1,106 research outputs found
Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques
Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to
be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning
methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories.
We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that
proposes a new form of interaction between users and digital libraries, where the latter are adapted to users
and their surroundings
Challenges in distributed information search in a semantic digital library
Nowadays an enormous quantity of heterogeneous and distributed information is stored in the current digital
libraries. Access to these collections poses a serious challenge, however, because present search techniques
based on manually annotated metadata and linear replay of material selected by the user do not scale
effectively or efficiently to large collections. The artificial intelligent and semantic Web provides a common
framework that allows knowledge to be shared and reused. In this paper we propose a comprehensive
approach for discovering information objects in large digital collections based on analysis of recorded
semantic metadata in those objects and the application of expert system technologies. We suggest a
conceptual architecture for a semantic and intelligent search engine. OntoFAMA is a collaborative effort
that proposes a new form of interaction between people and Digital Library, where the latter is adapted to
individuals and their surroundings. We have used Case Based-Reasoning methodology to develop a
prototype for supporting efficient retrieval knowledge from digital library of Seville University
Intelligent Integrated Management for Telecommunication Networks
As the size of communication networks keeps on growing, faster connections, cooperating technologies and the divergence of equipment and data communications, the management of the resulting networks gets additional important and time-critical. More advanced tools are needed to support this activity. In this article we describe the design and implementation of a management platform using Artificial Intelligent reasoning technique. For this goal we make use of an expert system. This study focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We propose a new paradigm where the intelligent network management is integrated into the conceptual repository of management information called Managed Information Base (MIB). This paper outlines the development of an expert system prototype based in our propose GDMO+ standard and describes the most important facets, advantages and drawbacks that were found after prototyping our proposal
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
Monitoring and Fault Location Sensor Network for Underground Distribution Lines
One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous
supply of electricity to their customers. The primary distribution network is a critical part of these
facilities because a fault in it could affect thousands of customers. However, the complexity of
this network has been increased with the irruption of distributed generation, typical in a Smart
Grid and which has significantly complicated some of the analyses, making it impossible to apply
traditional techniques. This problem is intensified in underground lines where access is limited. As a
possible solution, this paper proposes to make a deployment of a distributed sensor network along
the power lines. This network proposes taking advantage of its distributed character to support new
approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed
network (adapted to the power grid) based on nodes that use power line communication and energy
harvesting techniques. In this sense, it also describes the implementation of a real prototype that
has been used in some experiments to validate this technological adaptation. Additionally, beyond
a simple use for monitoring, this paper also proposes the use of this approach to solve two typical
distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016
Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources
Nowadays, the proliferation of heterogeneous data sources provided by different
research and innovation projects and initiatives is proliferating more and more and
presents huge opportunities. These developments create an increase in the number
of different data sources, which could be involved in the process of decisionmaking
for a specific purpose, but this huge heterogeneity makes this task difficult.
Traditionally, the expert systems try to integrate all information into a main
database, but, sometimes, this information is not easily available, or its integration
with other databases is very problematic. In this case, it is essential to establish
procedures that make a metadata distributed integration for them. This process
provides a “mapping” of available information, but it is only at logic level. Thus, on
a physical level, the data is still distributed into several resources. In this sense, this
chapter proposes a distributed rule engine extension (DREE) based on edge computing
that makes an integration of metadata provided by different heterogeneous
data sources, applying then a mathematical decomposition over the antecedent of
rules. The use of the proposed rule engine increases the efficiency and the capability
of rule-based expert systems, providing the possibility of applying these rules over
distributed and heterogeneous data sources, increasing the size of data sets that
could be involved in the decision-making process
The Evolution of OSI Network Management by Integrated the Expert Knowledge
The management of modern telecommunications networks must satisfy
ever-increasing operational demands. Operation and quality service requirements
imposed by the users are also an important aspect to consider. In
this paper we have carried out a study for the improvement of intelligent administration
techniques in telecommunications networks. This task is achieved
by integrating knowledge base of expert system within the management information
used to manage a network. For this purpose, an extension of OSI management
framework specifications language has been added and investigated
in this study. A new property named RULE has also been added, which gathers
important aspects of the facts and the knowledge base of the embedded
expert system. Networks can be managed easily by using this proposed integration
Poster Abstract: Practical issues in image acquisition and transmission over wireless sensor network
Multimedia data have become an important objective in
wireless sensor networks. Due to the node resource constraints
(energy consumption, memory capacity, network
latency and throughput) the incorporation of image sensor
at the nodes is currently a challenge.
In this paper, we study different node architectures,
evaluating processing time, energy consumption, image
quality and data delivery issues. The study shows that
a specialized image co-processor is an optimal solutionJUnta de Andalucía P07-TIC-0247
Intelligent information processing in a digital library using semantic web
With the explosive growth of information, it is
becoming increasingly difficult to retrieve the relevant
documents with current search engine only. The
information is treated as an ordinary database that
manages the contents and positions. To the individual
user, there is a great deal of useless information in
addition to the substantial amount of useful information.
This begets new challenges to docent community
and motivates researchers to look for intelligent
information retrieval approach and ontologies that
search and/or filter information automatically based on
some higher level of understanding are required. We
study improving the efficiency of search methods and
classify the search patrons into several models based on
the profiles of agent based on ontology.
We have proposed a method to efficiently search for
the target information on a Digital Library network with
multiple independent information sources. This paper
outlines the development of an expert prototype system
based in an ontology for retrieval information of the
Digital Library University of Seville. The results of this
study demonstrate that by improving representation by
incorporating more metadata from within the
information and the ontology into the retrieval process,
the effectiveness of the information retrieval is enhanced.
We used Jcolibri and Prótége for developing the
ontology and creation the expert system respectively
Expert knowledge management based on ontology in a digital library
The architecture of the future Digital Libraries should be able to allow any users to access available
knowledge resources from anywhere and at any time and efficient manner. Moreover to the individual user,
there is a great deal of useless information in addition to the substantial amount of useful information. The
goal is to investigate how to best combine Artificial Intelligent and Semantic Web technologies for semantic
searching across largely distributed and heterogeneous digital libraries. The Artificial Intelligent and
Semantic Web have provided both new possibilities and challenges to automatic information processing in
search engine process. The major research tasks involved are to apply appropriate infrastructure for specific
digital library system construction, to enrich metadata records with ontologies and enable semantic
searching upon such intelligent system infrastructure. We study improving the efficiency of search methods
to search a distributed data space like a Digital Library. This paper outlines the development of a CaseBased
Reasoning prototype system based in an ontology for retrieval information of the Digital Library
University of Seville. The results demonstrate that the used of expert system and the ontology into the
retrieval process, the effectiveness of the information retrieval is enhanced
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