Modeling of combined Bayesian networks and cognitive framework for decision-making in C2

The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approach,Bayesian networks（BNs） provide a framework in which a decision is made by combining the experts＇ knowledge and the specific data.In addition,an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker.The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets（CPNs）,and the consequences of execution manifest such combination can perfectly present the decision-making process in C2.

ZTE Signs Network Global Framework Agreement with Vodafone

ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, has signed a network equipment Global Framework Agreement (GFA) with Vodafone on spanning ZTE’s complete telecoms infrastructure equipment portfolio.

A Framework for Multi-Hop Ad-Hoc Networking over Wi-Fi Direct with Android Smart Devices

The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.

A New Communication Framework for Networked Mobile Games

This paper introduces a two-layer UDP datagram-based communication framework for developing networked mobile games. The framework consists of a physical layer and a data-link layer with a unified interface as a network communication mechanism. A standalone two-player mobile game, such as a chess game and the like, can be easily plugged on to the communication framework to become a corresponding networked mobile game.

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

This paper presents a comprehensive review of various traditional systems of crude oil distillation column design, modeling, simulation, optimization and control methods. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithm (GA) framework were chosen as the best methodologies for design, optimization and control of crude oil distillation column. It was discovered that many past researchers used rigorous simulations which led to convergence problems that were time consuming. The use of dynamic mathematical models was also challenging as these models were also time dependent. The proposed methodologies use back-propagation algorithm to replace the convergence problem using error minimal method.

Research on Weighted Directed Dynamic Multiplexing Network of World Grain Trade Based on Improved MLP Framework

As the main food source for humans, the global movement of the three major grains significantly impacts human survival and development. To investigate the evolution of the world cereal trade network and its development trend, a weighted directed dynamic multiplexed network was established using historical data on cereal trade, cereal import dependency ratio, and arable land per capita. Inspired by the MLP framework, we redefined the weight determination method for computing layer weights and edge weights of the target layer, modified the CN, RA, AA, and PA indicators, and proposed the node similarity indicator for weighted directed networks. The AUC metric, which measures the accuracy of the algorithm, has also been improved in order to finally obtain the link prediction results for the grain trading network. The prediction results were processed, such as web-based presentation and community partition. It was found that the number of generalized trade agreements does not have a decisive impact on inter-country cereal trade. The former large grain exporters continue to play an important role in this trade network. In the future, the world trade in cereals will develop in the direction of more frequent intercontinental trade and gradually weaken the intracontinental cereal trade.

一种基于Parlay Framework API的应用侧框架系统的设计与实现

支持采用安全的、开放式API(应用编程接口)的业务访问方式是下一代网络业务层发展的必然趋势.介绍了下一代网络开放式的应用编程接口ParlayAPI,重点分析了其中Framework(框架)接口的特点及其功能,提出了一种安全、高效的基于ParlayFrameworkAPI的应用侧框架系统结构,包括Framework应用侧模块CORBA(公共对象请求代理体系结构)适配层、Framework应用侧模块实际功能层、安全工具包,并讨论了其原型实现.

A dynamic and resource sharing virtual network mapping algorithm

Network virtualization can effectively establish dedicated virtual networks to implement various network functions.However,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and priority.In addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation redundancy.Based on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology properties.In the node mapping process,three properties of the node are used to measure its mapping ability.Second,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link mapping.Finally,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource allocation.The former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision probability.Simulation results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenue by 15%and 38%,and reduce the network cost and link pressure by 25%and 17%.

Traffic prediction using a self-adjusted evolutionary neural network

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.

