Interference Mitigation and Joint Decoding for Uplink LDPC-Coded Relay Cooperation

This paper proposes an efficient inter- ference mitigation and joint decoding scheme for uplink LDPC-coded relay cooperation over a Rayleigh fading channel, where a concatenation of Minimum-mean-squared error linear detectors （MMSE） and BP-based joint itera- tire decoding based on the introduced treble-layer Tanner graph are effectively designed to filter and decode the cor- rupted received sequence at a base station. It is demon- strated by theoretical analysis and numerical simulations that the proposed design can well combine the gains from coding and diversity, which consequently leads to a sig- nificant performance improvement over the conventional cooperation system under the same conditions.     

网络图像检索重排序系统建模和仿真

基于关键词的网络图像检索得到的结果往往是海量且无序的,给用户造成不便。本文构建了一个网络图像检索结果重排序系统并进行了仿真。提出了一种利用图像SIFT局部特征构建图像间视觉Graph结构对图像进行聚类的重排序算法,并引入检索关键词的网络翻译以提高检索效率。仿真结果证明,系统的重排序结果满足网络用户的检索期望,验证了算法的有效性。     

Semantic guided knowledge graph for large-scale zero-shot learning

Zero-shot learning has received growing attention, which aims to improve generalization to unseen concepts. The key challenge in zero-shot tasks is to precisely model the relationship between seen and unseen classes. Most existing zero-shot learning methods capture inter-class relationships via a shared embedding space, leading to inadequate use of relationships and poor performance. Recently, knowledge graph-based methods have emerged as a new trend of zero-shot learning. These methods use a knowledge graph to accurately model the inter-class relationships. However, the currently dominant method for zero-shot learning directly extracts the fixed connection from off-the-shelf WordNet, which will inherit the inherent noise in WordNet. In this paper, we propose a novel method that adopts class-level semantic information as a guidance to construct a new semantic guided knowledge graph (SG-KG), which can correct the errors in the existing knowledge graph and accurately model the inter-class relationships. Specifically, our method includes two main steps: noise suppression and semantic enhancement. Noise suppression is used to eliminate noise edges in the knowledge graph, and semantic enhancement is used to connect two classes with strong relations. To promote high efficient information propagation among classes, we develop a novel multi-granularity fusion network (MGFN) that integrates discriminative information from multiple GCN branches. Extensive experiments on the large-scale ImageNet-21K dataset and AWA2 dataset demonstrate that our method consistently surpasses existing methods and achieves a new state-of-the-art result.     

广义AND/OR图的自底向上的启发式搜索算法BFA0

本文首先根据三角模及其扩张运算概念,定义了一类新的更具普遍意义的模糊广义AND/OR图。根据新定义的启发式函数h(n,x)以及模糊广义AND/OR图的最佳解树之所有子树亦是最佳子解树的原理,提出了自底向上的启发式搜索算法BFAO·。文中证明了算法BFAO·的可采纳性。本文还提出了两类新的启发式函数的单调限制概念,并据此研究了算法BFAO·的单调限制性质,研究了两个BFAO·算法间的比较性质。     

基于顺序图着色方法的地面数字电视频率指配

介绍了频率指配的数学模型和顺序图着色方法,并举例说明了顺序图着色方法在地面电视频率指配规划中的实际应用.     

An Simple Enumeration of All Minimal Cutsets of an All-Terminal Graph

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Minimum delay algorithm based on time aggregated graph in DTN network

The DTN network has the characteristics of dynamic topology change,thus the routing algorithm of static network can not solve the routing problem of time-varying network,and can not support fast transmission of given tasks.The existing time-variant routing algorithm CGR (contact graph routing) uses the earliest contact to obtain the shortest path,but because of the influence of the order of the connectivity period,the CGR algorithm has low link utilization.To solve this problem,the method of finding the shortest path was proposed.Besides,in order to characterize the relation between the different connect periods of the same link,the time series of node cache was added to the time aggregated graph.Based on this,the end-to-end multi-path shortest delay routing algorithm for the known task requirements was proposed.Finally,the feasibility of the algorithm was proved by an example.     

基于图论的指标体系优化方法研究

针对指标体系中指标间存在相互作用,采用层次分析法难以对其进行评估,提出了基于图论的指标体系优化方法,设计了基于最短路径的简化指标相互作用算法和基于可达矩阵的指标层次化算法;将复杂的指标体系简化为层次结构,有效地对指标体系优化.     

Improving Knowledge Graph Completion Using Soft Rules and Adversarial Learning

Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks.However,the existing knowledge graph are incomplete.Therefore,knowledge graph completion technology has become a research hotspot in the field of artificial intelligence,but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge embedding.Based on the logic rules of knowledge and the role of adversarial learning in knowledge embedding,we proposes a model to improve the completion of knowledge graph:soft Rules and graph adversarial learning (RUGA).Firstly,the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method,and high-quality negative samples are obtained.Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model.The whole model will benefit from both high-quality samples and logical rules.In addition,we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively.Finally,the experimental results show that the model can effectively improve the effect of knowledge graph completion.     

导弹仿真系统可信度评估的层次分析法

利用层次分析法对导弹仿真系统进行了可信度评估，不仅给出了可信度评估的方法，而且得到客观、量化的可信度结果．为仿真用户提供了科学的决策依据，也进一步完善了导弹武器仿真系统VV＆A工作。