Dongming Wang

Fanfei Xu, Shengheng Liu, Zihuan Mao et al.

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and max-min problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves $20\%$ of overall performance and $120\%$ of lowest performance.

Xiaoyu Chen, Dongming Wang

We study the data space $D$ of any given data set $X$ and explain how functions and relations are defined over $D$. From $D$ and for a specific domain $Δ$ we construct the information space $I$ of $X$ by interpreting variables, functions, and explicit relations over $D$ in $Δ$ and by including other relations that $D$ implies under the interpretation in $Δ$. Then from $I$ we build up the knowledge space $K$ of $X$ as the product of two spaces $K_T$ and $K_P$, where $K_T$ is obtained from $I$ by using the induction principle to generalize propositional relations to quantified relations, the deduction principle to generate new relations, and standard mechanisms to validate relations and $K_P$ is the space of specifications of methods with operational instructions which are valid in $K_T$. Through our construction of the three topological spaces the following key observation is made clear: the retrieval of information from the given data set for $Δ$ consists essentially in mining domain objects and relations, and the discovery of knowledge from the retrieved information consists essentially in applying the induction and deduction principles to generate propositions, synthesizing and modeling the information to generate specifications of methods with operational instructions, and validating the propositions and specifications. Based on this observation, efficient approaches may be designed to discover profound knowledge automatically from simple data, as demonstrated by the result of our study in the case of geometry.

Xiaoyu Chen, Dan Song, Dongming Wang

We propose an approach to generate geometric theorems from electronic images of diagrams automatically. The approach makes use of techniques of Hough transform to recognize geometric objects and their labels and of numeric verification to mine basic geometric relations. Candidate propositions are generated from the retrieved information by using six strategies and geometric theorems are obtained from the candidates via algebraic computation. Experiments with a preliminary implementation illustrate the effectiveness and efficiency of the proposed approach for generating nontrivial theorems from images of diagrams. This work demonstrates the feasibility of automated discovery of profound geometric knowledge from simple image data and has potential applications in geometric knowledge management and education.