Application of associative memory in human face detection

[[abstract]]In this paper we present an associative-memory-based face detection system. First, the symmetry of human faces is used to quickly locate all the candidates of human faces with all possible sizes and locations. Then two associative memories are used to decide whether or not a human face exists at the locations. Some experimental results are given.[[conferencetype]]國際[[conferencedate]]19990710~19990716[[booktype]]紙本[[conferencelocation]]Washington, DC, US

An efficient initialization scheme for the self-organizing feature map algorithm

[[abstract]]It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters of the algorithm, namely, the learning-rate parameter and the neighborhood function. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Two data sets are tested to illustrate the performance of the proposed method.[[conferencetype]]國際[[conferencedate]]19990710~19990716[[booktype]]紙本[[conferencelocation]]Washington, DC, US

Improving the self-organizing feature map algorithm using an efficient initialization scheme

[[abstract]]It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters (i.e. the learning-rate parameter and the neighborhood set) of the algorithm. They usually have to be counteracted by the trial-and-error method; therefore, often time consuming retraining procedures have to precede before a neighborhood preserving feature amp is obtained. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Several data sets are tested to illustrate the performance of the proposed method.[[notice]]補正完

Application of neural networks in spatio-temporal hand gesture recognition

[[abstract]]Several successful approaches to spatio-temporal signal processing such as speech recognition and hand gesture recognition have been proposed. Most of them involve time alignment which requires substantial computation and considerable memory storage. In this paper, we present a neural-network-based approach to spatio-temporal pattern recognition. This approach employs a powerful method based on hyperrectangular composite neural networks (HRCNNs) for selecting templates, therefore, considerable memory is alleviated. In addition, it greatly reduces substantial computation in the matching process because it obviates time alignment. Two databases consisted of 51 spatio-temporal hand gestures were utilized for verifying its performance. An encouraging experimental result confirmed the effectiveness of the proposed method.[[conferencetype]]國際[[conferencedate]]19980504~19980509[[booktype]]紙本[[conferencelocation]]Anchorage, AK, US

Genetic-algorithms-based approach to self-organizing feature map and its application in cluster analysis

[[abstract]]In the traditional form of the self-organizing feature map (SOFM) algorithm, the criterion for stopping training is either to terminate the training procedure when no noticeable changes in the feature map are observed or to stop training when the number of iterations reaches a prespecific number. In this paper we propose an efficient method for measuring the degree of topology preservation. Based on the method we apply genetic algorithms (GAs) in two stages to form a topologically ordered feature map. We then use a special method to interpret a SOFM formed by the proposed GA-based method to estimate the number and the locations of clusters from a multidimensional data set without labeling information. Two data sets are used to illustrate the performance of the proposed methods[[conferencetype]]國際[[conferencedate]]19980504~19980509[[iscallforpapers]]Y[[conferencelocation]]Anchorage, AK, US

Facial image morphing by self-organizing feature maps

[[abstract]]We propose a new facial image morphing algorithm based on the Kohonen self-organizing feature map (SOM) algorithm to generate a smooth 2D transformation that reflects anchor point correspondences. Using only a 2D face image and a small number of anchor points, we show that the proposed morphing algorithm provides a powerful mechanism for processing facial expressions.[[conferencetype]]國際[[conferencedate]]19990710~19990716[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Washington, DC, US

Rule extraction for voltage security margin estimation

[[abstract]]Research efforts have been devoted to estimating voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. One main disadvantage of these techniques is that they require large computations, therefore, they are not efficient for on-line use in power control centers. In this paper, we propose a technique based on hyperrectangular composite neural networks (HRCNNs) and fuzzy hyperrectangular composite neural networks (FHRCNNs) for voltage security margin estimation. The technique provides us with much faster assessments of voltage security than conventional techniques. The two classes of HRCNNs and FHRCNNs integrate the paradigm of neural networks with the rule-based approach, rendering them more useful than either. The values of the network parameters, after sufficient training, can be utilized to generate crisp or fuzzy rules on the basis of preselected meaningful features. Extracted rules are helpful to explain the whole assessment procedure so the assessments are more capable of being trusted. In addition, the power system operators or corresponding experts can delete unimportant features or add some additional features to improve the performance and computational efficiency based on the evaluation of the extracted rules. The proposed technique was tested on 3000 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficienc

Neural-network-based fuzzy model and its application to transient stability prediction in power systems

[[abstract]]We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP's) in terms of computational burden and classification rat

[[alternative]]The Implementation of Human-Computer Interfaces for Patients with High-Level Cervical Injuries(I)

計畫編號：NSC89-2614-E032-012研究期間：200008~200107研究經費：678,000[[sponsorship]]行政院國家科學委員

Use of neural networks as medical diagnosis expert system

[[notice]]補正完畢[[conferencetype]]國內[[conferencedate]]19960101~1996010