An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variable

Augmented Models of High-Frequency Transformers for SMPS

The modeling of high-frequency transformers via augmented equivalent circuits is addressed. The augmented models are composed of a low-frequency equivalent and a supplemental element modeled via real rational fitting. They offer both high accuracy levels and a physical meaning that helps the interpretation of simulation results. Parasitics effects between the windings and between the windings and the carrying board can be also included. The use of an augmented model for the simulation of a dc-dc converter is demonstrate

Qualitative System Identification from Imperfect Data

Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data

Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle

To restore functional use of paralyzed muscles by automatically controlled stimulation, an accurate quantitative model of the stimulated muscles is desirable. The most commonly used model for isometric muscle has had a Hammerstein structure, in which a linear dynamic block is preceded by a static nonlinear function, To investigate the accuracy of the Hammerstein model, the responses to a pseudo-random binary sequence (PRBS) excitation of normal human plantarflexors, stimulated with surface electrodes, were used to identify a Hammerstein model but also four local models which describe the responses to small signals at different mean levels of activation. Comparison of the local models with the Linearized Hammerstein model showed that the Hammerstein model concealed a fivefold variation in the speed of response. Also, the small-signal gain of the Hammerstein model was in error by factors up to three. We conclude that, despite the past widespread use of the Hammerstein model, it is not an accurate representation of isometric muscle. On the other hand, local models, which are more accurate predictors, can be identified from the responses to short PRBS sequences. The utility of local models for controller design is discussed

A Simulation Model for Logical and Operative Clash Detection

The introduction of the Building Information Modeling (BIM) approach has facilitated the management process of documents produced by different kinds of professionals involved in the design and/or renovation of a building, through identification and subsequent management of geometrical interferences (Clash Detection). The methodology of this research proposes a tool to support Clash Detection, introducing the logical-operative dimension, that may occur with the presence of a construction site within a hospital structure, through the integration of a BIM model within a Game Engine environment, to preserve the continuity of daily hospital activities and trying to reduce negative impacts, times and costs due to construction activities

An Objective-Based Perspective on Assessment of Model-Supported Policy Processes

Simulation models, being in use for a long time in natural sciences and engineering domains, are diffusing to a wider context including policy analysis studies. The differences between the nature of the domain of application, as well as the increased variety of usage partially induced by this difference naturally imply new challenges to be overcome. One of these challenges is related to the assessment of the simulation-based outcomes in terms of their reliability and relevance in the policy context being studied. The importance of this assessment is twofold. First of all, it is all about conducting a high quality policy study with effective results. However, the quality of the study does not necessarily imply acceptance of the results by the clients and/or colleagues. This problem of policy analysts increases the importance of such an assessment; an effective assessment may induce the acceptance of the conclusions drawn from the study by the clients and/or colleagues. The main objective of this paper is to introduce an objective-based assessment perspective for simulation model-supported policy studies. As a first step towards such a goal, an objective-based classification of models is introduced. Based on that, we will discuss the importance of different aspects of the assessment for each type. In doing so, we aim to provide a structured discussion that may serve as a sort of methodological guideline to be used by policy analysts, and also by clients.Simulation, Validation, Model Assessment, Policy Analysis, Model Typology

Single-vehicle data of highway traffic - a statistical analysis

In the present paper single-vehicle data of highway traffic are analyzed in great detail. By using the single-vehicle data directly empirical time-headway distributions and speed-distance relations can be established. Both quantities yield relevant information about the microscopic states. Several fundamental diagrams are also presented, which are based on time-averaged quantities and compared with earlier empirical investigations. In the remaining part time-series analyses of the averaged as well as the single-vehicle data are carried out. The results will be used in order to propose objective criteria for an identification of the different traffic states, e.g. synchronized traffic.Comment: 12 pages, 19 figures, RevTe