数字制造系统的建模理论及方法研究

阐述了基于数字化工厂的设备环境建模、面向数字化加工的过程建模和数字制造系统的信息建模等理论问题，为系统地描述整个数字制造系统提供了新的思路和方法。     

开发知识级问题求解建模

开发知识级问题求解建模正在导致知识工程的重大变革。鉴于知识表示的本体论可以作为开发知识级建模的基础理论，我们提出描述问题求解过程组织的本体论ＲＡ和刻划领域世界结构的本体论ＯＴ，并设计了基于这两个本体论的问题求解建模方法。由于面向知识级建模分析的特点，支持该方法的建模环境ＫＭＥＰＳ有效地克服了面向符号级实现的知识工程的弱点。     

基于活动方法的协同设计过程管理系统研究

通过分析活动的组成及与过程建模的关系，提出了一种协同环境下设计过程管理的体系结构。根据产品开发过程的现状和特点，讨论了过程建模、多活动单元设计环境、过程监控以及系统间的数据交换等一系列重要的问题。最后，给出了协同设计过程管理和过程监控显示情况。     

小卫星装配建模与仿真的实现

采用基于模板的参数化特征建模方法，基于数据库驱动，主控零件驱动以及系统全关联数据结构的变形设计方法，基于装配规划和动画技术的装配仿真方法，基于多媒体技术和一次开发技术的用户界设计方法，在Pro/ENGINEER环境下实现了小卫星的参数化特征建模、变形设计、零件与装配的联动设计、装配仿真、干涉性检验和用户界面设计等。     

面向信息化战争模拟的体系对抗交互模型研究

从研究信息化战争模拟体系对抗交互模型的有关问题入手，界定了体系对抗交互概念，分析了体系对抗交互的复杂性特征及体系对抗交互作用的主要途径．在探讨体系对抗交互仿真建模内容以及与之相适应的新型建模方法的基础上，对体系对抗总体交互建模、实体交互建模、交互效果建模等问题进行了研究，并给出了有关交互模型的实例．     

书名：《舰船冲击理论及应用》

本书系统介绍水下非接触爆炸冲击环境下，舰船及设备冲击响应特性仿真分析理论与实践。全书共分10章，包括绪论、冲击输入的时域和频域表示、冲击响应特性分析、谱跌和动态设计法的主模态理论、多刚体一弹性体的子结构建模方法、抗冲击设计的有限元缩聚建模、GAP单元在非线性建模中的应用、冲击脉冲和冲击谱的数据分析方法、冲击试验机和冲击脉冲的确定、舰船水下爆炸冲击等。     

基于图像的虚拟环境建模技术

综述了基于图像的虚拟现实环境的建模技术，并给出了各种模型表示的理论依据和建模过程的定义。把环境表示分为四类：全景图，光场数据库，深度图像和多投影中心图。全景图的生成包括基于图像相关性和基于图像整合变换的算法。光场数据库的分类主要依据全光函数。同时论述了在各种表示下的采样和建模技术。最后讨论了当前建模技术研究的热点及所需要解决的问题。     

铁路接触网钢桁架柱的计算机建模与力学分析

本项目通过对几种接触网钢桁架柱的计算机建模与力学分析，探讨利用ansys等通用的有限元分析程序解决接触网钢桁架柱的建模、力学特性分析、环境影响分析和结果评判等相关问题较为简便的解决方法。     

武器系统研制费投资分布规律的仿真研究

针对大型项目多峰、复杂费用分布的传统仿真建模方法模型选取过于复杂、且难以达到精度要求等不足，提出将一种基于支持向量机的机器学习方法应用于大型复杂武器系统研制费投资分布研究．计算结果表明，这种方法有效弥补了传统建模方法的缺陷，有更好的泛化能力和实用性，因而可以作为研究此类问题的一种新方法．     

基于UML的税收管理员工作平台系统建模研究

税收管理员工作平台是税务系统管理的一个重要环节，是税务综合业务管理系统到末端的延伸。在介绍了系统建模语言UML的基础上，讨论了税收管理员工作平台建模过程中，使用UML语言进行系统建模的方法和步骤，并使用Rational Rose工具作为系统建模环境。     

Constructing virtual environments for manufacturing simulation

Virtual environments (VE) are becoming an important tool for simulating manufacturing systems. A problem in their adoption is the lack of an easy and effective approach to building large and complex virtual environments that are required by many manufacturing simulation tasks. This paper reports on research into this problem and presents a novel approach - domain-analysis-based top-down construction - to constructing complex virtual manufacturing environments. Based on this approach, a software system is designed and implemented to provide easy-to-use and flexible tools that enable users to construct virtual environments rapidly and with minimum modelling effort.     

A data-driven approach to multi-product production network planning

The clearing function models the non-linear relationship between work-in-process and throughput and has been proposed for production planning in environments with queuing (congestion) effects. One approach in multi-product, multi-stage environments has been to model the clearing function at the bottleneck machine only. However, since the bottleneck shifts as the product release mix changes, this approach has its limitations. The other approach is the Alternative Clearing Function formulation, where the clearing function is first estimated at the resource level using piecewise linear regression from simulation experiments, and then embedded into a linear programme. This paper develops an alternative to the Allocated Clearing Function formulation, wherein system throughput is estimated at discrete work-in-process points. A mixed integer programming formulation is then presented to use these throughput estimates for discrete release choices. The strength of the formulation is illustrated with a numerical example and the new approach is compared with the ACF.     

