Simulation model for natural gas transmission pipeline network system

This paper focuses on developing a simulation model for the analysis of transmission pipeline network system (TPNS) with detailed characteristics of compressor stations. Compressor station is the key element in the TPNS since it provides energy to keep the gas moving. The simulation model is used to create a system that simulates TPNS with different configurations to get pressure and flow parameters. The mathematical formulations for the TPNS simulation were derived from the principles of flow of fluid through pipe, mass balance and compressor characteristics. In order to determine the unknown pressure and flow parameters, a visual C++ code was developed based on Newton–Raphson solution technique. Using the parameters obtained, the model evaluates the energy consumption for various configurations in order to guide for the selection of optimal TPNS. Results from the evaluations of the model with the existing TPNS and comparison with the existing approaches showed that the developed simulation model enabled to determine the operational parameters with less than 10 iterations. Hence, the simulation model could assist in decisions regarding the design and operations of the TPNS.     

On Conducting Simultaneous Versus Sequential Engineering Activities in Risky R&D

We consider development projects in which the activities can be classified in two types: uncertainty resolving R&D efforts that accumulate in the form of knowledge to eventually give rise to a discovery; and routine technical investments which aim at improving the profitability of the project once the discovery has been made. The advantage of initiating routine preparatory activities prior to the achievement of a technological breakthrough in such projects, sometimes termed as 'concurrent engineering', is analyzed within a suitable dynamic optimization framework. The optimal double expenditure policy is compared with the conservative delayed investment policy, under which all the routine engineering activities are delayed until the risky R&D efforts culminate in a breakthrough. A criterion for the optimality of the latter policy is developed on the basis of the probability distribution of the discovery date. The application of the criterion is illustrated for a variety of specifications regarding the uncertainty associated with the R&D process. We find that simultaneous investment is the optimal policy for a large class of probability distributions. However, if the conditional probability of immediate discovery is always below some critical value, it is optimal to delay the preparatory activities until the discovery.     

DISCWorld: an environment for service-based matacomputing

We describe our DISCWorld system for wide-area, high-performance metacomputing in which we adopt a high-level, service-based approach. Users’ client programs request combinations of services from a set of server nodes which communicate at a peer-based level. DISCWorld is a constrained metacomputing system, running only the service operations its participating resource administrators have chosen to provide and advertise, and provides a common integration environment for clients to access these services and developers to make them available. We discuss our software architecture and experiences building DISCWorld using Java and CORBA components, and the associated research issues for metacomputing that we are addressing.     

Returning basic research to the community: A relationship between scientist and citizen.

Demonstrates how citizens can use the information gained through a basic research project to benefit both the social scientist and the community being studied. A case study is presented based on the experience of the Neighborhood Participation Project, which studied citizen participation in a racially integrated neighborhood in Nashville, Tennessee. After gathering longitudinal data from residents, the researchers embarked on a process of working with a neighborhood agency (Neighborhood Housing Services) and a neighborhood organization (Sunnyside Community Citizens) to give away both the process and content of their research. The case study is presented to highlight some important issues for returning basic research to the community. The authors suggest that creating partnerships and linkages between social scientists and citizens can improve the quality of social science research, enhance the potential for using research, encourage public support for social science research, and help people help themselves. (37 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Scalable approach to uncertainty quantification and robust design of interconnected dynamical systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks.     

Regranulation: A granular algorithm enabling communication between granular worlds

In this paper, we describe a granular algorithm for translating information between two granular worlds, represented as fuzzy rulebases. These granular worlds are defined on the same universe of discourse, but employ different granulations of this universe. In order to translate information from one granular world to the other, we must regranulate the information so that it matches the information granularity of the target world. This is accomplished through the use of a first-order interpolation algorithm, implemented using linguistic arithmetic, a set of elementary granular computing operations. We first demonstrate this algorithm by studying the common “fuzzy-PD” rulebase at several different granularities, and conclude that the “3 × 3” granulation may be too coarse for this objective. We then examine the question of what the “natural” granularity of a system might be; this is studied through a 10-fold cross-validation experiment involving three different granulations of the same underlying mapping. For the problem under consideration, we find that a 7 × 7 granulation appears to be the minimum necessary precision.     

CDUL: Class directed unsupervised learning

A novel neural network called Class Directed Unsupervised Learning (CDUL) is introduced. The architecture, based on a Kohonen self-organising network, uses additional input nodes to feed class knowledge to the network during training, in order to optimise the final positioning of Kohonen nodes in feature space. The structure and training of CDUL networks is detailed, showing that (a) networks cannot suffer from the problem of single Kohonen nodes being trained by vectors of more than one class, (b) the number of Kohonen nodes necessary to represent the classes is found during training, and (c) the number of training set passes CDUL requires is low in comparison to similar networks. CDUL is subsequently applied to the classification of chemical excipients from Near Infrared (NIR) reflectance spectra, and its performance compared with three other unsupervised paradigms. The results thereby obtained demonstrate a superior performance which remains relatively constant through a wide range of network parameters.     

Enriching our theoretical repertoire: the role of evolutionary psychology in technology acceptance

Information systems (IS) research has drawn heavily on social and cognitive psychology to explain technology adoption. Indeed, the many variations of the technology acceptance model all share these same theoretical foundations. Focusing exclusively on the socio-cognitive lens can lead to overlooking enhanced explanations of technology acceptance, such that new theoretical perspectives may be warranted. In this qualitative grounded theory study, we discovered how the lens of evolutionary psychology, as embodied in the Four-Drive model, was helpful in understanding technology acceptance across three organizational sites. We contend that evolutionary psychology is an important addition to the theoretical repertoire of IS researchers, and propose including ‘evolved psychological mechanisms’ within traditional models of technology acceptance.     

On the Properties of Fuzzy Switching Functions

Abstract

A strictly binary approach to the treatment of switching circuits today is not always adequate to describe systems in the real world. This approach is partly due, to the relative simplicity of designing binary switching systems, and to the fact that basic switching modules in common use are two-positional. Consequently, every variable in Boolean logic is assumed to be two-valued. However, because of real-world constraints, the attributes of system variables are often ambiguously defined. In other words, quite often variables might have values other than falsehood and truth. Cases with such attributes arise, for example in artifical intelligence and related subjects. Ever since Zadeh introduced the idea of fuzzy set theory [11 by utilizing the concept of membership grade, a number of authors have been concerned with the analysis and applications of fuzzy models. Especially, the relation of fuzzy to switching systems have been discussed in [2]–[12] and by other researchers in relation to other topics.

In this paper we are concerned with the study of fuzzy switching functions and their properties. Special attention is devoted to their minimization, enumeration, and their application to Boolean static hazard detection.     

Evaluating opportunistic networks in disaster scenarios

Forwarding data in scenarios where devices have sporadic connectivity is a challenge. An example scenario is a disaster area, where forwarding information generated in the incident location, like victims' medical data, to a coordination point is critical for quick, accurate and coordinated intervention. New applications are being developed based on mobile devices and wireless opportunistic networks as a solution to destroyed or overused communication networks. But the performance of opportunistic routing methods applied to emergency scenarios is unknown today. In this paper, we compare and contrast the efficiency of the most significant opportunistic routing protocols through simulations in realistic disaster scenarios in order to show how the different characteristics of an emergency scenario impact in the behaviour of each one of them.