Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

An Empirical Study of Communication Infrastructures Towards the Smart Grid: Design, Implementation, and Evaluation

Abstract: The smart grid features ubiquitous interconnections of power equipments to enable two-way flows of electricity and information for various intelligent power management applications, such as accurate relay protection and timely demand response. To fulfill such pervasive equipment interconnects, a full-fledged communication infrastructure is of great importance in the smart grid. There have been extensive works on disparate layouts of communication infrastructures in the smart grid by surveying feasible wired or wireless communication technologies, such as power line communications and cellular networks. Nevertheless, towards an operable, cost-efficient and backward-compatible communication solution, more comprehensive and practical understandings are still urgently needed regarding communication requirements, applicable protocols, and system performance. Through such comprehensive understandings, we are prone to answer a fundamental question, how to design, implement and integrate communication infrastructures with power systems. In this paper, we address this issue in a case study of a smart grid demonstration project, the Future Renewable Electric Energy Delivery and Management (FREEDM) systems. By investigating communication scenarios, we first clarify communication requirements implied in FREEDM use cases. Then, we adopt a predominant protocol framework, Distributed Network Protocol 3.0 over TCP/IP (DNP3 over TCP/IP), to practically establish connections between electric devices for data exchanges in a small-scale FREEDM system setting, Green Hub. Within the real-setting testbed, we measure the message delivery performance of the DNP3-based communication infrastructure. Our results reveal that diverse timing requirements of message deliveries are arguably primary concerns in a way that dominates viabilities of protocols or schemes in the communication infrastructure of the smart grid. Accordingly, although DNP3 over TCP/IP is widely considered as a smart grid communication solution, it cannot satisfy communication requirements in some time-critical scenarios, such as relay protections, which claim a further optimization on the protocol efficiency of DNP3.

An Efficient and Fine-Grained Big Data Access Control Scheme With Privacy-Preserving Policy

Abstract: How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.

A Large Scene Deceptive Jamming Method for Space-Borne SAR

Abstract: Based on the synthetic aperture radar (SAR) geometric model, a novel, fast algorithm of large scene deceptive jamming against the space-borne SAR is proposed. First, we divide the jamming scene template into sub-templates according to the depth of focus in the range dimension. Next, each sub-template is decomposed into the slow-time-dependent and slow-time-independent terms in the range frequency-azimuth time domain. The slow-time-independent terms are generated off-line while the slow-time-dependent terms are generated by real-time 1-D frequency modulation. Then, the sub-templates are convolved with the intercepted SAR signals simultaneously. Finally, fast deceptive jamming is achieved by incorporating all the sub-templates together. In the proposed method, the two-step realization of the sub-templates and the parallel sub-block processing improves the algorithm efficiency. The simulation results prove the validity of the proposed algorithm.

A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems

Abstract: The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi-period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II).

Label Consistent Matrix Factorization Hashing for Large-Scale Cross-Modal Similarity Search

Abstract: Multimodal hashing has attracted much interest for cross-modal similarity search on large-scale multimedia data sets because of its efficiency and effectiveness. Recently, supervised multimodal hashing, which tries to preserve the semantic information obtained from the labels of training data, has received considerable attention for its higher search accuracy compared with unsupervised multimodal hashing. Although these algorithms are promising, they are mainly designed to preserve pairwise similarities. When semantic labels of training data are given, the algorithms often transform the labels into pairwise similarities, which gives rise to the following problems: (1) constructing pairwise similarity matrix requires enormous storage space and a large amount of calculation, making these methods unscalable to large-scale data sets; (2) transforming labels into pairwise similarities loses the category information of the training data. Therefore, these methods do not enable the hash codes to preserve the discriminative information reflected by labels and, hence, the retrieval accuracies of these methods are affected. To address these challenges, this paper introduces a simple yet effective supervised multimodal hashing method, called label consistent matrix factorization hashing (LCMFH), which focuses on directly utilizing semantic labels to guide the hashing learning procedure. Considering that relevant data from different modalities have semantic correlations, LCMFH transforms heterogeneous data into latent semantic spaces in which multimodal data from the same category share the same representation. Therefore, hash codes quantified by the obtained representations are consistent with the semantic labels of the original data and, thus, can have more discriminative power for cross-modal similarity search tasks. Thorough experiments on standard databases show that the proposed algorithm outperforms several state-of-the-art methods.