新型集体经济与内源式发展:基于江苏省苏州市的案例研究

内源式发展是激发新型集体经济内生动力的重要举措。本文选取江苏省苏州市四个集体经济组织作为案例研究的对象,基于行动者网络理论构建了包括问题呈现、路径整合、利益赋予、征召动员和互动调整五个环节的整合性行动分析框架,归纳出新型集体经济实现内源式发展的演化机理。研究发现,集体经济组织是发展新型集体经济的核心主体;因地制宜地盘活优势资源要素并构建多方共赢的紧密利益联结机制是其实现内源式发展的核心。

泛在空间定位的概念框架与服务

全球导航卫星系统(GNSS)时空信息服务面临复杂场景的信号覆盖性难题,无法解决“无处不在”的泛在时空服务需求。为此,开展基于北斗卫星导航系统(BDS)信号的室内外无缝导航定位技术研究和系统研制,实现泛在空间定位服务非常必要。本文首先定义了“地上/室外定位+地下/室内定位+云上定位”的泛在空间定位体系概念,提出了泛在空间定位体系架构、信号信息架构、应用服务架构,给出了体系关键技术、应用服务类型、应用模式,构建了泛在空间定位试验网络,开展了针对典型场景的地上/室外定位、地下/室内定位和云上定位性能的初步示范验证、测试结果分析与服务能力评估。研究结果表明:以BDS为核心的泛在空间定位试验网络可提供泛在时空信息服务,能够将BDS导航定位服务从开阔空间全面拓展到半遮蔽及全遮蔽空间。

Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.

A Network Analysis of Skill Game Dynamics

Casino games can be classified in two main categories, i.e. skill games and gambling. Notably, the former refers to games whose outcome is affected by the strategies of players, the latter to those games whose outcome is completely random. For instance, lotteries are easily recognized as pure gambling, while some variants of Poker (e.g. Texas Hold’em) are usually considered as skill games. In both cases, the theory of probability constitutes the mathematical framework for studying their dynamics, despite their classification. Here, it is worth to consider that when games entail the competition between many players, the structure of interactions can acquire a relevant role. For instance, some games as Bingo are not characterized by this kind of interactions, while other games as Poker, show a network structure, i.e. players interact each other and have the opportunity to share or exchange information. In this paper, we analyze the dynamics of a population composed of two species, i.e. strong and weak agents. The former represents expert players, while the latter beginners, i.e. non-expert ones. Here, pair-wise interactions are based on a very simple game, whose outcome is affected by the nature of the involved agents. In doing so, expert agents have a higher probability to succeed when playing with weak agents, while the success probability is equal when two agents of the same kind face each other. Numerical simulations are performed considering a population arranged in different topologies like regular graphs and in scale-free networks. This choice allows to model dynamics that we might observe on online game platforms. Further aspects as the adaptability of agents are taken into account, e.g. the possibility to improve (i.e. to becomean expert). Results show that complex topologies represent a strong opportunity for experts and a risk for both kinds of agents.

基于四维分析框架的中国省级可再生能源政策量化研究

将可再生能源政策文本进行多维度量化分析,是政策制定和政策优化的必要工作之一,对保证政策执行效果最大化具有重要意义。以中国30个省级行政区2010—2022年间的616份可再生能源政策文本为研究对象,构建政策目标—政策主体—政策工具—政策效果的四维分析框架,运用政策文本分析、社会网络分析、PMC指数分析等方法,对可再生能源政策进行量化研究。结果发现:①目标较清晰的政策文本比例有所提高但政策数量整体有所下降;②政策主体数量较多且存在一定程度上合作,但存在部分政策主体“边缘化”现象;③3种政策工具均有所覆盖,但需求型政策工具较为缺乏;④政策效果大多数为优秀或良好,但少数政策效果一般,需加强政策性质、发布机构和政策重点多元化。基于以上发现,从提升政策目标清晰程度等4个方面提出相关政策建议。

一种基于SDN的网络安全框架及安全策略设计

软件定义网络(Softeware Defined Network, SDN)是一种新型的网络体系架构,目前已成为下一代互联网研究的热点。为了解决SDN中的网络信息安全问题,文章对SDN中的控制平面、数据平面和应用平面进行分析,梳理并总结了SDN管理中的相关网络安全问题。提出了一种基于SDN的网络安全框架及安全策略,有效弥补传统网络结构中的网络安全缺陷问题,增强SDN网络安全级别,并建立一种基于终端用户限定与管理的SDN的网络安全框架及其安全策略。

