一种基于免疫克隆的偏好多维QoS云资源调度优化算法

针对云计算环境中高效资源调度问题,首先从理论上对云资源调度进行了建模,对用户应用偏好和多维QoS中的用户效用进行了量化,给出了多维QoS优化的目标函数.结合具有快速多目标优化能力的免疫克隆算法,提出了一种云资源调度优化算法.根据应用偏好信息为抗体分配偏好优先级,据此进行抗体的免疫克隆操作,提高抗体免疫基因操作的预见性,...     

资源约束多模式项目调度的遗传算法研究

针对以工期最短为优化目标的多模式资源约束优化调度问题进行研究,在建立数学模型基础上,采用改进遗传算法对模型进行求解.算法设计上,对各活动的执行模式进行统一编号,使模式的内部编号包含了时序约束、资源约束、工期等信息,针对活动执行模式采用特殊的整数编码方式,染色体基因由活动执行模式序号组成.对染色体结构设计了单点交叉和单点变异操作算子,采用锦标赛选择方法.通过实例验证了改进遗传算法在求解MMRCPSP上的有效性.     

并行多机调度问题的一种基于组合规则的遗传算法

本文对最小化完工时间并行多机调度问题提出了一种基于组合规则的遗传算法.用遗传算法来优化调度策略,使得在不同的调度阶段,可采用不同的调度规则以提高算法性能,并用计算实例表明了该遗传算法优于基于机器编码的模拟退火算法和遗传算法,并能适用于大规模并行多机调度问题,算法计算量小,鲁棒性强.     

考虑运输代价的制造工作流调度算法

在云制造环境下,因制造服务资源所在地域的差异性,多目标制造工作流调度不仅考虑制造服务所需时间、费用,还需考虑产品运输所需时间、费用,原有工作流调度算法无法有效优化运输代价.针对此问题,结合遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,提出多目标混合遗传粒子群(MOGA - PSO)算法.仿真结果表明混合算法能够有效降低运输代价,使得工作流调度得到进一步优化,可适用于云制造环境.     

微波与激光混合链路中继卫星动态调度快速启发式算法

针对微波与激光混合链路中继卫星系统实际应用中的动态变化,分析了混合系统动态扰动条件,依据中继卫星资源调度动态调整原则,研究了混合系统动态资源调度策略。以最大化调度任务优先级之和,以及扰动发生后对初始方案扰动变化最小为目标,建立了具有两级优化目标的混合系统动态调度模型。提出了动态插入快速启发式算法并对模型求解算法,算法包括直接插入任务操作、移动插入任务操作和删除插入任务操作。仿真结果表明与完全重调度的算法相比,动态插入算法的方案变化率平均降低了60.10%,且计算速度明显高于重新调度算法,能够有效解决混合链路中继卫星系统动态调度问题。     

自适应遗传算法的数据中继卫星光网络资源调度算法

以数据中继卫星光网络系统资源、任务和约束条件为参量,以任务对资源的选择为优化对象,提出了一种基于自适应遗传算法的数据中继卫星光网络资源调度算法.综合考虑多中继星、多时间窗口、多光学天线以及任务优先级要求,建立调度模型;采用当前任务调度时间的确定和后续任务可见时间窗口的更新的调度操作,对不同资源的任务集合进行调度安排并实现了可见时间窗口的动态更新,获得调度任务的总权值并将其作为参量计算适应度值,最后通过改进的自适应遗传算法对不同调度方案进行寻优.以3颗中继星、12颗用户星,6个光天线,60个任务为条件设置了仿真场景,仿真结果表明该算法在收敛速度、调度效率方面具有优势,适应于多任务、多天线的数据中继卫星光网络系统资源调度.     

