An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

Best Practices for Evaluating Flight Deck Interfaces for Transport Category Aircraft with Particular Relevance to Issues of Attention, Awareness, and Understanding CAST SE-210 Output 2 Report 6 of 6

Attention, awareness, and understanding of the flight crew are a critical contributor to safety and the flight deck plays a critical role in supporting these cognitive functions. Changes to the flight deck need to be evaluated for whether the changed device provides adequate support for these functions. This report describes a set of diverse evaluation methods. The report recommends designing the interface-evaluation to span the phases of the device development, from early to late, and it provides methods appropriate at each phase. It describes the various ways in which an interface or interface component can fail to support awareness as potential issues to be assessed in evaluation. It summarizes appropriate methods to evaluate different issues concerning inadequate support for these functions, throughout the phases of development

How to recognise a kick : A cognitive task analysis of drillers’ situation awareness during well operations

Acknowledgements This article is based on a doctoral research project of the first author which was sponsored by an international drilling rig operator. The views presented are those of the authors and should not be taken to represent the position or policy of the sponsor. The authors wish to thank the industrial supervisor and the drilling experts for their contribution and patience, as well as Aberdeen Drilling School for allowing the first author to attend one of their well control courses.Peer reviewedPostprin

Robustness-Driven Resilience Evaluation of Self-Adaptive Software Systems

An increasingly important requirement for certain classes of software-intensive systems is the ability to self-adapt their structure and behavior at run-time when reacting to changes that may occur to the system, its environment, or its goals. A major challenge related to self-adaptive software systems is the ability to provide assurances of their resilience when facing changes. Since in these systems, the components that act as controllers of a target system incorporate highly complex software, there is the need to analyze the impact that controller failures might have on the services delivered by the system. In this paper, we present a novel approach for evaluating the resilience of self-adaptive software systems by applying robustness testing techniques to the controller to uncover failures that can affect system resilience. The approach for evaluating resilience, which is based on probabilistic model checking, quantifies the probability of satisfaction of system properties when the target system is subject to controller failures. The feasibility of the proposed approach is evaluated in the context of an industrial middleware system used to monitor and manage highly populated networks of devices, which was implemented using the Rainbow framework for architecture-based self-adaptation

AsmetaF: A Flattener for the ASMETA Framework

Abstract State Machines (ASMs) have shown to be a suitable high-level specification method for complex, even industrial, systems; the ASMETA framework, supporting several validation and verification activities on ASM models, is an example of a formal integrated development environment. Although ASMs allow modeling complex systems in a rather concise way -and this is advantageous for specification purposes-, such concise notation is in general a problem for verification activities as model checking and theorem proving that rely on tools accepting simpler notations. In this paper, we propose a flattener tool integrated in the ASMETA framework that transforms a general ASM model in a flattened model constituted only of update, parallel, and conditional rules; such model is easier to map to notations of verification tools. Experiments show the effect of applying the tool to some representative case studies of the ASMETA repository.Comment: In Proceedings F-IDE 2018, arXiv:1811.09014. The first two authors are supported by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST. Funding Reference number: 10.13039/501100009024 ERAT