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This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber–physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework. Moreover, by analyzing the correlations of the decision variables in the objective function, it is shown that the attack parameters and energy allocation strategy can be derived by two steps without loss of optimality, such that the optimal attack scheme is acquired by solving a multivariate semi-definite programming (SDP) problem and a linear 0–1 programming problem respectively. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method. • A novel energy constrained fusion attack model is proposed as the linear combination of transmitted innovations. • Under the proposed attack framework, the remote state estimation errors are analyzed. • By analyzing the structure of the objective function, the optimal attack scheme is found by solving a multivariate SDP problem and a linear 0–1 programming problem. [ABSTRACT FROM AUTHOR]

• The established deep learning model is capable of considering the impacts of 3D heterogeneity of oil shale on production and energy consumption. • The optimization procedure simultaneously optimized well constraints and well locations. • The evolution patterns of optimal parameter values across varying levels of energy constraints are identified. The in-situ conversion process (ICP) involves complicated thermal-reactive-compositional flow processes where the production parameters are closely interdependent. Therefore, manual optimization of the production parameters of ICP through numerical simulation is arduous and time-consuming. This paper introduces a computational framework that integrates deep learning techniques and particle swarm optimization (PSO) to automatically optimize the values of production parameters of ICP, and thus locating the highest energy efficiency and the optimum energy usage. This approach utilizes a 3D CNN model to predict key metrics. The prediction process considers the detailed 3D heterogeneity of oil shale, resulting in remarkably high prediction accuracy, as evidenced by determination coefficients (R2) above 0.99. Subsequently, the trained CNN model is integrated to the PSO algorithm to automatically fine-tune the production parameters to optimize the energy efficiency of ICP. The optimization results yield three significant findings. First, as the energy consumption limit increases, the optimal number of heaters, well spacing, and heating temperature exhibit an upward trend, but stabilized beyond the threshold of 7 × 105 kWh. Secondly, the optimal input energy (7 × 105 kWh) is found for the given ICP model. Lastly, the analysis reveals that variations in initial reservoir pressure or bottomhole pressure have limited impact on cumulative oil production and energy usage. [ABSTRACT FROM AUTHOR]

In this paper, we formulate and analyze the problem of secure control in the context of networked linear parameter varying (LPV) systems. We consider an energy-constrained, pulse-width modulated (PWM) jammer, which corrupts the control communication channel by performing a denial-of-service (DoS) attack. In particular, the malicious attacker is able to erase the data sent to one or more actuators. In order to achieve secure control, we propose a virtual actuator technique under the assumption that the behavior of the attacker has been identified. The main advantage brought by this technique is that the existing components in the control system can be maintained without need of retuning them, since the virtual actuator will perform a reconfiguration of the plant, hiding the attack from the controller point of view. Using Lyapunov-based results that take into account the possible behavior of the attacker, design conditions for calculating the virtual actuators gains are obtained. A numerical example is used to illustrate the proposed secure control strategy. [ABSTRACT FROM AUTHOR]

An efficient method to solve the long-term security constrained unit commitment (SCUC) is a requisite for power system operators in many countries, particularly those with many coal-fired generating units, which require a long time to start up and shut down. However, the complexity that is introduced by the energy or fuel constraints in the long-term SCUC problem often results in exorbitant computation time. In this paper, a new type of approach, which is termed the inducing-objective-function (IOF)-based method, is proposed. This method significantly improves the computational efficiency. In the proposed method, the inducing factors are introduced into the objective function of the SCUC relaxation problem to induce more UC state variables to satisfy the integrality requirement after several iterations. According to the relaxation solution, the objective function of the original SCUC model is modified. Thus, the process of solving the modified model using the branch-and-cut algorithm can be more efficient. Because the solution of this modified model can be near-optimal for the original model, the gap between the near-optimal and the optimal solution is estimated to decide whether the near-optimal solution is acceptable for practical application. If the near-optimal solution is not acceptable, it will be used as the initial solution to solve the original model. Because the initial solution is of high quality, it can significantly speed up the branch-and-cut process. Two case studies with different scales demonstrate the robustness, the efficiency and the prospect of the proposed method for practical applications. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we study the problem of stabilizing a linear system with a single long time-varying delay in the input. Under the assumption that the open-loop system is stabilizable and not exponentially unstable, a finite dimensional static time-varying linear state feedback controller is obtained by truncating the predictor based controller and by adopting the parametric Lyapunov equation based controller design approach. As long as the time-varying delay is exactly known and bounded, an explicit condition is provided to guarantee the stability of the closed-loop system. It is also shown that the proposed controller achieves semi-global stabilization of the system if its actuator is subject to either magnitude saturation or energy constraints. Numerical examples show the effectiveness of the proposed approach. [Copyright &y& Elsevier]