Random matrix theory for modeling uncertainties in computational mechanics

This paper deals with data uncertainties and model uncertainties issues in computational mechanics. If data uncertainties can be modeled by parametric probabilistic methods, for a given mean model, a nonparametric probabilistic approach can be used for modeling model uncertainties. The first part is devoted to random matrix theory for which we summarize previous published results and for which two new ensembles of random matrices useful for the nonparametric models are introduced. In a second part, the nonparametric probabilistic approach of random uncertainties is presented for linear dynamical systems and for nonlinear dynamical systems constituted of a linear part with additional localized nonlinearities. In a third part, a new method is proposed for estimating the parameters of the nonparametric approach from experiments. Finally, examples with experimental comparisons are given.     

The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling,scenario discovery,and (multi-objective) robust decision making

There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches being put forward in the literature. A key idea shared among these is the use of models for exploratory rather than predictive purposes. Exploratory modeling aims at exploring the implications for decision making of the various presently irresolvable uncertainties. This is achieved by conducting series of computational experiments that cover how the various uncertainties might resolve. This paper presents an open source library supporting this. The Exploratory Modeling Workbench is implemented in Python. It is designed to (i) support the generation and execution of series of computational experiments; and (ii) support the visualization and analysis of the results from the computational experiments. The Exploratory Modeling Workbench enables users to easily perform exploratory modeling with existing models, identify the policy-relevant uncertainties, assess the efficacy of policy options, and iteratively improve candidate strategies.     

An Efficient and Scalable Approach to Correct Class Model Refinement

Today, programmers benefit immensely from Integrated Development Environments (IDEs), where errors are highlighted within seconds of their introduction. Yet, designers rarely benefit from such an instant feedback in modeling tools. This paper focuses on the refinement of UML-style class models with instant feedback on correctness. Following the Model-Driven Architecture (MDA) paradigm, we strongly believe in the benefit of maintaining high-level and low-level models separately to 1) document the lower level model and 2) continuously ensure the correctness of the low-level model during later evolution (i.e., high- or low-level models may be evolved independently). However, currently the refinement and subsequent evolution lack automated support, let alone an instant feedback on their correctness (i.e., consistency). Traditional approaches to consistency checking fail here because of the computational cost of comparing class models. Our proposed instant approach first transforms the low-level model into an intermediate model that is then easier comparable with the high-level model. The key to computational scalability is the separation of transformation and comparison so that each can react optimally to changes—changes that could happen concurrently in both the high- and low-level class models. We evaluate our approach on eight third-party design models. The empirical data show that the separation of transformation and comparison results in a 6 to 11-fold performance gain and a ninefold reduction in producing irrelevant feedback. While this work emphasizes the refinement of class models, we do believe that the concepts are more generally applicable to other kinds of modeling languages, where transformation and subsequent comparison are computationally expensive.     

Discovering expressive process models by clustering log traces

Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant" models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound mDdel, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability.     

增强现实环境下的汽车A立柱视野评估方法

针对目前汽车造型设计过程中A立柱视野评估缺乏从设计师视角主动参与的使能工具,提出基于增强现实技术的汽车A立柱评估方法.首先建立了汽车内饰评估的增强现实软硬件环境,将虚拟汽车模型准确、实时地叠加到真实的驾驶环境中;其次建立了虚拟视锥体几何模型,设计了用户左右眼视锥的标定方法以及基于视线碰撞检测的A柱盲区角计算方法,并分析了标定误差对盲区角计算精度的影响,结果表明标定方法可靠.最后采用某型轿车模型进行应用验证,通过跟踪用户人眼位置驱动虚拟视锥体,实现不同驾姿下的A立柱盲区角的快速计算,主观地评估A立柱对前方视野的影响.结果表明,该方法操作简单有效,为汽车视野A立柱评估提供了一个新型工具.     

