一种基于间接互惠的计算网格合作激励机制研究

基于间接互惠理论,构建了计算网格合作激励机制,建立了三维声誉系统以及声誉管理机制,对计算网格中资源拥有者的"合作行为"进行有效激励,并对"拒绝合作行为"中的"恶意拒绝合作"和"偶发拒绝合作"进行了区分。通过对恶意拒绝合作行为进行严厉的惩罚,提升合作稳定性,同时对偶发的拒绝合作行为采取宽容策略,以避免偶发的拒绝合作陷入最坏的"一报还一报"结果。最后,利用合作博弈理论,分析得出在激励机制下,资源拥有者的最优策略是最大可能地共享闲置资源以及不间断合作,以提升自己的合作诚信度,从而验证了激励机制的有效性。     

一种基于窄带信号多普勒频率测量的运动目标直接定位方法

相比于常规的两步定位方法,目标直接定位方法具有更高的定位精度,但现有的直接定位方法主要是针对静止目标所提出的,并具有较大的运算量.本文提出了一种针对匀速运动目标的多普勒频率直接定位方法.文中首先基于最大似然准则推导了直接估计目标初始位置和运动速度的优化模型,针对该优化模型是以矩阵特征值的形式给出而难以数值求解的问题,提出了一种基于矩阵特征值扰动定理的Newton型迭代算法,该算法可以避免多维参数网格搜索所导致的庞大运算量.此外,文中还推导了关于目标初始位置、运动速度以及运动航迹估计的克拉美罗界的闭式表达式.数值实验表明新方法的目标位置估计方差可以达到克拉美罗界,并且具有较少的运算量.     

基于网格部分细化的DOA估计方法

计算复杂度和估计精确度一直是波达方向(DOA)估计研究的重点。现有基于压缩感知的DOA估计算法与传统算法相比具有一定优势,但这些稀疏信号重建模型都是将角度空间等间距划分,仍存在算法计算复杂度较高和估计精确度较低的问题。针对这些问题,提出一种对角度空间网格进行部分细化的DOA估计方法。该方法包括裂变过程和学习过程,裂变过程通过产生新网格点对角度空间进行细化,学习过程通过迭代不断逼近波达方向。仿真结果表明,提出的算法耗时较少,而且在非常稀疏的初始网格划分的条件下(初始间隔为20°),仍可以获得较高的估计精确度。     

BAAQ: an infrastructure for application integration and knowledge discovery in bioinformatics.

The emerging grid computing technologies enable bioinformatics scientists to conduct their researches in a virtual laboratory, in which they share public databases, computational tools as well as their analysis workflows. However, the development of grid applications is still a nightmare for general bioinformatics scientists, due to the lack of grid programming environments, standards and high-level services. Here, we present a system, which we named Bioinformatics: Ask Any Questions (BAAQ), to automate this development procedure as much as possible. BAAQ allows scientists to store and manage remote biological data and programs, to build analysis workflows that integrate these resources seamlessly, and to discover knowledge from available resources. This paper addresses two issues in building grid applications in bioinformatics: how to smoothly compose an analysis workflow using heterogeneous resources and how to efficiently discover and re-use available resources in the grid community. Correspondingly an intelligent grid programming environment and an active solution recommendation service are proposed. Finally, we present a case study applying BAAQ to a bioinformatics problem.     

对藏棋“久”的分阶段算法研究

藏族久棋是2019年中国计算机博弈锦标赛新设棋种,在此之前,国内外对该棋种的博弈策略研究相对较少。本文基于久棋两个博弈阶段规则和目的差异性大的特点,提出一种分阶段的博弈策略:下子阶段,考虑到无明显胜负判别的因素,提出一种基于相对胜负的改进蒙特卡洛树搜索算法以获取最佳下子点;行棋阶段,考虑到过程中的行棋方式会对后续模拟局面造成一定的影响,提出一种加入过程分值的改进Alpha-Beta剪枝搜索算法以获取最优行棋方案。在上述算法模拟博弈树的过程中,通过下子阶段优先集中在中心区域,行棋阶段优先形成褡裢的估值策略,给出了一份完整的估值评估表。实验结果表明,使用上述博弈策略及估值表实现的博弈程序棋力较高。     

Level-set-based motion estimation algorithm for multiple reference frame motion estimation

Motion estimation (ME) has a variety of applications in image processing, pattern recognition, target tracking, and video compression. In modern video compression standards such as H.264/AVC and HEVC, multiple reference frame ME (MRFME) is adopted to reduce the temporal redundancy between successive frames in a video sequence. In MRFME, the motion search process is conducted using additional reference frames, thereby obtaining better prediction signal as compared to single reference frame ME (SRFME). However, its high computational complexity makes it difficult to be utilized in real-world applications. In order to reduce the computational complexity of MRFME, this paper proposes a level-set-based ME algorithm (LSME) without any penalty in the rate-distortion (RD) performance. First, the proposed algorithm partitions the motion search space into multiple level sets based on a rate constraint. The proposed algorithm then controls the ME process on the basis of the predetermined level sets. Experimental results show that the proposed algorithm reduces the ME time by up to 83.46% as compared to the conventional full search (FS) algorithm.     

