Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

SECURE STEGANOGRAPHY BASED ON BINARY PARTICLE SWARM OPTIMIZATION

The objective of steganography is to hide message securely in cover objects for secret communication.How to design a secure steganographic algorithm is still major challenge in this re-search field.In this letter,developing secure steganography is formulated as solving a constrained IP(Integer Programming) problem,which takes the relative entropy of cover and stego distributions as the objective function.Furthermore,a novel method is introduced based on BPSO(Binary Particle Swarm Optimization) for achieving the optimal solution of this programming problem.Experimental results show that the proposed method can achieve excellent performance on preserving neighboring co-occurrence features for JPEG steganography.

Resource allocation with minimum transmit power in multicast OFDM systems

To minimize the total transmit power for multicast service in an orthogonal frequency division multiplexing（OFDM） downlink system,resource allocation algorithms that adaptively allocate subcarriers and bits are proposed.The proposed algorithms select users with good channel conditions for each subcarrier to reduce the transmit power,while guaranteeing each user＇s instantaneous minimum rate requirement.The resource allocation problem is first formulated as an integer programming（IP） problem,and then,a full search algorithm that achieves an optimal solution is presented.To reduce the computation load,a suboptimal algorithm is proposed.This suboptimal algorithm decouples the joint resource allocation problem by separating subcarrier and bit allocation.Greedy-like algorithms are employed in both procedures.Simulation results illustrate that the proposed algorithms can significantly reduce the transmit power compared with the conventional multicast approach and the performance of the suboptimal algorithm is close to the optimum.