scholarly journals Using S-TaLiRo on Industrial Size Automotive Models

10.29007/kwp3 ◽  
2018 ◽  
Author(s):  
Bardh Hoxha ◽  
Houssam Abbas ◽  
Georgios Fainekos
Keyword(s):  
Embedded Systems ◽  
Automotive Industry ◽  
Industrial Scale ◽  
High Fidelity ◽  
Formal Specifications ◽  
Engine Model ◽  
Model Based

In Model Based Development (MBD) of embedded systems, it is often desirable to verify or falsify certain formal specifications. In some cases it is also desirable to find the range of specification parameters for which the specification does not hold on the system. We illustrate these methods on a challenge problem from the automotive industry on a high-fidelity, industrial scale engine model.

scholarly journals Industrial Examples of Formal Specifications for Test Case Generation

10.29007/z9ph ◽  
2018 ◽  
Author(s):  
Hendrik Roehm ◽  
Rainer Gmehlich ◽  
Thomas Heinz ◽  
Jens Oehlerking ◽  
Matthias Woehrle
Keyword(s):  
Embedded Systems ◽  
Requirements Engineering ◽  
Automotive Industry ◽  
Test Case Generation ◽  
Test Case ◽  
Formal Specifications ◽  
Discrete Events ◽  
The Past ◽  
Formal Representations

While requirements engineering has received considerable attention inacademia over the past years, formalization of requirements for physicallyinfluenced systems is still a difficult task in practice. In this paper, we giveformal representations of some typical requirement classes arising in theautomotive industry. We divide these patterns into three main classes:those mostly referring to properties of continuous signals, those mostlyreferring to discrete events and those referring to similarity to a referencesignal. We discuss these patterns on concrete examples from automotiveembedded systems, where specifications are used for test case generation.

Automated Model-Based Optimization of Data-Adaptable Embedded Systems

2020 ◽  
Vol 19 (1) ◽  
pp. 1-22
Author(s):  
Adrian Lizarraga ◽  
Jonathan Sprinkle ◽  
Roman Lysecky
Keyword(s):  
Embedded Systems ◽  
Model Based

scholarly journals MODES: model-based optimization on distributed embedded systems

2021 ◽  
Author(s):  
Junjie Shi ◽  
Jiang Bian ◽  
Jakob Richter ◽  
Kuan-Hsun Chen ◽  
Jörg Rahnenführer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Learning Model ◽  
Black Box ◽  
Distributed Embedded Systems ◽  
Data Set ◽  
Individual Model ◽  
Model Based ◽  
Machine Learning Model ◽  
Distributed Machine Learning

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.

Optimization of a Shock Absorber Design Using Model-Based Approach

2012 ◽  
Vol 452-453 ◽  
pp. 1351-1355 ◽  
Author(s):  
Grzegorz Wszołek ◽  
Piotr Czop ◽  
Dawid Jakubowski ◽  
Damian Slawik
Keyword(s):  
Automotive Industry ◽  
Shock Absorber ◽  
Flow Characteristic ◽  
Optimization Method ◽  
Design Parameters ◽  
Coupled Models ◽  
Model Based ◽  
Noise Evaluation ◽  
Optimal Values ◽  
Application Scope

The aim of this paper is to demonstrate a possibility to optimize a shock absorber design to minimize level of vibrations with the use of model-based approach. The paper introduces a proposal of an optimization method that allows to choose the optimal values of the design parameters using a shock absorber model to minimize the level of vibrations. A model-based approach is considered to obtain the optimal pressure-flow characteristic by simulations conducted with the use of coupled models, including the damper and the servo-hydraulic tester model. The presence of the tester model is required due to high non-linear coupling of the tested object (damper) and the tester itself to be used for noise evaluation. This kind of evaluation is used in the automotive industry to investigate dampers, as an alternative to vehicle-level tests. The paper provides numerical experimental case studies to show application scope of the proposed method

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