基于无传感器的PMSM电流控制策略的研究

提出一种新型的滑模观测器,并分别研究在4种不同的电流控制策略下,基于该新型滑模观测器的PMSM伺服系统的控制问题。该新型滑模观测器引进了Sigmoid函数作为控制函数用来抑制抖振,并根据PMSM的反电动势模型构建了反电势观测器来提取所需的连续信号,因此取消了传统的一阶低通滤波器和相角补偿环节。为了对电机的转子位置和转速进行更加精确的估算,引入了转子位置锁相环结构。在Matlab/Simulink平台基础上建立了4种不同的电流控制策略,基于新型滑模观测器PMSM无位置传感器三闭环控制系统仿真模型,分别对反电动势估算、速度估算、位置估算和突加负载扰动进行了仿真分析。仿真结果表明,该新型滑模观测器在4种不同的电流控制策略下,对电机转子位置和转速的估算、电磁转矩以及定子三相电流有着不同的影响,验证了该新型滑模观测器算法的可行性。     

游戏产品有效粘合用户时间的策略研究

通过对游戏的分类、发展历程梳理以及用户的需求分析,结合游戏产生过程给出了游戏产品有效粘合用户时间的策略,最后预测了游戏的发展趋势。     

新媒体环境下的安徽城市形象传播策略研究

新媒体是在传统媒体基础上发展出的新型传播模式。新媒体构建全方位、立体传播体系,善于利用新型传播载体,以网络和移动客户端为代表。相比于传统媒体,新媒体形式多样生动,更加亲民,其突破了原有的灌输式宣传方式,增强了民众的互动性,民众知情权得到了一定程度的保障。但正是这样,新媒体环境下,城市形象的宣传难度大大增加,宣传传播体系途径也愈加复杂,为此必须制定出符合安徽省省情的新媒体传播方式、创新思维,发挥各种传播途径方式的优势建立立体、全面的传播模式,建立正面积极的安徽城市新形象。     

浅探品牌传播中的广告创意策略

广告已经成为品牌竞争的主要形式和经济社会发展的重要引擎,广告效果的产生是一个连续性和积累性的过程,这个过程与消赞者的购买心理和行为保持一致。怎样抓住品牌传播的阶段性特征,在品牌推广的不同时期推出适当的广告创意策略是品牌研究的重要研究课题。     

中外动画专业教学改革现状比较研究

文章分析了美国、日本、加拿大、韩国等动画发达国家的发展情况,总结优缺点结合国内动画专业教学改革的现状,进行比较,得出有利于我国动画教育发展的策略。     

基于本体的特征模型演化的一致性验证

针对特征模型的演化导致特征间的冲突问题, 从特征模型的演化方面进行研究, 提出了一种基于本体的特征模型演化的一致性验证方法。首先采用本体理论方法对特征模型的演化进行分析和建模, 建立本体的特征模型元模型; 其次基于上述元模型, 为网上购物系统实例建立相应的网上购物的领域特征模型, 根据需求裁剪出产品特征模型; 然后在Eclipse集成开发环境下, 通过Jena推理机加载规则和产品特征模型进行一致性验证, 当检测到冲突时, 采用演化策略来消除冲突; 最后通过实例研究说明了该方法的有效性。     

高维数据挖掘中基于中位数回归的特征提取新方法

为降低噪声对数据特征提取(变量选择)效果的不利影响,基于中位数回归分析方法,利用变量选择降维技术(正则化估计),提出了一种稳健、有效的特征提取(变量选择)新方法,并具体给出了估计算法,该算法具有快速计算的特点.实验结果表明,新方法能够有效地对高维数据集进行估计和变量选择,且具有较高的准确性,即使数据中的信噪比很低时,该方法仍具有较好的效果.因此,该方法为高维数据挖掘特征提取提供了稳健且有效的方法.     

网吧“零点断网”自动控制策略——以华为接入交换机S3xxx系列为例

该文在分析网吧“零点断网”手工操作的复杂性和低效性基础上,提出并在华为S3xxx系列接入交换机实现了网吧“零点断网”自动控制策略,并分别以二层ACL和三层ACL作了说明。本法极大提高了“零点断网”的效率和精确性,具有极高的推广价值。     

A reactive extension of the OpenMusic visual programming language

Objectives: OpenMusic (OM) is a domain-specific visual programming language designed for computer-aided music composition. This language based on Common Lisp allows composers to develop functional processes generating or transforming musical data, and to execute them locally by demand-driven evaluations. As most historical computer-aided composition environments, OM relies on a transformational declarative paradigm, which is hard to conciliate with reactive data-flow (an evaluation scheme more adequate to the development of interactive systems). We propose to link these two evaluation paradigms in the same and consistent visual programming framework.Methods: We establish a denotational semantics of the visual language, which gives account for its demand-driven evaluation mechanism and the incremental construction of programs. We then extend this semantics to enable reactive computations in the functional graphs.Results: The resulting language merges data-driven executions with the existing demand-driven mechanism. A conservative implementation is proposed.Conclusions: We show that the incremental construction of programs and their data-driven and demand-driven evaluations can be smoothly integrated in the visual programming workflow. This integration allows for the propagation of changes in the programs, and the evaluation of graphically designed functional expressions as a response to external events, a first step in bridging the gap between computer-assisted composition environments and real-time musical systems.     

A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy

In engineering, it is computationally prohibitive to directly employ costly models in optimization. Therefore, surrogate-based optimization is developed to replace the accurate models with cheap surrogates during optimization for efficiency. The two key issues of surrogate-based optimization are how to improve the surrogate accuracy by making the most of the available training samples, and how to sequentially augment the training set with certain infill strategy so as to gradually improve the surrogate accuracy and guarantee the convergence to the real global optimum of the accurate model. To address these two issues, a radial basis function neural network (RBFNN) based optimization method is proposed in this paper. First, a linear interpolation (LI) based RBFNN modelling method, LI-RBFNN, is developed, which can enhance the RBFNN accuracy by enforcing the gradient match between the surrogate and the trend observed from the training samples. Second, a hybrid infill strategy is proposed, which uses the surrogate prediction error based surrogate lower bound as the optimization objective to locate the promising region and meanwhile employs a linear interpolation-based sequential sampling approach to improve the surrogate accuracy globally. Finally, extensive tests are investigated and the effectiveness and efficiency of the proposed methods are demonstrated.