Dynamic modelling and estimation of the error due to asynchronism in a redundant asynchronous multiprocessor system

The use of Redundant Asynchronous Multiprocessor System to achieve ultrareliable Fault Tolerant Control Systems shows great promise. The development has been hampered by the inability to determine whether differences in the outputs of redundant CPU's are due to failures or to accrued error built up by slight differences in CPU clock intervals. This study derives an analytical dynamic model of the difference between redundant CPU's due to differences in their clock intervals and uses this model with on-line parameter identification to idenitify the differences in the clock intervals. The ability of this methodology to accurately track errors due to asynchronisity generate an error signal with the effect of asynchronisity removed and this signal may be used to detect and isolate actual system failures

Independent Orbiter Assessment (IOA): Assessment of the manned maneuvering unit

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA effort first completed an analysis of the Manned Maneuvering Unit (MMU) hardware, generating draft failure modes and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contain within the NASA FMEA/CIL documentation. The IOA results were then compared to the proposed Martin Marietta FMEA/CIL Post 51-L updates. A discussion of each discrepancy from the comparison is provided through additional analysis as required. These discrepancies were flagged as issues, and recommendations were made based on the FMEA data available at the time. The results of this comparison for the Orbiter MMU hardware are documented. The IOA product for the MMU analysis consisted of 204 failure mode worksheets that resulted in 95 potential critical items being identified. Comparison was made to the NASA baseline which consisted of 179 FMEAs and 110 CIL items. This comparison produced agreement on all 121 FMEAs which caused differences in 92 CIL items

Tour-based Travel Mode Choice Estimation based on Data Mining and Fuzzy Techniques

This paper extends tour-based mode choice model, which mainly includes individual trip level interactions, to include linked travel modes of consecutive trips of an individual. Travel modes of consecutive trip made by an individual in a household have strong dependency or co-relation because individuals try to maintain their travel modes or use a few combinations of modes for current and subsequent trips. Traditionally, tour based mode choice models involved nested logit models derived from expert knowledge. There are limitations associated with this approach. Logit models assumes i) specific model structure (linear utility model) in advance; and, ii) it holds across an entire historical observations. These assumptions about the predefined model may be representative of reality, however these rules or heuristics for tour based mode choice should ideally be derived from the survey data rather than based on expert knowledge/ judgment. Therefore, in this paper, we propose a novel data-driven methodology to address the issues identified in tour based mode choice. The proposed methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performances are compared with the state-of-the-art approaches in this area

An Agent Based Model for the Simulation of Transport Demand and Land Use

Agent based modelling has emerged as a promising tool to provide planners with insights on social behaviour and the interdependencies characterising urban system, particularly with respect to transport and infrastructure planning. This paper presents an agent based model for the simulation of land use and transport demand of an urban area of Sydney, Australia. Each individual in the model has a travel diary which comprises a sequence of trips the person makes in a representative day as well as trip attributes such as travel mode, trip purpose, and departure time. Individuals are associated with each other by their household relationship, which helps define the interdependencies of their travel diary and constrains their mode choice. This allows the model to not only realistically reproduce how the current population uses existing transport infrastructure but more importantly provide comprehensive insight into future transport demands. The router of the traffic micro-simulator TRANSIMS is incorporated in the model to inform the actual travel time of each trip and changes of traffic density on the road network. Simulation results show very good agreement with survey data in terms of the distribution of trips done by transport modes and by trip purposes, as well as the traffic density along the main road in the study area

GIDS: Global Interlinked Data Store

This paper introduces the Global Interlinked Data Store1 (GIDS), a technique to support the easy creation and retrieval of interlinked semantic data within a web-scale distributed network environment such as the World Wide Web (WWW). The GIDS enables the network to be treated as a data store without worrying about files, databases or other traditional data storage concerns. Data created on the network can be subsequently accessed and navigated by end users and software agents alike. The GIDS proposes a novel three-stage data storage process which enables the data to be stored in up to three contextually relevant locations to enhance subsequent retrieval opportunities. We believe that the capability offered by the GIDS will be of significant use to rapidly formed diverse coalitions who wish to communicate and exchange semantic data in a large network environment such as the WWW

The Cognitive Virtues of Dynamic Networks

For the most part, studies in the network science literature tend to focus on networks whose functional connectivity is largely invariant with respect to some episode of collective information processing. In the real world, however, networks with highly dynamic functional topologies tend to be the norm. In order to improve our understanding of the effect of dynamic networks on collective cognitive processing, we explored the problem-solving abilities of synthetic agents in dynamic networks, where the links between agents were progressively added throughout the problem-solving process. The results support the conclusion that (at least in some task contexts) dynamic networks contribute to a better profile of problem-solving performance compared to static networks (whose topologies are fixed throughout the course of information processing). Furthermore, the results suggest that constructive networks (like those used in the present study) strike a productive balance between autonomy and social influence. When agents are allowed to operate independently at the beginning of a problem-solving process, and then later allowed to communicate, the result is often a better profile of collective performance than if extensive communication had been permitted from the very outset of the problem-solving process. These results are relevant, we suggest, to a range of phenomena, such as groupthink, the common knowledge effect and production blocking, all of which have been observed in group problem-solving contexts

The ART of IAM: The Winning Strategy for the 2006 Competition

In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for others, may betray that trust by not performing the actions as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. This situation has led to the development of a number of trust and reputation models, which aim to facilitate an agent's decision making in the face of uncertainty regarding the behaviour of its peers. However, these multifarious models employ a variety of different representations of trust between agents, and measure performance in many different ways. This has made it hard to adequately evaluate the relative properties of different models, raising the need for a common platform on which to compare competing mechanisms. To this end, the ART Testbed Competition has been proposed, in which agents using different trust models compete against each other to provide services in an open marketplace. In this paper, we present the winning strategy for this competition in 2006, provide an analysis of the factors that led to this success, and discuss lessons learnt from the competition about issues of trust in multiagent systems in general. Our strategy, IAM, is Intelligent (using statistical models for opponent modelling), Abstemious (spending its money parsimoniously based on its trust model) and Moral (providing fair and honest feedback to those that request it)