Uncertainty quantification of MEMS using a data-dependent adaptive stochastic collocation method

This paper presents a unified framework for uncertainty quantification (UQ) in microelectromechanical systems (MEMS). The goal is to model uncertainties in the input parameters of micromechanical devices and to quantify their effect on the final performance of the device. We consider different electromechanical actuators that operate using a combination of electrostatic and electrothermal modes of actuation, for which high-fidelity numerical models have been developed. We use a data-driven framework to generate stochastic models based on experimentally observed uncertainties in geometric and material parameters. Since we are primarily interested in quantifying the statistics of the output parameters of interest, we develop an adaptive refinement strategy to efficiently propagate the uncertainty through the device model, in order to obtain quantities like the mean and the variance of the stochastic solution with minimal computational effort. We demonstrate the efficacy of this framework by performing UQ in some examples of electrostatic and electrothermomechanical microactuators. We also validate the method by comparing our results with experimentally determined uncertainties in an electrostatic microswitch. We show how our framework results in the accurate computation of uncertainties in micromechanical systems with lower computational effort.     

An agent-based distributed computational experiment framework for virtual supply chain network development

This paper focuses on research on virtual supply chain networks instead of real supply chain networks by making use of agent technology and computational experiment method. However, the recent research is inefficient in computational experiment modeling and lack of a related methodological framework. This paper proposes an agent-based distributed computational experiment framework with in-depth study of material flow, information flow and time flow modeling in supply chain networks. In this framework, a matrix-based formal representation method for material flow, a task-centered representation method for information flow and an agent-based time synchronization mechanism for time flow are proposed to aid building a high quality computational experiment model for a multi-layer supply chain network. In order to conduct the model, a computational experiment architecture for virtual supply chain networks is proposed. In this architecture, coordination mechanisms among agents based on material flow, information flow and time flow as well as consistency check methods for computational experiment models are discussed. Finally, an implementation architecture of the framework is given and a case of virtual supply chain network is developed to illustrate the application of the framework. The computational experiment results of the case show that the proposed framework, not only feasible but correct, has sound advantages in virtual supply chain network development, computational experiment modeling and implementation.     

In-depth behavior understanding and use: The behavior informatics approach

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (BI), in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of BI in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific means by finding deeper and more informative patterns leading to greater in-depth behavior understanding. BI creates new directions and means to enhance the quantitative, formal and systematic modeling and analysis of behaviors in both physical and virtual organizations.     

Context inclusive function evaluation: a case study with EM-based multi-scale multi-granular image classification

Many statistical queries such as maximum likelihood estimation involve finding the best candidate model given a set of candidate models and a quality estimation function. This problem is common in important applications like land-use classification at multiple spatial resolutions from remote sensing raster data. Such a problem is computationally challenging due to the significant computation cost to evaluate the quality estimation function for each candidate model. For example, a recently proposed method of multi-scale, multi-granular classification has high computational overhead of function evaluation for various candidate models independently before comparison. In contrast, we propose an upper bound based context-inclusive approach that reduces computational overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. We also prove that an upper bound exists for each candidate model and the proposed algorithm is correct. Experimental results using land-use classification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly.     

A Drug Candidate Design Environment Using Evolutionary Computation

This paper describes the candidate design environment we developed for efficient identification of promising drug candidates. Developing effective drugs from active molecules is a challenging problem which requires the simultaneous satisfaction of many factors. Traditionally, the drug discovery process is conducted by medicinal chemists whose vital expertise is not readily quantifiable. Recently, in silico modeling and virtual screening have been emerging as valuable tools despite their mixed results early on. Our approach combines the capabilities of computational models with human knowledge using a genetic algorithm and interactive evolutionary computation. We enable the chemist's expertise to play a key role in every stage of the discovery process. Our evolved structures are guaranteed to be within the chemistry space specified by the medicinal chemist, thereby making the results plausible. In this paper, we describe our approach, introduce a case study to test our methodology, and present our results.     

Deterministic approach to robust adaptive learning of fuzzy models

This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of H/sup /spl infin// estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.     

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-A11 binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes.     

