适用于路由算法研究的动态网络仿真系统

合适的网络模拟系统是进行智能路由算法研究的支撑环境。采用基于事件驱动的方法,研究实现一个动态网络路由仿真系统。给出构造网络拓扑、生成流量负载、实现网络协议、展现性能结果的设计过程。实验结果表明,该模型能对网络动态和非精确状态信息进行有效模拟且支持群体智能路由仿真。     

一种基于规则的双层四边通道布线专家系统原型

四边通道布线问题(switchbox routing problem)是VLSI布图设计(layout)中的一个关键问题。从计算的观点看,它是一个NP-hard问题。因此,基于知识的方法是解决这一问题的有效方法。 本文中,提出了一种以PROLOG为核心的基于规则的双层四边通道布线专家系统的原型(prototype)。它能把人类布线专家的规划、推理能力及布线经验同计算机的高速数据处理能力有机地结合起来,从而取得较高的布线效率和布通率。     

非对称门限服务与完全服务的性能解析

为了能在无线传感器网络选择一种合适的非对称轮询服务,对非对称门限服务与完全服务的性能进行了分析和比较判定了两种服务在不同情况下其各自特性的优越性.通常在分析非对称轮询服务的时候,一般采用由浅入深的分析方法.所以两队列的服务模型将会作为基础,借此进行拓展,对多队列的非对称服务进行解析.分析过程中使用了马尔科夫链和概率母函数的方法构建了服务系统的数学模型.通过对数学模型的解析给出了非对称服务系统平均排队队长和平均查询周期的表达式.根据理论值的精确计算与实验仿真值的对比结果,可以验证出二者是保持一致的.并且,对未来在无线传感器网络中实现非对称的门限服务和完全服务进行了初步设计,可以实现将多跳的路由协议,转变成单跳的轮询协议,减少数据传输的冲突性.     

基于群组移动模型的OLSR协议性能仿真与评估

在研究移动自组网移动模型和无线路由协议的基础上,选取移动自组网中具有代表性的参考点群移动模型,基于ns2网络模拟平台,以参考点群组移动模型生成移动场景文件,应用层使用cbr数据流量,对OLSR无线路由协议在不同参数条件下的性能进行了模拟测试。通过对测试结果数据的详细分析,总结得出了OLSR无线路由协议在参考点群移动模型下端到端时延、报文交付率和路由负载三个方面的特点。为进一步研究移动自组网下基于OLSR协议的各种应用提供参考。     

Modular multicast ATM switching architecture

A modular multicast packet switching architecture is proposed for the B-ISDN/ATM. In this switching architecture, we use the abilities of a broadcast bus suitable for multicasting services. Instead of using the concatenation of a copying network and a routing network, we use the same switching network to perform both copying and routing of multicast packets. This multicast switch architecture treats unicast and multicast packets in the same way; the delay time for these two kinds of packets has the same characteristics for the affective load in the switching. Through the modularity of this switching architecture, it is easy to dimension the switching system. We also include a mathematical analysis of this architecture.     

基于改进支持向量机的无线传感器网络路由优化算法

为了解决当前无线传感器网络路由算法能耗大的缺陷,设计了基于改进支持向量机的无线传感器网络路由算法(PSO-LSSVM)。首先建立了无线传感器网络路由能耗的数学模型,然后通过组合模型的节点剩余能量进行在线估计,选择能耗最小的路由进行数据传输,最后在Matlab 平台上对该算法的性能进行测试。结果表明,PSO-LSSVM可以快速找到能耗最小的路由,改善了数据传输的可靠性,降低了数据的传输时延,而且综合性能优于对比的无线传感器网络路由算法。     

Dynamic multiscale region search algorithm using vitality selection for traveling salesman problem

Traveling salesman problem (TSP) is a classical mathematical model. Many industry, network, and engineering optimization problems on expert and intelligent system are able to be expressed by using TSP-based mathematical model, such as production planning, vehicle routing, resource scheduling, and so on. Vitality selection (VS) is proposed as a new modification scheme based on delete-oldest selection for TSP. The evaluation criterion of individuals in VS is the individual-made progress in the local successive generations. This is different from the pure fitness criterion. Theoretical comparison and behavior feature analysis demonstrate that VS is effective to avoid the premature convergence and escape from the local optimum. On the other hand, dynamic multiscale region search algorithm (DMRSA) using VS is proposed. DMRSA is characterized by the subregion-segmentation and the selected-city methods. These methods for one individual are not only dynamic in one generation, but also variable from the first generation to the last generation. These dynamic-variable search rules are effective to improve the performance of the local search, and different from variable neighborhood search. To demonstrate the effectiveness of DMRSA, experiments about the convergence, the percentage deviation of the average solution to the best known solution, and the average execution time were done. We compared DMRSA with 9 compared algorithms for 27 TSP instances of TSPLIB. DMRSA found the 22 best known solutions for 27 TSP instances. The experiment-proof robustness and adaptability of DMRSA is trustworthy for solving TSP-based mathematical model applications on expert and intelligent system.     