Stochastic partner selection for virtual enterprises: a chance-constrained approach

A virtual enterprise (VE) is a temporary organisation that pools the core competencies of its member enterprises in order to exploit fast-changing market opportunities. Making successful collaborative partnerships is, in this context, a major challenge in today’s competitive business environments. The success of such a ‘virtual’ organisation is strongly dependent on its composition, and the selection of partners becomes therefore a crucial issue. This problem is particularly difficult because of the uncertainties related to information, market dynamics, customer expectations and technology speed-up, with a strongly stochastic decision-making context. In this paper, a chance-constrained approach to rank alternative VE configurations in business environments with uncertainty, and vague and random information, is proposed. This approach is based on a two-stage model: a chance-constraint multi-objective directional Tabu Search metaheuristic, complemented by a 2-tuple fuzzy linguistic representation model. Preliminary computational results clearly demonstrate the potential of the approach for practical application.     

Thermo-mechanical damage modeling of polymer matrix sandwich composites in fire

The objective of this research is to develop a modeling and simulation approach for predicting the thermo-mechanical damage of composite materials subjected to fire environments. A 3D thermal damage model is developed for glass-reinforced polymer composite materials subject to high temperature and radiative environments. Homogenization methods are used to formulate the damaged material in terms of fiber, resin and char. The thermal damage model is implemented in Abaqus via an overlaid element approach. The solution of the mechanical response uses the existing functions in Abaqus for large-displacement analysis. Composite sandwich panels with balsa core are examined. Reasonable agreement in temperature is obtained between predictions and available experimental data. For the sandwich panels, delamination failure is predicted at the sandwich interface – consistent with the experiments. Comparisons of time-to-failure of the sandwich panel show the predictions are reasonable.     

Learning effective dispatching rules for batch processor scheduling

Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.     

An Efficient Approach to Fabricate Air-Stable Perovskite Solar Cells via Addition of a Self-Polymerizing Ionic Liquid

Despite the excellent photovoltaic properties achieved by perovskite solar cells at the laboratory scale, hybrid perovskites decompose in the presence of air, especially at high temperatures and in humid environments. Consequently, high-efficiency perovskites are usually prepared in dry/inert environments, which are expensive and less convenient for scale-up purposes. Here, a new approach based on the inclusion of an in situ polymerizable ionic liquid, 1,3-bis(4-vinylbenzyl)imidazolium chloride ([bvbim]Cl), is presented, which allows perovskite films to be manufactured under humid environments, additionally leading to a material with improved quality and long-term stability. The approach, which is transferrable to several perovskite formulations, allows efficiencies as high as 17% for MAPbI₃ processed in air % relative humidity (RH) ≥30 (from an initial 15%), and 19.92% for FAMAPbI₃ fabricated in %RH ≥50 (from an initial 17%), providing one of the best performances to date under similar conditions.     

Formal Approach to Workflow Application Fragmentations Over Cloud Deployment Models

Workflow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments. Especially, such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workflow processes and applications with scalable on-demand services. In this paper, we focus on the distribution paradigm and its deployment formalism for such very large-scale workflow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments. We propose a formal approach to vertically as well as horizontally fragment very large-scale workflow processes and their applications and to deploy the workflow process and application fragments over three types of cloud deployment models and architectures. To concretize the formal approach, we firstly devise a series of operational situations fragmenting into cloud workflow process and application components and deploying onto three different types of cloud deployment models and architectures. These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workflow process and applications as an implementing component for cloud workflow management systems. Finally, we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workflow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.     

Adaptive exponential smoothing versus conventional approaches for lumpy demand forecasting: case of production planning for a manufacturing line

Production planning in a lumpy demand environment can be tenuous, with potentially costly forecasting errors. This paper addresses the issue of selecting the smoothing factor used in lumpy demand forecasting models. We propose a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineer's experience and subjective judgment. The Kalman filter approach developed in this study processes measurements to estimate the state of a linear system and utilises knowledge from states of measurements and system dynamics. Performances of an array of forecasting models that have been shown to work well in lumpy demand environments are compared with respect to the proposed adaptive smoothing factor and the conventional smoothing constant across a spectrum of lumpy demand scenarios. All models using the adaptive smoothing factor based on Kalman filter weighting function generate smaller errors than their conventional counterparts, especially under high lumpiness demand environments. Our proposed approach is particularly useful when production management is concerned about simplicity and transferability of knowledge due to constant personnel turnaround and low retention rate of expertise.     

移动机器人在大尺度未知环境下的自主导航方法——改进的FastSLAM算法

在移动机器人同时定位和地图创建过程中,标准FastSLAM算法（对SLAM因式分解的一种快速算法）通常假设机器人观测值和环境陆标之间的数据关联是已知的（即采用预置陆标）来回避数据关联问题。针对标准FastSLAM算法的这一缺陷,提出了一种适合大尺度未知环境（即数据关联未知）下的基于单个粒子最大似然数据关联和环境否定信息相结合的FastSLAM改进算法。该算法用基于单个粒子最大似然数据关联法保证当前运动噪声对下一步关联数据精度的影响和“失踪”问题的出现,而用环境否定信息法避免错误陆标添加到环境地图中。仿真结果表明,改进的FastSLAM算法解决了大尺度未知环境下的数据关联问题,提高了机器人自身定位和地图创建的精度,可真正实现机器入在大尺度未知环境的自主导航。