Human error is a crucial factor leading to maritime traffic accidents. The effect of human–computer interaction (HCI) also plays a leading role in human error. The objective of this study is to propose a method of interaction strategies based on a cognitive-processing model in crews’ daily navigation tasks. A knowledge-based ship HCI framework architecture is established. It provides an extensible framework for the HCI process in the maritime domain. By focusing on the cognitive process of a crew in the context of accident and risk handling during ship navigation, based on the information, decision, and action in crew context (IDAC) model, in combination with the maritime accident dynamics simulation (MADS) system, the MADS-IDAC system was developed and enhanced by the HCI structure and function design of the dynamic risk analysis platform for maritime management. The results indicate that MADS enhanced by HCI can effectively generate a strategy set of various outcomes in preset scenarios. Moreover, it provides a new method and thought for avoiding human error in crew interaction and to lower the risk of ship collision as well as effectively improving the reliability of HCI.

This paper proposes an ontology‐based noise source identification method, establishes an ontology knowledge expression model in the field of noise, vibration, and harshness (NVH), and provides an extensible framework for sharing noise diagnosis knowledge in the field. Based on the key features extracted from the noise and vibration signals at different positions and the prior knowledge, mechanical engineers can construct an ontology rule and locate noise sources by identifying the intrinsic relationship between signal characteristics through ontology reasoning. A case study is conducted to demonstrate the effectiveness of the proposed method in resolving the problem of integrating multisource heterogeneous knowledge and exchanging noise diagnosis knowledge information in the field of NVH for agricultural machines. Thus, our study facilitates the sharing and reuse of knowledge and advances the development of intelligent noise diagnosis expert systems to a certain extent.