大数据环境下MES作业计划与调度能力云服务化研究

云制造技术给制造企业带来机遇的同时,也为其制造执行系统MES的设计与实现带来了新的挑战。为了解决单件小批MES中作业计划与调度优化问题,首先设计了一个从作业计划静态制定,到作业执行情况实时监控与主动感知,再到异常事件智能响应,最后到作业调度动态调节的闭环体系结构。接着针对异常信息实时获取与异常事件发现、异常事件智能化处理以及作业计划与调度优化算法计算能力服务化三个子问题,依次进行了问题分析并给出了技术解决方案。最后,以哈尔滨电机厂为案例对象,综合利用IEC/ISO 62264标准、大数据分析与挖掘方法以及由虚拟化、服务化和SOA等组成的云计算技术实现了单件小批MES作业计划与调度综合优化系统,验证了上述理论与方法的有效性。     

一种基于EEMD的新型谐波误差分析策略

针对电力系统中严重的谐波干扰问题,为提高其运行的安全有效性,去除谐波干扰并提高系统稳定性能成为研究热点。电网中常存在频率强烈波动或者白噪声强烈干扰的现象,虽说三谱线插值的FFT算法(基于Kaiser窗)的抗干扰能力很强,谐波参数估计也可以有较高的精准度,但是远不及使其与平均经验模态的总体水平来分解谐波的检测算法(EEMD)与直线度误差分析相结合。应用直线度误差来分析信号的谐波成分,有机结合了三谱线插值的FFT(基于Kaiser窗的)谐波检测分析法。通过对整体谐波分量的在线监测,不仅能够提高稳态信号的检测精度,还能提高系统的动态性能。本分析策略大限度地提高了计算精度以及系统稳定性。通过仿真分析得知,所述方法可以使系统具有较强的抑制频谱泄露能力,并能够显著降低栅栏效应的发生概率,进而使电力谐波信号的幅值误差、初相位误差和频率误差分别低于0.004 1%、0.002 4%和0.003 9%。     

分布式系统概念的教学案例设计与实践

分布式系统概念没有公认的定义,不同定义之间甚至存在较大的差异,给学生理解、掌握分布式系统的内涵带来困难。分布性、协作性是分布式系统的根本特征。设计了多个案例,通过课堂研讨促进学生对分布式系统本质的理解,在分布式系统教学实践中取得良好效果。     

以培养计算思维为导向的大学计算机基础课程教学改革与实践

为培养大学生的计算机能力,计算机基础课程教育有必要改"知识传授为导向"为"计算思维能力培养为导向",重新构建凸显"面向应用、突出实践、着眼能力"特点的大学计算机基础课程体系和以信息素养和应用能力培养为主线的"模块化、多层次"的实验教学体系,对"大学计算机基础"课程和课程体系中第二层次的重点核心课程进行全面改革,在课堂教学中运用与专业相融合的多元化教学模式,并在建设多元化教学资源平台基础上将能力培养渗透整个专业学科课程教育中,全面提升大学计算机基础课程教育质量,对大学计算机基础课程的教学改革进行了初步的研究与实践。     

基于人工蜂群的连续域蚁群优化算法

连续域蚁群优化算法是蚁群优化算法的一个重要研究方向,针对连续域蚁群优化算法(ACOR)计算时间较长、易陷入局部最优的问题,提出了一种基于人工蜂群的连续域蚁群优化算法(ABCACOR)。首先,引入一种替代机制来选择指导解,以替换原来的基于排序的选择方式,目的是节约计算时间和尽可能地保持搜索的多样性;其次,结合人工蜂群算法的搜索策略来提高算法的全局搜索能力,进一步减少计算时间和提高求解精度。通过对大量的测试函数进行仿真实验,结果表明,ABC-ACOR算法较现有的一些连续域蚁群算法具有更好的寻优能力。     

基于加权支持向量机的凸轮升程误差拟合方法

针对凸轮磨削加工过程中存在局部升程误差严重超差的问题,提出了加权支持向量机的凸轮升程误差补偿方法。首先根据圆率的符号判断凸轮升程误差值是否连续,然后建立了不连续点与相邻点之间夹角与加权值的关系。通过加权支持向量机对凸轮升程误差值进行拟合。然后将原始凸轮升程值减去经拟合后的凸轮升程误差值,获得新的凸轮升程值。再根据圆率自动调整加权值的系数,使凸轮升程值光滑点数最大,即获得最优的凸轮升程值。对上述算法进行仿真和编程,经YTM8336-16数控凸轮轴磨床进行磨削验证。实验表明,凸轮升程误差由原来的±0.059 mm降至±0.011 mm,表面粗糙度由Ra0.32μm降至Ra0.25μm,获得良好的效果。     

Managing a Very Connected World: A Report on APNOMS2015

This article presents a report on APNOMS2015, which was held August 19–21, 2015 in Busan, Korea. The theme of APNOMS2015 was “Managing a Very Connected World.”     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

Entropy measures and granularity measures for interval and set-valued information systems

In many practical situations, some of the attribute values for an object may be interval and set-valued. The interval and set-valued information systems have been introduced. According to the semantic relation of attribute values, interval and set-valued information systems can be classified into two categories, disjunctive (type 1) and conjunctive (type 2) systems. This paper mainly focuses on semantic interpretation of type 1. Then, a new fuzzy preference relation for interval and set-valued information systems is defined. Moreover, based on the new fuzzy preference relation, the concepts of fuzzy information entropy, fuzzy rough entropy, fuzzy knowledge granulation and fuzzy granularity measure are studied and relationships between entropy measures and granularity measures are investigated. Finally, an illustrative example to substantiate the theoretical arguments is given. These results may supply a further understanding of the essence of uncertainty in interval and set-valued information systems.     

Chaotic feature analysis and forecasting of Liujiang River runoff

Because most of runoff time series with limited amount of data reveal inherently nonlinear and stochastic characteristics and tend to show chaotic behavior, strategies based on chaotic analysis are popular methods to analyze them from real systems in nonlinear dynamics. Only one kind of predicted method for yearly rainfall-runoff forecasting cannot achieve perfect performance. Thus, a mixture strategy denoted by WT-PSR-GA-NN, which is composed of wavelet transform (WT), phase space reconstruction (PSR), neural network (NN) and genetic algorithm (GA), is presented in this paper. In the WT-PSR-GA-NN framework, the process to deal with time series gathered from Liujiang River runoff data is given as follows: (1) the runoff time series was first decomposed into low-frequency and high-frequency sub-series by wavelet transformation; (2) the two sub-series were separately and independently reconstructed into phase spaces; (3) the transformed time series in the reconstructed phase spaces were modeled by neural network, which is trained by genetic algorithm to avoid trapping into local minima; (4) the predicted results in low-frequency parts were combined with the ones of high-frequency parts, and reconstructed with wavelet inverse transformation, to form the future behavior of the runoff. Experiments show that WT-PSR-GA-NN is effective and its forecasting results are high in accuracy not only for the short-term yearly hydrological time series but also for the long-term one. The comparison results revealed that the overall forecasting performance of WT-PSR-GA-NN proposed by us is superior to other popularity methods for all the test cases. We can conclude that WT-PSR-GA-NN can not only increase the forecasted accuracy, but also its own competitiveness in efficiency, effectiveness and robustness.