Mu-Chun Su

A new cluster validity measure and its application to image compression

Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure that can deal with this situation. In addition, we also propose a modified K-means algorithm that can assign more cluster centres to areas with low densities of data than the conventional K-means algorithm does. First, several artificial data sets are used to test the performance of the proposed measure. Then the proposed measure and the modified K-means algorithm are applied to reduce the edge degradation in vector quantisation of image compression.

Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements

The ability to rapidly acquire synchronized phasor measurements from around a power system opens up new possibilities for power system protection and control. In this paper, the authors develop a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements to provide fast transient stability swings prediction for use with high-speed control. Primary features of the method include constructing a fuzzy neural network for all fault locations, using a short window of realistic-precision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.

Fast self-organizing feature map algorithm

We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N x N neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine-tuned by the traditional Kohonen self-organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three-stage method, a topologically ordered feature map would be formed very quickly instead of requiring a huge amount of iterations to fine-tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm. Three data sets are utilized to illustrate the proposed method.

A fuzzy rule-based approach to spatio-temporal hand gesture recognition

Gesture based applications widely range from replacing the traditional mouse as a position device to virtual reality and communication with the deaf. The article presents a fuzzy rule based approach to spatio-temporal hand gesture recognition. This approach employs a powerful method based on hyperrectangutar composite neural networks (HRCNNs) for selecting templates. Templates for each hand shape are represented in the form of crisp IF-THEN rules that are extracted from the values of synaptic weights of the corresponding trained HRCNNs. Each crisp IF-THEN rule is then fuzzified by employing a special membership function in order to represent the degree to which a pattern is similar to the corresponding antecedent part. When an unknown gesture is to be classified, each sample of the unknown gesture is tested by each fuzzy rule. The accumulated similarity associated with all samples of the input is computed for each hand gesture in the vocabulary, and the unknown gesture is classified as the gesture yielding the highest accumulative similarity. Based on the method we can implement a small-sized dynamic hand gesture recognition system. Two databases which consisted of 90 spatio-temporal hand gestures are utilized for verifying its performance. An encouraging experimental result confirms the effectiveness of the proposed method.

Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition

Abstract Often a major difficulty in the design of rule-based systems is the process of acquiring the requisite knowledge in the form of If–Then rules. This paper presents a class of fuzzy degraded hyperellipsoidal composite neural networks (FDHECNNs) that are trained to provide appealing solutions to the problem of knowledge acquisition. The values of the network parameters, after sufficient training, are then utilized to generate If–Then rules on the basis of preselected meaningful features. In order to avoid the risk of getting stuck in local minima during the training process, a real-valued genetic algorithm is proposed to train FDHECNNs. The effectiveness of the method is demonstrated on two problems, namely, the “truck backer-upper” problem as well as real-world application of a hypothesis regarding the pathophysiology of diabetes.

A novel algorithm for data clustering

Abstract An efficient clustering algorithm is proposed in an unsupervised manner to cluster the given data set. This method is based on regulating a similarity measure and replacing movable vectors so that the appropriate clusters are determined by a performance for the classification validity. The proposed clustering algorithm needs not to predetermine the number of clusters, to choose the appropriate cluster centers in the initial step, and to choose a suitable similarity measure according to the shapes of the data. The location of the cluster centers can be efficiently determined and the data can be correctly classified by the proposed method. Several examples are considered to illustrate the effectiveness of the proposed method.

Neuro-fuzzy networks for voltage security monitoring based on synchronized phasor measurements

The ability to rapidly acquire synchronized phasor measurements from around a power network opens up new possibilities for power system operation and control. A novel neuro-fuzzy network, the fuzzy hyperrectangular composite neural network, is proposed for voltage security monitoring (VSM) using synchronized phasor measurements as input patterns. This paper demonstrates how neuro-fuzzy networks can be constructed offline and then utilized online for monitoring voltage security. The neuro-fuzzy network is tested on 3000 simulated data from randomly generated operating conditions on the IEEE 30-bus system to indicate its high classification rate for voltage security monitoring.

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

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 (MLPs) in terms of computational burden and classification rate.

SOM-based optimization

A new approach to optimization problems based on the self-organizing feature maps is proposed. We name the new optimization algorithm the SOM-based optimization (SOMO) algorithm. Through the self-organizing process, good solutions to an optimization problem can be simultaneously explored and exploited. An additional advantage of the algorithm is that the outputs of the neural network allow us to transform a multi-dimensional fitness landscape into a three-dimensional projected fitness landscape. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm.

Evaluation of a haptics-based virtual reality temporal bone simulator for anatomy and surgery training

INTRODUCTION Virtual reality simulation training may improve knowledge of anatomy and surgical skills. We evaluated a 3-dimensional, haptic, virtual reality temporal bone simulator for dissection training. METHODS The subjects were 7 otolaryngology residents (3 training sessions each) and 7 medical students (1 training session each). The virtual reality temporal bone simulation station included a computer with software that was linked to a force-feedback hand stylus, and the system recorded performance and collisions with vital anatomic structures. Subjects performed virtual reality dissections and completed questionnaires after the training sessions. RESULTS Residents and students had favorable responses to most questions of the technology acceptance model (TAM) questionnaire. The average TAM scores were above neutral for residents and medical students in all domains, and the average TAM score for residents was significantly higher for the usefulness domain and lower for the playful domain than students. The average satisfaction questionnaire for residents showed that residents had greater overall satisfaction with cadaver temporal bone dissection training than training with the virtual reality simulator or plastic temporal bone. For medical students, the average comprehension score was significantly increased from before to after training for all anatomic structures. Medical students had significantly more collisions with the dura than residents. The residents had similar mean performance scores after the first and third training sessions for all dissection procedures. DISCUSSION The virtual reality temporal bone simulator provided satisfactory training for otolaryngology residents and medical students.