Discussion: Latent variable graphical model selection via convex optimization

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].Comment: Published in at http://dx.doi.org/10.1214/12-AOS980 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Combined network intrusion and phasor data anomaly detection for secure dynamic control centers

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions

Atom Strapdown: Toward Integrated Quantum Inertial Navigation Systems

We present an alternative technique for estimating the response of a cold atom interferometer (CAI). Using data from a conventional inertial measurement unit (IMU) and common strapdown terminology, the position of the atom wave packet is tracked in a newly introduced sensor frame, enabling hybridization of both systems in terms of acceleration and angular rate measurements. The sensor frame allows for an easier mathematical description of the CAI measurement and integration into higher-level navigation systems. The dynamic terms resulting from the transformation of the IMU frame into the CAI sensor frame are evaluated in simulations. The implementation of the method as a prediction model in an extended Kalman filter is explained and demonstrated in realistic simulations, showing improvements of over two orders of magnitude with respect to the conventional IMU strapdown solution. Finally, the implications of these findings for future hybrid quantum navigation systems are discussed