7,491 research outputs found
Query processing of geometric objects with free form boundarie sin spatial databases
The increasing demand for the use of database systems as an integrating
factor in CAD/CAM applications has necessitated the development of database
systems with appropriate modelling and retrieval capabilities. One essential
problem is the treatment of geometric data which has led to the development of
spatial databases. Unfortunately, most proposals only deal with simple geometric
objects like multidimensional points and rectangles. On the other hand, there has
been a rapid development in the field of representing geometric objects with free
form curves or surfaces, initiated by engineering applications such as mechanical
engineering, aviation or astronautics. Therefore, we propose a concept for the realization
of spatial retrieval operations on geometric objects with free form
boundaries, such as B-spline or Bezier curves, which can easily be integrated in
a database management system. The key concept is the encapsulation of geometric
operations in a so-called query processor. First, this enables the definition of
an interface allowing the integration into the data model and the definition of the
query language of a database system for complex objects. Second, the approach
allows the use of an arbitrary representation of the geometric objects. After a
short description of the query processor, we propose some representations for free
form objects determined by B-spline or Bezier curves. The goal of efficient query
processing in a database environment is achieved using a combination of decomposition
techniques and spatial access methods. Finally, we present some experimental
results indicating that the performance of decomposition techniques is
clearly superior to traditional query processing strategies for geometric objects
with free form boundaries
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
On Range Searching with Semialgebraic Sets II
Let be a set of points in . We present a linear-size data
structure for answering range queries on with constant-complexity
semialgebraic sets as ranges, in time close to . It essentially
matches the performance of similar structures for simplex range searching, and,
for , significantly improves earlier solutions by the first two authors
obtained in~1994. This almost settles a long-standing open problem in range
searching.
The data structure is based on the polynomial-partitioning technique of Guth
and Katz [arXiv:1011.4105], which shows that for a parameter , , there exists a -variate polynomial of degree such that
each connected component of contains at most points
of , where is the zero set of . We present an efficient randomized
algorithm for computing such a polynomial partition, which is of independent
interest and is likely to have additional applications
Multi-Paradigm Reasoning for Access to Heterogeneous GIS
Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn
the web into a geospatial web services enabled place. Mediation
architectures like VirGIS overcome syntactical and semantical heterogeneity
between several distributed sources. On mobile devices,
however, this kind of solution is not suitable, due to limitations,
mostly regarding bandwidth, computation power, and available storage
space. The aim of this paper is to present a solution for providing
powerful reasoning mechanisms accessible from mobile applications
and involving data from several heterogeneous sources.
By adapting contents to time and location, mobile web information
systems can not only increase the value and suitability of the
service itself, but can substantially reduce the amount of data delivered
to users. Because many problems pertain to infrastructures
and transportation in general and to way finding in particular, one
cornerstone of the architecture is higher level reasoning on graph
networks with the Multi-Paradigm Location Language MPLL. A
mediation architecture is used as a âgraph providerâ in order to
transfer the load of computation to the best suited component â
graph construction and transformation for example being heavy on
resources. Reasoning in general can be conducted either near the
âsourceâ or near the end user, depending on the specific use case.
The concepts underlying the proposal described in this paper are
illustrated by a typical and concrete scenario for web applications
Towards Generalized Noise-Level Dependent Crystallographic Symmetry Classifications of More or Less Periodic Crystal Patterns
Geometric Akaike Information Criteria (G-AICs) for generalized noise-level dependent crystallographic symmetry classifications of two-dimensional (2D) images that are more or less periodic in either two or one dimensions as well as Akaike weights for multi-model inferences and predictions are reviewed. Such novel classifications do not refer to a single crystallographic symmetry class exclusively in a qualitative and definitive way. Instead, they are quantitative, spread over a range of crystallographic symmetry classes, and provide opportunities for inferences from all classes (within the range) simultaneously. The novel classifications are based on information theory and depend only on information that has been extracted from the images themselves by means of maximal likelihood approaches so that these classifications are objective. This is in stark contrast to the common practice whereby arbitrarily set thresholds or null hypothesis tests are employed to force crystallographic symmetry classifications into apparently definitive/exclusive states, while the geometric feature extraction results on which they depend are never definitive in the presence of generalized noise, i.e., in all real-world applications. Thus, there is unnecessary subjectivity in the currently practiced ways of making crystallographic symmetry classifications, which can be overcome by the approach outlined in this review
An Evolutionary Learning Approach for Adaptive Negotiation Agents
Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications
K-nearest neighbor search for fuzzy objects
The K-Nearest Neighbor search (kNN) problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. Existing research on fuzzy objects mainly focuses on modelling basic fuzzy object types and operations, leaving the processing of more advanced queries such as kNN query untouched. In this paper, we propose two new kinds of kNN queries for fuzzy objects, Ad-hoc kNN query (AKNN) and Range kNN query (RKNN), to find the k nearest objects qualifying at a probability threshold or within a probability range. For efficient AKNN query processing, we optimize the basic best-first search algorithm by deriving more accurate approximations for the distance function between fuzzy objects and the query object. To improve the performance of RKNN search, effective pruning rules are developed to significantly reduce the search space and further speed up the candidate refinement process. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments using both synthetic and real datasets
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