Jorge Ropero

Classification of Electrical Disturbances in Real Time Using Neural Networks

Power-quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. This paper presents a novel system based on neural networks for the classification of electrical disturbances in real time. In addition, an electrical pattern generator has been developed in order to generate common disturbances which can be found in the electrical grid. The classifier obtained excellent results (for both test patterns and field tests) thanks in part to the use of this generator as a training tool for the neural networks. The neural system is integrated on a software tool for a PC with hardware connected for signal acquisition. The tool makes it possible to monitor the acquired signal and the disturbances detected by the system.

A Fuzzy Logic intelligent agent for Information Extraction: Introducing a new Fuzzy Logic-based term weighting scheme

In this paper, we propose a novel method for Information Extraction (IE) in a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. The aim of this system is to design and implement an intelligent agent to manage any set of knowledge where information is abundant, vague or imprecise. The method was applied to the case of a major university web portal, University of Seville web portal, which contains a huge amount of information. Besides, we also propose a novel method for term weighting (TW). This method also is based on Fuzzy Logic, and replaces the classical TF-IDF method, usually used for TW, for its flexibility.

OLIMPO, an ad-hoc wireless sensor simulator for public utilities applications

This paper introduces OLIMPO, an useful simulation tool for researchers who are developing wireless sensor communication protocols. OLIMPO is a discrete-event simulator design to be easily reconfigured by the user, providing a way to design, develop and test communication protocols. In particular, we have designed a self-organizing wireless sensor network for low data rate. Our premise is that, due to their inherent spread location over large areas, wireless sensor networks are well-suited for SCADA applications, which require relatively simple control and monitoring. To show the facilities of our simulator, we have studied our network protocol with OLIMPO, developing several simulations. The purpose of these simulations is to demonstrate, quantitatively, the capability of our network to support this kind of applications.

Information extraction in a set of knowledge using a fuzzy logic based intelligent agent

A method for Information Extraction (IE) in a set of knowledge is proposed in this paper in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portal.

TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD VS. CLASSICAL TF-IDF METHOD FOR WEB INFORMATION EXTRACTION

Solving Term Weighting problem is one of the most important tasks for Information Retrieval and Information Extraction. Tipically, the TF-IDF method have been widely used for determining the weight of a term. In this paper, we propose a novel alternative fuzzy logic based method. The main advantage for the proposed method is the obtention of better results, especially in terms of extracting not only the most suitable information but also related information. This method will be used for the design of a Web Intelligent Agent which will soon start to work for the University of Seville web page.

A fuzzy logic system for classifying the contents of a database and searching consultations in natural language

This paper presents a method for the classification of the contents in a database in order to answer to user consultations using natural language. Artificial intelligence (AI) is used to relate these consultations to the database contents. The system is based on a fuzzy logic engine to take advantage of its so suitable properties for this application and is ideal for sets of accumulated knowledge that can be built in hierarchic levels by a tree structure. The eventual aim of this system is the implementation of a virtual Web assistant for an Internet portal

SABIO: SOFT AGENT FOR EXTENDED INFORMATION RETRIEVAL

In the current study, an integrated system called SABIO is presented. The current system applies Information Retrieval (IR) techniques developed for collections of textual documents to nontextual corpa. SABIO integrates a fuzzy logic-based procedure for IR. Its search algorithm improves the IR efficiency and decreases the computational burden by using a fuzzy logic-based procedure for IR. This procedure is integrated in a flexible and fault-tolerant, human-reasoning-based search algorithm. The Accumulated Knowledge Set (AKS) of the system is sorted in a hierarchic multilevel tree-structure-like ontology. The objects in the AKS are represented using a novel human-reasoning-based-method. This representation takes into account the occurrence of related terms. The system uses a novel fuzzy logic-based term-weighting (TW) method. The developed fuzzy logic method improves the classical term frequency–inverse document frequency (TF/IDF) method, generally used for TW. The abovementioned system is the core of a wizard for search into the website of the University of Seville, www.us.es, which is currently in testing.

Term Weighting for Information Retrieval Using Fuzzy Logic

The rising quantity of available information has constituted an enormous advance in our daily life. However, at the same time, some problems emerge as a result from the existing difficulty to distinguish the necessary information among the high quantity of unnecessary data. Information Retrieval has become a capital task for retrieving the useful information. Firstly, it was mainly used for document retrieval, but lately, its use has been generalized for the retrieval of any kind of information, such as the information contained in a database, a web page, or any set of accumulated knowledge. In particular, the so-called Vector Space Model is widely used. Vector Space Model is based on the use of

A Hybrid Intelligent Multiagent System for the Remote Control of Solar Farms

ABSTRACT This paper describes a multiagent architecture integrated system designed to supervise infrastructures in solar farms. The system enables monitoring the environment by means of sensor networks that are in charge of collecting data. It is designed using a hybrid model composed of an inference engine and an ontology. The former makes the system intelligent, while the latter structures knowledge. We have also developed a tool to configure and use the multiagent system in a simple and intuitive way.

A precise electrical disturbance generator for neural network training with real level output

Power Quality is defined as the study of the quality of electric power lines. The detection and classification of the different disturbances which cause power quality problems is a difficult task which requires a high level of engineering expertise. Thus, neural networks are usually a good choice for the detection and classification of these disturbances. This paper describes a powerful tool, developed by the Institute for Natural Resources and Agrobiology at the Scientific Research Council (CSIC) and the Electronic Technology Department at the University of Seville, which generates electrical patterns of disturbances for the training of neural networks for PQ tasks. This system has been expanded to other applications (as comparative test between PQ meters, or test of effects of power-line disturbances on equipment) through the addition of a specifically developed high fidelity power amplifier, which allows the generation of disturbed signals at real levels.