VyrdMC: Driving Runtime Refinement Checking with Model Checkers

This paper presents VyrdMC, a runtime verification tool we are building for concurrent software components. The correctness criterion checked by VyrdMC is refinement: Each execution of the implementation must be consistent with an atomic execution of the specification. VyrdMC combines testing, model checking, and Vyrd, the runtime refinement checker we developed earlier. A test harness first drives the component to a non-trivial state which serves as the starting state for a number of simple, very small multi-threaded test cases. An execution-based model checker explores for each test case all distinct thread interleavings while Vyrd monitors executions for refinement violations. This combined approach has the advantage of improving the coverage of runtime refinement checking at modest additional computational cost, since model checkers are only used to explore thread interleavings of a small, fixed test program. The visibility and detailed checking offered by using refinement as the correctness criterion differentiate our approach from simply being a restricted application of model checking. An important side benefit is the reduction in program instrumentation made possible if VyrdMC is built using a model checker with its own virtual machine, such as Java PathFinder [Guillaume Brat, Klaus Havelund, Seung-Joon Park, and Willem Visser. Model Checking Programs. In IEEE International Conference on Automated Software Engineering (ASE), September 2000]. We are investigating the use of two different model checkers for building VyrdMC: Java PathFinder, an explicit-state model checker and Verisoft, a “stateless” model checker [P. Godefroid. Model Checking for Programming Languages using VeriSoft. In Proceedings of the 24th ACM Symposium on Principles of Programming Languages, pages 174–186, Paris, January 1997].     

Continuous space maximal coverage: Insights,advances and challenges

Siting facilities in continuous space to maximize coverage of regional demand is an important planning problem. It is challenging to solve because demand is continuously distributed in a region and facilities may be sited anywhere in the region, suggesting that an infinite number of locations must be considered both as demand for service as well as a potential facility. An ingenious approach for solving continuous space coverage problems is abstracting continuously distributed demand as discrete spatial objects (points, lines or polygons), generating a finite dominating set from continuous space as candidate facility sites, and applying a discrete location coverage model. However, abstracting continuous demand and potential facility sites often introduces errors and uncertainties in coverage of regional demand, rendering findings (service and optimal facility locations) less than definitive. In this paper we examine the continuous space maximal coverage problem. Studies focused on locating antennas in Paradise Valley, Arizona and emergency warning sirens in Dublin, Ohio, are used to highlight issues and challenges in continuous space modeling. Potential paths forward are suggested based on empirical insights.     

车辆虚拟试验场的路面建模方法研究

路面建模是车辆虚拟试验场研究的关键问题。为此提出了1种三维路面生成的通用方法,首先通过滤波白噪声法生成一定等级路面不平度序列,然后按照一定规则将所生成的大量不平度序列转化成ADAMS/Car识别的虚拟路面文件。该方法可用于生成各类虚拟试验场路面,为建立基于ADAMS/Car的虚拟试验场仿真路面提供了有效的方法和手段。     

一种基于物理意义的快速力反馈形变模型及实时力觉响应算法

虚拟物体在受力作用时的形变建模是虚拟环境中力/触觉人机交互的关键.文中提出了一种新的基于物理意义的形变建模方法,不仅计算速度快,满足力反馈的实时性要求,而且能够同时保证接触力和形变的计算具有较高的精度,适用于具有较大变形量的柔性物体的力反馈计算,满足精细作业对虚拟现实系统的要求.     

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data. In recent years, the types and vari ables of industrial data have increased significantly, making data driven models more challenging to develop. To address this prob lem, data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data. This paper systematically explores and discusses th...     

基于MA和BP的虚拟企业动态建模及关键技术研究

竞争迫使企业走到一起，组成虚拟企业，是现代企业的特征．但如何迅速构建虚拟 企业，实现联盟企业间的重组、重用和柔性，是传统企业要解决的首要问题．文章分析了虚 拟企业建模的特点和需求，讨论了该领域的研究动态，论述了企业建模的关键技术，提出了 基于多智能代理（MA）和事务过程（BP）的建模方法，并介绍了该模型的应用实例．为现代 企业的组织形态和运营方式提供一种理论指导和解决方案．     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

An abstract architecture for virtual organizations: The THOMAS approach

Today, the need for architectures and computational models for large-scale open multi-agent systems is considered to be a key issue for the success of agent technology in real-world scenarios. This paper analyzes the significant unsolved problems that must be taken into account in order to develop real, open multi-agent systems. It identifies requirements and related open issues, discusses how some of these requirements have been tackled by current technologies, and explains how the THOMAS architecture is able to give support to these open issues. This paper also describes the THOMAS abstract architecture and computational model for large-scale open multi-agent systems based on a service-oriented approach that specifically addresses the design of virtual organizations. An application example for the management of a travel agency system, which demonstrates the new features of the proposal, is also presented.