scholarly journals Improving System Reliability Assessment of Safety-Critical Systems using Machine Learning Optimization Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 49-65
Author(s):  
Ibrahim Alagoz ◽  
Thomas Hoiss ◽  
Reinhard German
Keyword(s):  
Machine Learning ◽  
System Reliability ◽  
Reliability Assessment ◽  
Optimization Techniques ◽  
Critical Systems ◽  
Safety Critical ◽  
Safety Critical Systems

scholarly journals Machine Learning Approach in Heterogeneous Group of Algorithms for Transport Safety-Critical System

2020 ◽  
Vol 10 (8) ◽  
pp. 2670 ◽  
Author(s):  
Jaehyung An ◽  
Alexey Mikhaylov ◽  
Keunwoo Kim
Keyword(s):  
Machine Learning ◽  
Optimal Algorithm ◽  
Heterogeneous Group ◽  
Type Model ◽  
Learning Approach ◽  
Critical Systems ◽  
Safety Critical ◽  
Machine Learning Approach ◽  
Transport Safety ◽  
Safety Critical Systems

This article presents a machine learning approach in a heterogeneous group of algorithms in a transport type model for the optimal distribution of tasks in safety-critical systems (SCS). Applied systems in the working area identify the determination of their parameters. Accordingly, in this article, machine learning models are implemented on various subsets of our transformed data and repeatedly calculated the bounds for 90 percent tolerance intervals, each time noting whether or not they contained the actual value of X. This approach considers the features of algorithms for solving such important classes of problem management as the allocation of limited resources in multi-agent SCS and their most important properties. Modeling for the error was normally distributed. The results are obtained, including the situation requiring solutions, recorded and a sample is made out of the observations. This paper summarizes the literature review on the machine learning approach into new implication research. The empirical research shows the effect of the optimal algorithm for transport safety-critical systems.

scholarly journals Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems Using Feature Importance Fusion

2021 ◽  
Vol 11 (24) ◽  
pp. 11854
Author(s):  
Divish Rengasamy ◽  
Benjamin C. Rothwell ◽  
Grazziela P. Figueredo
Keyword(s):  
Machine Learning ◽  
Synthetic Data ◽  
Ground Truth ◽  
Decision Fusion ◽  
Critical Systems ◽  
Safety Critical ◽  
Ensemble Strategy ◽  
Feature Importance ◽  
Safety Critical Systems

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features’ importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance in estimates and to improve the quality of explanations. Our hypothesis is that this leads to more robust and trustworthy explanations of the contribution of each feature to machine learning predictions. To test this hypothesis, we propose an extensible model-agnostic framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models, (ii) predictive machine learning, (iii) feature importance quantification, and (iv) feature importance decision fusion using an ensemble strategy. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the quality of the feature importance ensembles studied. The results show that, overall, our feature importance ensemble framework produces 15% less feature importance errors compared with existing methods. Additionally, the results reveal that different levels of noise in the datasets do not affect the feature importance ensembles’ ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features. We also discuss the implications of our findings on the quality of explanations provided to safety-critical systems.

Cyber Physical Systems Management

2015 ◽  
pp. 286-305
Keyword(s):  
System Reliability ◽  
High Volume ◽  
Critical Systems ◽  
State Variables ◽  
Systems Management ◽  
Safety Critical ◽  
Service Interruption ◽  
Continuous State ◽  
Safety Critical Systems ◽  
Set Up

Most of the systems are unsuccessful during integration due to insignificant consequences occurring in them. This is due to lack of system scalability that fails to provide an improved workload of the system. This chapter describes the parameters to be measured while evaluating the scalability of the structure. The parameters to be measured are described in a scalability review that represents the problems in it. The primary requirement of CPSs is system reliability because an unreliable system yields service interruption and financial cost. A CPS cannot be set up in critical applications in which system reliability and predictability are inefficient. To provide safety critical systems, a high volume of data is dealt, containing operator-in-loop and operating online constantly. The combined characteristics of physical and computational components allow CPSs to use hybrid dynamical models to integrate discrete and continuous state variables that use computational tools to resolve composite problems.

Sign in / Sign up

Export Citation Format

Share Document