Catálogo de publicaciones - libros

Large collections of full-text document are now commonly used in automated information retrieval. When the stored document texts are long, the retrieval of complete documents may not be in the users’ best interest and extract () is not accurate. In such circumstances, efficient and effective retrieval may be obtained by using passage retrieval strategies designed to retrieve text excerpts of varying size in response to statements of user interest.

New approaches are described in this study for implementing selective passage retrieval systems, and identifying text passage response to particular user needs. Moreover an automated system is using for extract accurate from that passage and evaluate the usefulness of the proposed method. From the experimental results, when passage retrieval are accessible leading to the retrieval of additional extracted relevant with corresponding improvements in Recall and Precision. Therefore, and improved by 30% than using whole texts and traditional methods.

With increasing popularity of the Internet and tremendous amount of on-line text, automatic document classification is important for organizing huge amounts of data. Readers can know the subject of many document fields by reading only some specific Document fields can be decided efficiently if there are many and if the rate is high. This paper proposes a method for automatically building new . A search engine is used to extract candidates from document corpora. New candidates in each field are automatically compared with previously determined . Then new are appended to an dictionary. From the experiential results, our new system can automatically appended around 44% of new words to the existence Dictionary. Moreover, the concentration ratio 0.9 is also effective for extracting relevant that needed for the system design to build automatically.

A simple method for reducing undesirable distortion effects of mixed noise filters for digital images is presented. The method is based on a simple 2-input 1-output neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images generated on a computer. The method can be used with any type of mixed noise filters since its operation is completely independent of the filter. The proposed method is applied to two representative mixed noise filters from the literature under different noise conditions and image properties. Results indicate that the proposed method may efficiently be used with any type of mixed noise filters to effectively reduce their distortion effects.

In this paper we propose a split and merge texture segmentation method. The presented approach is characterised by the introduction of a novel operator, the Local Fuzzy Pattern for texture discrimination, and the employment of a neuro-fuzzy decision support strategy, which supervises the overall split and merge procedure. The effectiveness of the proposed approach is evaluated on a set of artificial and natural texture images.

We proposed the use of Lukasiewicz’s operators for lineal image compression. These operators have been applied to the approximation of piecewise linear functions. In this sense we showed two basic piecewise lineal static image compression techniques in which the use of this operators lets to reduce hardware resources.

In this paper we model the concept of ”coarseness”, typically used in texture image descriptions, by means of fuzzy sets. Specifically, we relate representative measures of this kind of texture with its presence degree. To obtain these ”presence degrees”, we collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using this collected data, and some statistics as reference set, the membership function corresponding to the fuzzy set ”coarseness” is modelled.

A motion adaptive algorithm for video de-interlacing is presented in this paper. It is based on a fuzzy inference system, which performs an interpolation between two linear techniques as a function of the motion level. Fuzzy systems with different number of ’if-then’ rules have been analyzed and compared in terms of complexity as well as efficiency in de-interlacing benchmark video sequences.

The correct web site text content must be help to the visitors to find what they are looking for. However, the reality is quite different, many times the web page text content is ambiguous, without meaning and worst, it don’t have relation with the topic that is shown as the main theme. One reason to this problem is the lack of contents with concept meaning in the web page, i.e., the utilization of words and sentences that show concepts, which finally is the visitor goal. In this paper, we introduce a new approach for improving the web site text content by extracting Concept-Based Knowledge from data originated in the web site itself. By using the concepts, a web page can be rewrite for showing more relevant information to the eventual visitor. This approach was tested in a real web site, showing its effectiveness

Where customers with different membership and position, use computers as in the university network systems, it often takes much time and efforts for them to cope with the change of the system management. This is because the requirements for the respective computer usage are different in the network and security policies. In this paper, a new destination addressing control system (DACS) scheme for the university network services is proposed. The DACS Scheme performs the network services efficiently through the communication management of a client. As the characteristic of DACS Scheme, only the setup modification is required by a system administrator, when the configuration change is needed in the network server. Then, the setup modification is unnecessary by a customer, which shows a merit for both a system administrator and a customer. This paper describes the instruction and the prototype for DACS Protocol as the implementation of DACS Scheme. Then, the simplicity of the system management in DACS Scheme, is examined from the customer and the system administrator viewpoints.

We proposed the prediction system of electric power generation of solar cell using neural network. Recently, the solar cell system is developing in many fields. However this system is easily to influence by the weather condition. In the practical application, it has been required the prediction of electric power generation. By this system, it is possible to make the planning of supply and the security of alternative power source. This prediction system is used neural network system and it can predict the integral power consumption, largest electric power and time-serial prediction.