Catálogo de publicaciones - libros

An R-functions based classification approach along with a regularization framework is proposed. The abnormal software process detection problem was used as the test bed. The R-functions based classification method is termed as the R-cloud method. The approach was validated both on synthetic and real-world data. Regularization allows to achieve good generalization and classification performance. In addition, the R-cloud approach gives the benefit of the analytical representation of the decision boundary. The introductory study on practical use of the R-cloud classifiers yielded promising results. The prototyping has shown that application of the R-functions based pattern recognition technique is a significant and practical tool for fault detection in providing fault tolerant computing.

Locality preserving projections (LPP) can find an embedding that preserves local information and discriminates data well. However, only one projection matrix over the whole data is not enough to discriminate complex data. In this paper, we proposed locality preserving projections mixture models (LPP mixtures), where the set of all data were partitioned into several clusters and a projection matrix for each cluster was obtained. In each cluster, We performed LPP via QR-decomposition, which is efficient computationally in under-sampled situations. Its theoretical foundation was presented. Experiments on a synthetic data set and the Yale face database showed the superiority of LPP mixtures.

Video shot boundary detection (SBD) is an important step in many video applications. In this paper, previous temporal multi-resolution analysis (TMRA) framework was extended by first using SVM (Supported Vector Machines) classify the video frames within a sliding window into normal frames, gradual transition frames and CUT frames, then clustering the classified frames into different shot categories. The experimental result on ground truth, which has about 26 hours (13,344 shots) news video clips, shows that the new framework has relatively good accuracy for the detection of shot boundaries. It basically solves the difficulties of shot boundaries detection caused by sub-window technique in video. The framework also greatly improves the accuracy of gradual transitions.

The paper introduces the method of probabilistic neural network (PNN) and its classifying principle. It constructs two PNN structures which are used to recognize both the two patterns and the three patterns respectively. The structure of the two patterns classification of PNN is used to classify the 106 listed companies of China in 2000 into two groups. The classification accuracy rate is 87.74%. The structure of the three patterns classification of PNN is used to classify the 96 listed companies of China in 2000 into three groups. The classification accuracy rate is 85.42%.

Human gait recognition is the process of identifying individuals by their walking manners. The gait as one of newly coming biometrics has recently gained more and more interests from computer vision researchers. In this paper, we propose a new method for model-free recognition of gait based on silhouette in computer vision sequences. The silhouette shape is represented by a novel approach which includes not only the spatial body contour but also the temporal information. First, a background subtraction is used to separate objects from background. Then, we represent the spatial shape of walker and their motion by the temporal matrix, and use Discrete Fourier analysis to analyze the gait feature. The nearest neighbor classifier is used to distinguish the different gaits of human. The performance of our approach is tested using different gait databases. Recognition results show this approach is efficient.

This paper presents an improved method which is suitable for gradient-threshold edge detectors. The proposed method takes into account the basic characteristics of the human visual system (HVS) and precisely determines the local masking regions for the edges with arbitrary shape according to the image content. Then the gradient image is masked with the luminance and the activity of local image before edge labelling. The experimental results show that the edge images obtained by our algorithm are more consistent with the perceptive edge images.

This paper addresses how intelligent characters, having learning capability based on the neural network technology, automatically adapt to environmental changes in computer games. Our adaptation solution includes an autonomous adaptation scheme and a cooperative adaptation scheme. With the autonomous adaptation scheme, each intelligent character steadily assesses changes of its game environment while taking into consideration recently earned scores, and initiates a new learning process when a change is detected. Intelligent characters may confront various opponents in many computer games. When each intelligent character has fought with just part of the opponents, the cooperative adaptation scheme, based on a genetic algorithm, creates new intelligent characters by composing their partial knowledge of the existing intelligent characters. The experimental results show that intelligent characters can properly accommodate to the changes with the proposed schemes.

In this study, we will employ a multi-agent for searching and extraction of data. We will use Integrator Agent based on CORBA architecture for the proposed model on hierarchical Clustering and Association Rule Discovery (HCARD). The model will address the inadequacy of other data mining tools in processing performance and efficiency when use for knowledge discovery. The result revealed faster searching using the agents. Our experiment also shows that the HCARD generated isolated but imperative association rules which in return could be practically explained for decision making purposes. Shorter processing time had been noted in computing for smaller clusters implying ideal processing period than dealing with the entire dataset.

Both a concept of the optimal set of elementary siphons and a deadlock prevention policy based on integer programming are presented to solve deadlock problems arising in flexible manufacturing systems(FMS). Furthermore, an algorithm based on modified particle swarm optimization(PSO) is illustrated to show its efficiency to deal with such problems. Numerical simulation shows that this policy can minimize the number of newly additional control places and arcs while improving the dynamic performance of the resultant system.

A multi-objective evolutionary algorithm (MOEA) based approach to Power System Stabilizer (PSS) and Static Var Compensators (SVC) tuning has been investigated in this paper. The coordinated design problem of PSS and SVC is formulated as a multi-objective optimization problem, in which the system response is optimized by minimizing several system-behavior measure criterions. MOEA is employed to search optimal controller parameters.Design of the multi-objective optimization aims to find out the Pareto optimal solution which is a set of possible optimal solutions for controller parameters. And effectiveness of the proposed control scheme has been demonstrated in a multiple power system.