Expert systems and economic policy analysis: An application to OCS auction leasing

In this paper we argue that expert systems can be powerful tools for modelling microeconomic systems, including both individual decision making and the coordination of individual agents in a resource allocation mechanism. Using the fact that expert systems are essentially computerized versions of decision processes, we illustrate how they can be viewed as generalized process models of decision-making. We argue that the expert system approach is beneficial because it allows a policy analyst to explore the implication of policy alternatives without having to incur the generally prohibitive cost of field implementation studies. Further, enables the incorporation and updating of decision strategies and qualitative information, which human experts typically use but which is not amenable to pure mathematical modelling.One particular microeconomic system we suggest could be modelled as an expert system is the OCS offshore oil lease auction process. Moreover, we argue that constructing such an expert system model would require the development of two integrated expert systems: one for the auction process and subsequent resource allocation and the other to model the individual bidding behavior of the auction participants. We set out the structure of the auction expert system in some detail and discuss rules of thumb used by bidders inferred from our empirical research on past OCS auctions.Such an expert system of an auction leasing process could provide benefits to both bidders (e.g., oil companies) and the auctioneer (e.g., the Department of the Interior) as well. Bidders, by trying different strategies against different hypothesized strategies by their opponents could use such an integrated expert system to improve their bidding performances. The auctioneer, on the other hand, could test the efficiency of various proposed auction institutions under different assumptions about bidding behavior. In some circumstances, it might be desirable to even automate the auction process with a network coordinating the expert systems used by the individual firms and a computerized auctioneer.     

Best routes selection in international intermodal networks

This study focuses on one of the intermodal operational issues: how to select best routes for shipments through the international intermodal network. International intermodal routing is complicated by three important characteristics: (1) multiple objectives; (2) scheduled transportation modes and demanded delivery times; and (3) transportation economies of scale. In this paper, the international intermodal routing problem is formulated as a multiobjective multimodal multicommodity flow problem (MMMFP) with time windows and concave costs. The objectives of this paper are to develop a mathematical model encompassing all three essential characteristics, and to propose an algorithm that can effectively provide answers to the model. The problem is NP-hard. It follows that the proposed algorithm is a heuristic. Based on relaxation and decomposition techniques, the original problem is broken into a set of smaller and easier subproblems. The case studies show that it is important to incorporate the three characteristics into the international intermodal routing problem, and our proposed algorithm can effectively and efficiently solve the MMMFP with time windows and concave costs.     

Comparison of intelligent systems in detecting a child’s mathematical gift

This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children’s mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child’s mathematical gift identified in previous research. The expert system estimated a child’s gift based on heuristically defined logic rules, while the scientifically confirmed psychological evaluation of gift based on Raven’s standard progressive matrices was used at the output of neural network models. Three neural network algorithms were tested on a Croatian dataset. The results show that both the expert system and the neural network recognize more pupils as mathematically gifted than teachers do. The expert system produces the highest average hit rate, although the highest accuracy in classifying gifted children is obtained by the radial basis neural network algorithm, which also yields lower type II error. Due to the ability of expert systems to explain the result, it can be suggested that both the expert system and the neural network model have potential to serve as effective intelligent decision support tools in detecting mathematical gift in early stage, therefore enabling its further development.     

The use of learning algorithms in telephone traffic routing—A methodology

A theoretical as well as conceptual framework for the use of learning algorithms in telephone traffic routing is given. The approach is distinctly different from the mathematical programming methods generally used in such cases. Learning algorithms at the network nodes update their strategies for routing traffic on the basis of success or failure in completing calls. The entire system is described as a Markov process and different learning schemes are shown to lead to different flow patterns in the steady state.     

WSN中基于蚁群算法的QoS路由协议

蚁群算法的随机自适应性使得它很适合应用于无线传感器网络( WSN)环境中,所以本文针对WSN中的数据拥塞、传输延迟、能量消耗等问题,提出了一种基于蚁群算法的QoS路由协议.协议将如何搜索最佳路径问题抽象为组合规划问题,根据最小费用流规则定义了高带宽和低时延路径的判决条件,利用蚁群优化算法,寻找到不同目标函数的路径,达到...     

