横倾状态下舰船液舱容量计量修正方法研究

本文介绍了横倾状态下舰船液舱容量计量修正方法的研究。重点阐述了利用三次样条曲线拟合和双圆弧样条函数逼近的液舱容量计量计算和修正方法     

基于核Logistic回归的乐器音乐辨识

基于统计学习的音频分类具有理论基础坚实,实现机制简单等特点受到广泛关注并被很多音频分类系统所采用。本文对核Logistic回归算法(KLR)进行了深入分析,提出基于KLR的音频分类器设计方法,应用其解决同类型的乐器音乐分类问题。结合所采集的小提琴中提琴和大提琴的音乐信号样本进行了分类仿真试验,并与传统的Logistic回归(LR)和支持向量机(SVM)进行对比。结果表明,核Logistic回归模型具有较为优越的分类性能和非线性处理能力。     

尺寸可变的装箱问题的近似算法的研究

给定物品系列,要求将所有物品装入到不同类型的箱子中,以实现从第1个箱子到最后1个箱子被使用的箱子的总尺寸最小化.用最坏情况绝对性能研究在线算法,给出了一种最坏情况绝对性能比是3的近似算法.作为这种算法的应用,给出了一种脱线算法,其最坏情况绝对性能比是2.     

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.     

Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

Improving the efficiency of ship optimization is crucial for modern ship design. Compared with traditional methods, multidisciplinary design optimization (MDO) is a more promising approach. For this reason, Collaborative Optimization (CO) is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.     

Dynamic pricing for managed lanes with multiple entrances and exits

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPLs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0–27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5–50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the “jam-and-harvest” behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.     

NUCLEAR OPERATORS ON C（Ω，X）

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一类双线性规划的线性逼近算法

讨论了一类双线性规划的优化问题，利用对偶原理，将双线性规划问题转化为极大极小问题，研究了该极大极小问题的线性逼近算法，并证明了该算法在有限步内收敛，采用Karmarkar算法优化初始迭代点，使线性逼近算法更为有效。     

巴尔姆流的模拟与应用

巴尔姆流是交通运输生产与控制过程中普遍存在的一种事件流 ;通过对巴尔姆流的模拟 ,提出了一种将排队服务系统马尔可夫化的方法 ;运用该方法能科学定量地分析排队服务系统的各项运营指标 ;所提出的理论在交通运输生产过程与控制的研究中有广泛的应用价值     

基于阻力和伴流不均匀度的多用途船型线优化

为探究多用途船首尾型线变化对船舶水动力性能的影响,采用RBF(Radial Basis Functions)曲面变形方法分别对多用途船首尾型线进行变换,使用优化拉丁超立方法设计计算样本,引入Kriging近似模型解决传统CFD计算耗时的问题。在此基础上,以船舶的阻力性能以及桨盘面伴流不均匀度作为优化目标,利用带精英策略的非支配排序遗传算法(NSGA-Ⅱ)完成整个型线优化流程。计算结果表明,对于该多用途船,船首形状朝V型发展有利于减小兴波阻力,船尾的V型横剖面在接近推进器处逐渐向U型转变,可以在获得更均匀伴流场的同时对阻力性能影响较小。