Biblio

This paper focuses on the system on wireless sensor networks. The system is linear and the time of the system is discrete as well as variable, which named discrete-time linear time-varying systems (DLTVS). DLTVS are vulnerable to network attacks when exchanging information between sensors in the network, as well as putting their security at risk. A DLTVS with privacy-preserving is designed for this purpose. A set-membership estimator is designed by adding privacy noise obeying the Laplace distribution to state at the initial moment. Simultaneously, the differential privacy of the system is analyzed. On this basis, the real state of the system and the existence form of the estimator for the desired distribution are analyzed. Finally, simulation examples are given, which prove that the model after adding differential privacy can obtain accurate estimates and ensure the security of the system state.

With fast development of distributed generation, storages and control techniques, a growing number of microgrids are interconnected with distribution networks. Microgrid capacity that a local distribution system can afford, is important to distribution network planning and microgrids well-organized integration. Therefore, this paper focuses on estimating consumption ability of distribution system interconnected with microgrids. The method to judge rationality of microgrids access plan is put forward, and an index system covering operation security, power quality and energy management is proposed. Consumption ability estimation procedure based on rationality evaluation and interactions is built up then, and requirements on multi-scenario simulation are presented. Case study on a practical distribution system design with multi-microgrids guarantees the validity and reasonableness of the proposed method and process. The results also indicate construction and reinforcement directions for the distribution network.

Recently, deep neural networks based hashing methods have greatly improved the multimedia retrieval performance by simultaneously learning feature representations and binary hash functions. Inspired by the latest advance in the asymmetric hashing scheme, in this work, we propose a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing. The core idea is that two deep convolutional models are jointly trained such that their output codes for a pair of images can well reveal the similarity indicated by their semantic labels. A pairwise loss is elaborately designed to preserve the pairwise similarities between images as well as incorporating the independence and balance hash code learning criteria. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly. Experiments on three image benchmarks show that DAPH achieves the state-of-the-art performance on large-scale image retrieval.