A Model based Safety Assessment for Multirotors

Unmanned Aerial Vehicles (UAVs) must be safe and reliable to prevent fatal accidents in densely populated areas. This research makes the first steps to create a framework which can integrate safety and reliability considerations in the design process. The conceptual design process should consider creating design models coupling sizing with system architecture. Additionally, the multirotor has safety challenges from the propulsor configuration. They lose flight control and show erroneous flight behaviour when propulsors fail. Hence, the design models of multirotor should also incorporate a controllability assessment method to identify and isolate uncontrollable events. For this matter, an appropriate tool should be considered to create such design models. A combination of OpenAltarica, System Analyst and Python is used to create design models of multirotor in a model-based safety assessment framework. These models are developed by integrating system architecture and controllability assessment following the etiquettes of the process. A case study is used to validate the framework and to demonstrate its ability to explore innovative designs. The reliability analysis confirms that the multirotors are fault-tolerant except quadrotor and some configurations are potentially highly reliable. The results demonstrate the feasibility of the multirotor system modelling methods in terms of reliability and pave the way to further develop the model-based safety assessment framework with sizing methodologies. The models can also be further enhanced with the addition of a component fault library, additional failure modes and implementation of diagnosability analysis, fault detection and identification analysis. Fault libraries and failure modes can help in foreseeing uncontrollable cases. In contrast, diagnosability analysis, fault detection and identification analysis can integrate detect, isolate and recover mechanisms, and ensure redundancy optimization effectively. Additionally, the framework should also be combined with multidisciplinary design optimization for sizing. Such design models can contribute to the emergence of UAVs for safety-critical applications

Model-Based Safety and Assessment: 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings

This book constitutes the proceedings of the 6th International Symposium on Model-Based Safety and Assessment, IMBSA 2019, held inThessaloniki, Greece, in October 2019.The 24 revised full papers presented were carefully reviewed and selected from 46 initial submissions. The papers are organized in topical sections on safety models and languages; dependability analysis process; safety assessment; safety assessment in automotive industry; AI in safety assessmen

Safety Analysis Concept and Methodology for EDDI development (Initial Version)

Executive Summary:This deliverable describes the proposed safety analysis concept and accompanying methodology to be defined in the SESAME project. Three overarching challenges to the development of safe and secure multi-robot systems are identified — complexity, intelligence, and autonomy — and in each case, we review state-of-the-art techniques that can be used to address them and explain how we intend to integrate them as part of the key SESAME safety and security concept, the EDDI.The challenge of complexity is largely addressed by means of compositional model-based safety analysis techniques that can break down the complexity into more manageable parts. This applies both to scale — modelling systems hierarchically and embedding local failure logic at the component-level — and to tasks, where different safety-related tasks (including not just analysis but also requirements allocation and assurance case generation) can be handled by the same set of models. All of this can be combined with the existing DDI concept to create models — EDDIs — that store all of the necessary information to support a gamut of design-time safety processes.Against the challenge of intelligence, we propose a trio of techniques: SafeML and Uncertainty Wrappers for estimating the confidence of a given classification, which can be used as a form of reliability measure, and SMILE for explainability purposes. By enabling us to measure and explain the reliability of ML decision making, we can integrate ML behaviour as part of a wider system safety model, e.g. as one input into a fault tree or Bayesian network. In addition to providing valuable feedback during training, testing, and verification, this allows the EDDI to perform runtime safety monitoring of ML components.The EDDI itself is therefore our primary solution to the twin challenges of autonomy and openness. Using the ConSert approach as a foundation, EDDIs can be made to operate cooperatively as part of a distributed system, issuing and receiving guarantees on the basis of their internal executable safety models to collectively achieve tasks in a safe and secure manner. Finally, a simple methodology is defined to show how the relevant techniques can be applied as part of the EDDI concept throughout the safety development lifecycle