考虑多工序设备权重的资源协同综合调度算法

针对多品种、小批量复杂产品综合调度研究中,没有考虑加工较多工序设备上的工序间调度空隙会对调度结果产生重要影响的问题,该文提出考虑多工序设备权重的资源协同综合调度算法。该算法在综合调度中首先提出多工序设备和工序权重值的定义,其次提出以权重值为主的调度策略,提高了工序纵向连续加工的紧密度;最后提出最佳调度时刻的调整策略,提高了工序横向并行优化的力度。实验结果表明,该算法在提高综合调度设备整体利用率和减少复杂产品时间成本等方面,具有更优性。     

一种基于多Agent协同的准并行遗传算法

提出了一种基于多Agent协同操作的准并行遗传算法结构.该算法由若干运行简单遗传算法的计算单元组成,每个单元也就是独立的计算Agent.算法依照资源分配向量为各单元分配不同的计算资源,并根据个体迁移矩阵驱动它们进行个体交换.从多Agent系统的观点看,资源的分配体现了算法对各Agent的协调,个体的迁移则体现了Agent之间的协作.该算法很容易在串行计算机上实现,此时各个计算单元具有微观上串行、宏观上并行的准并行关系.对二维准并行算法动态性能的分析表明:由于统筹考虑了各计算单元间的协同关系,算法能够更充分有效地利用有限的计算资源,在解决不同的优化问题时表现出了很高的性能.     

基于混合云的教学平台资源调度机制研究

文章通过研究当前典型的混合云调度模型架构,提出一个基于集群式管理的多云资源调度模型:云内资源调度算法基于优化初始种群选择的遗传算法,提高算法收敛速度的同时,用设定海明距离的方法防止早熟局部收敛;云间的资源调度算法在最少完成时间适应度的基础上加入了基于最少支出费用的适应度,并基于小生境Pareto非劣解排序的方法得到个体的双目标共享适应度。     

面向异构计算的能效感知调度研究

异构调度可使大规模计算系统采用并行方式聚合广域分布的各种资源以提高性能.传统调度目标追时限约束求高性能而忽视高效能,远不能适应绿色计算科学发展要求.因此,本文在理论上一方面建立融合能效感知的调度模型;另一方面提出适于超计算机混合体系的多学科背景的元启发式优化算法.从技术上解决了面向不同环境目标的调度实施条件界定及调度指标(时间、能耗)实时变化描述等问题.大量仿真实验结果表明:与三个元启发式调度器相比,论文方法在能效及可扩展等方面优势明显;对于高维实例,整体性能改善分别达到8%,15%和17%.     

A novel target tracking method based on OSELM

Target tracking is an important branch of computer vision, which includes three stages: image sequence optimizing, target expressing and target detecting. The target detecting stage is an important factor that influences the tracking performance. Therefore, how to obtain a more accurate and robust target detecting method becomes an urgent problem. Online sequential extreme learning machine (OSELM) is a kind of online learning method based on extreme learning machine. OSELM completes incremental learning by combining with the existed model when dynamic training samples are arriving. That OSELM has advantages including fast-speed and incremental learning suggests that is suitable for target detecting. Nevertheless, the target detecting process is different from the traditional classification for two causes: (1) target detecting is the dynamic process in that the position and rotation of the target are changing with time, and therefore the original OSELM method fails to obtain the most optimal target object from classified samples, (2) the tracking result frame depends on the previous frame, thus if the noisy sample is used as the target object, it would generates an impact to the tracking performance. To alleviate above-mentioned problems, this paper proposes an interesting and efficient target tracking method based on OSELM. In this method, we obtain the appropriate target object by judging the position relationship between each classified sample and the classification boundary. Moreover, we develop a kind of method that is similar to clustering to avoid tracking drift from noisy samples. The new target tracking method improves the performance remarkably, and eliminates the tracking drift from noisy samples. The proposed method is validated on six kinds of challenging image sequences.     

Data-driven homologue matching for chromosome identification

Karyotyping involves the visualization and classification of chromosomes into standard classes. In “normal” human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying “normal” chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors' homologue matcher for identifying class 17 within normal and abnormal metaphase spreads     

On-line estimation of myoelectric signal spectral parameters andnonstationarities detection

A method for detecting nonstationarities of random time series with an approximately Gaussian distribution of amplitudes is presented. This method is suitable for real time implementation. Some results obtained by applying them to a time series of spectral parameters of surface myoelectric signals are reported. The computerized system used to implement the detector of nonstationarity is described. This system realizes on-line estimation and display of spectral parameters, as well as detection of their nonstationarities, featuring a sampling frequency up to 20 k samples/s. A user friendly interface, fully menu driven, allows the user to select different options during the system's operation by means of hot keys. The accuracy of the system was tested by comparing its estimates with those of an off-line system, previously characterized. The estimates of spectral parameters obtained by means of the two systems were always consistent. The on-line stationarity detector was able to recognize rates of variation of the spectral parameters as small as 1% during contractions lasting 10-15 s. This sensitivity makes it suitable for clinical application     