Hierarchical Hypercube Based Pairwise Key Establishment Scheme for Sensor Networks

Security schemes of pairwise key establishment, which enable sensors to communicate with each other se-curely, play a fundamental role in research on security issue in wireless sensor networks. A general frame-work for key predistribution is presented, based on the idea of KDC (Key Distribution Center) and polyno-mial pool schemes. By utilizing nice properties of H2 (Hierarchical Hypercube) model, a new security mechanism for key predistribution based on such model is also proposed. Furthermore, the working per-formance of tolerance resistance is seriously inspected in this paper. Theoretic analysis and experimental fig-ures show that the algorithm addressed in this paper has better performance and provides higher possibilities for sensor to establish pairwise key, compared with previous related works.

Framework to Classify and Analyze Social Media Content

In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involves machine learning classifying techniques. To facilitate this, we propose a general framework that can be applied to any social media content to develop an intelligent system. The framework consists of three main parts: an interface, classifier and ana-lyzer. The analyzer uses media recognition to identify specific features. Then, the classifier uses these features and involves them in the classification process. The interface organizes the interaction between the system compo-nents. We tested the framework and developed a system to be applied to im-age-based social media networks (Instagram). The system was implemented as a mobile application (My Interests) that works as a recommendation and filtering system for Instagram users and reduces the time they spend on irre-levant information. It analyzes the images, categorizes them, identifies the in-teresting ones, and finally, reports the results. We used the Cloud Vision API as a tool to analyze the images and extract their features. Furthermore, we adapted support vector machine (SVM), a machine learning method, to classify images and to predict the preferred ones.

一种基于Framework架构的智能化过覆盖分析工具的应用研究

采用Framework架构,自主开发工具,结合基础工参、全量客户TA分布和站间距,实现快速定位越区覆盖小区,形成越区覆盖问题小区库;另外通过链路预算和三角函数算法,实现分场景差异化的问题小区自动快速高效识别和更加精准的优化调整方案输出,准确输出下倾角调整方案,弥补了现有现场天馈调整优化缺少有效手段,需反复现场查勘调整的空缺,有效支撑地市一线生产,提高生产效率和缩小优化成本。

基于STM32的塔机数据采集和监控系统设计

针对塔机作业对运行状态参数和作业环境实时监测的需求,设计了一种基于STM32的塔机数据采集和监控系统。该系统采用STM32F103RCT6为核心的微处理器对塔机运行各项状态参数结合传感器进行数据采集和处理,传感器信号经带数字隔离的A/D转换模块和RS485转TTL模块传输到微处理器,微处理器通过串口转以太网模块将采集的传感器数据发送到由工业交换机和网络摄像机组建的局域网中。最后,采用Java语言结合Spring Boot框架开发系统上位机页面对传感器数据和视频监控画面进行显示。通过测试,该系统能够对塔机运行状态参数和塔机作业环境进行实时监测,传感器采集数据的平均相对误差为0.74%,摄像机监控画面平均延时20.4 ms,实现了数据高速准确的实时监测,满足了监测塔机的系统需求。

图情领域二模网络研究:概念、脉络及应用

二模网络能够全面深入细致地挖掘众多复杂网络场景中的丰富信息。图情领域中的众多研究对象组成的网络具有天然的二分属性,二模网络在该领域的应用取得了较好的研究成果。本文系统梳理了图情领域二模网络的研究现状。首先,对二模网络相关概念进行了界定,包括隶属网络、二分网络等。其次,厘清了二模网络的研究脉络,包括二模网络的研究方法、基本流程和应用场景。再其次,总结了二模网络在知识管理与知识发现、科研合作与学术评价、信息传播与网络舆情、用户需求与信息服务等图情领域的应用逻辑。最后,探讨了二模网络在图情领域的研究前景,为信息资源管理学科体系构建贡献了基础方法。