Catálogo de publicaciones - libros

In order to enhance robustness and security of the embedded watermark, proposed a novel blind digital watermark algorithm based on neural network and chaotic map Firstly, a better chaotic sequence is generated by Cellular Neural Network (CNN) and Chebyschev map, using the chaotic sequence encrypted the watermark and its spectrum is spread. Then, BPN is trained to memorize the relationship among pixels of each sub-block image. Furthermore, the adaptive embedding algorithm is adopted to enhance the characters of the watermarking system. Simulation results are given which show that this scheme is practical, secure and robust.

In the analysis of biological data artificial neural networks are a useful alternative to conventional statistical methods. Because of its advantage in analyzing time courses the Multilevel Hypermap Architecture (MHA) is used for analysis of stimulus related data, exemplified by fMRI studies with auditory stimuli. Results from investigations with the MHA show an improvement of discrimination in comparison to statistical methods. With an interface to the well known BrainVoyager software and with a GUI for MATLAB an easy usability of the MHA and a good visualization of the results is possible.

The MHA is an extension of the Hypermap introduced by Kohonen. By means of the MHA it is possible to analyze structured or hierarchical data (data with priorities, data with context, time series, data with varying exactness), which is difficult or impossible to do with known self-organizing maps so far.

Traditional text clustering methods require enormous computing resources, which make them inappropriate for processing large scale data collections. In this paper we present a clustering method based on the word category map approach using a two-level Growing Self-Organising Map (GSOM). A significant part of the clustering task is divided into separate sub-tasks that can be executed on different computers using the emergent Grid technology. Thus enabling the rapid analysis of information gathered globally. The performance of the proposed method is comparable to the traditional approaches while improves the execution time by 15 times.

In this paper, the discriminative training criterion of maximum-minimum similarity (MMS) is used to improve the performance of text extraction based on Gaussian mixture modeling of neighbor characters. A recognizer is optimized in the MMS training through maximizing the similarities between observations and models from the same classes, and minimizing those for different classes. Based on this idea, we define the corresponding objective function for text extraction. Through minimizing the objective function by using the gradient descent method, the optimum parameters of our text extraction method are obtained. Compared with the maximum likelihood estimation (MLE) of parameters, the result trained with the MMS method makes the overall performance of text extraction improved greatly. The precision rate decreased little from 94.59% to 93.56%, but the recall rate increased a lot from 80.39% to 98.55%.

The feature selection is an important part in automatic text classification. In this paper, we use a Chinese semantic dictionary – Hownet to extract the concepts from the word as the feature set, because it can better reflect the meaning of the text. We construct a combined feature set that consists of both sememes and the Chinese words, propose a CHI-MCOR weighing method according to the weighing theories and classification precision. The effectiveness of the competitive network and the Radial Basis Function (RBF) network in text classification are examined. Experimental result shows that if the words are extracted properly, not only the feature dimension is smaller but also the classification precision is higher, the RBF network outperform competitive network for automatic text classification because of the application of supervised learning. Besides its much shorter training time than the BP network’s, the RBF network makes precision and recall rates that are almost at the same level as the BP network’s.

Text-based information accounts for more than 80% of today’s Web content. They consist of Web pages written in different natural languages. As the semantic Web aims at turning the current Web into a machine-understandable knowledge repository, availability of multilingual ontology thus becomes an issue at the core of a multilingual semantic Web. However, multilingual ontology is too complex and resource intensive to be constructed manually. In this paper, we propose a three-layer model built on top of a soft computing framework to automatically acquire a multilingual ontology from domain specific parallel texts. The objective is to enable semantic smart information access regardless of language over the Semantic Web.

This paper described two kinds of neural networks for text categorization, multi-output perceptron learning (MOPL) and back-propagation neural network (BPNN), and then we proposed a novel algorithm using improved back-propagation neural network. This algorithm can overcome some shortcomings in traditional back-propagation neural network such as slow training speed and easy to enter into local minimum. We compared the training time and the performance, and tested the three methods on the standard Reuter-21578. The results show that the proposed algorithm is able to achieve high categorization effectiveness as measured by the precision, recall and F-measure.

With the popularization of the concept knowledge economic management, it not only propels the whole development of knowledge economy but also directs the industry of “Basic Agent Service” of becoming the mainstream in the present markets. This research institute constructed a system called, “” (KBSC). It allows the customers to submit economic or business field questions online in the form of their natural language. By using Chinese phrase-cutting, key words weighted value calculations, and professional categorizations, it can automatically analyze the nature of the customer’s problem and search for the relevant information in the HR database to list the most suitable names of specialists as the assigned coordinator for the clients. When each matching procedure was finished, the questionnaire was given to examine the correctness of the data search following adjustments of the system.

It’s natural and direct to identify the structural stiffness based on the measurement of static displacement; In addition, considering that the lower frequencies of structures can be tested with high precision and can reflect the global dynamic properties of structures, static displacements at partial nodes and several low frequencies were used to constitute the input parameter vectors for neural networks. A damage numerical verification on an arch bridge model was carried out using a radical basis function (RBF) network. Identification results indicate that the neural network has an excellence capability to identify the location and extent of structural damage with the limited noises and incomplete measured data.

In this paper, we investigate whether the word frequency effect and the word similarity effect could be applied to Korean lexical decision task (henceforth, LDT). Also we propose a computational model of Korean LDT and present comparison results between human and computational model on Korean LDT. We found that the word frequency effect and the similarity effect in Korean LDT were language general phenomena in both the behavioral experiment and the proposed computational simulation.