Adaptive algorithm for optimal real‐time pricing in cognitive radio enabled smart grid network

Integration of multiple communication technologies in a smart grid (SG) enables employing cognitive radio (CR) technology for improving reliability and security with low latency by adaptively and effectively allocating spectral resources. The versatile features of the CR enable the smart meter to select either the unlicensed or the licensed band for transmitting data to the utility company, thus reducing communication outage. Demand response management is regarded as the control unit of the SG that balances the load by regulating the real‐time price that benefits both the utility company and consumers. In this study, joint allocation of the transmission power to the smart meter and consumer's demand is formulated as a two stage multi‐armed bandit game in which the players select their optimal strategies noncooperatively without having any prior information about the media. Furthermore, based on historical rewards of the player, a real‐time pricing adaptation method is proposed. The latter is validated through numerical results.     

Fusion of artificial intelligence and game theory for resource allocation in non-orthogonal multiple access-assisted device-to-device cooperative communication

Device-to-device (D2D) communication offers a low-cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non-orthogonal multiple access (NOMA)-based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.     

一种启发式的足球机器人传球路径搜索算法

在机器人足球比赛的动态和不确定环境中,机器人一般选择带球进攻,为了增强比赛的水平和观赏性,可以开发更多的传球策略,这里特别针对前场传切进攻战术,提出了启发式的传球路径搜索算法。该算法考虑了影响传球路线的多方面因素,能在有效时间为持球机器人提供更多的可用传球路线,并生成更好的传球路线。     

A joint communication and computing resource management scheme for pervasive grid networks

The last years have been characterized by an increasing interest in the grid and cloud computing that allow the implementation of high performance computing structures in a distributed way by exploiting multiple processing resources. The presence of mobile terminals has extended the paradigm to the so called pervasive grid networks, where multiple heterogeneous devices are interconnected to form a distributed computing resource. In such a scenario, there is the need of efficient techniques for providing reliable wireless connections among network nodes. This paper deals with the proposal of a suitable resource management scheme relying on a routing algorithm able to perform jointly the resource discovery and task scheduling for implementing an efficient pervasive grid infrastructure in a wireless ad hoc scenario. The proposed solutions have been considered within two different parallelization processing schemes, and their effectiveness has been verified by resorting to computer simulations. Copyright © 2011 John Wiley & Sons, Ltd.     

基于网格工作流的政务协同研究

随着网格基础设施的逐渐完善以及网格应用研究的深入,网格环境下电子政务中协同处理、协同服务的应用越来越复杂。针对网格环境下政务工作流特点以及传统支持跨部门工作流模型的缺点,在研究网格工作流基础上,提出了一个QoS约束的政务协同工作模型。最后通过一个应用实例来实现了模型的应用。     

A distributed architecture for online power systems security analysis

Phenomena that can compromise power systems operation need to be carefully analyzed in order to evaluate their impact on the security and reliability levels of the electrical networks. The real-time assessment of the system's security and reliability levels, especially under unforeseen contingencies, is known as online power system security analysis. For complex networks this process requires large computational efforts whereas computation times should be less than a few minutes for the information to be useful. To address this problem a distributed architecture based on the Web is proposed. The architecture integrates a network of remotely controlled units distributed in the most critical sections of the electrical network for fields data acquisition and safety check violations, a distributed solution engine for the online analysis of the system security, and a Web-based interface for graphical synoptic and reporting development. The results obtained from an intensive experimentation demonstrate the validity of the architecture and stimulate the enhancement of the solution engine through the use of a computational grid able to dynamically acquire the needed resources.     

基于网络连接性的移动网格任务调度算法

针对移动网格任务调度过程中,移动资源的移动性和网络连接的不确定性问题,通过改进全局搜索能力强的遗传算法过程以适应于移动网格的调度场景,提出一种基于移动性和连接性的移动网格任务调度算法。通过理论分析和仿真验证,该算法能有效避免因网络失效导致的资源浪费,提高了资源利用率,同时,能精确任务调度的完成时间,提高用户满意度。     

基于马尔可夫决策过程的网格资源发现

传统的网格资源发现方法没有考虑节点和资源本身性能的优劣性。针对这一问题,提出了基于马尔可夫决策过程(MDP)的网格资源发现方法,利用其对固定目标的最优搜索理论建立MDP模型实现报酬最优的资源发现,并对该模型仿真分析,验证其性能。     

An Adaptive Smart Grid Management Scheme Based on the Coopetition Game Model

Recently, the idea of the smart grid has been gaining significant attention and has become a hot research topic. The purpose of this paper is to present a novel smart grid management scheme that uses game theory principles. In our proposed scheme, power appliances in the smart grid adaptively form groups according to the non‐cooperative hedonic game model. By exploiting multi‐appliance diversity, appliances in each group are dynamically scheduled in a cooperative manner. For efficient smart grid management, the proposed coopetition game approach is dynamic and flexible to adaptively respond to current system conditions. The main feature is to maximize the overall system performance while satisfying the requirements of individual appliances. Simulation results indicate that our proposed scheme achieves higher energy efficiency and better system performance than other existing schemes.     

