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.
2175115
Phase Transitions in Self-Organising Sensor Networks
In this paper we consider a multi-cellular sensing and communication network, embedded in an ageless aerospace vehicle, that is expected to detect and react to impact location, intensity and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and measure their spatiotemporal robustness. The presented quantitative information-theoretic techniques clearly identify phase transitions, separating chaotic dynamics from ordered and robust patterns.
28403175
GI/S: A Graphical User Interface For Symbolic Computation Systems
The design and implementation of GI/S, a Graphical user Interface for Symbolic computation systems, is described. The system provides a multiple window environment for the high-resolution 2-D display of mathematical expressions, the ability to select and manipulate parts of expressions with a mouse, and graphics plotting of mathematical expressions. GI/S also provides command line editing and a history mechanism for editing and re-executing past commands. GI/S is written in Franz Lisp for the Macsyma system and has been implemented on the Tektronix 4404 workstation.
17913196
Community Learning in Information Technology Fashion
In their striving to learn about information technology innovations, organizations draw on knowledge resources available in the diverse organizational communities that converge around those innovations. But even as such organizations learn about an innovation, so too does the larger community. Community learning takes place as its members reflect upon their learning and contribute their experiences, observations, and insights to the community’s on-going innovation discourse. Community learning and organizational learning thus build upon one another in a reciprocal process, or cycle, over time, as the stock of interpretations, adoption rationales, implementation strategies, and utilization patterns is expanded and refined. Relative to this overall cycle, we explore the neglected aspect that concerns how community learning draws on organizational learning. Analyzing the community discourse on enterprise resource planning (ERP) over the past 14 years, we found that different types of organizational actors played different roles, at different times, in contributing different types of knowledge to the discourse. Research analysts and technology vendors took leadership early on in articulating the know-what (conceptualization and interpretation) and know-why (justification) for ERP. Later on, adopters came to dominate the discourse through contributions of know-how (capacitation). We situate these observations in a larger model of the learning cycle, and outline a number of areas for future research on the crucial interactions between organizational and community learning in IT innovation.
6086436
3D Line Image Analysis - A Heuristic Parallel Approach with Learning and Recognition
Abstract A heuristic parallel approach for (3d) line image analysis is introduced by using the concept of coordinated graph, layered graph representation, and parallel matching techniques and it significantly reduces the time required for dealing with three-dimensional line image analysis problems. Their fundamental properties and the concept of finite representations are investigated, and an interactive and iterative recognition algorithm with learning and recognition as a part of each other is presented. Several interested examples including curved and disconnected line images are illustrated. In addition to its importance in theoretical studies, this approach can also be applied to 3d object recognition, image processing, and computer vision in industrial, military, and medical fields.
17343502
IAMC architecture and prototyping: a progress report
Internet Accessible Mathematical Computation (IAMC) is a distributed framework to supply mathematical computing powers over the Internet. Presented are conceptual and experimental work on the IAMC architecture, a client prototype (Dragonfly), client GUI, a server prototype (Starfish), the Mathematical Computation Protocol (MCP), mathematical data encoding, and the external compute engine interface.
15177304
A new method of color image segmentation based on intensity and hue clustering
A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and the center in the hue space, based on which the hue-clustering algorithm is implemented. Utilised in medical image processing, the new method gives a good performance.