93,092 research outputs found
A feature-based reverse engineering system using artificial neural networks
Reverse Engineering (RE) is the process of reconstructing CAD models from
scanned data of a physical part acquired using 3D scanners. RE has attracted a
great deal of research interest over the last decade. However, a review of the
literature reveals that most research work have focused on creation of free form
surfaces from point cloud data. Representing geometry in terms of surface patches
is adequate to represent positional information, but can not capture any of the
higher level structure of the part. Reconstructing solid models is of importance
since the resulting solid models can be directly imported into commercial solid
modellers for various manufacturing activities such as process planning, integral
property computation, assembly analysis, and other applications.
This research discusses the novel methodology of extracting geometric features
directly from a data set of 3D scanned points, which utilises the concepts of
artificial neural networks (ANNs). In order to design and develop a generic
feature-based RE system for prismatic parts, the following five main tasks were
investigated. (1) point data processing algorithms; (2) edge detection strategies;
(3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD
model exchanger into other CAD/CAM systems via IGES.
A key feature of this research is the incorporation of ANN in feature recognition.
The use of ANN approach has enabled the development of a flexible feature-based
RE methodology that can be trained to deal with new features. ANNs
require parallel input patterns. In this research, four geometric attributes extracted
from a point set are input to the ANN module for feature recognition: chain codes,
convex/concave, circular/rectangular and open/closed attribute. Recognising each
feature requires the determination of these attributes. New and robust algorithms
are developed for determining these attributes for each of the features.
This feature-based approach currently focuses on solving the feature recognition
problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss,
which are common and crucial in mechanical engineering products. This approach
is validated using a set of industrial components. The test results show that the
strategy for recognising features is reliable
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Extracting Boolean rules from CA patterns
A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois
Identification of the neighborhood and CA rules from spatio-temporal CA patterns
Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule
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Toward the automation of business process ontology generation
Semantic Business Process Management (SBPM) utilises semantic technologies (e.g., ontology) to model and query process representations. There are times in which such models must be reconstructed from existing textual documentation. In this scenario the automated generation of ontological models would be preferable, however current methods and technology are still not capable of automatically generating accurate semantic process models from textual descriptions. This research attempts to automate the process as much as possible by proposing a method that drives the transformation through the joint use of a foundational ontology and lexico-semantic analysis. The method is presented, demonstrated and evaluated. The original dataset represents 150 business activities related to the procurement processes of a case study company. As the evaluation shows, the proposed method can accurately map the linguistic patterns of the process descriptions to semantic patterns of the foundational ontology to a high level of accuracy, however further research is required in order to reduce the level of human intervention, expand the method so as to recognise further patterns of the foundational ontology and develop a tool to assist the business process modeller in the semi-automated generation of process models
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