8410122
On connectivity of reconfigurable impact networks in ageless aerospace vehicles
The research results presented in this paper were obtained as part of the joint CSIRO-NASA Ageless Aerospace Vehicle (AAV) project. We describe the underlying principles, methodology, and preliminary results of modelling and simulating a multi-cellular sensor and communication network in a dynamic decentralised setting, motivated by a self-monitoring, self-repairing AAV. Such networks are expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we address the problem of forming a reconfigurable network (a minimum spanning tree) connecting cells that detected non-critical impacts, in presence of connectivity disruptions caused by critical impacts. The presented algorithm is based on the ant colony metaphor and may be complemented by gradient-based techniques. In addition, we measure the robustness of impact networks and present quantitative metrics that clearly identify phase transitions in network connectivity, separating chaotic dynamics from ordered and robust patterns. © 2005 Elsevier B.V. All rights reserved.
15078369
Self-organising impact boundaries in ageless aerospace vehicles
Self-monitoring, self-repairing aerospace vehicles require modular, flexible and adaptive sensing and communication networks. In general, a modular (multi-cellular) sensing and communication network is expected to detect and react to impact location, energy and damage over a wide range of impacts. It is critical that global response emerges as a result of interactions involving transfer of information embedded locally, avoiding single points-of-failure. This work presents mechanisms ensuring self-organisation of autonomous cells into robust and continuously connected impact boundaries. The spatiotemporal stability is demonstrated for a variety of cell shapes in a dynamic environment with varying energy dissipation and damage probability models.
16423538
On Decentralised Clustering in self-monitoring networks
A Decentralised Adaptive Clustering (DAC) algorithm for multiagent networks is contrasted with a Fixed-order Centralised Adaptive Clustering algorithm (FCAC). The clustering is done on sensor readings detected within a self-monitoring impact sensing network. Simulation results show that DAC algorithm scales well with increasing network and data sizes and in some cases outperforms FCAC algorithm. While the common-sense intuition suggests that centralised algorithm is always superior, we support the simulation results with a simple counter-example.
17885591
Towards Adaptive Clustering in Self-monitoring Multi-agent Networks
A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.
17905930
Evaluating Team Performance at the Edge of Chaos
We introduce a concise approach to teamwork evaluation on multiple levels – dealing with agent’s behaviour spread and multi-agent coordination potential, and abstracting away the team decision process. The presented quantitative information-theoretic methods measure behavioural and epistemic entropy, and detect phase transitions – the edge of chaos – in team performance. The techniques clearly identify under-performing states, where a change in tactics may be warranted. This approach is a step towards a unified quantitative framework on behavioural and belief dynamics in complex multi-agent systems.
487167
On Self-organising Diagnostics in Impact Sensing Networks
Structural health management (SHM) of safety-critical structures requires multiple capabilities: sensing, assessment, diagnostics, prognostics, repair, etc. This paper presents a capability for self-organising diagnosis by a group of autonomous sensing agents in a distributed sensing and processing SHM network. The diagnostics involves acoustic emission waves emitted as a result of a sudden release of energy during impacts and detected by the multi-agent network. Several diagnostic techniques identifying the nature and severity of damage at multiple sites are investigated, and the self-organising maps (Kohonen neural networks) are shown to outperform the standard k-means algorithm in both time- and frequency domains.
13349953
Heuristics and normative models of judgment under uncertainty
Abstract Psychological evidence shows that probability theory is not a proper descriptive model of intuitive human judgment. Instead, some heuristics have been proposed as such a descriptive model. This paper argues that probability theory has limitations even as a normative model. A new normative model of judgment under undertainty is designed under the assumption that the system's knowledge and resources are insufficient with respect to the questions that the system needs to answer. The proposed heuristics in human reasoning can also be observed in this new model, and can be justified according to the assumption.
62548937
Performance Modeling and Prediction of Face Recognition Systems
It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper presents generic methods to model and predict the face recognition performance based on analysis of similarity measurement. We first introduce a concept of "perfect recognition", which only depends on the intrinsic structure of a recognition system. A metric extracted from perfect recognition similarity scores (PRSS) allows modeling the face recognition performance without empirical testing. This paper also presents an EM algorithm to predict the recognition rate of a query set. Furthermore, features are extracted from similarity scores to predict recognition results of individual queries. The presented methods can select algorithm parameters offline, predict recognition performance online, and adjust face alignment online for better recognition. Experimental results show that the performance of recognition systems can be greatly improved using presented methods.
1532988
Symbolic Computation and Parallel Software
Two aspects of parallelism as related to symbolic computing are presented: (1) the implementation of parallel programs for the factorization of polynomials, and (2) the automatic derivation and generation of parallel codes for finite element analysis. The former illustrates the use of parallel programming to speed up symbolic manipulation. The latter shows how symbolic systems can help create parallel software for scientific computation. Through these two case studies, the promise of parallelism in symbolic computation is demonstrated.
13042163
Non-Axiomatic Reasoning System (Version 4.1)
NARS (Non-Axiomatic Reasoning System) is an intelligent reasoning system. It answers questions according to the knowledge originally provided by its user. What makes it different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. The NARS 4.1 demo is a Java applet. It comes with help information and simple examples to show how the system does deduction, induction, abduction, analogy, belief revision, membership evaluation, relational inference, backward inference, new concept formation, and so on, in a unified manner. The demo also allows its user to create new examples to test the system, as well as to see the internal structure and process when the system is running. The on-line help document contains links to relevant publications. A previous version of the system, NARS 3.0, is described in detail in (Wang, 1995), which, and other related publications, are available at the author’s web page.
2424601
Users' interaction with World Wide Web resources: an exploratory study using a holistic approach
Abstract This paper presents the first part of the research on user–Web interaction: a multidimensional model, methodology, and general findings. The objectives of this study are three-fold: (1) to explore factors of user–Web interaction in finding factual information and what happens during this interaction; (2) to develop a conceptual framework for studying user–Web interaction; and (3) to apply a process-tracing method for conducting holistic user–Web studies. The proposed model consists of three components: user, interface, and the World Wide Web. User–Web interaction is viewed as a communication process facilitated through an interface. A process-tracing technique has been designed to capture the processes of user-Web interactions. Twenty-four graduate students participated in this study. Prior to the interaction, each participant was given a questionnaire to report his/her computer and Web experience, the State Trait Anxiety Inventory (form Y1 and form Y2) to measure affective states, and an individually administered Embedded Figure Test to measure cognitive style. Each participant used the Web to find answers to two factual questions. Both the processes (continuous screen shots) and the concurrent verbalizations of thoughts were recorded in synchronized video–audio data. The findings provided rich information on users’ cognitive, affective and physical behaviors. The proposed model is used to present the findings of user behavior in connections with interfaces and the Web.