In this paper, the security issue in the state estimation problem is investigated for networked control systems. The communication channels between the sensors and the remote estimator are vulnerable to attacks from malicious adversaries. The false data injection attacks (FDIAs) are considered. We aim to find the so-called insecurity conditions under which the estimation system is insecure in the sense that there exist FDIAs that can bypass the anomaly detector but still lead to unbounded estimation errors. In particular, a new necessary and sufficient condition for the insecurity is derived in the case that all communication channels are compromised by the adversary. Furthermore, a specific algorithm is proposed for generating attacks with which the estimation system is insecure. Moreover, for the insecure system, we propose a system protection scheme through which only a few (rather than all) communication channels require protection against FDIAs. A simulation example is utilized to demonstrate the usefulness of the proposed conditions/algorithms in the secure estimation problem for a flight vehicle.
P-glycoprotein (P-gp) is one of the major ABC transporters and involved in many essential processes such as lipid and steroid transport across cell membranes but also in the uptake of drugs such as HIV protease and reverse transcriptase inhibitors. Despite its importance, reliable models predicting substrates of P-gp are scarce. In this study, we have built several computational models to predict whether or not a compound is a P-gp substrate, based on the largest data set yet published, employing 332 distinct structures. Each molecule is represented by ADRIANA.Code, MOE, and ECFP_4 fingerprint descriptors. The models are computed using a support vector machine based on a training set which includes 131 substrates and 81 nonsubstrates that were evaluated by 5-, 10-fold, and leave-one-out (LOO) cross-validation. The best model gives a Matthews Correlation Coefficient of 0.73 and a prediction accuracy of 0.88 on the test set. Examination of the model based on ECFP_4 fingerprints revealed several substructures which could have significance in separating substrates and nonsubstrates of P-gp, such as the nitrile and sulfoxide functional groups which have a higher frequency in nonsubstrates than in substrates. In addition structural isomerism in sugars was found to result in remarkable differences regarding the likelihood of a compound to be a substrate for P-gp.
In this study, we report a feasible strategy for fabricating high-dielectric-constant polymer composites for applications in energy storage devices and embedded capacitors. Hierarchical flower-like TiO2 particles were prepared via a facile solvothermal process and incorporated into the P(VDF-HFP) matrix. The temperature and frequency dependent dielectric properties of flower-like TiO2/P(VDF-HFP) composites as well as commercial TiO2/P(VDF-HFP) composites were investigated. The results reveal that the flower-like TiO2 particles are more effective in increasing the dielectric constant of P(VDF-HFP) when compared with commercial TiO2. Typically, the dielectric constant of the P(VDF-HFP) composite filled with 20 vol % flower-like TiO2 reaches 83.1 at 100 Hz, in contrast to 43.4 for the composite filled with 20 vol % commercial TiO2 and 11.3 for pristine P(VDF-HFP). Also, the flower-like TiO2-filled composites exhibit similar characteristic breakdown strengths to their commercial TiO2-filled counterparts. The significant improvement in the dielectric constant could be attributed to the enhancement of Maxwell-Wagner-Sillars polarization, which originates from the sophisticated morphology of flower-like TiO2 particles.
Converter-driven direct current (DC) motors exhibit various advantages in industry, but impose several challenges to higher-precision speed regulation in the presence of parametric uncertainties and exogenous, timevarying load torque disturbances. In this paper, the robust predictive speed regulation problem of a generic DC-DC buck converter-driven permanent magnet DC motors is addressed by using an output feedback discrete-time model predictive control (MPC) algorithm. A new discrete-time reduced-order generalized proportional-integral observer (GPIO) is proposed to reconstruct the virtual system states as well as the lumped disturbances. The estimates of GPIO are then collected for output speed prediction. An optimized duty ratio law of the converter is obtained by solving a constrained receding horizon optimization problem, where the operational constraint on control input is explicitly taken into account. Finally, the effectiveness of the proposed new algorithm is demonstrated by various experimental testing results.
Abstract-In this paper, a hybrid filter algorithm is developed to deal with the state estimation problem for power systems by taking into account the impact from the phasor measurement units (PMU). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control centre, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed state estimation problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved by using the particle swarm optimization (PSO) algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.
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