自主-合作教学模式在C＋＋程序设计教学改革中的应用

针对在C＋＋程序设计课程教学中存在的问题,结合对自主-合作教学模式的研究,提出以自主学习教学模式贯穿教学始终;案例驱动的精讲与泛读相结合;手写程序与机器调试相结合;自主学习与合作学习相结合的教学新思路。并阐述了上述方法在教学改革实践中的具体应用及效果。     

An analysis on decentralized adaptive MAC protocols for cognitive radio networks

The scarcity of bandwidth in the radio spectrum has become more vital since the demand for more and more wireless applications has increased. Most of the spectrum bands have been allocated although many studies have shown that these bands are significantly underutilized most of the time. The problem of unavailability of spectrum and inefficiency in its utilization has been smartly addressed by the cognitive radio (CR) technology which is an opportunistic network that senses the environment, observes the network changes, and then uses knowledge gained from the prior interaction with the network to make intelligent decisions by dynamically adapting their transmission characteristics. In this paper, some of the decentralized adaptive medium access control (MAC) protocols for CR networks have been critically analyzed, and a novel adaptive MAC protocol for CR networks, decentralized non-global MAC (DNG-MAC), has been proposed. The results show the DNG-MAC outperforms other CR-MAC protocols in terms of time and energy efficiency.     

A suboptimal joint bandwidth and power allocation for cooperative relay networks＂ a cooperative game theoretic approach

In commercial networks, user nodes operating on batteries are assumed to be selfish to consume their resources (i.e., bandwidth and power) solely maximizing their own benefits (e.g., the received signal-to-noise ratios (SNRs) and datarates). In this paper, a cooperative game theoretical framework is proposed to jointly perform the bandwidth and power allocation for selfish cooperative relay networks. To ensure a fair and efficient resource sharing between two selfish user nodes, we assume that either node can act as a source as well as a potential relay for each other and either node is willing to seek cooperative relaying only if the datarate achieved through cooperation is not lower than that achieved through noncooperation (i.e., direct transmission) by consuming the same amount of bandwidth and power resource. Define the cooperative strategy of a node as the number of bandwidth and power that it is willing to contribute for relaying purpose. The two node joint bandwidth and power allocation (JBPA) problem can then be formulated as a cooperative game. Since the Nash bargaining solution (NBS) to the JBPA game (JBPAG) is computationally difficult to obtain, we divide it into two subgames, i.e., the bandwidth allocation game (BAG) and the power allocation game (PAG). We prove that both the subgames have unique NBS. And then the suboptimal NBS to the JBPAG can be achieved by solving the BAG and PAG sequentially. Simulation results show that the proposed cooperative game scheme is efficient in that the performance loss of the NBS result to that of the maximal overall data-rate scheme is small while the maximal-rate scheme is unfair. The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

Fair relay selection in decode-and-forward cooperation based on outage priority

The opportunistic cooperation schemes,where only the "best" relay is selected to forward the message,have been widely investigated recently for their good performance in terms of outage probability.However,the unfair selections of relays may cause unbalance power consumptions among relays,which reduces the lifetime of energy constrained networks.In this paper,we introduce a novel concept of outage priority based fairness(OPF),aiming at improving the selection fairness among relays appropriately without outage performance deterioration.Then,a cooperation scheme is proposed to meet this concept,and corresponding theoretical analysis is also provided.Afterward,based on OPF,the achievable upper bound of the fairness is derived,and an optimal cross-layer designed scheme is also provided to achieve the bound.Numerical simulations are carried out finally,which not only validate the theoretical analysis,but also show that taking advantages of the proposed schemes,the fairness among all relays,as well as the network lifetime,can be greatly improved without any loss of outage performance,especially in high SNR regime.     

灾害中生活类物资的供应分析——以汶川地震重灾区物资供应为例

救灾生活类物资在供应中面临着优化运输路线、降低运输成本、缩短运输时间3个难题。文章采用相关机会多目标规划模型,以汶川地震为例,总结并完善了自然灾害救助中的多物资的协作供应模式,为突发性重大灾害中多物资的协作供应的组织、实施、管理提供参考支持。     

篮球比赛记时记分器设计

以P89V51RB2单片机为核心结合ZLG7289B外围显示管理芯片,使用LED点阵和七段数码管共同进行显示,完成了记时与记分的功能;该装置可以显示当前节次,比赛剩余时间,双方比分,并能够根据实际情况对比赛的时间及分数进行修改。     

网络游戏引擎技术的研究

随着网络规模的不断扩大,网络速度的不断提升,网络游戏也在迅速地推广开来,成为众多网民娱乐的一部分。网络游戏的核心技术是网络引擎技术,它是整个网络游戏的主要控制程序,随着网络游戏的不断发展而快速进步。引擎可以说是一个平台,它提供接口给游戏的研发人员,可以提高开发效率,节约成本。3D游戏是目前众多玩家的最爱,重点介绍3D方面的引擎。     

一个基于XCS的同质团队学习模型

同质团队学习是实现多Agent协作的一种方法。但是,传统方法仅在系统运行的前后对目标Agent进行修改,系统运行过程没有直接对Agent的改进做出贡献。本文利用合作策略,在学习分类器系统XCS的基础上提出了一种同质团队学习模型,弥补了传统方法的上述不足。文中还在模型的基础上实验分析了相关因素,如规则积累、通信以及发现新规则等对多Agent协作效率的影响。     

Static Bayesian games with finite fuzzy types and the existence of equilibrium

This paper studies a special game with incomplete information, in which the payoffs of the players are both random and fuzzy. Such a game is considered in the context of a Bayesian game with the uncertain types characterized as fuzzy variables. A static fuzzy Bayesian game is then introduced and the decision rules for players are given based on credibility theory. We further prove the existence of the equilibrium of the game. Finally, a Cournot competition model with fuzzy efficiency under asymmetric information is investigated as an application and some results are presented.     

Application of reinforcement learning to the game of Othello

Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such complex problems are associated with some difficulties. As we discuss in this article, these methods are plagued by the so-called curse of dimensionality and the curse of modelling. In this article, we discuss reinforcement learning, a machine learning technique for solving sequential decision making problems with large state spaces. We describe how reinforcement learning can be combined with a function approximation method to avoid both the curse of dimensionality and the curse of modelling. To illustrate the usefulness of this approach, we apply it to a problem with a huge state space—learning to play the game of Othello. We describe experiments in which reinforcement learning agents learn to play the game of Othello without the use of any knowledge provided by human experts. It turns out that the reinforcement learning agents learn to play the game of Othello better than players that use basic strategies.