Model transformation for multi-objective architecture optimisation for dependable systems

Model-based engineering (MBE) promises a number of advantages for the development of embedded systems. Model-based engineering depends on a common model of the system, which is refined as the system is developed. The use of a common model promises a consistent and systematic analysis of dependability, correctness, timing and performance properties. These benefits are potentially available early and throughout the development life cycle. An important part of model-based engineering is the use of analysis and design languages. The Architecture Analysis and Design Language (AADL) is a new modelling language which is increasingly being used for high dependability embedded systems development. AADL is ideally suited to model-based engineering but the use of new language threatens to isolate existing tools which use different languages. This is a particular problem when these tools provide an important development or analysis function, for example system optimisation. System designers seek an optimal trade-off between high dependability and low cost. For large systems, the design space of alternatives with respect to both dependability and cost is enormous and too large to investigate manually. For this reason automation is required to produce optimal or near optimal designs.There is, however, a lack of analysis techniques and tools that can perform a dependability analysis and optimisation of AADL models. Some analysis tools are available in the literature but they are not able to accept AADL models since they use a different modelling language. A cost effective way of adding system dependability analysis and optimisation to models expressed in AADL is to exploit the capabilities of existing tools. Model transformation is a useful technique to maximise the utility of model-based engineering approaches because it provides a route for the exploitation of mature and tested tools in a new model-based engineering context. By using model transformation techniques, one can automatically translate between AADL models and other models. The advantage of this model transformation approach is that it opens a path by which AADL models may exploit existing non-AADL tools.There is little published work which gives a comprehensive description of a method for transforming AADL models. Although transformations from AADL into other models have been reported only one comprehensive description has been published, a transformation of AADL to petri net models. There is a lack of detailed guidance for the transformation of AADL models.This thesis investigates the transformation of AADL models into the HiP-HOPS modelling language, in order to provide dependability analysis and optimisation. HiP-HOPS is a mature, state of the art, dependability analysis and optimisation tool but it has its own model. A model transformation is defined from the AADL model to the HiP-HOPS model. In addition to the model-to-model transformation, it is necessary to extend the AADL modelling attributes. For cost and dependability optimisation, a new AADL property set is developed for modelling component and system variability. This solves the problem of describing, within an AADL model, the design space of alternative designs. The transformation (with transformation rules written in ATLAS Transformation Language (ATL)) has been implemented as a plug-in for the AADL model development tool OSATE (Open-source AADL Tool Environment). To illustrate the method, the plug-in is used to transform some AADL model case-studies

Effect of IDT position parameters on SAW yarn tension sensor sensitivity

In this paper, the effect of the interdigital transducer (IDT) position parameters on the surface acoustic wave (SAW) yarn tension sensor sensitivity is investigated. The stress–strain characteristic of substrate was studied by the combination of finite element simulation and regression analysis method. According to this characteristic, the function relationship between the SAW yarn tension sensor sensitivity and the IDT position parameters was built using the regression analysis method. The monotonicity of the regression function was also given. On this basis, a novel sensitivity optimal scheme was proposed and solved by the quadratic programming method. Its solution demonstrates that the optimum sensitivity can be obtained when the IDT is 8.9 mm to the left side of the substrate and the IDT is 0.3 mm to the top edge of the substrate within a domain of the IDT position parameters. The SAW yarn tension sensor with corresponding IDT position parameters was fabricated to validate the correctness of the sensitivity optimal scheme. The measured results indicate that the SAW yarn tension sensor sensitivity can reach 813.69 Hz/g, which confirms that the novel scheme is effective

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Multi-objective architecture optimisation modelling for dependable systems

The design of dependable systems must address both cost and dependability (i.e. safety, reliability, availability and maintainability) concerns. For large systems, the design space of alternatives with respect to both dependability and cost is very large and automation is essential to explore this space. The model-based approach to the development and analysis of complex dependable systems is increasingly popular and recently, the Architecture Analysis and Design Language (AADL) has emerged as a potential future standard for model-based development of dependability-critical systems. The paper tackles the problem of describing, within an AADL model, the design space of alternative designs. A new AADL property set is proposed for modelling component and system variability for cost and dependability optimisation. The proposed method is illustrated with an example of an AADL model of a safety critical embedded system. © 2013 IFAC

Automatic generation of Temporal Fault Trees from AADL models

The Architecture Analysis and Design Language (AADL) is gaining growing acceptance in the aerospace, automobile and avionics industries. These industries are increasingly concerned with systems exhibiting sequence-dependent failures. About dependability (i.e. safety, reliability, availability and maintainability) analysis of AADL models, there is still a lack of techniques that can take into account the sequencing of failure events and determine minimal failure scenarios, i.e. which are made up of the relevant events causing a system to fail as a whole. In this paper, we present how we address this problem through an intelligent transformation, which captures the significant temporal ordering of faults and failures expressed by the AADL error models, to synthesise system Temporal Fault Trees (TFTs). © 2014 Taylor & Francis Group, London

