以决策为导向的信息系统需求建模研究

提出了“以决策为导向”的方法,从方向上把握建模的整个过程,力图解决需求获取的目的性．     

圆锥凸轮特征造型方法的研究与系统实现

特征造型有利于将零件设计意图传递到后续加工环节,保证设计模型与加工模型数据表达的一致性.以圆锥凸轮机构为研究对象,采用已推导出的圆锥凸轮机构设计模型表达式,在特征造型软件MDT开发平台上,通过API编程,研制了一套具有设计计算和三维特征造型功能的圆锥凸轮机构虚拟设计系统.实例证明该系统具有一定的实用价值.     

数字化设备功能建模及其综合评价

根据数字化设备的特点,采用特征建模技术将设备信息分为6大类,并建立数字化制造资源特征模型.为适应快速多变的加工模式,提出设备加工能力评价体系:根据设备信息模型,提出特征筛选规则,建立设备加工能力规则库;采用综合模糊评价技术,建立综合评价模型,并且说明特征信息映射及其模糊化表示规则,给出了评价值规范化处理方法.通过实例分析可知,结果符合实际生产情况,实现了数字化设备的优化选择.     

Silica Nanotube Reactors for Catalytic Polymerization of Styrene and Olefins

Summary: Silica nanotube reactor (SNTR) has been designed and used as a novel catalytic polymerization reactor device to synthesize syndiotactic polystyrene (sPS), and polyethylene with Cp*Ti(OCH₃)₃ and rac-Et(indenyl)₂ZrCl₂ metallocene catalysts in conjunction with methylaluminoxane (MAO). The highly crystalline sPS molecules polymerized within the SNTR form nano-scale polymeric fibrils of 30–50 nm in diameter that further intertwine to fill the nanopore. The polymer molecular weight of sPS has been found to increase significantly in the SNTR. A simplified mathematical reaction model for the SNTR suggests that hindered chain transfer reactions in the nanopores filled with rigid polymeric nanofibrils might have caused the extended polymer chain length. The morphological characteristics of polymeric nanofibrils and the effect of SNTR's geometric confinement on the polymer properties are also discussed. It is also demonstrated that the liberated SNTR can be a novel ‘See-Through’ tool for visual observation when it is analyzed by transmission electron microscopy.     

Travel time models for automated warehouses with aisle transferring storage and retrieval machine

This paper presents analytical travel time models for the computation of travel time for automated warehouses with the aisle transferring S/R machine (in continuation multi-aisle AS/RS). These models consider the operating characteristics of the storage and retrieval machine such as acceleration and deceleration and the maximum velocity. Assuming uniform distributed storage rack locations and pick aisles and using the probability theory, the expressions of the cumulative distribution functions with which the mean travel time is calculated, have been determined. The computational models enable the calculation of the mean travel time for the single and dual command cycles, from which the performance of multi-aisle AS/RS can be evaluated. A simulation model of multi-aisle AS/RS has been developed to compare the performances of the proposed analytical travel time models. The analyses show that regarding all examined types of multi-aisle AS/RS, the results of proposed analytical travel time models correlate with the results of simulation models of multi-aisle AS/RS.     

No-arbitrage conditions,scenario trees,and multi-asset financial optimization

Many numerical optimization methods use scenario trees as a discrete approximation for the true (multi-dimensional) probability distributions of the problem’s random variables. Realistic specifications in financial optimization models can lead to tree sizes that quickly become computationally intractable. In this paper we focus on the two main approaches proposed in the literature to deal with this problem: scenario reduction and state aggregation. We first state necessary conditions for the node structure of a tree to rule out arbitrage. However, currently available scenario reduction algorithms do not take these conditions explicitly into account. State aggregation excludes arbitrage opportunities by relying on the risk-neutral measure. This is, however, only appropriate for pricing purposes but not for optimization. Both limitations are illustrated by numerical examples. We conclude that neither of these methods is suitable to solve financial optimization models in asset–liability or portfolio management.     

Considering endogeneity for optimal catalog allocation in direct marketing

The majority of catalog allocation models using historical data ignore endogeneity of past catalog decisions. We investigate two alternative approaches which either impose a relationship between the number of catalogs allocated to a customer and customer-specific coefficients of the sales response function or use instrumental variables. Heterogeneity across customers is modeled by cluster effects following a nonparametric distribution derived from a Dirichlet process prior. Models are estimated by Markov chain Monte Carlo simulation methods and evaluated by cross-validation predictive densities. Models which consider endogeneity imply much lower effects for sending a higher number of catalogs. These models also lead to optimal allocations which differ strongly from optimal allocations obtained for models which ignore endogeneity. Higher values of both posterior model probabilities and model average profits suggest to allocate catalogs based on the instrumental variables approach.     

SOIL EROSION AND DEGRADATION BASED ON SAND PARTICLES TRANSPORT CAUSED BY WIND BLOWING

Abstract In this paper, the effect of sand particles transport caused by wind blowing and its role in the land degradation and desertification process is considered. For the modeling of the 3D landscape, a grayscale height map has been used, the vegetation has been modeled using a Lindenmayer system, and the sand particles have been modeled as a 3D mesh‐free particles system. It was assumed that both the sand motion and the wind motion are incompressible continuum systems and their behavior follows the Navier–Stokes equations. To simulate the sand transport, the Navier–Stokes equations are discretized using the moving particle Semi‐implicit (MPS) method. Different types of revegetation patterns (windbreakers) have been used to show some effective measures preventing soils from erosion.     

A PGM framework for recursive modeling of players in simple sequential Bayesian games

We consider the situation where two agents try to solve each their own task in a common environment. In particular, we study simple sequential Bayesian games with unlimited time horizon where two players share a visible scene, but where the tasks (termed assignments) of the players are private information. We present an influence diagram framework for representing simple type of games, where each player holds private information. The framework is used to model the analysis depth and time horizon of the opponent and to determine an optimal policy under various assumptions on analysis depth of the opponent. Not surprisingly, the framework turns out to have severe complexity problems even in simple scenarios due to the size of the relevant past. We propose two approaches for approximation. One approach is to use Limited Memory Influence Diagrams (LIMIDs) in which we convert the influence diagram into a set of Bayesian networks and perform single policy update. The other approach is information enhancement, where it is assumed that the opponent in a few moves will know your assignment. Empirical results are presented using a simple board game.