Sung-Suk Kim

Development of Quantum-Based Adaptive Neuro-Fuzzy Networks

In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.

C-ANFIS based fault diagnosis for voltage-fed PWM motor drive systems

Since most of the induction motors are operated by the inverter, an unexpected fault of the inverter can cause serious troubles such as downtime of equipment, heavy loss, and etc. Therefore, the studies on the robust drive system for induction motors to protect the system under the fault modes are gaining more interests. This paper investigates the fault diagnosis for open-switch damages in a voltage-fed PWM motor drive system. For diagnosing the conditions of a inverter, we transform the current signal to the d-q axis. And then, we obtain the features consisting of the trajectories of d-q phase currents for each fault mode. In the ideal cases, a set of fault modes can be classified by using the shape of these trajectories. There are, however, many variational elements such as load torque and the electrical/mechanical variable parameters. So, we propose a robust diagnosis method based on the neuro-fuzzy algorithm. For this, we adopted the Clustering Adaptive Neuro Fuzzy Inference System(C-ANFIS) to recognize the various and vague fault patterns. Finally, we implement the method for the diagnosis module of the inverter with MATLAB and show its usefulness.

Sound source localization with the aid of excitation source information in home robot environments

This paper is concerned with multiple microphone-based sound source localization in home robot environments. For this purpose, we use the excitation source information to determine the time-delay between each two microphones from speech source when robots name is called. Furthermore, we present a novel method to estimate the reliable localization angle from the obtained time-delay values. Thus, it can be used as a core technique in conjunction with human-robot interaction that can naturally interact between human and robots in home robot applications. The experimental results on sound localization database revealed that the presented method showed a good performance in comparison with the conventional methods such as time difference of arrival (TDOA) and generalized cross correlation- phase transform (GCC-PHAT) in distant-varying, noise and reverberant environments.

Incremental modeling with rough and fine tuning method

In this paper, we propose a new learning approach for designing an incremental model that has a cascade learning structure combined with a rough and fine tuning method for the learning scheme. Recently, various fuzzy logic-based modeling methods, with fuzzy if-then type rules, have been proposed in an attempt to obtain good approximations and generalization performances. In contrast to these various modeling methods, the new proposed incremental modeling scheme presented here is combined with a rough and fine tuning scheme, to learn and construct the best architecture for the model. A compensation idea is introduced in the fine tuning stage to solve the over-fitting problem caused from testing data. For this purpose, a construct of an extreme learning machine (ELM) is used as a global model, and this is compensated through a conditional fuzzy C-means (CFCM)-based fuzzy inference system (FIS) with a Takagi-Sugeno-Kang (TSK)-type method, which captures the remaining localized nonlinearities of the model. The experimental results, obtained by the proposed model have proved to show better performances in comparison with previous works.

Face Recognition for Expressive Face Images

In this paper, we deal with a face recognition method for the expressive face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated. The face recognition becomes a very difficult problem if we consider the facial expression. This situation requires a robust face recognition algorithm. So, we use a fuzzy linear discriminant (LDA) algorithm with the wavelet transform. The fuzzy LDA is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information.

Detection and Diagnosis of Induction Motor Using Conditional FCM and Radial Basis Function Network

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

Facial Expression Recognition using ICA-Factorial Representation Method

In this paper, we proposes a method for recognizing the facial expressions using ICA(Independent Component Analysis)-factorial representation method. Facial expression recognition consists of two stages. First, a method of Feature extraction transforms the high dimensional face space into a low dimensional feature space using PCA(Principal Component Analysis). And then, the feature vectors are extracted by using ICA-factorial representation method. The second recognition stage is performed by using the Euclidean distance measure based KNN(K-Nearest Neighbor) algorithm. We constructed the facial expression database for six basic expressions(happiness, sadness, angry, surprise, fear, dislike) and obtained a better performance than previous works.

A Neuro-Fuzzy Modeling using the Hierarchical Clustering and Gaussian Mixture Model

In this paper, we propose a neuro-fuzzy modeling to improve the performance using the hierarchical clustering and Gaussian Mixture Model(GMM). The hierarchical clustering algorithm has a property of producing unique parameters for the given data because it does not use the object function to perform the clustering. After optimizing the obtained parameters using the GMM, we apply them as initial parameters for Adaptive Network-based Fuzzy Inference System. Here, the number of fuzzy rules becomes to the cluster numbers. From this, we can improve the performance index and reduce the number of rules simultaneously. The proposed method is verified by applying to a neuro-fuzzy modeling for Box-Jenkins s gas furnace data and Sugeno`s nonlinear system, which yields better results than previous oiles.

A Neuro-Fuzzy System Modeling using Gaussian Mixture Model and Clustering Method

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.