scholarly journals Lightweight Convolution Neural Networks for Mobile Edge Computing in Transportation Cyber Physical Systems

2019 ◽  
Vol 10 (6) ◽  
pp. 1-20 ◽  
Author(s):  
Junhao Zhou ◽  
Hong-Ning Dai ◽  
Hao Wang
Keyword(s):  
Neural Networks ◽  
Cyber Physical Systems ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Physical Systems ◽  
Convolution Neural Networks

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

scholarly journals Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Meiyi Ma ◽  
John Stankovic ◽  
Ezio Bartocci ◽  
Lu Feng
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Recurrent Neural Networks ◽  
Cyber Physical Systems ◽  
Uncertainty Estimation ◽  
Physical Systems ◽  
Confidence Levels ◽  
Novel Approach ◽  
Infinite Set

Predictive monitoring—making predictions about future states and monitoring if the predicted states satisfy requirements—offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.

An Intelligent Edge-Computing-Based Method to Counter Coupling Problems in Cyber-Physical Systems

IEEE Network ◽  
2020 ◽  
Vol 34 (3) ◽  
pp. 16-22 ◽  
Author(s):  
Tian Wang ◽  
Yuzhu Liang ◽  
Yi Yang ◽  
Guangquan Xu ◽  
Hao Peng ◽  
...  
Keyword(s):  
Cyber Physical Systems ◽  
Edge Computing ◽  
Physical Systems

FPGA on Cyber-Physical Systems for the Implementation of Internet of Things

2020 ◽  
pp. 82-96
Author(s):  
Rajit Nair ◽  
Preeti Nair ◽  
Vidya Kant Dwivedi
Keyword(s):  
High Performance ◽  
Management Strategies ◽  
Cyber Physical Systems ◽  
Edge Computing ◽  
Reconfigurable Systems ◽  
Smart Power ◽  
Physical Systems ◽  
Computing Performance ◽  
High Level ◽  
Processing Architecture

Today, in cyber-physical systems, there is a transformation in which processing has been done on distributed mode rather than performing on centralized manner. Usually this type of approach is known as Edge computing, which demands hardware time to time when requirements in computing performance get increased. Considering this situation, we must remain energy efficient and adaptable. So, to meet the above requirements, SRAM-based FPGAs and their inherent run-time reconfigurability are integrated with smart power management strategies. Sometimes this approach fails in the case of user accessibility and easy development. This chapter presents an integrated framework to develop FPGA-based high-performance embedded systems for Edge computing in cyber-physical systems. The processing architecture will be based on hardware that helps us to manage reconfigurable systems from high level systems without any human intervention.

Functional verification of cyber-physical systems containing machine-learnt components

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Farzaneh Moradkhani ◽  
Martin Fränzle
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Cyber Physical Systems ◽  
Satisfiability Modulo Theories ◽  
Activation Functions ◽  
Physical Systems ◽  
Smt Solvers ◽  
Non Linear ◽  
Artificial Neural

Abstract Functional architectures of cyber-physical systems increasingly comprise components that are generated by training and machine learning rather than by more traditional engineering approaches, as necessary in safety-critical application domains, poses various unsolved challenges. Commonly used computational structures underlying machine learning, like deep neural networks, still lack scalable automatic verification support. Due to size, non-linearity, and non-convexity, neural network verification is a challenge to state-of-art Mixed Integer linear programming (MILP) solvers and satisfiability modulo theories (SMT) solvers [2], [3]. In this research, we focus on artificial neural network with activation functions beyond the Rectified Linear Unit (ReLU). We are thus leaving the area of piecewise linear function supported by the majority of SMT solvers and specialized solvers for Artificial Neural Networks (ANNs), the successful like Reluplex solver [1]. A major part of this research is using the SMT solver iSAT [4] which aims at solving complex Boolean combinations of linear and non-linear constraint formulas (including transcendental functions), and therefore is suitable to verify the safety properties of a specific kind of neural network known as Multi-Layer Perceptron (MLP) which contain non-linear activation functions.

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