Hybrid model of inter-stage spectrum trading in multistage game-theoretic framework

Dynamic spectrum access(DSA),consisting of spectrum sharing and spectrum trading stage,becomes a promising approach to increase the efficiency of spectrum usage and system performance.In this paper,from the perspective of individual interest optimization,we focus on strategy adaptation of network users and their interaction in spectrum trading process.Considering adverse effects on decision-making accuracy and the fairness among network users via local information acquirement,a hybrid game model based on global information of relevant spectrum is proposed to formulate intelligent behaviors of both primary and secondary users.Specifically,by using the evolutionary game theory,a spectrum-selection approach for the evolution process of secondary users is designed to converge to the evolutionary equilibrium gradually.Moreover,competition among primary users is modeled as a non-cooperative game and an iterative algorithm is employed to achieve the Nash equilibrium.The simulation results show that the proposed hybrid game model investigates network dynamics under different network parameter settings.     

Area-Efficient Parallel Syndrome Generators for Linear Block Codes

The common subexpression elimination (CSE) method has been applied to reduce the computational cost of the parallel syndrome generators, which are essential components for decoding of linear block codes. In this work, we present an alternative expression for the parallel syndrome generation formula, with which CSE is able to search redundant computations in a maximally expanded space to achieve a significantly higher optimization efficiency. In particular, two search space expansion schemes are studied for syndrome generators of BCH and RS codes. Experiments demonstrate at most 81 % of area savings using the proposed optimization schemes, and the post-layout results show the power saving of 41 ～ 45 % compared to a straightforward design.     

Harmonic Minimization in Multilevel Inverters Using Modified Species-Based Particle Swarm Optimization

Harmonic minimization in multilevel inverters is a complex optimization problem that involves nonlinear transcendental equations having multiple local minima. In this paper, a solution to the harmonic minimization problem using a novel particle swarm optimization (PSO) approach based on species-based PSO (SPSO) is presented. The original SPSO is modified, which increased the robustness of algorithm to find global optimum of the search space. The proposed method is able to find the optimum switching angles when their number is increased, while it is not possible to determine them using either conventional iterative techniques or resultant theory method. Theoretical results are verified by experiments and simulations for an 11-level H-bridge inverter. Results show that the proposed method effectively minimizes a large number of specific harmonics, and the output voltage results in very low total harmonic distortion and switching frequency.      

分布式环境中基于协商的虚拟网映射算法

网络虚拟化技术可以在共享的底层物理网络上为用户同时提供多种可定制的服务网络。目前的虚拟网映射算法比较依赖于集中式的管理节点,使其在可靠性和适用范围等方面存在诸多问题。为此,提出了一种分布式环境下的虚拟网映射算法,该算法通过多个节点之间的相互协商来完成虚拟网的映射,并且在降低通信开销和缩短虚拟链路的路径长度方面进行了相应改进。实验结果表明,该算法与同类型算法相比,在资源利用率和通信开销方面具有一定的优越性。     

A Radio Resource Management Framework for Multi-User Multi-Cell OFDMA Networks Based on Game Theory

This work proposes a radio resource management framework employing game theoretic concepts for orthogonal frequency division multiple access, the most prevalent multiple access technique for the next generation wireless networks. The subcarrier allocation problem is encountered as a combinatorial auction, where the base station auctions the subcarriers and the users bid for and buy bundles of subcarriers, aiming at minimising their required transmit power. Subsequently, each allocated subcarrier is loaded with a number of bits, decided by each user independently, and the power control process is set up as a non-cooperative game. Each user responds to the interference sensed in his environment and, through a best responses process, the game converges to the unique, Pareto optimal, Nash equilibrium. In order to guarantee convergence, a limit is imposed to the maximum modulation level for each subcarrier. Simulation results show that the auction algorithm follows closely the performance of the optimal algorithm, whereas it is of lower computational complexity and requires less feedback information. Similarly, the proposed distributed bit loading and power control scheme achieves lower transmit power per offered bit rate unit. However, the distributed nature of the algorithm results in lower total offered bit rate, because of the partial knowledge and exploitation of channel state information.