Catálogo de publicaciones - libros

Seeds based semi-supervised clustering algorithms often utilize a seeds set consisting of a small amount of labeled data to initialize cluster centroids, hence improve the performance of clustering over whole data set. Researches indicate that both the scale and quality of seeds set greatly restrict the performance of semi-supervised clustering. A novel semi-supervised clustering algorithm named DE-Tri-training semi-supervised K means is proposed. In new algorithm, prior to initializing cluster centroids, the training process of a semi-supervised classification approach named Tri-training is used to label the unlabeled data and add them into initial seeds to enlarge the scale. Meanwhile, to improve the quality of enlarged seeds set, a Nearest Neighbor Rule based data editing technique named Depuration is introduced into the Tri-training process to eliminate and correct the noise and mislabeled data among the enlarged seeds. Experiments show that novel algorithm can effectively improve the initialization of cluster centroids and enhance clustering performance.

The implicit knowledge in the databases can be extracted of automatic form. One of the several approaches considered for this problem is the construction of graphical models that represent the relations between the variables and regularities in the data. In this work the problem is addressed by means of an algorithm of search and scoring. These kind of algorithms use a heuristic mechanism search and a function of score to guide themselves towards the best possible solution.

The algorithm, which is implemented in the semifunctional language Lisp, is a searching mechanism of the structure of a bayesian network (BN) based on concurrent processes.

Each process is assigned to a node of the BN and effects one of three possible operations between its node and some of the rest: to put, to take away or to invert an edge. The structure is constructed using the metric MDL (made up of three terms), whose calculation is made of distributed way, in this form the search is guided by selecting those operations between the nodes that minimize the MDL of the network.

In this work are presented some results of the algorithm in terms of comparison of the structure of the obtained network with respect to its gold network.

This paper presents a method of collaborative design optimization based on knowledge discovery. Firstly, a knowledge discovery approach based on simulation data is presented. Rules are extracted by knowledge discovery algorithm, and each rule is divided into several intervals. Secondly, a collaborative optimization model is established. In the model, the consistency intervals are derived from intervals of knowledge discovery. The model is resolved by genetic arithmetic. Finally, The method is demonstrated by a parameter design problem of piston-connecting mechanism of automotive engine. The proposed method can improve the robustness of collaborative design optimization.

In telecommunication networks, alarms are usually useful for identifying faults, and therefore solving them. However, for large systems the number of alarms produced is so large that the current management systems are overloaded. One way of overcoming this problem is to filter and reduce the number of alarms before the faults can be located. In this paper, we describe a new approach for fault recognition and classification in telecommunication networks. We study and evaluate its performance using real-world data collected from 3G telecommunication networks.

This paper introduces MineSP, a relational-like operator to mine sequential patterns from databases. It also shows how an inductive query can be translated into a traditional query tree augmented with MineSP nodes. This query tree is then optimized, choosing the mining algorithm that best suits the constraints specified by the user and the execution environment conditions. The SPMiner prototype system supporting our approach is also presented.

Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically.

Robot Soccer is a challenging research domain for Artificial Intelligence, which was proposed in order to provide a long-term problem in which researchers can investigate the construction of systems involving multiple agents working together in a dynamic, uncertain and probabilistic environment, to achieve a specific goal. This work focuses on the design and implementation of a fast and robust computer vision system for a team of small size robot soccer players. The proposed system combines artificial intelligence and computer vision techniques to locate the mobile robots and the ball, based on global vision images. To increase system performance, this work proposes a new approach to interpret the space created by a well-known computer vision technique called Hough Transform, as well as a fast object recognition method based on constraint satisfaction techniques. The system was implemented entirely in software using an off-the-shelf frame grabber. Experiments using real time image capture allows to conclude that the implemented system are efficient and robust to noises and lighting variation, being capable of locating all objects in each frame, computing their position and orientation in less than 20 milliseconds.

In mobile robotics, the inference of the 3D layout of large-scale indoor environments is a critical problem for achieving exploration and navigation tasks. This article presents a framework for building a 3D model of an indoor environment from partial data using a mobile robot. The modeling of a large-scale environment involves the acquisition of a huge amount of range data to extract the geometry of the scene. This task is physically demanding and time consuming for many real systems. Our approach overcomes this problem by allowing a robot to rapidly collect a set of intensity images and a small amount of range information. The method integrates and analyzes the statistical relationships between the visual data and the limited available depth on terms of small patches and is capable of recovering complete dense range maps. Experiments on real-world data are given to illustrate the suitability of our approach.

In this paper, an effective method is proposed for automatic facial expression recognition from static images. First, a modified Active Appearance Model (AAM) is used to locate facial feature points automatically. Then, based on this, facial feature vector is formed. Finally, SVM classifier with a sample selection method is adopted for expression classification. Experimental results on the JAFFE database demonstrate an average recognition rate of 69.9% for novel expressers, showing that the proposed method is promising.

In this paper, a new feature extraction called principal component net analysis (PCNA) is developed for face recognition. It looks a face image upon as two orthogonal modes: row channel and column channel and extracts Principal Components (PCs) for each channel. Because it does not need to transform an image into a vector beforehand, much more spacial discrimination information is reserved than traditional PCA, ICA etc. At the same time, because the two channels have different physical meaning, its extracted PCs can be understood easier than 2DPCA. Series of experiments were performed to test its performance on three main face image databases: JAFFE, ORL and FERET. The recognition rate of PCNA was the highest (PCNA, PCA and 2DPCA) in all experiments.