Situation awareness modeling for emergency management on offshore platforms

Intelligent Guidance and Control Methods for Missile Swarm

High-speed unmanned aerial vehicles (UAVs) are more and more widely used in both military and civil fields at present, especially the missile swarm attack, and will play an irreplaceable key role in the future war as a special combat mode. This study summarizes the guidance and control methods of missile swarm attack operation. First, the traditional design ideas of the guidance and control system are introduced; then, the typical swarm attack guidance and control methods are analyzed by taking their respective characteristics into considering, and the limitations of the traditional design methods are given. On this basis, the study focuses on the advantages of intelligent integrated guidance and control design over traditional design ideas, summarizes the commonly used integrated guidance and control design methods and their applications, and explores the cooperative attack strategy of missile swarm suitable for the integrated guidance and control system. Finally, the challenges of missile swarm guidance and control are described, and the problems worthy of further research in the future are prospected. Summarizing the guidance and control methods of missile will contribute to the innovative research in this field, so as to promote the rapid development of unmanned swarm attack technology.

Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains

The idea of remote controlling ships for operational and commercial uses has developed beyond concepts. Controlling and monitoring vessels from a distant location requires updating the concept and requirements of shore control centers (SCCs), where human operators control the fleet via cameras, GPS, and many other types of sensors. While remote ship operation promises to reduce operational and maintenance costs, while increasing loading capacity and safety, it also brings significant uncertainty related to both the human-machine and human-human interactions which will affect operations. Achieving safe, reliable, and efficient remote ship operations requires consideration of both technological, cultural, social and human factor aspects of the system. Indeed, operators will act as captain and crew remotely, from the SCC, introducing new types of hardware and software interactions. This paper provides an overview of human factor issues that may affect human-machine and human-human interactions in the course of remote ship operations. In doing so, the literature related to remote operations in the domains of shipping, aerial vehicles, cranes, train transportation, automobiles, and mining is reviewed. Findings revealed that human factor issues are likely to fall into 13 distinct groups based on the type of human interactions that take place in SCCs.

Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots' Unsafe Behaviors Using Quantitative Modeling

Situation awareness (SA) of pilots’ unsafe behavior can ensure safety onboard. Thus, the cognitive mechanism that controls the SA leading to unsafe behavior must be articulated. This study employs the SA model and theory of planned behavior (TPB) to articulate a quantitative model of ship safe piloting. Firstly, the hierarchical classification framework of unsafe behaviors was constructed as an analytical foundation for rational and unconscious behaviors in sight of cognitive processes, and then the measurement elements of the cognitive mechanisms for behaviors were identified. Subsequently, based on the structural model, a hypothetical model of the cognitive path for unsafe behaviors was proposed by using the extended TPB, where there are four independent variables (i.e., attitude (ATD), subjective norm (SN), and perceived behavioral control (PBC)), one mediating variables (i.e., SA) and two dependent variables (i.e., behavioral intention (BI) and unsafe behaviors (BE)). Finally, this hypothetical model was analyzed with the data resources from extended TPB questionnaire of 295 pilots. Analysis results show that relationships of causation and mediation in the cognitive mechanism are in line with the behavior pattern and SA have a pronounced mediating effect and a strong relevance to the causal chain of extended TPB framework. This study integrated the SA three-level model to understand the motivation–cognition–action–feedback (MCAF) mechanism of pilots’ unsafe behaviors under cognitive mode of information processing through structural model. It would make a valuable contribution to the assessment and intervention of safety behaviors, and provide a basic framework for monitoring the situation awareness of pilot by man-machine interactive measurement technology in the future.

Dynamic Offloading Model for Distributed Collaboration in Edge Computing: A Use Case on Forest Fires Management

With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric and task-centric offloading. The offloading model can reduce the edge node overload based on a centralized, inefficient distribution and trade-off occurring in the edge node. That is the leading cause of edge node overload. So, this paper shows a collaborative offloading model in edge computing that guarantees real-time and prevention overload prevention based on data-centric offloading and task-centric offloading. Also, we present an intelligent offloading model based on several scenarios of forest fire ignition.