论文标题
Safe-OCC：卷积神经网络传感器的新颖性检测框架及其在过程控制中的应用
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control
论文作者
论文摘要
我们提出了卷积神经网络（CNN）传感器的新颖性检测框架，我们称之为传感器激活的特征提取一级分类（Safe-OCC）。我们表明，该框架可以安全使用计算机视觉传感器在过程控制体系结构中。紧急控制应用程序使用CNN模型将视觉数据映射到可以由控制器解释的状态信号。合并此类传感器会引入重要的系统操作漏洞，因为CNN传感器在暴露于新颖（异常）视觉数据时会显示出高预测误差。不幸的是，实时识别这种新颖性是不平凡的。为了解决这个问题，Safe-OCC框架利用CNN的卷积块创建有效的功能空间，使用所需的一级分类技术进行新颖性检测。这种方法产生了与CNN传感器使用的特征空间直接对应的，并避免了得出独立潜在空间的需求。我们通过模拟控制环境证明了安全性OCC的有效性。
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.
