Catálogo de publicaciones - libros

The authors have been involved in developing an automated inspection system, based on machine vision, to improve the repair coating quality control (RCQ control) in can ends of metal containers for fish food. The RCQ of each end is assesed estimating its average repair coating quality (ARCQ). In this work we present a fuzzy model building to make the acceptance/rejection decision for each can end from the information obtained by the vision system. In addition it is interesting to note that such model could be interpreted and supplemented by process operators. In order to achieve such aims, we use a fuzzy model due to its ability to favour the interpretability for many applications. Firstly, the easy open can end manufacturing process, and the current, conventional method for quality control of easy open can end repair coating, are described. Then, we show the machine vision system operations. After that, the fuzzy modeling, results obtained and their discussion are presented. Finally, concluding remarks are stated.

Negotiation is an important function for e-commerce system to be efficient. However, negotiation is complicated, time-consuming and difficulty for participants to reach an agreement. This paper aims to establish an automated negotiation mechanism based on fuzzy method in order to alleviate the difficulty of negotiation. This automated negotiation is performed by autonomous agents that use fuzzy logic and issue-trading strategies in finding mutually-agreed contracts.

The fault diagnosis on diesel engine is a difficult problem due to the complex structure of the engine and the presence of multi-excite sources. A new kind of fault diagnosis system based on Rough Set Theory and Support Vector Machine is proposed in the paper. Integrating the advantages of Rough Set Theory in effectively dealing with the uncertainty information and Support Vector Machine’s greater generalization performance. The diagnosis of a diesel demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis, and verified the feasibility of engineering application.

This paper presents the feature recognition technique that recognizes the features from 2D ship drawings using the fuzzy inference system. Generally, ship drawings include a lot of symbols and texts. They were complicatedly combined each other. So, it is very difficult to recognize the feature from 2D ship model. The fuzzy inference system is suitable to solve these problems. Input information for fuzzy inference is connection type of drawing elements and properties of element. Output value is the correspondence between target feature and candidate feature. The recognition rule is the fuzzy rule that has been predefined by designer. In this study, the midship section drawing of general cargo ship was used to verifying suggested methodology. Experimental results showed that this approach is more efficient than existing methods and reflects the human knowledge for recognition of the feature.

In this oppaper, camera calibration and video tracking technology are used to get the vehicle location information so as to calibrate the Gazis-Herman-Rothery (GHR) model and fuzzy car-following model. The detail analyses about the models’ parameters and accuracy show that the fuzzy model is easy to understand and have better performance.

To study the spatial relationships with the instability is becoming one of the hot spots and the difficulties in studying the spatial relationships. This paper express and apply the information of relationships among spatial objects in the real world in computer system from the cognitive view, study the fuzzy extension about description of spatial relationships at the base. Guided by the spatial query, we makes the model on the base of regular indefinite spatial inferring, applies fuzzy theory and spatial relationship theory in the spatial query and solves the fuzzy location problems in applying GIS network resource management.

SAGE data can be used to learn classification models to aid cancer classification. In this paper, maximum entropy models are built for SAGE data classification by estimating the conditional distribution of the class variable given the samples. In experiments we compare accuracy and precision to SVMs (one of the most effective classifiers in performing accurate cancer diagnosis from gene expression data) and show that maximum entropy is better. The results indicate that maximum entropy is a promising technique for SAGE data classification.

It is difficult to build a gene regulatory network directly. So the main interest focuses on the gene-gene regulation relationship mining, which reveals an active or repressive action from one gene to another. The previous methods, such as , didn’t solve the gene regulatory relationship with a great succeed. In this paper, we propose a new method by introducing several more relational techniques. The results demonstrate the complete and detailed information between the genes.The data set and software will be available upon request.

In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the different techniques, we considered Nasdaq–100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. We analyzed 7 year’s Nasdaq 100 main index values and 4 year’s NIFTY index values. The parameters of the different techniques are optimized by the particle swarm optimization algorithm. This paper briefly explains how the different learning paradigms could be formulated using various methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the models considered could represent the stock indices behavior very accurately.

The main objective of this paper is to use the auto segmentation with morphological technique to find out predictable region of interest (ROI), especially the center and margin area of the tumor. The proposed method has employed moving average method for detecting edge of tumor after estimating the corresponding center using the aid of medical domain knowledge. In our re-search, after computing distance between center and edge of tumor we get factual and numerical data of tumor to calculate multi-deviation and circularity test. It is useful to construct tumor profiling by splitting up the lesion into 4 divisions with the mean of multi-standard deviation (benign: 13.7, malignancies: 38.32) and 8 divisions with the mean of multi-standard deviation (benign: 3.36, malignancies: 15.29) with equal segments. We used K-means algorithm to make classification between benign and malignance tumor. This technique has been fully validated by using more than 100 ultrasound images of the patients and found to be accurate with 90% degree of confidence. This study will help the physicians and radiologist to improve the efficiency in accurate detection of the image and appropriate diagnosis of the cancer tumor.