基于双螺旋桨差动调节的双体船航向智能控制研究

设计了基于双螺旋桨差动调节的双体船航向智能控制系统,通过系统辨别得到双体船航向运动的数学模型,并在该模型的基础上运用模糊控制算法,将得到的参数赋予双螺旋桨控制,同时通过双螺旋桨转速的差动调节,达到控制双体船航向的作用.仿真结果表明,该系统对于海情的变化具有较强的自适应能力.     

一个自学习本体支持的辅助学习系统

介绍一个智能辅助学习平台的设计理念、功能和结构.它根据相同领域的电子资料集合自动学习领域本体,基于所学本体自动建立不同资料之间的语义关联.应用语义关联自动实现学习资料之间的参考关系.其结果是将相同领域的原来相互独立的多种资料自动按语义融合为一个整体,以提高学习和研究工作的资料应用效率.     

羧甲基纤维素交联缩合反应的动力学研究

本文研究了羧甲基纤维素和水溶性酚醛树脂的交联缩合动力学的测试方法并得到了该反应的动力学方程。因为水溶性酚醛树脂是多种活性中间体的混合物,羧甲基纤维素是受羧甲基取代度和聚合度影响的大分子,两者的交联缩合反应可以同时发生在多点、多分子之间,动力学研究较为复杂,所以本文分别采用Borchardt-Daniels模型和Kissinger模型方法,根据差示扫描量热仪(DSC)测定不同升温速率下的羧甲基纤维素和水溶性酚醛树脂交联缩合反应的热流曲线数据,计算得到反应动力学方程。利用非等温单一扫瞄速率法的Borchardt-Daniels模型得到的动力学参数为:反应级数n 1.05,反应活化能E 93.86kJ/mol,指前因子InA16.23。采用非等温多加热扫描速率法的Kissinger模型计算得到的动力学参数为:反应级数n 1.04,反应活化能E 94.37 kJ/mol,指前因子InA15.96。3个热力学参数值分别相差0.55%、1.71%和1.14%,证明2种模型计算结果较一致。水溶性酚醛树脂与羧甲基纤维素缩合交联缩合反应的动力学方程为(dα)/(dt)=e~(-16.24E/(RT))(1-α)~(1.05)。     

新型国产近红外分析仪的菜籽菜粕快速检测技术研究

近红外光谱分析作为1种快速无损的绿色分析技术,可应用于检测油料原料和油脂产品品质,为粮油企业带来巨大经济效益.本文采用一款新型国产化的近红外漫反射光谱分析仪对菜籽和菜粕进行了检测分析,分别对菜籽的水分、蛋白、含油量、脂肪酸、硫苷含量和菜粕水分、蛋白和含油量指标建立模型并验证.结果显示近红外检测结果与国标参考方法检测结果相关性好、准确度高.表明新型国产化的近红外分析仪可满足油脂企业对菜籽和菜粕产品的质量控制要求.     

基于PPDR模型的关键应用信息系统防御体系

本文通过对关键应用信息系统存在的风险和安全隐患进行分析,采用PPDR动态网络安全模型,结合企业信息系统特点和笔者多年的信息安全管理实践,将基于安全策略的系统性能提升、安全访问区域保护、信息系统的自身防护、入侵防御和漏洞扫描体系以及响应和恢复机制等技术方法和手段综合应用,全方位构筑关键应用信息系统的动态安全防御体系,保证关键应用信息系统的可用性、可靠性和安全性.     

ZSM5-Y型分子筛催化剂复配体系裂化性能数值研究

采用流动和反应耦合模型模拟ZSM5-Y复配催化剂体系在提升管反应器内的催化裂化过程。结果表明,重油催化裂化反应主要发生在喷嘴附近,该处有45%的重油转化为汽油和柴油。对于ZSM5-Y型复配催化剂体系,ZSM5加入沸石催化剂的显然影响着汽油和液化气的产率,反应产物中柴油馏分及重油馏分的产率基本上不受影响。加入少量择形催化剂便大大降低提升管反应器汽油馏分收率,并提高液化气收率,但汽油与液化气产率显著变化仅发生在择形催化剂加量较小的时候,一旦超过10%以后,对气油和液化气的产率影响反而较小。     

Unified computational methods for regression analysis of zero-inflated and bound-inflated data

Bounded data with excess observations at the boundary are common in many areas of application. Various individual cases of inflated mixture models have been studied in the literature for bound-inflated data, yet the computational methods have been developed separately for each type of model. In this article we use a common framework for computing these models, and expand the range of models for both discrete and semi-continuous data with point inflation at the lower boundary. The quasi-Newton and EM algorithms are adapted and compared for estimation of model parameters. The numerical Hessian and generalized Louis method are investigated as means for computing standard errors after optimization. Correlated data are included in this framework via generalized estimating equations. The estimation of parameters and effectiveness of standard errors are demonstrated through simulation and in the analysis of data from an ultrasound bioeffect study. The unified approach enables reliable computation for a wide class of inflated mixture models and comparison of competing models.     

Real-time foveation filtering using nonlinear Mipmap interpolation

In recent years, several techniques have been proposed to simulate the gaze effect of the Human Visual System (HVS). It is believed that this effect is due to the foveation filtering. Current techniques to simulate the foveation filtering in computer graphics are either slow or suffer from artifacts and limitations. In this paper, we present a new approach of foveation filtering based on the Mipmap Pyramid of the current view by considering the relationship between the Gaussian kernel and Mipmap level. Due to the nonlinear Mipmap interpolation under the Bilateral Filtering scheme, we are able to simulate the foveation filtering more naturally and efficiently than in previous work. Moreover, a detail enhancement method based on the Cornsweet illusion is proposed to augment the gazing effect. We demonstrate our new approach with a variety of examples and provide comparisons with recent approaches.     

An MLS-based cartoon deformation

We present an image deformation method driven by skeleton; it is based on MLS deformation algorithm (Schaefer et al. in SIGGRAPH, vol. 25, pp. 533–540, 2006). We improve the MLS deformation by defining a new weight function based on skeleton. Being different from the weight function based on control points, our weight function has benefited from the shape information of undeformed object and keeps deformation local, therefore our method can achieve a realistic effect. In cartoon video, we propose a new method to track the skeleton in the video, to build new origin skeleton and new target skeleton on each frame, and to apply our image deformation method to each frame and maintain spatiotemporal consistency. Results demonstrate that our method is able to decrease the effect of squeeze and use less control points.     

A heuristic method for learning Bayesian networks using discrete particle swarm optimization

Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.