Queuing Theory and Discrete Events Simulation for Health Care

This chapter describes applications of the discrete events simulation (DES) and queuing analytic (QA) theory as a means of analyzing healthcare systems. There are two objectives of this chapter: (i) to illustrate the use and shortcomings of QA compared to DES by applying both of them to analyze the same problems, and (ii) to demonstrate the principles and power of DES methodology for analyzing both simple and rather complex healthcare systems with interdependencies. This chapter covers: (i) comparative analysis of QA and DES methodologies by applying them to the same processes, (ii) effect of patient arrival and service time variability on patient waiting time and throughput, (iii) comparative analysis of the efficiency of dedicated (specialized) and combined resources, (iv) a DES model that demonstrates the interdependency of subsystems and its effect on the entire system throughput, and (v) the issues and perspectives of practical implementation of DES results in health care setting.

Teaching Principles of Petri Nets in Hardware Courses and Students Projects

The paper presents the principles of using Petri Net formalism in hardware design courses, especially in the course “Architecture of peripheral devices”. Several models and results obtained by student individual or group projects are mentioned. First the using of formalism as a modeling tool is presented consecutively from Place/Transition nets to Coloured Petri nets. Then the possible Petri Nets using as a hardware specification for direct hardware implementation (synthesized VHDL for FPGA) is described. Implementation and simulation results of three directly implemented models are presented.

An Integrated Data Mining and Simulation Solution

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

The Simulation of Spiking Neural Networks

This chapter is an overview of the simulation of spiking neural networks that relates discrete event simulation to other approaches and includes a case study of recent work. The chapter starts with an introduction to the key components of the brain and sets out three neuron models that are commonly used in simulation work. After explaining discrete event, continuous and hybrid simulation, the performance of each method is evaluated and recent research is discussed. To illustrate the issues surrounding this work, the second half of this chapter presents a case study of the SpikeStream neural simulator that covers the architecture, performance and typical applications of this software along with some recent experiments. The last part of the chapter suggests some future trends for work in this area.

Applications of Visual Algorithm Simulation

Understanding data structures and algorithms is an integral part of software engineering and elementary computer science education. However, people usually have difficulty in understanding abstract concepts and processes such as procedural encoding of algorithms and data structures. One way to improve their understanding is to provide visualizations to make the abstract concepts more concrete. In this chapter, we represent a novel idea to promote the interaction between the user and the algorithm visualization system called visual algorithm simulation. As a proof of concept, we represent an application framework called Matrix that encapsulates the idea of visual algorithm simulation. The framework is applied by the TRAKLA2 learning environment in which algorithm simulation is employed to produce algorithm simulation exercises. Moreover, we discuss the benefits of such exercises and applications of visual algorithm simulation in general.

Experimental Error Measurement in Monte Carlo Simulation

This chapter describes the set up step series, developed by the Genoa Research Group on Production System Simulation at the beginning of the ’80s, as a sequence, through which it is possible at first statistically validate the simulator, then estimate the variables which effectively affect the different target functions, then obtain, through the regression meta-models, the relations linking the independent variables to the dependent ones (target functions) and, finally, proceed to the detection of the optimal functioning conditions. The authors pay great attention to the treatment, the evaluation and control of the Experimental Error, under the form of Mean Square Pure Error (MSPE), a measurement which is always culpably neglected in the traditional experimentation on the simulation models but, that potentially can consistently invalidate with its magnitude the value of the results obtained from the model.

Simulation Environments as Vocational and Training Tools

This paper investigates over 50 simulation packages and simulators used in vocational and course training in many fields. Accordingly, the 50 simulation packages were categorized in the following fields: Pilot Training, Chemistry, Physics, Mathematics, Environment and ecological systems, Cosmology and astrophysics, Medicine and Surgery training, Cosmetic surgery, Engineering – Civil engineering, architecture, interior design, Computer and communication networks, Stock Market Analysis, Financial Models and Marketing, Military Training and Virtual Reality. The incentive for using simulation environments as vocational and training tools is to save live, money and effort.

Using Simulation Systems for Decision Support

This chapter describes the use of simulation systems for decision support in support of real operations, which is the most challenging application domain in the discipline of modeling and simulation. To this end, the systems must be integrated as services into the operational infrastructure. To support discovery, selection, and composition of services, they need to be annotated regarding technical, syntactic, semantic, pragmatic, dynamic, and conceptual categories. The systems themselves must be complete and validated. The data must be obtainable, preferably via common protocols shared with the operational infrastructure. Agents and automated forces must produce situation adequate behavior. If these requirements for simulation systems and their annotations are fulfilled, decision support simulation can contribute significantly to the situational awareness up to cognitive levels of the decision maker.

Virtual Reality

The chapter introduces a modern and advanced view and implementations of Virtual reality systems. Considering the VR systems as tools that can be used in order to alter the perceived information from real world and allow perceiving the information from virtual world. Virtual Reality grounds the main concepts for interactive 3D simulations. The chapter emphasizes the use of the 3D interactive simulations through virtual reality systems in order to enable designers to operationalize the theoretical concepts for empirical studies. This emphasize takes the form of presenting most recent case studies for employing the VR systems. The first emphasizes the role of realistic 3D simulation in a virtual world for the purpose of pipelining complex systems production for engineering application. This requires highly realistic simulations, which involves both realism of object appearance and object behaviour in the virtual world. The second case emphasizes the evolution from realism of virtual reality towards additional reality. Coupling interactions between virtual and real worlds is an example of using the VR system to allow human operators to interactively communicate with real robot through a VR system. The robots and the human operators are potentially at different physical places. This allows for 3D-stereoscopic robot vision to be transmitted to any or all of the users and operators at the different sites.

Efficient Discrete Simulation of Coded Wireless Communication Systems

Simulation can be a valuable tool for wireless communication system’s (WCS) designers to assess the performance of its radio interface. It is common to use the Monte Carlo simulation method (MCSM), although this is quite time inefficient, especially when it involves forward error correction (FEC) with very low bit error ratio (BER). New techniques were developed to efficiently evaluate the performance of the new class of TCH (Tomlinson, Cercas, Hughes) codes in an additive white Gaussian noise (AWGN) channel, due to their potential range of applications. These techniques were previously applied using a satellite channel model developed by Lutz with very good results. In this chapter, we present a simulation method, named accelerated simulation method (ASM), that provides a high degree of efficiency and accuracy, namely for lower BER, where the application of methods like the MCSM is prohibitive, due to high computational and time requirements. The present work generalizes the application of the ASM to a WCS modelled as a stochastic discrete channel model, considering a real channel, where there are several random effects that result in random energy fluctuations of the received symbols. The performance of the coded WCS is assessed efficiently, with soft-decision (SD) and hard-decision (HD) decoding. We show that this new method already achieves a time efficiency of two or three orders of magnitude for SD and HD, considering a BER = 1x10-4 , when compared to MCSM. The presented performance results are compared with the MCSM, to check its accuracy.