网格市场中服务质量驱动下的任务调度算法

针对资源提供方不能完成用户所有任务的情况,提出一种服务质量驱动下的任务调度算法,即预算和截止时间限制下的最大任务完成数调度算法(DBCN).这种批调度算法结合了Min-min算法吞吐量较高和线性规划全局优化的优点,不仅考虑了用户的所有任务,同时还考虑了优先级较高的任务.实验结果表明,该算法在任务完成总数方面比经典算法Min-min和DBCT分别提高了约10.6%和22.0%,在优先级高的任务完成总数方面也有大幅度提高,分别约为20%和40%.     

基于用户使用频率的Linux系统智能化任务调度机制

如今,智能手机的普及率越来越高。在数字化的生活里,人们不仅仅满足于手机的智能化操作,更追求系统运行的速度与效率,追求手机的节能与省电。而在大部分使用智能手机的人群中,以安卓系统为代表的群体占据了相当一部分的比例。本文基于安卓手机的操作系统,对Linux系统的任务调度机制增加了一种全新的功能,实现了系统对特定用户使用进程频率的识别、统计以及高效率的调度。     

A novel design method for optimal IIR system identification using opposition based harmony search algorithm

In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.     

基于资源组合预测的云计算任务调度算法

云计算资源负荷状态的动态变化,使得如何预测其变化情况,从而合理地实施任务调度,成为亟待解决的问题。针对该问题,提出一种基于资源组合预测的云计算任务调度算法。该模型采用偏最小二乘回归从各种单一模型的预测结果中提取有效成分,建立组合预测模型;再依据对资源结点的资源预测结果和最小完成时间原则实施任务调度。仿真测试结果表明,该算法具有较好的性能与负载均衡性。     

Model-free algorithm for consensus of discrete-time multi-agent systems using reinforcement learning method

In this work, we investigate consensus issues of discrete-time (DT) multi-agent systems (MASs) with completely unknown dynamic by using reinforcement learning (RL) technique. Different from policy iteration (PI) based algorithms that require admissible initial control policies, this work proposes a value iteration (VI) based model-free algorithm for consensus of DTMASs with optimal performance and no requirement of admissible initial control policy. Firstly, in order to utilize RL method, the consensus problem is modeled as an optimal control problem of tracking error system for each agent. Then, we introduce a VI algorithm for consensus of DTMASs and give a novel convergence analysis for this algorithm, which does not require admissible initial control input. To implement the proposed VI algorithm to achieve consensus of DTMASs without information of dynamics, we construct actor-critic networks to online estimate the value functions and optimal control inputs in real time. At last, we give some simulation results to show the validity of the proposed algorithm.     

异构多核平台上基于任务划分和调度的性能评估方法

提出一种异构多核平台上的性能评估方法.该方法与多核任务的动态划分和调度算法相结合,可以获得给定任务集在不同硬件平台上的任务加速比.与此同时,针对不同层次的实时性需求,该算法可以给出硬件平台配置及任务划分方案.仿真实验和FPGA原型系统的测试数据表明,在任务之间存在依赖关系的情况下,该方法能够获得90%的精确度,在无依赖关系时,算法的精度为98.9%.     

A collaborative filtering recommender system using genetic algorithm

This paper presents a novel genetic-based recommender system (BLI_GA) that depends on the semantic information and historical rating data. The main contribution of this research lies in evaluating the possible recommendation lists instead of evaluating items then forming the recommendation list. BLI_GA utilizes the genetic algorithm to find the best list of items to the active user. Thus, each individual represents a candidate recommendation list. BLI_GA hierarchically evaluates the individuals using three fitness functions. The first function uses semantic information about items to estimates the strength of the semantic similarity between items. The second function estimates the similarity in satisfaction level between users. The third function depends on the predicted ratings to select the best recommendation list.BLI_GA results have been compared against recommendation results from alternative collaborative filtering methods. The results demonstrate the superiority of BLI_GA and its capability to achieve more accurate predictions than the alternative methods regardless of the number of K-neighbors.     

PF-BTS: A Privacy-Aware Fog-enhanced Blockchain-assisted task scheduling

In recent years, the deployment of Cloud Computing (CC) has become more popular both in research and industry applications, arising form various fields including e-health, manufacturing, logistics and social networking. This is due to the easiness of service deployment and data management, and the unlimited provision of virtual resources (VR). In simple scenarios, users/applications send computational or storage tasks to be executed in the cloud, by manually assigning those tasks to the available computational resources. In complex scenarios, such as a smart city applications, where there is a large number of tasks, VRs, or both, task scheduling is exposed as an NP-Hard problem. Consequently, it is preferred and more efficient in terms of time and effort, to use a task scheduling automation technique. As there are many automated scheduling solutions proposed, new possibilities arise with the advent of Fog Computing (FC) and Blockchain (BC) technologies. Accordingly, such automation techniques may help the quick, secure and efficient assignment of tasks to the available VRs. In this paper, we propose an Ant Colony Optimization (ACO) algorithm in a Fog-enabled Blockchain-assisted scheduling model, namely PF-BTS. The protocol and algorithms of PF-BTS exploit BC miners for generating efficient assignment of tasks to be performed in the cloud’s VRs using ACO, and award miner nodes for their contribution in generating the best schedule. In our proposal, PF-BTS further allows the fog to process, manage, and perform the tasks to enhance latency measures. While this processing and managing is taking place, the fog is enforced to respect the privacy of system components, and assure that data, location, identity, and usage information are not exposed. We evaluate and compare PF-BTS performance, with a recently proposed Blockchain-based task scheduling protocol, in a simulated environment. Our evaluation and experiments show high privacy awareness of PF-BTS, along with noticeable enhancement in execution time and network load.     

