The dynamic frequency assignment problem

In this paper, we consider a frequency assignment problem occurring in a military context. The main originality of the problem pertains to its dynamic dimension: new communications requiring frequency assignments need to be established throughout a battlefield deployment. The problem resolution framework decomposes into three phases: assignment of an initial kernel of communications, dynamic assignment of new communication links and a repair process when no assignment is possible. Different solution methods are proposed and extensive computational experiments are carried out on realistic instances.     

Graceful reassignment of excessively long communications paths in networks

Modern broadband telecommunications networks transport diverse classes of traffic through flexible end-to-end communications paths. For instance, Internet Protocol (IP) networks with Multi-Protocol Label Switching (MPLS) carry traffic through label switched paths. These flexible paths are often changed in real, or near-real, time in response to congestion and failures detected in the network. As a result, over time, some of these communications paths become excessively long (referred to as out-of-kilter), leading to poor service performance and waste of network resources. An effective reassignment scheme may require reassignment of communications paths with acceptable length (referred to as in-kilter) in order to generate spare capacity on certain links for the out-of-kilter paths. A graceful reassignment solution provides an ordered sequence of reassignments that satisfies the following: (i) the total number of reassigned communications paths does not exceed a specified limit, (ii) no temporary capacity violations are incurred on any network link during the execution of the sequence of reassignments (reassignments are executed sequentially, one at a time), (iii) a communications path is reassigned only as a unit without being split among multiple alternate routes (iv) all reassigned communications paths will be in-kilter, (v) none of the reassignments of communications paths that were originally in-kilter can be excluded from the specified solution without resulting in some capacity violation, and (vi) the sequence of reassignments approximately optimizes a predefined objective, such as maximizing the number of reassigned out-of-kilter communications paths or maximizing the total load reassigned from out-of-kilter communications paths. The resulting problem is formulated as a multi-period, multi-commodity network flow problem with integer variables. We present a search heuristic that takes advantage of certain problem properties to find subsequences of reassignments that become part of the solution, without performing an exhaustive search. Each subsequence reassigns at least one out-of-kilter communication path.     

On the construction of a nonlinear recursive predictor

In this paper, we present a novel approach for constructing a nonlinear recursive predictor. Given a limited time series data set, our goal is to develop a predictor that is capable of providing reliable long-term forecasting. The approach is based on the use of an artificial neural network and we propose a combination of network architecture, training algorithm, and special procedures for scaling and initializing the weight coefficients. For time series arising from nonlinear dynamical systems, the power of the proposed predictor has been successfully demonstrated by testing on data sets obtained from numerical simulations and actual experiments.     

System reliability analysis of slope stability using generalized Subset Simulation

Slope failure mechanisms (e.g., why and where slope failure occurs) are usually unknown prior to slope stability analysis. Several possible failure scenarios (e.g., slope sliding along different slip surfaces) can be assumed, leading to a number of scenario failure events of slope stability. How to account rationally for various scenario failure events in slope stability reliability analysis and how to identify key failure events that have significant contributions to slope failure are critical questions in slope engineering. In this study, these questions are resolved by developing an efficient computer-based simulation method for slope system reliability analysis. The proposed approach decomposes a slope system failure event into a series of scenario failure events representing possible failure scenarios and calculates their occurrence probabilities by a single run of an advanced Monte Carlo simulation (MCS) method, called generalized Subset Simulation (GSS). Using GSS results, representative failure events (RFEs) that are considered relatively independent are identified from scenario failure events using probabilistic network evaluation technique. Their relative contributions are assessed quantitatively, based on which key failure events are determined. The proposed approach is illustrated using a soil slope example and a rock slope example. It is shown that the proposed approach provides proper estimates of occurrence probabilities of slope system failure event and scenario failure events by a single GSS run, which avoids repeatedly performing simulations for each failure event. Compared with direct MCS, the proposed approach significantly improves computational efficiency, particularly for failure events with small failure probabilities. Key failure events of slope stability are determined among scenario failure events in a cost-effective manner. Such information is valuable in making slope design decisions and remedial measures.     

Optimal tactics for close support operations: Part VI,summary of numerical results

A new generation of C³ (command, control, and communication) models for military cybernetics has been developed in recent papers. Recursive equations for the solution of the C³-problem have been derived for an amphibious campaign with linear time-varying dynamics. Air and ground commanders are assumed to have different modes of intelligence and communications. Numerical results are summarized, and the relative importance of intelligence and communications is discussed.     

