基于软测量的化工精馏过程推断控制策略

针对化工精馏过程产品成分无法在线检测及其用温度间接控制产品成分的常规控制策略存在着控制精度低的问题,提出基于软测量的精馏过程成分非线性串级推断控制策略。该控制策略首先提出核岭回归的实时软测量方法,即利用满足Mercer条件的核函数改进线性岭回归算法,实现精馏过程产品成分的在线检测;然后在此基础上,提出一种新的非线性串级推断控制策略,即副环采用常规的温度间接控制,主环采用基于核岭回归软测量的推断控制策略。通过Mejedell等建立的精馏塔动态模型分别对单端和双端成分非线性串级推断控制策略性能进行分析,仿真结果表明,与传统控制方案比较,新控制策略的控制质量有了较大提高,控制结构简单,易于实施。     

Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

Citations do not solve problems.

D. T. Cambell and D. W. Fiske (1959) made the now-obvious statement that measures of the same variable made by different methods should agree (converge) and certainly should agree better than measures of different variables made by those several methods. Almost everyone accepted that statement, yet the multitrait–multimethod matrices published today show little or no improvement over the published examples cited in 1959. The article raised the conceptual problem of how a variable and its measurement should be linked and the methodological problem of clarifying the nature of methods and their effects. Given these unresolved problems, it is perhaps not surprising that the question of the appropriate statistical analysis of these matrices has also no consensual answer. Perhaps methods and traits or contents are so thoroughly intertwined that their interaction cannot be adequately analyzed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于图像内容层次表征的遥感图像 分割方法

提出基于图像内容层次表征的高分辨率遥感图像快速多精度分割方法。首先根据初始分割结果建立区域邻接图(RAG),并将其定义为马尔可夫随机场(MRF);然后引入光谱、形状和边缘等图像特征进行层次合并,通过记录层次合并过程获得图像内容的层次表征;最后根据层次表征中不同层级对象之间的关系快速生成任意不同精度的分割结果,以满足不同应用的需求。利用QuickBird卫星图像进行实验和评价的结果表明,本文方法具有较高的精度和效率。     

基于传感器网络能量均衡分簇算法仿真研究

研究传感网络能量均衡分簇延长网络寿命问题。针对LEACH算法中簇首分布不均及簇首与基站一跳通信能耗大的问题,造成网络簇头的不均匀分布带来的能耗不均衡问题以及簇头的瓶颈等,为了解决上述问题,提出了一种基于能量高效的无线传感器网络分簇路由算法。算法首先在簇头选择过程中利用节点的能量、邻节点数以及每一轮中簇头的个数等参数设置节点当选簇头的优先度,使簇头均匀地分布在网络中;在簇的组建过程中利用能量参数设置簇的重建条件,达到减小簇的重建频率的目的。     

S-57电子海图数据读取和自动多模式分层显示

电子海图显示与信息系统(Electronic Chart Display and Information System,简称ECDIS)是以数字形式储存的海图并与多种传感设备、图形显示终端等高度融合的船舶航行综合信息处理系统,而S-57是电子海图国际通用的数据格式。在Linux/Fedora系统下,针对于S-57格式,研究其数据结构模型,从最基本的点、线、面出发,读取海图数据并根据时间和天气数据实现S-57海图数据自动多模式显示,并提出了一种根据物标重要性进行分层显示的方法。     

基于隐枚举法的快速分级切负荷优化方法

目前稳控装置广泛采用的穷举法已经很难满足快速、精准切负荷的要求。为了高效地求解切负荷优化问题,为紧急决策争取更多时间,提出了基于隐枚举法的快速分级切负荷优化方法。该方法协调低频和低压切负荷动作轮次,建立切负荷优化模型,通过制定合适的搜索策略和剪枝策略,仅在可行解范围内进行寻优,大大提高了计算最优解的效率。切负荷算例结果表明同等条件下该方法可以节约至少50%以上的计算时间,实现了稳控装置的快速切负荷,保证了紧急情况下电网的安全稳定运行。     

基于层次认知模型的大学计算机课程多元混合教学模式设计与实施

根据计算思维理论中从知识到能力转变的递进过程,构建了基于层次认知模型的多元混合式教学课程体系和教学模式,并以大学计算机课程为例,以学生自主学习和协作学习为教学出发点,开发、整合出优质教学资源,设计了多样化教学活动,以培养学生综合应用计算机的软硬件知识解决本专业的实际问题为目的,最终应用到“SPOC+翻转课堂”混合教学中,达到教学目标。     

基于局部线性嵌入和支持向量机回归的TBM施工参数预测

依托吉林引松工程开展隧道掘进机（TBM）施工参数预测研究,提出TBM施工数据分段提取算法,提取上升段前30 s的总推进力、刀盘转速、推进速度、刀盘扭矩、刀盘转速电位器设定值、推进速度电位器设定值、贯入度、贯入度指数(FPI)、扭矩切深指数(TPI)9个参数作为输入;通过局部线性嵌入（LLE）完成对上升段数据特征的降维;基于支持向量机回归（SVR）建立TBM施工控制参数（推进速度、刀盘转速）和负载参数（总推进力、刀盘扭矩）预测模型. 分析是否结合前一掘进循环的FPI、TPI指数进行预测对预测效果的影响. 结果表明,上述方法在推进速度、刀盘转速、总推进力、刀盘扭矩的预测中均取得了较好的预测效果,平均预测绝对百分比误差均小于15%,验证了该预测方法的有效性,该方法可以为TBM现场施工提供指导.