一种分布式无线传感器网络跨层移动通信技术研究

针对无线多媒体传感器网络对带宽和实时性等的高要求,建立了一种基于能量优化和跨层协同交互的分布式多路径路由技术,并应用于移动通信领域。该技术采用了遗传算法优化节点传输多媒体数据能耗,建立节点剩余能量预测模型,根据感知能量建立跨层协同工作体系,以较小代价在动态无线网络拓扑中选择最优路径。仿真实验和数学分析表明,该技术能够高效地支持实时移动视频通信,适合于计算能力、存储能力和能量等受限的无线多媒体传感器网络。     

A fuzzy ANP approach to shipyard location selection

It is the first duty of investors to transform financial resources into investments in the right places at the right times and earn benefits. However, where to invest and how to invest is always a risky and complicated problem. The decision which will be made under a variety of factors might result in a terrible mistake if it is not felicitous. It is necessary to utilize scientific methods when deciding to make these types of complicated investments which can cause large financial losses. Analytical network process (ANP) can show us the best alternative based on different criteria from a variety of alternatives. However, the network structure should model the actual in a very good way. Analytical network process will give us the best result in span after the correctly built network structure’s comparisons are made correctly. It is important to design the structure flexibly in this work in order for it to lead to improvements. Shipyard location is selected in this work which provides reference to investors based on numerical and solid essences. At the end, the study has received a successful application with the help of expert people and the correct fuzzy analytical network process’s success over the criteria which have a close relationship with each other.     

基于蚁群系统的多约束QoS路由模型设计

研究多约束QoS路由问题,设计一种基于蚁群系统的多约束QoS路由模型,该模型适合于多约束QoS网络服务环境,并具有QoS路由有效性、可扩展性和鲁棒性等特点.     

Scalable evacuation routing in a dynamic environment

In emergency management, tools are needed so we can take the appropriate action at different stages of an evacuation. Recent wildfires in California showed how quickly a natural disaster can affect a large geographical area. Natural disasters can create unpredicted traffic congestion or can temporarily block urban or rural roads. Evacuating a large area in an emergency situation is not possible without prior knowledge of the road network and the ability to generate an efficient evacuation plan. An ideal evacuation routing algorithm should be able to generate realistic and efficient routes for each evacuee from the source to the closest shelter. It should also be able to quickly update routes as the road network changes during the evacuation. For example, if a main road is blocked during a flood, the evacuation routing algorithm should update the plan based on this change in the road network. In this article major works in evacuation routing have been studied and a new algorithm is developed that is faster and can generate better evacuation routes. Additionally, it can quickly adjust the routes if changes in the road network are detected. The new algorithm's performance and running time are reported.     

利用改进ILP和二进制穷举择优法的低成本物联网流量多目标路由感知方法

针对无线网络不能为多样化应用需求提供支持及卸载移动通信核心成本较高的问题,提出了一种改进整数线性规划模型(IILP)结合二进制穷举择优法的低成本混合物联网流量多目标路由感知方法。首先,基于IILP对混合物联网流量路由感知进行建模,获得准确的能量感知模型;其次,采用多目标MAXI路由感知算法对多目标路由感知模型进行了求解,降低了流量路由求解的延时;最后,采用二进制穷举择优法对流量路由感知的吞吐量进行扩展。仿真实验表明,与现有算法相比,提出方法降低了求解的延时,提高了流量的吞吐量,减少了流量的丢包率,同时还降低了混合物联网多目标路由感知的成本。     

SUPERVISORY EXPERT SYSTEM-BASED INTELLIGENT OPTIMIZATION OF A MICROBIOREACTOR

□ Microbioreactors with immobilized yeast cells are conventionally packed uniformly. A recent study has shown, however, that a topologically optimized distribution of cells yields much greater outputs of the desired product. Because topology optimization is a complex method requiring a good mathematical model, artificial intelligence (AI) has been employed here as an alternative method. For the same system—in other words, immobilized genetically modified yeast cells—an expert system selected online the better of two AI methods—a fuzzy neural network (FNN) and a genetic algorithm (GA)—according to the output of the product recombinant glucoamylase. Progressing in short time intervals enables the expert system to shift continually between the FNN and the GA, thereby maintaining optimal performance at all times. This method is more robust than topology optimization, easier to implement, does not require a mathematical model, and improves glucoamylase output even further.     

可编程数据平面下基于DDPG的路由优化方法

针对于数据中心网络不均衡的流量分布,和在使用固定功能交换机的软件定义网络中部署强化学习模型时,不能精确感知网络状态导致的路由决策偏差问题,设计了一种在具有可编程数据平面的软件定义网络中,基于深度确定性策略梯度(DDPG)强化学习模型的路由优化方法.通过在可编程数据平面自定义数据包处理逻辑,获取细粒度、高精度的网络状态参...     

MGB-IPH model for hydrological and hydraulic simulation of large floodplain river systems coupled with open source GIS

Large-scale hydrological models are useful tools for water resources studies, however, river network flow routing is generally represented using simplified methods, which may lead to simulation errors in flat regions. We present recent improvements to the large-scale hydrological model MGB-IPH to improve its capability of simulating large river basins with extensive floodplains. We also describe the coupling of MGB-IPH to an open source GIS and a large set of developed pre-processing tools with a user-friendly interface for remote sensing data preparation and output visualization. The new features implemented are demonstrated applying the model to the whole Araguaia river basin (380,000 km²). Results are compared to the previous MGB-IPH routing method, observed flow and water level data and remote sensing imagery, showing improvement in the representation of floodplain inundation dynamics. The test case also shows that the proposed model software framework amplifies possibilities of large-scale simulation of ungauged basins.