On the Full Idle Time in Multiphase Queueing Systems

The modern queueing theory is a powerful tool for a quantitative and qualitative analysis of communication systems, computer networks, transportation systems, and many other technical systems. The paper is designated to the analysis of queueing systems arising in the network theory and communications theory (such as the so-called multiphase queueing systems, tandem queues, or series of queueing systems). We present heavy traffic limit theorems for the full idle time in multiphase queueing systems. We prove functional limit theorems for values of the full idle time of a queueing system, which is its important probability characteristic. __________ Translated from Lietuvos Matematikos Rinkinys, Vol. 45, No. 3, pp. 367–386, July–September, 2005.     

Using activity focus networks to pressure terrorist organizations

The predominant idea for using network concepts to fight terrorists centers on disabling key parts of their communication networks. Although this counternetwork strategy is clearly a sound approach, it is vulnerable to missing, incomplete, or erroneous information about the network. This paper describes a different and complementary application of network concepts to terrorist organizations. It is based on activity focus networks (AFNs), which represent the complex activity system of an organization. An activity focus is a conceptual or physical entity around which joint activity is organized. Any organization has a number of these, which are in some cases compatible and in some cases incompatible. The set of foci and their relations of compatibility and incompatibility define the AFN. A hypothetical AFN for a terrorist organization is specified and tested in a simulation called AQAS. It shows that certain activity foci, and in particular one combination, have high potential as pressure points for the activity system. The AFN approach complements the counternetwork approach by reducing the downside risk of incomplete information about the communication network, and enhancing the effectiveness of counternetwork approaches over time. Steven R. Corman is Professor in the Hugh Downs School of Human Communication at Arizona State University and Chair of the Organizational Communication Division of the International Communication Association. His research interests include communication networks and activity systems, high-resolution text and discourse analysis, and modeling and simulation of human communication systems.     

Dynamic evolution of economically preferred facilities

In a sustained development scenario, it is often the case that an investment is to be made over time in facilities that generate benefits. The benefits result from joint synergies between the facilities expressed as positive utilities specific to some subsets of facilities. As incremental budgets to finance fixed facility costs become available over time, additional facilities can be opened. The question is which facilities should be opened in order to guarantee that the overall benefit return over time is on the highest possible trajectory. This problem is common in situations such as ramping up a communication or transportation network where the facilities are hubs or service stations, or when introducing new technologies such as alternative fuels for cars and the facilities are fueling stations, or when expanding the production capacity with new machines, or when facilities are functions in a developing organization that is forced to make choices of where to invest limited funding.     

Optimal tactics for close-support operations— III. Degraded intelligence and communications

A new generation of C³ (command, control, and communication) models for military cybernetics is developed. Recursive equations for the solution of the C³ problem are derived for an amphibious campaign with linear time-varying dynamics. Air and ground commanders are assumed to have no intelligence and no communications. Numerical results are given for the optimal decision rules.     

基于模糊时间序列的传感器网络感知数据预测模型

传感器网络监控系统属于大型复杂系统,由感知节点以一定的时间间隔向sink节点发送感知数据,以实现对应用环境的监控。由于网络本身及应用环境的影响,得到的感知数据往往存在不确定性。此外,周期性报告数据模式影响到实时监控数据的精确性。本文应用时间序列模型预测传感器数据以响应用户查询,可有效降低网络通信量。通过对无线传感器网络的数据分析,引入多属性模糊时间序列预测模型,充分考虑了无线传感器网络时间序列中存在的趋势因素,并提出了适合于传感器网络的修正预测模型。实验结果表明模糊时间序列模型可有效预测传感器网络数据,且能提高预测精度。     

基于能岗匹配的管理人员配置过程定性模拟研究

能岗匹配是人力资源配置的重要原则。实施动态环境下的能岗匹配需要模拟出管理者素质变量与岗位职能变量的变化趋势与程度，以及这两者的实时匹配程度。首先描述了管理者素质与管理岗位职能的变量，建立了用于能岗匹配的定性模拟模型，设计了状态变量知识表示方法及其转换规则。采用BP神经网络设计了管理人员能岗匹配度计算函数。最后设计了定性模拟步骤并给出了一个模拟应用实例。     

Optimal tactics for close support operations: Part IV,perfect intelligence and communications

A new generation ofC ³ (command, control, and communication) models for military cybernetics is developed. Recursive equations for the solution of theC ³ problem are derived for an amphibious campaign with linear, time-varying dynamics. Air and ground commanders are assumed to have perfect intelligence and perfect communications. Numerical results are given for the optimal decision rules.     

ARIMA模型在达尔文通信模式研究中的运用

首先利用ARIMA模型,研究了进化论的提出者查尔斯·达尔文(Charles R.Darwin,1809/02/12-1882/04/19)从1866到1879年期间的通信量变化规律,并根据模型优化的AIC和BIC准则求得ARIMA优化模型.接着,应用ARIMA优化模型,对达尔文1880至1881年的月通信数量的时间序列进行了预测.预测的月通信数量与实际通信数量的对比说明,ARIMA优化模型拟合效果良好,并能够较好的反映达尔文学术生涯后期实际通信量的时间变化规律.研究结果表明,ARIMA优化模型可以作为一种研究人类通信模式的有效模型加以广泛应用.     

