Synchrophasor Broadcast over Internet Protocol for Distributed Generator Synchronization

Synchronous islanded operation involves continuously holding an islanded power network in virtual synchronism with the main power system to aid paralleling and avoid potentially damaging out-of-synchronism reclosure. This requires phase control of the generators in the island and the transmission of a reference signal from a secure location on the main power system. Global positioning system (GPS) time-synchronized phasor measurements transmitted via an Internet protocol (IP) are used for the reference signal. However, while offering low cost and a readily available solution for distribution networks, IP communications have variable latency and are susceptible to packet loss, which can make time-critical control applications difficult. This paper investigates the ability of the phase-control system to tolerate communications latency. Phasor measurement conditioning algorithms that can tolerate latency are used in the phase-control loop of a 50-kVA diesel generator. © 2010 IEEE.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.