基于记忆细胞剪切和非线性资源分配的人工免疫识别系统

为减少人工免疫识别系统(AIRS)的记忆细胞数量并提高AIRS的分类准确率,提出一种基于记忆细胞剪切和非线性资源分配的人工免疫识别系统(PNAIRS).PNAIRS采用样本属性离散化来压缩训练空间,利用记忆细胞剪切来淘汰低适应度细胞,并使用非线性资源分配来优化分类器.PNAIRS对6个UCI数据集进行分类测试,测试结果与其它分类算法结果对比,显示PNAIRS具有较小规模的记忆细胞群体和较高的分类准确率,而且算法运行速度快.这表明PNAIRS算法是一个性能良好的分类算法,具有潜在的应用价值.     

一种基于效用函数的网格资源优化策略

基于市场机制提出了一种以资源代理为基础、面向服务的网格资源管理模型——SBAGRM,在该模型的框架下提出了一种基于效用函数的网格资源分配方法,该方法以满足用户的QoS需求为出发点,旨在追求系统资源的全局最优化。SBAGRM模型可以避免非线性优化带来过高的计算复杂度,以市场模式根据效用函数配置资源,因此计算复杂度将大大降低,模拟结果显示性能明显提高。     

统计推荐模型中的异构数据源资源配置

信息系统中的统计推荐模型需要获取、分析和汇总多个来源的数据。这些多源异构的数据集在特征和价值方面可能存在显著差异,因而影响模型性能。为了提升统计推荐模型的整体性能,这项研究工作采用凸优化理论和方法,解决了统计推荐模型中异构数据源的最优资源配置问题。在不同的数据源资源配置下,该工作对比了同一推荐模型的性能变化。实验结果表明该工作提出的资源分配算法在NDCG（normalized discounted cumulative gain）和召回率这两个推荐系统主要评价指标上有效地提升了模型性能。这项工作的结论是：针对多个异构的数据源,适当的资源划分和分配策略可以显著影响推荐模型的整体性能。     

基于子载波选择配对及功率优化分配的多跳中继系统研究

为了提升OFDM协作通信系统的网络覆盖率及网络容量,提出了一种基于子载波选择配对及功率优化分配的多跳中继OFDM系统优化算法。首先,通过在OFDM的系统模型上进行中继配对和非中继配对的性能分析,将子载波选择配对转换为一个整数规划问题,并采用基于匈牙利算法的规划方法进行配对矩阵的计算。接着,根据OFDM系统的功率分配问题,通过KKT条件对中继功率及电源功率进行优化,从而实现中继系统的功率优化分配 。最终的仿真结果表明,与统计质量QoS保证的资源分配方案以及OFDM中继系统异构服务的资源分配算法相比,所提出的方法在提高网络覆盖率和容量上均表现出更好的效果。     

Improved classification with allocation method and multiple classifiers

Classification is the most used supervized machine learning method. As each of the many existing classification algorithms can perform poorly on some data, different attempts have arisen to improve the original algorithms by combining them. Some of the best know results are produced by ensemble methods, like bagging or boosting. We developed a new ensemble method called allocation. Allocation method uses the allocator, an algorithm that separates the data instances based on anomaly detection and allocates them to one of the micro classifiers, built with the existing classification algorithms on a subset of training data. The outputs of micro classifiers are then fused together into one final classification. Our goal was to improve the results of original classifiers with this new allocation method and to compare the classification results with existing ensemble methods. The allocation method was tested on 30 benchmark datasets and was used with six well known basic classification algorithms (J48, NaiveBayes, IBk, SMO, OneR and NBTree). The obtained results were compared to those of the basic classifiers as well as other ensemble methods (bagging, MultiBoost and AdaBoost). Results show that our allocation method is superior to basic classifiers and also to tested ensembles in classification accuracy and f-score. The conducted statistical analysis, when all of the used classification algorithms are considered, confirmed that our allocation method performs significantly better both in classification accuracy and f-score. Although the differences are not significant for each of the used basic classifier alone, the allocation method achieved the biggest improvements on all six basic classification algorithms. In this manner, allocation method proved to be a competitive ensemble method for classification that can be used with various classification algorithms and can possibly outperform other ensembles on different types of data.     

Optimal water allocation through a multi-objective compromise between environmental,social, and economic preferences

Raising water demands and insufficient freshwater resources are the main reasons of water conflicts in transboundary watersheds. Sustainable water allocation can be a resolution for water disputes as it addresses simultaneously economic, social and environmental benefits. In this paper, a multi-objective model is introduced, which leads to sustainable water allocation of transboundary watersheds taking into account all these three aspects. Five water allocation objectives are proposed for this model in which three of them address the social factors and others represent the economic and environmental preferences. The Compromise Programming technique is employed to solve the applied model to the Sefidrud Basin, Iran and several water allocation schemes are provided based on various weights combinations. The results of the model elucidate that the proposed model can allocate 83 percent of the Basin's water resources, to its stakeholders in a sustainable way while the environmental demand is satisfied.     

Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation

This paper presents a novel method for diagnosis of hepatitis disease. The proposed method is based on a hybrid method that uses feature selection (FS) and artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism. AIRS has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification. By hybridizing FS and AIRS with fuzzy resource allocation mechanism, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used hepatitis disease dataset which is taken from UCI machine learning repository. We obtained a classification accuracy of 92.59%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. The obtained classification accuracy of our system was 92.59% and it was very promising with regard to the other classification applications in literature for this problem. Also, sensitivity, and specificity values for hepatitis disease dataset were obtained as 100 and 85%.     

云计算环境下的资源监测模型研究

云计算环境下的资源监测是云计算平台资源管理的重要组成部分,为资源分配、任务调度和负载均衡等提供依据。由于云计算环境下资源的透明虚拟化和弹性化,并需要对用户使用资源进行计费,因此原有的资源监测方法不能完全满足云计算环境的要求。为此,根据云计算平台的特点,提出一种适应云计算环境下的资源监测模型,该模型通过虚拟机监测器和Java调用C/C++得到资源的状态信息。通过理论分析和实验表明,该模型可以较好地收集节点的资源监测信息,满足云计算平台特性的要求。     

QoS机制在IPSec安全设备上的分析与实现

提出了在IPSec 安全设备上实施QoS的体系方案。设计了适合IPSec安全设备特点的数据分类规则。为IPSec安全设备构建了一个系统排队理论模型,该模型以延迟作为系统运行目标,以带宽作为分配资源。据此设计了一个自适应带宽分配循环队列调度算法。实际运行的测试结果表明,在重载情况下,实施了QoS的IPSec 安全设备可以为重要业务流提供带宽和时延保障。     

WSN中层次型拓扑控制与网络资源配置联合设计方法

综合考虑异构无线传感器网络中节点速率分配、簇的划分规则和链路层网络频带资源占用情况, 提出一种基于拓扑控制与资源优化分配的层次型路由算法. 在网络层, 该算法根据成员节点和簇首节点的速率分配机制建立节点流量平衡模型. 在链路层, 分析无线传感器网络频谱共享行为, 研究邻近用户间访问冲突的规避抑制模型, 重构网络频带资源. 通过引入带宽比例因子将可用频带划分成若干子带, 提高网络频带资源的利用效率. 本文基于跨层联合设计思路, 建立一个混合整数非线性规划问题,对异构无线传感器网络中拓扑控制和网络资源分配问题联合设计, 得到最优的分簇结果和资源分配方案. 最后, 在设定网络拓扑中评估性能, 仿真结果证实该算法在网络频带资源充分利用的同时, 可实现最优的簇首匹配和路由建立结果.     

资源分配算法的比较和研究

许多领域都涉及资源分配问题,怎样合理的把各种有限的经济资源分配给企业内各生产部门,使得本企业在相对较低的成本投入下得到较大利润是每一个企业所追求的目标。于是就产生了如何分配以使工程目标或生产目的达到最优的问题。本文针对这一类资源分配问题,阐述了用动态规划方法和多段图方法的求解思想,并通过实例比较了两种算法。     

A rough set based approach to patent development with the consideration of resource allocation

Patent strategy is the overriding mechanism that helps direct investment, resource allocation, expectations, and policy development within an organization. Much studies of patent, for example, patent classification, patent analysis, patent management, patent strategy planning have been made. Due to the high cost of devoting to the research and development for a new patent application, it is essential for a company to develop the patent portfolio based on analyzing related information for fitting with cost constraint and maximizing the benefit. However, only few research attempts to develop new patents with the consideration of resource allocation, for example, optimizing budget utilization. In addition, the previous studies did not derive significant technologies and induct rules for resource allocation through patent analysis. In some cases, the patent analysis may process qualitative information that is difficult to analyze by standard statistical techniques. The rough set approach, which is suitable for processing qualitative information, is required to induct decision rules to derive critical technologies of patents. In this paper, a systematic approach to analyze existing patent information based on rough set theory with the consideration of resource allocation is developed. A case study is presented to demonstrate the contribution of the proposed approach which assists on decision-making in patent reform or invention with constraint resource.     

Resource allocation robustness in multi-core embedded systems with inaccurate information

Multi-core technologies are widely used in embedded systems and the resource allocation is vita to guarantee Quality of Service (QoS) requirements for applications on multi-core platforms. For heterogeneous multi-core systems, the statistical characteristics of execution times on different cores play a critical role in the resource allocation, and the differences between the actual execution time and the estimated execution time may significantly affect the performance of resource allocation and cause system to be less robust. In this paper, we present an evaluation method to study the impacts of inaccurate execution time information to the performance of resource allocation. We propose a systematic way to measure the robustness degradation of the system and evaluate how inaccurate probability parameters may affect the performance of resource allocations. Furthermore, we compare the performance of three widely used greedy heuristics when using the inaccurate information with simulations.     