Detection of a manoeuvring air target in strong sea clutter via joint time?frequency representation

Two new approaches for detecting a manoeuvring air target in strong sea clutter are proposed. Whereas one is based on the adaptive chirplet decomposition, the other is inspired by spectral subtraction. Unlike the other time-frequency transform-based methods, the approaches presented can both be used to compute a signal's time waveform with suppressed sea clutter. Experiments with real-world data samples show that both methods can effectively enhance the Doppler radar signals associated with a manoeuvring air target by more than 15-dB.     

相关杂波背景下的认知雷达序贯信号检测

针对相关杂波大大影响了传统雷达的检测性能问题, 本文提出了一种认知雷达的序贯信号检测方法,该方法在二元假设检验的基础上将雷达整个工作时间分成两个阶段:估计和检测阶段。在估计阶段,系统发射最优估计波形,同时通过雷达回波对目标冲击响应进行估计更新;在检测阶段,系统利用已经获得的先验知识设计最优检测波形且在接收端实现检测门限自适应。结果分析和实验仿真表明:由于联合了发射端的波形优化和接收端的序贯判决信息利用,因此大大提高了系统的检测性能;此外,随着时间的增加,目标冲击响应的估计值越来越逼近真实值,使得估计检测概率也越来越趋近于真实检测概率,从而在提高性能的同时也保证了决策的可靠性。      

一种新的消失点检测算法

该文在随机抽样一致性算法基础上,提出了一个基于预检验的随机抽样一致性Preview model Parameters Evaluation RANSAC(PERANSAC)消失点估计算法:该算法在原始RANSAC算法消失点检验前,加入一个预检验步骤,在保证计算结果精度不变的前提下,过滤掉大量偏差较大的消失点,减少了检验的计算量,大大提高了算法的整体效率。大量的实验结果表明,该算法的计算精度与RANSAC算法精度保持一致,计算速度远高于RANSAC算法。     

B样条曲面拟合在Mura缺陷获取中的应用

TFT-LCDMura缺陷表现为对比度低、亮度区域不均匀、边界模糊,通常大于一个像素,会给观察者带来视觉不适,是一种比较常见的缺陷。长期以来,对Mura缺陷的检测都是由经过训练的专业检验员完成。近年来,研究人员开始研究利用机器来代替人眼检测,但机器如何获取Mura缺陷一直是行业内公认的难题之一。本文提出了基于B样条曲面拟和的方法来获取Mura缺陷信息,并通过对大量真实Mura缺陷样本的检测验证了该方法具有高的获取准确率。     

制导激光编码信息识别技术研究

激光脉冲编码是半主动激光制导武器采用的一种抗干扰措施,为满足对抗半主动激光制导武器的需要,以激光信号的脉冲到达时间为参数,在分析现有各种码型及其识别方式的基础上,对激光脉冲编码信息逐次逼近,即在得到极少量的激光脉冲样本后,同步拟合解算不同码型参数,并结合模糊决策思想,预测下一脉冲的到达时刻,下达发射干扰脉冲的指令,同时侦测敌方目标指示器的后续脉冲到达时刻,在样本不断增长的过程中,对预测码型不断调整,逐步达到精确复制敌方编码信号的目的。试验结果表明,对常见的编码模式,该技术能通过较少的脉冲信息有效的识别脉冲编码规律。     

DS/LPI autocorrelation detection in noise plus random-toneinterference

An analysis is presented of a frequency-noncoherent, two-lag autocorrelation statistic for the wideband detection of random binary phase-shift keying (BPSK) signals in noise plus random multitone interference. It is shown that this detector is quite robust to the presence or absence of interference and its specific parameter values contrary to an energy detector. The rule assumes knowledge of the data rate and the active scenario under H₀. The purpose of the paper is to promote the real-time autocorrelation domain and its samples (lags) as a viable approach for detecting random signals in dense environments     

On detection of sparsely sampled data

The problem of detecting pulses of unknown amplitude and arrival time from sparse, noisy samples is considered. Several approaches are found to result in the square-law video amplifier, the performance of which is compared graphically with both single-sample detection and clairvoyant matched filtering.