A novel intelligent classification model for breast cancer diagnosis

Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.     

LTE系统中一种利用DRX降低UE功耗的视频流调度方法

提出一种利用DRX(discontinuous reception)降低UE(user equipment)功耗的视频流调度方法,该方法在保证数据包时延要求的同时,减少了InactivityTimer的开启次数,增加了UE的休眠时间.仿真结果表明,相比已有的DRX机制下的实时业务调度方法,该方法可以在丢包率相同的情况下,显著降低UE的功耗.     

A novel hybrid filter-based fault diagnosis algorithm for switched systems with a dual noise term

This study considers state and fault estimation for a switched system with a dual noise term. A zonotopic and Gaussian Kalman filter for state estimation is designed to obtain state estimation interval in the presence of both stochastic and unknown but bounded (UBB) uncertainties. The switching state and fault state of the system are distinguished by detecting whether the system measurement date is within the bounds of its predicted output. Once the switched time is detected in the system, the filter zonotopic and Gaussian Kalman functions are initialized. Once the fault time is detected, a zonotopic and Gaussian Kalman filter-based fault estimator is constructed to estimate the corresponding faults. Finally, a numerical simulation is presented to demonstrate the accuracy and effectiveness of the proposed algorithm.     

关于WIMAX的Qos调度算法分析

由于WiMAX能够支持多种多媒体业务,采用面向连接机制,其端到端的Qos机制显得尤为重要,基于数据业务的业务流管理成为WiMAX的最关键问题之一。为了提高系统频谱效率,满足用户间的公平性,同时很好地支持不同业务的Qos保证,需要研究和实现Qos解决机制的核心：MAC层的资源管理和调度算法。介绍了几个调度算法,并进行了分析。     

一种求解资源受限下项目进度问题的新方法

针对资源受限下的项目进度问题的特点,在已有对该问题的研究基础上,提出一种新方法,应用一种复合粒子群算法对问题求解,以此提高基本PSO算法的收敛速度和精度.以优先值法作为粒子表达RCRLP问题的方式:对并行进度生成机制进行处理以此扩大其搜索空间而增加找到最优解的机率,并用例子说明其有效性.最后结合PSPLIB问题集进行仿真实验,实验结果验证了本文求解RCPSP的新方法具有可行性与优越性.     

Optimization of fuzzy model using genetic algorithm for process control application

A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi-Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. This approach is applied to a process control rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach.     

一种集成可重构硬件的多核片上系统的软硬件任务划分与调度算法

提出了一种静态的软硬件任务划分与调度相结合的算法,可以同时获得给定任务集在该类平台上的软硬件任务划分和任务调度方案. 算法的时间复杂度为O(V(E+V)+V2logV+PVlogV). 实验结果表明了该算法的可行性和有效性.     

A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation

Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms.     

A novel trust-based false data detection method for power systems under false data injection attacks

This paper proposes a novel trust-based false data detection method for power systems under false data injection attacks (FDIAs). In order to eliminate the interference posed by false data to the power system in the state estimation process, a trust model is first established to estimate the reliability of the system bus. Then an algorithm is proposed to update the bus trust value, when all the trust value of neighbor buses at one bus node are quite low, then this bus is diagnosed as a malicious node and the false data are detected. This method guarantees that the power systems can estimate the state accurately against FDIAs based on the trust of bus. The simulations on the benchmark IEEE 14-bus, IEEE 30-bus and IEEE 57-bus test systems are used to demonstrate the feasibility and effectiveness of proposed algorithm.     

A control algorithm for systems with dead time

A least squares control algorithm for single-input single-output (SISO) systems is developed. The algorithm allows for a delay with large dead time and uses proportional- integral-derivative actions in their parallel form to achieve steady-state without error. Optimization of the controller parameters is achieved and the parameters of the controller are determined from the solution of a set of linear simultaneous equations. The control strategy is to optimize the controller parameters such that a desired well-behaved trajectory is obtained. The controller is shown to be robust and the algorithm is shown to function as well without or with large dead time, to have low sensitivity to changes in the dead time, and to allow an adaptive estimation of changing system parameters. The application of the developed algorithm to control the glucoregulatory system, based on a 4th-order digital model, is presented in two cases: free time delay and with large dead time.     

Gradient iterative algorithm for dual-rate nonlinear systems based on a novel particle filter

This paper proposes a novel particle filter based gradient iterative algorithm for the identification of dual-rate nonlinear systems. The novel particle filter is applied to estimate the missing outputs, and the measurable outputs are utilized to adjust the weights of particles during each interval of the slow sampled rate. Then the missing outputs and the unknown parameters can be estimated iteratively by the novel particle filter based gradient iterative algorithm. The simulation results indicate that the proposed method is more effective than the classical auxiliary model method.     

改进蝙蝠算法在Job-shop调度问题上的应用

针对蝙蝠算法求解Job-shop调度问题的局限性,采用字符串编码、NEH初始化种群粒子和增加随机扰动的方法,对现有蝙蝠算法进行改进。通过对Job-shop调度问题基准算例的求解,并和模拟退火算法、标准遗传算法和粒子群算法进行比较,验证了该算法操作简单,收敛速度快,结果精度高,能有效求解Job-shop调度问题。