Modern engineering systems are complex socio-technical structures with a mission to offer services of high quality, while in parallel ensuring profitability for their owners. However, practice has shown that accidents are inevitable, and the need for the use of systems-theoretic tools to support safety-driven design and operation has been acknowledged. As indicated in accident investigation reports, the degradation of risk situation awareness (SA) usually leads to safety issues. However, the literature lacks a methodology to compare existing systems with their ideal composition, which is likely to enhance risk SA. To fill this gap, the risk SA provision (RiskSOAP) is a comparison-based methodology and goes through three stages: (1) determine the desired/ideal system composition, (2) identify the as-is one(s), (3) employ a comparative strategy to depict the distance between the compared units. RiskSOAP embodies three methods: STPA (System Theoretic Process Analysis), EWaSAP (Early Warning Sign Analysis) and dissimilarity measures. The practicality, applicability and generality of RiskSOAP is demonstrated through its application to three case studies. The purpose of this work is to suggest the RiskSOAP indicator as a measure for safety in terms of the gap between system design and operation, thus increasing system’s risk SA. RiskSOAP can serve as a criterion for planning system modifications or selecting between alternative systems, and can support the design, development, operation and maintenance of safe systems.
Preprint submitted to Information Processing & Management Tags are a convenient way to label resources on the web. An interesting question is whether one can determine the semantic meaning of tags in the absence of some predefined formal structure like a thesaurus. Many authors have used the usage data for tags to find their emergent semantics. Here, we argue that the semantics of tags can be captured by comparing the contexts in which tags appear. We give an approach to operationalizing this idea by defining what we call paradigmatic similarity: computing co-occurrence distributions of tags with tags in the same context, and comparing tags using information theoretic similarity measures of these distributions, mostly the Jensen-Shannon divergence. In experiments with three different tagged data collections we study its behavior and compare it to other distance measures. For some tasks, like terminology mapping or clustering, the paradigmatic similarity seems to give better results than similarity measures based on the co-occurrence of the documents or other resources that the tags are associated to. We argue that paradigmatic similarity, is superior to other distance measures, if agreement on topics (as opposed to style, register or language etc.), is the most important criterion, and the main differences between the tagged elements in the data set correspond to different topics
The remarkable and continuous growth of the unmanned aircraft market has brought new safety related challenges, as those are recorded in various accident and incident reports. Although drones with an operating weight higher than 20-25Kgs are technologically advanced and often subject to standards (e.g., technical reliability, airspace management, licensing, certification), the regulatory framework for (ultra) light drones focuses almost exclusively on the limitations that the operator needs to consider. Thus, the protection from accidents seems to rely mostly on the competency of the operator to fly a drone safely, and his/her observance of the rules published by the respective authorities. In addition, the hazards lying in the interaction between an operator and a small drone have not been systematically studied. In this paper, we present (1) the first results from a System-Theoretic Process Analysis (STPA) based approach to the identification of hazards and safety requirements in small drone operations, and (2) an adaptation of the Risk Situation Awareness Provision Capability (RiskSOAP) methodology in order to quantify the differences amongst 4 drone models regarding the extent to which they fulfill the safety requirements identified through STPA. The results showed that the drones studied satisfy the safety requirements at low and moderate levels and they present high dissimilarities between them regarding the extent to which they meet the same safety requirements. Future work will include: (a) comparison of a larger sample of small drones against the safety requirements, as well as pairwise, and (b) assessment of the degree to which various regulatory frameworks worldwide address the safety requirements generated with STPA and assigned to the authority level.
