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NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness

机译：基于NLP的HMI状态序列预测方法以监控操作员的态势感知

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摘要

A novel approach presented herein transforms the Human Machine Interface (HMI) states, as a pattern of visual feedback states that encompass both operator actions and process states, from a multi-variate time-series to a natural language processing (NLP) modeling domain. The goal of this approach is to predict operator response patterns for time-step window given past HMI state patterns. The NLP approach offers the possibility of encoding (semantic) contextual relations within HMI state patterns. Towards which, a technique for framing raw HMI data for supervised training using sequence-to-sequence ( ) deep-learning machine translation algorithms is presented. In addition, a custom convolutional neural network (CNN) NLP model based on current state-of-the-art design elements such as attention, is compared against a standard recurrent neural network (RNN) based NLP model. Results demonstrate comparable effectiveness of both the designs of NLP models evaluated for modeling HMI states. RNN NLP models showed higher ( ) forecast accuracy, in general for both in-sample and out-of-sample test datasets. However, custom CNN NLP model showed higher ( ) validation accuracy indicative of less over-fitting with the same amount of available training data. The real-world application of the proposed NLP modeling of industrial HMIs, such as in power generating stations control rooms, aviation (cockpits), and so forth, is towards the realization of a non-intrusive operator situational awareness monitoring framework through prediction of HMI states.
机译：本文提出的一种新颖方法将人机界面（HMI）状态作为视觉反馈状态的一种模式，涵盖了操作员的动作和过程状态，从多元时间序列到自然语言处理（NLP）建模域。该方法的目标是根据给定的HMI状态模式来预测时间步窗的操作员响应模式。 NLP方法提供了在HMI状态模式内编码（语义）上下文关系的可能性。为此，提出了一种使用序列到序列（）深度学习机器翻译算法为监督训练构建原始HMI数据的技术。此外，将基于当前最新设计元素（例如注意力）的自定义卷积神经网络（CNN）NLP模型与基于标准递归神经网络（RNN）的NLP模型进行了比较。结果表明，两种用于建模HMI状态的NLP模型设计具有相当的有效性。 RNN NLP模型通常对样本内和样本外测试数据集显示较高的（）预测精度。但是，自定义CNN NLP模型显示出更高的（）验证准确性，表明在使用相同数量的可用训练数据的情况下，过度拟合的情况较少。提议的工业HMI的NLP建模在现实世界中的应用，例如在发电站控制室，航空（驾驶舱）等中，通过预测HMI来实现非侵入式操作员态势感知监视框架状态。

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