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A particularly challenging neural network application requiring high-speed and intensive image processing capability is target acquisition and discrimination. It requires spatio-temporal recognition of point and resolved targets at high speeds. A reconfigur able neural architecture may discriminate targets from clutter or classify targets once resolved. By mating a 64 x 64 pixel array infrared (IR) image sensor to a 3-D stack (cube) of 64 neural-net ICs along respective edges, every pixel would directly input to a neural network, thereby processing the infor-mation with full parallelism. Being mated to the infrared sensor array, the cube would operate at 90°K temperature with

In the last two decades, many different techniques have been developed and investigated by researchers for summarizing the output and drawing conclusions from simulation experiments. The lack of practitioner interest in using these techniques has been ignored by some researchers but has become a source of great existential angst among others. Some have suggested that the root cause of this disinterest is a lack of communication between researchers and practitioners. It seems appropriate to look at the different views of output analysis research. This paper is an attempt to (i) determine the level of practitioner usage in the methods developed by output analysis researchers, (ii) find out how the output analytic research is perceived by practitioners of simulation, and (iii) find out what topics simulation practitioners think are important for further investigation or development by researchers. The intent is to discuss these three main issues as well as any other relevant issues.

AutoStat/sup TM/ is an extension package for AutoMod/sup TM/ and AutoSched/sup TM/ models that provides complete support for simulation model experimentation and statistical analysis of outputs. Within a menu-driven, point and click environment, AutoStat provides assistance for setting up runs, automated execution of runs and consolidation of outputs across replications and scenarios. AutoStat automatically sets random number seeds to achieve statistically independent replications; it also sets factor levels (input parameters) to realize desired scenarios, without having to modify the underlying model. AutoStat has a number of advanced features such as datafile factors, responses (or performance measures) read from custom user output files, and user-defined combination responses (linear combinations of other responses). AutoStat offers several statistical methods, including confidence intervals, a ranking and selection procedure, design of experiments, and warm-up determination. Outputs can be displayed and printed in tabular and graphical formats and exported to spreadsheet and other analysis software. AutoStat saves simulation modelers considerable time and automates the effort of setting up and making runs, managing the input and output files, consolidating results and conducting analyses.

This presentation is for those who want to be a simulation consultant, whether in an independent consulting company or within a consulting group in a larger company. Both practical project management and good technical fundamentals are critical for success. Does a simulation consultant need to be an expert in all disciplines? Does he or she need to be an engineer, a modeler, a statistician, and a computer scientist? The ideas presented in this tutorial are based on many years of practical simulation experience. Examples from real world projects will be used to illustrate various points. The author's woeful experience will be used to illustrate pitfalls to avoid.

Presents a general model of a rapid transit system with automatic train control and automatic train protection (ATC/ATP). It has been used to study potential transit technologies, track layouts and block designs for ATC/ATP for the Miami State Road 836 East-West Multimodal Corridor Study Alternatives Analysis (AA). Results of the study have been used in a Draft Environmental Impact Statement (AA/DEIS) and a preliminary engineering report. The purpose of the "screening level" analysis presented in this paper is to propose a few technologically sound, cost-effective public transit technologies that can be carried forward into the conceptual design process, allowing further analysis of technology and alignment options as additional information is generated.

This paper provides a language-independent introduction to the major simulation modeling world views for discrete-event systems simulation. A world view is the modeling framework that a modeler uses to represent a system and its behavior. The main terminology and concepts include systems and models, system state variables, entities and their attributes, lists, resources, events, activities and delays. These concepts are covered from the perspective of the modeler. Finally, in the tutorial, we will attempt to make these ideas concrete through the use of a number of examples and exercises.

Simulation is a powerful tool for the analysis of new system designs, retrofits to existing systems and proposed changes to operating rules. Conducting a valid simulation is both an art and a science. This paper provides an introduction to simulation and modeling and the main concepts underlying simulation. It discusses a number of key issues regarding a simulation team, how to conduct a simulation study, the skills required and the steps involved. It also provides project management guidelines and outlines pitfalls to avoid.

The Red Book lies quietly on my shelf, its cover frayed through years of heavy use, some pages torn. The book and I were both young once. Before 1974 when Tom Schriber was still pregnant with the ideas for the book, I employed a pre-publication version to equip bright and eager students with the skills to practically apply this innovative problem-solving tool, GPSS. As the years went by, however, the book and I both became grayer. Updated versions of GPSS appeared on various computer platforms. Microcomputers became the tool of choice and friendlier versions of GPSS became available. Through it all, the Red Book never changed, although its price always rose. Rumor had it that Tom Schriber wanted to revise and expand the book so we waited and remained loyal, but nothing happened. We were very patient, but now it is 1990.

We consider the efficient estimation of mean delay in queue, d, mean wait in system, w, time average number in queue, Q, time average number in system, L, and time average amount of work in system, V, for simulated queueing systems. We prove for the regenerative GI/G/s queue that it is more efficient to estimate w, Q, L, and V from an estimate of d than it is to estimate them directly. This generalizes previous results for the M/G/1 queue and also confirms empirical studies on other GI/G/s queues.

A common problem faced by simulators is that of constructing a confidence interval for the steady-state mean of a stochastic process. We have reviewed the existing procedures for this problem and found that all but one either produce confidence intervals with coverages which may be considerably lower than desired or have not been adequately tested. Thus, in many cases simulators will have more confidence in their results than is justified. In this paper we present a new sequential procedure based on the method of batch means for constructing a confidence interval with coverage close to the desired level. The procedure has the advantage that it does not explicitly require a stochastic process to have regeneration points. Empirical results for a large number of stochastic systems indicate that the new procedure performs quite well.

A number of currently available simulation languages are based on, or offer, the process-interaction approach to simulation; among these languages are GPSS/H, SIMAN, SIMSCRIPT II.5 and SLAM II. This paper provides an introductory survey and descrip tion of these four languages, plus a briefer mention of a number of others. First, we give general information regarding the languages, including a brief description of the language's perspec tive, computers on which it is implemented, the address and telephone numbers for information, a partial listing of relevant texts and manuals, and presently available short courses on language instruction. Second, we explain the perspective of the four languages in greater detail, and model an example problem using each language. An overview of each solution plus a detailed statement-by-statement description is given.

We correct some errors in Spira’s algorithm for the all-pairs shortest-path problem, and empirically compare his algorithm (with two distinct sorting, routines) to Dijkstra’s procedure. The results show that Spira’s algorithm is only efficient for “large” networks. Furthermore, it is seen that the asymptotic number of additions and comparisons required by two algorithms may be a very poor indicator of their relative running times.

In this article, we introduce the reader to discrete-event simulation. The concepts of system and model, system state, entities, attributes and delays are defined in the general context of simulation. Using these concepts, event-scheduling, process-interaction, and activity-scanning perspectives are briefly described. To demonstrate the use of the concepts, a discrete system is modeled using the event-scheduling perspective. Simulation languages are classified in terms of the type of system being modeled, the application level, and the perspective taken. The features of a simulation language are discussed. Lastly, basic information is provided about an assortment of discrete-event simulation languages.