Uncertain dynamics in communication network, including random delays and packet losses make it difficult to guarantee stability of cyber-physical systems (CPSs). Many existing works consider the uncertainties of network channel with strong assumptions that network delay bounds and its distribution are known a priori and time-invariant. However, these assumptions could be invalidated in realistic CPSs by malicious attacks, system hardware faults, topology changes etc. A probability density function (PDF)-based tuning of stochastic optimal control (PTSOC) is proposed to manage the unknown dynamics in the embedded network. The update law of the proposed controller is derived and updated based on the PDF estimation of network delays that explicitly consider delays and its time-varying distribution. The results illustrate that the proposed PTSOC has a better performance in terms of the overshoot, convergence time, and cost when compared with the conventional stochastic optimal control.

Dialysis vascular accesses are critical for dialysis therapy, but they frequently suffer from stenotic complications. Higher patency rates and thrombosis rates are a concern to nephrology nurses and patients. These complications are complex events, including inflow stenosis, outflow stenosis, and coexistence of both. Therefore, a biophysical experimental model is employed to mimic the various combinations of stenoses and dialysis circulation circuits on a virtual adult hand. Considering the suggested signal preprocessing specifications, auscultation method and frequency analysis technique are used to extract the key frequency components from sufficient big spectral data. Key frequency components, depending on the degree of stenosis (DOS) (positive correlation), are validated using multiple regression models with multiple explanatory variables and response variables. A new machine learning method, radial-based colour relation analysis, is employed to identify the level of DOS at the inflow and outflow sites. In contrast to the multiple linear regression and traditional machine learning method, the experimental results indicated that the proposed screening model had higher accuracy (hit rate), true-positive rate, and true-negative rate in clinical indication.

Power grid design and maintenance are conducted to solve the problems caused by load growth over time and to stay within the constraints of voltage drop, power factor, etc. Typically, solutions to these problems are optimised individually. Considering multiple problems simultaneously and applying different solutions require vast design space exploration. This exclusively needs advanced algorithms and complex global optimisation methods which are not easily-applicable in different scenarios. In the state-of-the-art methods, for solving multiple problems simultaneously, these individually optimised solutions are applied sequentially to the power grid. In this so-called uncoordinated method, the final solution may not be optimal solution considering all the variables, since it is considering the overlapping effect of the solutions on the power grid. To validate the compartmentalisation method, a detailed distribution grid has been modeled. After analysing the possible solutions and optimisation, power loss was reduced 45% and total cost decreased by 71%, compared to the uncoordinated method.

Simulink/Stateflow is a popular commercial model-based development tool for many industrial domains. For safety and security concerns, verification and testing must be performed on the Simulink/Stateflow designs and the generated code. The authors present a test generation approach for Simulink/Stateflow by its reduction to reachability in a hybrid automaton, with its locations representing the computations of the Simulink/Stateflow model, and edges representing the computation-succession. A novel reachability resolution method is presented based on the refinement of the hybrid automaton such that the reachability is reduced to the reachability in the underlying graph (without the dynamics), whenever the refinement step terminates. The approach yields a technique that is effective in terms of achieving test coverage and efficient in terms of test generation time whenever the computation of each time step can be analytically solved for an arbitrary number of repetition. The approach is also applied on defect-detection and requirements-satisfaction, and illustrated through application to a bounded counter and a cyberphysical system of a thermal control unit.

Uncertainty analysis plays a pivotal role in identifying the important parameters affecting building energy consumption and estimate their effects at the early design stages. In this work, we consider the adaptive Lasso for uncertainty analysis in building performance simulation. This procedure has several appealing features: (1) We can introduce a large number of possible physical and environmental parameters at the initial stage to obtain a more complete picture of the building energy consumption. (2) The procedure could automatically select parameters and estimate influences simultaneously and no prior knowledge is required. (3) Due to computational efficiency of the procedure, non-linear relationship between the building performance and the input parameters could be accommodated. (4) The proposed adaptive Lasso can use a small number of samples to achieve high modeling accuracy and further reduce the huge computational cost of running building energy simulation programs. Furthermore, we propose a stable algorithm to rank input parameters to better identify important input parameters that affect energy consumption. A case study shows the superior performance of the procedure compared with LS and OMP in terms of modeling accuracy and computational cost.

Cyber-physical systems (CPS) help create new services and applications by revolutionising our world in different fields through their tight interactions and automated decisions. This is especially true with the ongoing increase in the number of physical things (sensors, actuators, smartphones, tablets, and so on) along with the explosive increase in the usage of online networking services and applications. Future fifth generation (5G) cellular networks will facilitate the enabling of CPS communications over current network infrastructure through different technologies such as device-to-device (D2D) communications. In this study, the authors discuss about the main challenges that cellular providers will face as the massive number of CPS devices attempt to access the cellular spectrum. A case study is presented on how to ease the spectrum access of these devices through D2D spatial spectrum sensing. Furthermore, the authors discuss about protecting these D2D links from eavesdropping, since security is becoming a critical aspect in the cyber-physical space, especially with the large amount of traffic that is constantly flowing through the network.