Synchronized states in a ring of four mutually coupled oscillators and experimental application to secure communications

Data encryption has become increasingly important for many applications including phone, internet and satellite communications. Considering the desirable properties of ergodicity and high sensitivity to initial conditions and control parameters, chaotic signals are suitable for encryption systems. Chaotic encryption systems generally have high speed with low cost, which makes them better candidates than many traditional ciphers for multimedia data encryption. In this paper, analytical and numerical methods as well as experimental implementation are used to prove partial and complete synchronized states in a ring of four autonomous oscillators in their chaotic states. Application to secure communication is discussed.     

Improving throughput in a wireless LAN with application to the Canadian army's situational awareness system

This paper employs optimization to improve the performance of the system the Canadian Army uses to give a Commander a picture of where his forces are. This system uses the Army's wireless battlefield communication network to send vehicle positions as data messages. The difficulty is that, when vehicles compete to send these reports, the messages sometimes collide and this wastes valuable network capacity. At the heart of the problem is the channel access control mechanism. We show how a simple change to this mechanism can improve the system's performance significantly.     

基于品牌手机未来销量预测

本文是用灰色预测GM（1,1）模型,探讨了手机销售总数量动态变化,为手机生产提供参考。同时用灰色关联度,来计算总销售量对需要预测的品牌影响,以关联度作为权重,来预测目标品牌。结果表明,对手机销售数量的历史趋势拟合程度较高,所以用此预测模型预测,具有一定的参考价值。     

Finding the needles in the haystack: efficient intelligence processing

As a result of communication technologies, the main intelligence challenge has shifted from collecting data to efficiently processing it so that relevant, and only relevant, information is passed on to intelligence analysts. We consider intelligence data intercepted on a social communication network. The social network includes both adversaries (eg terrorists) and benign participants. We propose a methodology for efficiently searching for relevant messages among the intercepted communications. Besides addressing a real and urgent problem that has attracted little attention in the open literature thus far, the main contributions of this paper are two-fold. First, we develop a novel knowledge accumulation model for intelligence processors, which addresses both the nodes of the social network (the participants) and its edges (the communications). Second, we propose efficient prioritization algorithms that utilize the processor’s accumulated knowledge. Our approach is based on methods from graphical models, social networks, random fields, Bayesian learning, and exploration/exploitation algorithms.     

基于时间序列的网络安全态势预测方法的研究

互联网上每天充斥着大量入侵与攻击事件,已经严重威胁到网络安全,如何把握网络目前安全态势并预测其发展趋势已成为国内外网络应急响应的热点研究内容之一.根据网络安全事件发生的统计规律性,分别研究了基于时间序列的神经网络和灰色系统理论对未来一段时间内的安全事件数量进行预测的方法,并在此基础上对未来的安全态势进行评估,从而得知网络的整体安全运行状况,有效指导了管理员对网络安全的控制.     

Evaluating the robustness of temporal networks considering spatiality of connections

Network robustness has been a hot research area of studies on complex networks. Finding out the explanations behind the phenomena that networked systems can still function efficiently after some structural damages or the malfunction of certain nodes is meaningful to both the design of solid systems and the defend against failures. It is still indistinct what kind of resilience networked systems which change their topological structures incessantly over time might have. Nevertheless, earlier studies have frequently overlooked to consider the temporal characteristics which in many real scenarios are of great concern, or have considered only the temporality without spatiality which is not reasonable in the real world case. In this paper, we first take the spatiality of connections and communications between nodes, except for the temporal ordering of connection events which has solely been noticed by previous studies, into consideration for measuring and assessing the robustness of the temporal network. We propose a novel temporal efficiency metric, and correspondingly, develop a new temporal robustness evaluation method for temporal network models. The proposed metric and method show their validity through numerical simulations of three temporal network models and we give our evaluations and discussions.     

An Overview of Scheduling Problems Arising in Satellite Communications

Satellite communications, like batches of work in a job shop, need to be scheduled in order to use their resources as efficiently as possible. The most common satellite communications system in use today is known as Time Division Multiple Access (TDMA), in which data from earth stations is buffered before being transmitted to the appropriate receiver on a satellite. Cycles of transmission are fixed for all stations. Since the same satellite will be used for routeing data in several different ways, a schedule must be devised to use the receivers, repeaters and transmitters on board to minimize the time needed for completion of a batch of work. This paper is a survey of current scheduling algorithms used for optimizing satellite communications resources. Apart from telecommunications, the methods presented here could be applied to more general scheduling problems with renewable resources but without precedence constraints.