IoP System Dependability Evaluation Method Based on AADL

The Internet of People(IoP)is characterized by the complex architecture and massive changing data, which adds to the difficulty of the analysis on IoP-based system dependability.Currently, there is still no robust dependability modelling and analysis method for IoP systems. This paper proposes an Architecture Analysis and Design Language (AADL)-based dependability evaluation method for IoP systems. By using AADL and its annex language, the dependability of IoP systems is modeled to support the qualitative analysis on the causes of system failures and risks. Furthermore, by combining the Ocarina model transformation technology, a quantitative evaluation algorithm based on the Continuous-Time Markov Chain(CTMC)is proposed. The algorithm transforms the AADL dependability model to the CTMC model, so that the dynamic and real-time attributes of IoP systems can be evaluated quantitatively. On this basis, a general IoP system model is designed to demonstrate the feasibility of the proposed method. The experimental results show that the proposed method can be used to model the IoP systems, and perform dependability analysis automatically and accurately, displaying a high application value

Research on College Computer-Computing and Information Literacy online course based on MOOC: Taking the North Minzu University as an example

In 2015, North Minzu University (NMU) introduced MOOC teaching method into the teaching practice of computer major. It is the first experiment of this teaching method in Ningxia Hui Autonomous Region. In the three years of teaching practice, NMU has made some achievements in MOOC online teaching, but at the same time, it has found that it lacks a set of MOOC teaching theories and methods suitable for the university's teaching situation and students' learning situation. In order to draw on the advantages of MOOC teaching and solve the problems encountered in the previous teaching practice in NMU, we plan to adopt the teaching philosophy of 'learning determines teaching, and student-learning is the center' and the new teaching method of 'MOOC+SPOC+Flipped Classroom' to design a new teaching scheme. The new teaching scheme was experimented with 2018 freshmen from the School of Mathematics and Information Science of NMU. The experimental results show that the new teaching method not only improves the quality of MOOC teaching in NMU and reduces the burden of teachers, but also enables students have more sense of acquisition, satisfaction, and achievement in the process of learning. This paper introduces the background, design, and practice of the 'MOOC+SPOC+Flipped Classroom' teaching method, and analyzes the experimental data to study its feasibility

Model transformation for analyzing dependability of AADL model by using HiP-HOPS

The Architecture Analysis and Design Language (AADL) has emerged as a potential future standard in aerospace, automobile and avionics industries for model-based development of dependability-critical systems. As AADL is relatively new, some existing analysis methods and tools are not able to accept AADL models. In this paper we show that, by using model transformation techniques, we can automatically transform AADL models into a form that is directly executable by fault-tree-based dependability analysis and optimisation tools. This model transformation opens a path by which AADL models may benefit from automatic synthesis and analysis of fault trees, temporal fault tree analysis, multiple failure mode and effects analysis and model architecture optimisation. In this paper, we present a new model transformation framework. The core of the framework is a novel transformation from a state machine-based error model to a fault-tree model. The framework has been implemented as a plug-in (AADL2HiP-HOPS) for the AADL model development tool OSATE. The plug-in may be used to transform AADL models into a state-of-the-art dependability analysis and optimisation tool: HiP-HOPS. To illustrate the transformation and subsequent HiP-HOPS analysis, an example AADL model is transformed

Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS

Currently, AADL has gradually become as one of the standards for the architecture design of complex embedded system. It is widely used in aerospace, automotive electronics and other fields for the design and analysis of high dependability-critical systems. Although the Error Model annex (EMA) of AADL can well support AADL error modeling, there is still a lack of technical method for multiobjective (based on dependability and cost) architecture optimisation analysis for dependable system. In order to achieve the optimisation analysis of AADL model, an effective method is to transform the AADL model into other equivalent models. This paper introduces the preliminary work of transforming AADL2 dependability model into a mature optimisation analysis tool model - HiP-HOPS model, and analyses the semantic mapping relationship between the two different models. Model transformation can not only integrate the dependability modeling field to the analysis field, but also enables the optimisation analysis process simple and effective. This also brings good technical and engineering value under the current economic and technological situation

System dependability modelling and analysis using AADL and HiP-HOPS

The Architecture Analysis and Design Language (AADL) is gaining widespread acceptance in aerospace, automobile and avionics industries for designing dependability-critical systems. The design process of dependable systems must address both cost and dependability (safety, reliability, availability, maintainability) concerns. This requires translating concepts of the design domain to the dependability analysis domain. We automate such a translation between AADL and the dependability analysis tool HiP-HOPS by using model transformation techniques. A generic primary-standby example system is used to show the mechanics of the transformation and the potential for highlighting problems and assisting design work using this technology. © 2012 IFAC