Explanation of the Model Checker Verification Results

Immer wenn neue Anforderungen an ein System gestellt werden, müssen die Korrektheit und Konsistenz der Systemspezifikation überprüft werden, was in der Praxis in der Regel manuell erfolgt. Eine mögliche Option, um die Nachteile dieser manuellen Analyse zu überwinden, ist das sogenannte Contract-Based Design. Dieser Entwurfsansatz kann den Verifikationsprozess zur Überprüfung, ob die Anforderungen auf oberster Ebene konsistent verfeinert wurden, automatisieren. Die Verifikation kann somit iterativ durchgeführt werden, um die Korrektheit und Konsistenz des Systems angesichts jeglicher Änderung der Spezifikationen sicherzustellen. Allerdings ist es aufgrund der mangelnden Benutzerfreundlichkeit und der Schwierigkeiten bei der Interpretation von Verifizierungsergebnissen immer noch eine Herausforderung, formale Ansätze in der Industrie einzusetzen. Stellt beispielsweise der Model Checker bei der Verifikation eine Inkonsistenz fest, generiert er ein Gegenbeispiel (Counterexample) und weist gleichzeitig darauf hin, dass die gegebenen Eingabespezifikationen inkonsistent sind. Hier besteht die gewaltige Herausforderung darin, das generierte Gegenbeispiel zu verstehen, das oft sehr lang, kryptisch und komplex ist. Darüber hinaus liegt es in der Verantwortung der Ingenieurin bzw. des Ingenieurs, die inkonsistente Spezifikation in einer potenziell großen Menge von Spezifikationen zu identifizieren. Diese Arbeit schlägt einen Ansatz zur Erklärung von Gegenbeispielen (Counterexample Explanation Approach) vor, der die Verwendung von formalen Methoden vereinfacht und fördert, indem benutzerfreundliche Erklärungen der Verifikationsergebnisse der Ingenieurin bzw. dem Ingenieur präsentiert werden. Der Ansatz zur Erklärung von Gegenbeispielen wird mittels zweier Methoden evaluiert: (1) Evaluation anhand verschiedener Anwendungsbeispiele und (2) eine Benutzerstudie in Form eines One-Group Pretest-Posttest Experiments.Whenever new requirements are introduced for a system, the correctness and consistency of the system specification must be verified, which is often done manually in industrial settings. One viable option to traverse disadvantages of this manual analysis is to employ the contract-based design, which can automate the verification process to determine whether the refinements of top-level requirements are consistent. Thus, verification can be performed iteratively to ensure the system’s correctness and consistency in the face of any change in specifications. Having said that, it is still challenging to deploy formal approaches in industries due to their lack of usability and their difficulties in interpreting verification results. For instance, if the model checker identifies inconsistency during the verification, it generates a counterexample while also indicating that the given input specifications are inconsistent. Here, the formidable challenge is to comprehend the generated counterexample, which is often lengthy, cryptic, and complex. Furthermore, it is the engineer’s responsibility to identify the inconsistent specification among a potentially huge set of specifications. This PhD thesis proposes a counterexample explanation approach for formal methods that simplifies and encourages their use by presenting user-friendly explanations of the verification results. The proposed counterexample explanation approach identifies and explains relevant information from the verification result in what seems like a natural language statement. The counterexample explanation approach extracts relevant information by identifying inconsistent specifications from among the set of specifications, as well as erroneous states and variables from the counterexample. The counterexample explanation approach is evaluated using two methods: (1) evaluation with different application examples, and (2) a user-study known as one-group pretest and posttest experiment

Дослідження можливості використання serverless технологій при побудові системи підтримки користувачів мобільного оператора

Метою дипломної роботи є: підвищення ефективності оператора мобільного зв’язку шляхом використання технології Serverless для побудови системи підтримки користувачів. В представленій роботі розглянуто технологію Serverless та її особливості, а також можливості, які вона надає для побудови системи підтримки користувачів мобільного оператора. Для закріплення теоретичних знання, на практиці було розроблено чат-бота використовуючи Serverless технологію.The purpose of the thesis is to analyze the serverless technology to build a support system for mobile operators, by developing a chat bot using serverless technology. This paper discusses Serverless technology and its features, as well as the opportunities it provides to build a support system for mobile operators. To consolidate theoretical knowledge, in practice a chatbot was developed using Serverless technology

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen