderstrom, Victor M. Zavala

We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.



中文翻译:

SAFE-OCC:卷积神经网络传感器的新奇检测框架及其在过程控制中的应用

我们提出了一个用于卷积神经网络 (CNN) 传感器的新奇检测框架,我们称之为传感器激活特征提取一类分类 (SAFE-OCC)。我们表明,该框架可以在过程控制架构中安全使用计算机视觉传感器。紧急控制应用程序使用 CNN 模型将视觉数据映射到可由控制器解释的状态信号。结合此类传感器会引入重大的系统操作漏洞,因为 CNN 传感器在暴露于新的(异常)视觉数据时会表现出很高的预测错误。不幸的是,实时识别这些新奇事物并非易事。为了解决这个问题,SAFE-OCC 框架利用 CNN 的卷积块创建有效的特征空间,以使用所需的一类分类技术进行新奇检测。这种方法产生了一个与 CNN 传感器使用的特征空间直接对应的特征空间,并且避免了导出独立潜在空间的需要。我们通过模拟控制环境证明了 SAFE-OCC 的有效性。

更新日期:2022-07-27
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