Investigating and predicting the perceptibility protect of channel mismatch in stereoscopic video

The degree of visual discomfort caused by watching stereoscopic scenes with channel mismatch is investigated and predicted. A scene with channel order mismatch is one in which the right and left views are swapped. A way of finding channelmismatch is used to analyze 105 3D films; the scenes found in this analysis are used for experimental study of the visual discomfort caused by channelmismatch. The experimental results are used to construct samplingwith a reference pattern. This sampling is used to learn different regression analysis algorithms, and the bestway of predicting visual discomfort caused by channel mismatch is chosen.     

Modelling of Technical Systems: Application to Hydrodynamic Torque Converters and Couplings

First a short survey of modelling of technical systems is presented. The different methods of modelling are applied to hydrodynamic torque converters and couplings. The results of linear modelling around an operating point, of nonlinear modelling for larger operating areas and of hybrid modelling using simplified physical laws are illustrated. Simulations for large excitations show the validity of the models. The advantages and disadvantages of the different methods are discussed.     

Multi-perspective modelling of complex phenomena

This conceptual paper discusses the limitations of a single-perspective hierarchical approach to modelling and proposes multi-perspective modelling as a way to overcome them. As it turns out, multi-perspective modelling is primarily a new methodology, using existing modelling techniques but extending the modelling hierarchy with a new epistemological level which integrates the different perspectives. The methodology will be presented in some detail, and its use will be demonstrated by analyzing an example taken from a socio-political context.     

Is simulation in health different?

It is often stated that health simulation is quite different and even that it is more difficult than in other sectors. But is simulation in health really different to simulation elsewhere? In this paper we explore this question through a survey of simulation modellers and academics. We elicit their opinions across a range of factors concerning the difficulties of health modelling against modelling in other domains. The analysis considers the responses of the whole group of respondents and the sub-group of respondents who have experience both in and outside of health modelling. The results show that overall, there is a perception that health modelling is different and that it is more difficult across a range of factors. The implications for simulation research and practice in health are discussed.     

Discursive positionings and emotions in modelling activities

Mathematical modelling is suggested as an activity through which students engage in meaningful mathematics. In the current research, the modelling activity of a group of four seventh-grade students was analysed using the discursive analysis framework. The research findings show that the positionings and emotions of the group members during their participation in the modelling activity changed as the activity proceeded. Overall, it can be said that three of the four group members acted as insiders, while the fourth acted as an outsider, and only, towards the end of the group's work on the activity, he acted as an insider. Moreover, the research findings point at four factors that affected the group members’ positionings and emotions during the modelling activity: the member's characteristics, the member's history of learning experiences, the activity characteristics and the modelling phases. Furthermore, the different positionings of the group members in the different modelling phases were accompanied by different emotions experienced by them, where being an insider and a collaborator resulted in positive emotions, while being an outsider resulted in negative emotions.     

Categorical approach to modelling and to coupling of models

Solution of any engineering problem starts with a modelling process, which typically involves a choice among different kinds of models. To create a realistic model, one has to think carefully about the modelling process. Particularly in the case of coupled problems when several models are coupled together to represent a given physical phenomenon. This paper presents an approach based on the category theory that allows to describe this modelling process on a more abstract level. Using the advantages of abstract level, one can describe the coupling process in a concise way and introduce certain criteria to check consistency of a coupled model. The main idea of the proposed approach is to introduce a structure in the modelling process, which allows to see how different models interact without a precise look into them. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling on fuzzy control systems

A kind of modelling method for fuzzy control systems is first proposed here, which is called modelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method that is different from two well-known modelling methods, that is, the first modelling method, mechanism modelling method (MMM), and the second modelling method, system identification modelling method (SIMM). This method can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inference rules describing a practice system into a kind of nonlinear differential equation with variable coefficients, called HX equations, so that the mathematical model of the system can be obtained. This means that we solve the difficult problem of how to get a model represented as differential equations on a complicated or fuzzy control system.     

