Workflow Automation for Cyber Physical System Development Processes

Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains to achieve ever-increasing demands for improved performance, reduced cost, and more system autonomy. Each engineering discipline commonly relies on domain-specific modeling languages, and analysis and execution of these models is often automated with appropriate tooling. However, integration between these heterogeneous models and tools is often lacking, and most of the burden for inter-operation of these tools is placed on system developers. To address this problem, we introduce a workflow modeling language for the automation of complex CPS development processes and implement a platform for execution of these models in the Assurance-based Learning-enabled CPS (ALC) Toolchain. Several illustrative examples are provided which show how these workflow models are able to automate many time-consuming integration tasks previously performed manually by system developers.

[1]  Hans Vangheluwe,et al.  The FTG+PM framework for multi-paradigm modelling: an automotive case study , 2012, MPM '12.

[2]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[3]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[6]  Charles Hartsell,et al.  Model-based design for CPS with learning-enabled components , 2019, DESTION@CPSIoTWeek.

[7]  H. Robbins A Stochastic Approximation Method , 1951 .

[8]  Gabor Karsai,et al.  An Analytical Framework for Smart Manufacturing , 2018 .

[9]  Arquimedes Canedo,et al.  Architectural Design Space Exploration of Cyber-physical Systems Using the Functional Modeling Compiler☆ , 2014 .

[10]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Joaquim R. R. A. Martins,et al.  OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization , 2019, Structural and Multidisciplinary Optimization.

[12]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[13]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[14]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[15]  Alberto L. Sangiovanni-Vincentelli,et al.  Scenic: Language-Based Scene Generation , 2018, ArXiv.

[16]  Xin Zhang,et al.  TFX: A TensorFlow-Based Production-Scale Machine Learning Platform , 2017, KDD.

[17]  Miklós Maróti,et al.  Next Generation (Meta)Modeling: Web- and Cloud-based Collaborative Tool Infrastructure , 2014, MPM@MoDELS.

[18]  ABOUT INTO-CPS Integrated Tool Chain for Model-based Design of Cyber-Physical Systems , 2015 .

[19]  Xenofon Koutsoukos,et al.  Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems , 2020, 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS).

[20]  Harris Papadopoulos,et al.  Inductive Conformal Prediction: Theory and Application to Neural Networks , 2008 .

[21]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[22]  Michael Gepp,et al.  Efficient Implementation of Task Automation to Support Multidisciplinary Engineering of CPS , 2018, 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE).