Optimal infrastructure design and expansion of broadband wireless access networks

This paper investigates the important infrastructure design and expansion problem for broadband wireless access networks subject to user demand constraints and system capacity constraints. For the problem, an integer program is derived and a heuristic solution procedure is proposed based on Lagrangean relaxation. In the computational experiments, our Lagrangean relaxation based algorithm can solve this complex design and expansion problem quickly and near optimally. Based on the test results, it is suggested that the proposed algorithm may be practically used for the infrastructure design and expansion problem for broadband wireless access networks.     

Improving multicut in directed trees by upgrading nodes

In this paper, we consider the network improvement problem for multicut by upgrading nodes in a directed tree T = (V, E) with multiple sources and multiple terminals. In a node based upgrading model, a node v can be upgraded at the expense of c(v) and such an upgrade reduces weights on all edges incident to v. The objective is to upgrade a minimum cost subset S ⊆ V of nodes such that the resulting network has a multicut in which no edge has weight larger than a given value D. We first obtain a minimum cardinality node multicut V_c for tree T, then find the minimum cost upgrading set based on the upgrading sets for the subtrees rooted at the nodes in V_c. We show that our algorithm is polynomial when the number of source–terminal pairs is upper bounded by a given value.     

Implementing an efficient minimum capacity cut algorithm

In this paper, we present an efficient implementation of theO(mn + n ² logn) time algorithm originally proposed by Nagamochi and Ibaraki (1992) for computing the minimum capacity cut of an undirected network. To enhance computation, various ideas are added so that it can contract as many edges as possible in each iteration. To evaluate the performance of the resulting implementation, we conducted extensive computational experiments, and compared the results with that of Padberg and Rinaldi's algorithm (1990), which is currently known as one of the practically fastest programs for this problem. The results indicate that our program is considerably faster than Padberg and Rinaldi's program, and its running time is not significantly affected by the types of the networks being solved.Corresponding author.     

Resource downgrading

This paper aims to develop models to optimally manage costs associated with resources that can be downgraded. These resources are reused a number of times before becoming unsuitable for their original purpose, and then they are assigned for some other purpose. The typical decisions are the quantity of resources to purchase, to downgrade and to hold in the inventory. A network-based model is developed to formulate the problem and to investigate several special cases. As the model becomes an integer program due to some side constraints, several heuristics are developed here to overcome the challenges associated with solving the resulting integer program. A semiconductor industry application for test wafer management is presented using real-life data.     

Survivor statistics and damage spreading on social network with power-law degree distributions

Damage spreading(DS) of the random graph networks with power-law degree distributions is investigated using Glauber dynamics. Various subgraphs defined by the probability of acquaintance show distinct features in DS as measured by Hamming distance. A heuristic understanding of the long-time value of damage is achieved through an analysis of the survivor statistics. All survivors are dynamical, flipping in unison for the controlled sample and the damaged sample. Verification of these dynamic survivors is achieved through the introduction of a new measure of self-damage.     

度量空间的k－映射像

Ｅ．Ｈａｌｆａｒ（［６］）引入了ｋ－映射概念，本文给出了度量空间ｋ－映像的一个内在刻划，并由此得到一些度量化定理。     

基于集值统计的模糊神经网络专家系统及其应用

建立基于集值统计的模糊神经网络专家评审系统，并应用于科研项目评审工作，实际应用表明该系统是可行的。     

结合区域提取和改进卷积神经网络的水下小目标检测

针对水下小目标信息量有限而难以提取有效特征导致的检测性能不佳问题,提出了一种结合区域提取和融合Hu矩特征的改进卷积神经网络水下小目标检测方法。该方法包含区域提取和分类两个步骤。首先以马尔可夫随机场分割算法为基础进行区域提取,对潜在目标定位的同时降低伪目标对后续分类的干扰;然后提取潜在目标区域的Hu矩特征并融入卷积神经网络,形成一种形状特征表征能力更强的改进卷积神经网络用于分类。声呐实测数据处理结果表明,该方法可以有效提升对水下小目标的发现概率和正确报警率,与其他目标检测方法相比,该方法具有更好的检测性能和泛化性。     

Mobility-aware coded caching in D2D communication networks

In this paper, we consider a cache-enable device-to-device (D2D) communication network with user mobility and design a mobility-aware coded caching scheme to exploit multicasting opportunities for reducing network traffic. In addition to the static cache memory that can be used to reap coded caching gains, we assign a dynamic cache memory to mobile users such that users who never meet can still exchange contents via relaying. We consider content exchange as an information flow among dynamic cache memories of mobile users and leverage network coding to reduce network traffic. Specifically, we transfer our storage and broadcast problem into a network coding problem. By solving the formulated network coding problem, we obtain a dynamic content replacement and broadcast strategy. Numerical results verify that our algorithm significantly outperforms the random and greedy algorithms in terms of the amount of broadcasting data, and the standard Ford–Fulkerson algorithm in terms of the successful decoding ratio.     

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.