Theoretical and empirical differentiations of phases in the modelling process

The reconstruction of pupils modelling processes can be found in many empirical studies within the literature on modelling. The empirical differentiations of the phases, which includes putting statements and actions of the pupils in the right phase, has not been reconstructed from a cognitive psychological point of view on a micro level thus far: In this article different modelling cycles are discussed with attention to distinctions in the various phases. The «modelling cycle under cognitive psychological aspects» is specifically emphasized in contrast to the other cycles. On the basis of the results of the COM²-project (Cognitive psychological analysis of modelling processes in mathematics lessons, Borromeo Ferri) the phases of the modelling processare described empirically. Some difficulties in the process of distinguishing the various phases are also pointed out.     

Co-ordinated movements to avoid collisions of a twin-robot system

A method of modelling collision avoidance between separate coordinated arms in the process of changing hands is presented. The shortest distance between two forearms is chosen as the criterion for kinematic modelling. Trajectories of various geometries are classified with respect to different collision situations. Parameters to be adjusted to avoid collisions are also discussed. To illustrate the modelling technique, two redundant anthropomorphic robots are considered to cooperate in rotating a wheel.     

What is the role and contribution of models to management and research in the health services? A view from Europe

Modelling in different forms has long been regarded as a cornerstone of health OR as in other fields of application. Models are now tending to become a standard tool in health services management and research. What are the lessons we as operational researchers have learnt during this development? How can health care managers and health service researchers benefit from modelling — with or without OR-analysts to guide them between the pitfalls? After an introductory overview concerning the nature and objectives of modelling, examples will be given of modelling applications from different health service areas in order to illustrate the versatility of the method. Further, the choice of methods and models is discussed with special attention to the problems of interpretation and implementation of results. From this overview some conclusions are drawn with regards to advantages and disadvantages of modelling as tools for policy planning and decision-making in the health area. Finally some observations are made concercing the conditions for the future development in the field.     

Modelling and analysis of multistage stochastic programming problems: A software environment

In many decision problems, time and uncertainty play important roles and stochastic programming models are well suited for capturing both these aspects. In this paper we focus our attention on the difficulties which arise in developing multi-period stochastic models and we outline those challenging aspects where the use of classical modelling techniques for deterministic problems prove to be inadequate. In particular, we discuss the development of a modelling and analysis environment which combines multidimensional databases (MDDB), declarative modelling languages and procedural languages. Our aim is to develop a versatile tool which generates multi-period stochastic models and supports the modeller in browsing of data and solutions across different time stages and among different scenarios.     

Application of mathematical models to simulate an extreme air pollution episode in the Bulgarian city of Stara Zagora

The application of complex atmospheric transport and transformation mesoscale models is becoming more common for study complex environmental problems. In this contribution, we present a pollution episode occurred in the area of Stara Zagora (Bulgaria), where a large SO₂ industrial emitter is present. This industrial emitter accounts for more than 75% of the total SO₂ emissions in Bulgaria. Two monitoring stations located in Stara Zagora and Mogila observed high SO₂ concentration peaks for 7–11 July 2007. We have used the MM5–CMAQ air quality modelling system with CBM-IV chemical mechanism. We have used two different emission modelling tools such as SMOKE and EMIMO. In the first simulation SMOKE is used. In the second simulation, we have used EMIMO including biogenic and anthropogenic sources coming from traffic, domestic sources, etc. Additionally, in the second simulation we have used a so-called ON–OFF approach, which means that we have run two time the modelling system with and without the emissions of the large industrial plant “Maritza-Iztok”, located in the surrounding area of the two monitoring stations. The use of MM5–CMAQ air quality modelling system with two different approaches, parameterizations, spatial resolution, emission modelling systems and vertical grid levels produce two significantly different results. The results show that the parameterizations and approaches for using a complex air quality modelling system such as MM5–CMAQ, are crucial for obtaining some similarities between observed and modelled data.     

A generic environment for modelling future launch operations—GEM-FLO: a success story in generic modelling

Several NASA programs have been established to study and improve the current launch capability to meet the need for more aggressive space exploration in the future. Numerous launch systems have been proposed by different government and commercial organizations with the potential goal of replacing the Space Shuttle. NASA must evaluate new designs and technologies with the objective of improving upon today's Shuttle cost, performance, and turnaround time, before the government or commercial organizations pursue the large undertaking of a new launch system. To address this issue, the Generic Simulation Environment for Modelling Future Launch Operations (GEM-FLO) was developed to accurately predict processing turnaround times and other effectiveness criteria and support making key business and program decisions. GEM-FLO utilizes a generic modelling paradigm to provide a single platform for modelling different designs, which helped significantly cut the cost of these studies. This paper documents a success story in generic simulation modelling.     

How well do discrete element granular flow models capture the essentials of mixing processes?

Flowing granular materials, undergoing both mixing and segregation, play important roles in industries ranging from minerals and food to pharmaceuticals and ceramics. Sometimes it is desirable to enhance the mixing and inhibit segregation and in other cases it is desirable to minimise the mixing and enhance the segregation. The fundamentals of these processes are poorly understood. Computational modelling of such granular flows offers a good opportunity to study these fundamentals. But how well do these discrete element based modelling techniques capture the essential features of mixing processes? The capabilities of our discrete element modelling package are described. Two different methods for measuring the rates of mixing are presented and three different configurations are studied. Qualitatively reasonable flows are obtained. A detailed study of the mixing demonstrates that the amount and nature of the mixing is quite sensitive to a range of physical parameters.     

Inverse modelling problems in linear algebra undergraduate courses

This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different presentations will be discussed. Finally, several results will be presented and some conclusions proposed.     

Orders of Validation in Mathematical Modelling

Recent work on validation has demonstrated the paucity of validation in operational research projects. What little validation is done usually takes place when the modeller builds a model directly for a decision-maker. This paper begins by reviewing and exploring the processes of model-building and usage with different levels of separation between the model-builder and the ultimate user of the model outputs. The different types of validation that these levels of separation imply are then discussed, with emphasis on validation within linear programming. A checklist of validation questions is suggested. New modelling tools coming onto the market affect the levels at which mathematical modelling takes place and alter the ways in which this modelling should best be carried out in business.     

Decision development in facilitated modelling workshops

While the facilitated modelling literature recognises the importance of the group process within facilitated modelling workshops, published empirical research rarely examines their dynamic nature. In this paper, we address this gap in the literature in two ways. First, we propose to locate facilitated modelling workshops as the main focus of investigation, and adopt decision development as the analytical lens. Second, we provide guidance regarding how to implement a research strategy that is informed by such a focus. We start by mapping the different conceptualisations of decision development that seem embedded within the facilitated modelling tradition, and contrast them with theoretical models from the group communication field. Our analysis identifies a number of potentially useful areas for the study of facilitated modelling workshops from a decision development perspective, and articulates a number tentative research questions and testable propositions amenable to empirical research. Central to our proposal are research methods for the study of dynamic group processes. We thus discuss the steps required to extract group process data from facilitated modelling workshops that are usable and open to analysis. This includes a review of issues regarding research design, coding scheme development, data coding and choice of analytical techniques. Finally, we offer conclusions and briefly discuss some feasibility issues related to the implementation of our proposal.     

Rethinking tradeoffs and OR/MS methodology

The purpose of this paper is to explore the role of a process known as the ‘evaporating cloud’ (EC) as a way of enhancing OR/MS modelling. While other authors have described the use of the EC process to explore the tradeoffs in a traditional Economic Order Quantity model, this paper demonstrates the use of the EC with a facilities location problem, which was originally solved via mixed integer programming. We relate our findings to OR/MS modelling in general, and argue that the EC could contribute effectively to different phases of the problem-solving process, for example, aiding the problem-structuring phase and helping to find better solutions in many OR/MS modelling situations. We demonstrate that the EC is a valuable tool to use in modelling practice as a problem-structuring tool, as a way of dealing specifically with tradeoffs, and that it can therefore be a useful complement to OR/MS methodology, and contribute to OR/MS practice and teaching.