A survey on resource allocation in high performance distributed computing systems

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.     

基于双层规划的高超声速飞行器预警资源分配方法

针对高超声速飞行器预警系统中资源难以合理利用的问题,提出一种基于双层规划的预警资源分配方法.首先,建立高超声速飞行器运动状态的马尔可夫模型,提出威胁评估的方法;其次,基于隐马尔可夫模型和卡尔曼滤波,提出双层规划的高超声速飞行器预警资源分配模型,下层规划以单位资源损耗下信息增益为目标函数,上层规划以风险的降低为目标函数;...     

A robust approach for multi-agent natural resource allocation based on stochastic optimization algorithms

Natural resource allocation is a complex problem that entails difficulties related to the nature of real world problems and to the constraints related to the socio-economical aspects of the problem. In more detail, as the resource becomes scarce relations of trust or communication channels that may exist between the users of a resource become unreliable and should be ignored. In this sense, it is argued that in multi-agent natural resource allocation settings agents are not considered to observe or communicate with each other. The aim of this paper is to study multi-agent learning within this constrained framework. Two novel learning methods are introduced that operate in conjunction with any decentralized multi-agent learning algorithm to provide efficient resource allocations. The proposed methods were applied on a multi-agent simulation model that replicates a natural resource allocation procedure, and extensive experiments were conducted using popular decentralized multi-agent learning algorithms. Experimental results employed statistical figures of merit for assessing the performance of the algorithms with respect to the preservation of the resource and to the utilities of the users. It was revealed that the proposed learning methods improved the performance of all policies under study and provided allocation schemes that both preserved the resource and ensured the survival of the agents, simultaneously. It is thus demonstrated that the proposed learning methods are a substantial improvement, when compared to the direct application of typical learning algorithms to natural resource sharing, and are a viable means of achieving efficient resource allocations.     

Performance analysis of IRS-bands for land use/land cover classification system using Maximum Likelihood Classifier

The allocation of confidence level to a classification product is considered to be essential. The acquisition of site-specific data to check the classification was discussed. A statistical approach to the determination of an appropriate confidence level from the check data was presented. The test scene was classified using Maximum likelihood classification with various band combinations. These combinations were used as statistical variables. The results are analysed and the best possible combinations selected for accurate classification system. Keeping the percentage overall accuracy in view, these tests demonstrated the complexity of relations between the land cover classes and the data processing variables. Further, they indicated the variables best suited to the classification of certain classes, based on the performance of variable by class. Here, Hyderabad City is taken as a test site.     

基于分类挖掘的网格资源分配研究

根据用户访问网格资源的历史信息,采用分类算法对此信息进行挖掘,得出用户使用集群资源的访问规则和模式,在此基础上构造一种基于分类挖掘的资源调度模型、用户调度UA算法以及资源调度CDMRA算法,分别将用户请求调度到各个集群中闲置的CPU资源.实验证明,采用基于分类挖掘的资源分配策略相比其他算法可以减少资源分配过程中对资源的重新分配次数,可以提高网格资源的利用率.     

容器云中基于改进遗传算法的资源分配策略

容器很容易针对Web应用程序提供包装、迁移和配置等服务,近年来已成为研究热点;提出了容器云中基于改进遗传算法的资源分配策略Double-GA;Double-GA是一种包括两个层次的资源分配策略:容器到虚拟机的资源分配和虚拟机到物理主机的资源分配;设计了容器云的两层资源分配的数学模型,以容器云中的整体物理主机能量消耗作为Double-GA策略的目标函数;Double-GA以遗传算法为基础,设计了双染色体的表达方式并处理好了遗传算法的初始化、进化、交叉、变异等操作;真实的实验实例数据结果表明:Double-GA双染色体算法明显优于普通遗传算法GA和递减最好适用算法。     

Taguchi-factorial type-2 fuzzy random optimization model for planning conjunctive water management with compound uncertainties

In this study, a type-2 fuzzy random optimization (TFRO) method is developed for planning conjunctive water management system associated with compound uncertainties. TFRO can effectively address compound uncertainties expressed as type-2 fuzzy sets, probability distributions, and type-2 fuzzy random variables. Solution algorithm based on the degree of probability and the information of plausibility is proposed to transform nonlinear objective function and constraints into their linear equivalents. A real case of water-resources allocation problem in Zhangweinan River Basin (China) is employed to demonstrate the applicability of the proposed method. A Taguchi-factorial type-2 fuzzy random model is also formulated through introducing Taguchi design and ANOVA technique into the TFRO framework. Results obtained can help reveal the relationship among multiple impact factors of economic, environmental and resource (water conveyance efficiency, water delivery cost, and system violation risk), as well as quantify their contributions to the variability of system benefit and water allocation schemes.