Myung-Geun Chun

TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization

Since the fuzzy time series forecasting methods provide a powerful framework to cope with vague or ambiguous problems, they have been widely used in real applications. The forecasting accuracy of these methods usually, however, depend on their universe of discourse and the length of intervals. So, we present a new forecasting method using two-factors high-order fuzzy time series and particle swarm optimization (PSO) for increasing the forecasting accuracy. To show the effectiveness of the proposed method, we applied our method for the Taiwan futures exchange (TAIFEX) forecasting and the Korea composite price index (KOSPI) 200 forecasting. The results show better forecasting accuracy than previous methods.

Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring

This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring of alertness of a vehicle driver. Most existing approaches to visual detection of nonalert driving patterns rely either on eye closure or head nodding angles to determine the driver drowsiness or distraction level. The proposed scheme uses visual features such as eye index (EI), pupil activity (PA), and HP to extract critical information on nonalertness of a vehicle driver. EI determines if the eye is open, half closed, or closed from the ratio of pupil height and eye height. PA measures the rate of deviation of the pupil center from the eye center over a time period. HP finds the amount of the drivers head movements by counting the number of video segments that involve a large deviation of three Euler angles of HP, i.e., nodding, shaking, and tilting, from its normal driving position. HP provides useful information on the lack of attention, particularly when the drivers eyes are not visible due to occlusion caused by large head movements. A support vector machine (SVM) classifies a sequence of video segments into alert or nonalert driving events. Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.

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.

A similarity-based bidirectional approximate reasoning method for decision-making systems

This paper presents a similarity-based bidirectional approximate reasoning method which can express the decision makers disposition. For this, we shall propose a new type of similarity measure based on the ordered weighted aggregation (OWA) operator. The proposed similarity measure has a structure to involve the disposition of a decision maker by choosing a suitable weighting vector of the OWA operator. From this property, we derive a simple and flexible type of similarity-based bidirectional approximate reasoning method which can support the making of a decision under more complex situations such as industrial inspection systems for example.

Parameter Optimization of Extreme Learning Machine Using Bacterial Foraging Algorithm

Recently, Extreme learning machine(ELM), a novel learning algorithm having much faster than the traditional gradient-based learning algorithm, was proposed for single-hid den-layer feedforward neural networks (SLFNs). Usually, the initial input weights and hidden biases of ELM are randomly chosen, and then the output weights are analytically determined by using Moore-Penrose (MP) generalized inverse. However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. In this paper, an optimization method based on the bacterial foraging (BF) algorithm is proposed to adjust the input weights and hidden biases. Experimental result shows that this method can achieve better performance for problems having higher dimension than others.

Hybrid Feature Selection Using Genetic Algorithm and Information Theory

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

ASSOCIATION ANALYSIS OF SOFTWARE MEASURES

Software measures (metrics) provide software engineers with an important means of quantifying essential features of software products and software processes such as software reliability, maintenance, reusability and alike. Software measures interact between themselves. Some of them may be deemed redundant. Software measures are used to construct detailed prediction models. The objective of this study is to pursue an association analysis of software measures by revealing dependencies (associations) between them. More specifically, the introduced association analysis is carried out at the local level by studying dependencies between information granules of the software measures. This approach is contrasted with a global level such as e.g., regression analysis. We discuss the role of information granules as meaningful conceptual entities that facilitate analysis and give rise to a user-friendly, highly transparent environment.

Neuro-Fuzzy Rule Generation for Backing up Navigation of Car-like Mobile Robots

An automatic neuro-fuzzy rule generation scheme is proposed for backing up navigation of car-like mobile robots. The proposed method is based on the Conditional Fuzzy C-Means (CFCM) and Fuzzy Equalization (FE) methods. The CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data, and also the FE method is used to systematically construct the fuzzy membership functions for the ANFIS. From these, a compact size of fuzzy rules can be automatically obtained, which satisfy the given goal. The proposed method has been applied to a truck, and also to a truck-trailer backing up navigation problem, and good results have been achieved in comparison to previous work.

Application of ANFIS for coagulant dosing process in a water purification plant

It is very important to optimize the turbidity of the treated water by dosing coagulant in water purification plant. The coagulant reaction to the turbidity is, however, not yet to be clarified and the amount of coagulant can not be easily calculated. In this work an adaptive network-based fuzzy inference system (ANFIS) based on conditional fuzzy c-means is employed to model the coagulant reaction to the turbidity of the treated water and the historical jar-test data are used to train the ANFIS. From this, we obtained a better performance than previous works using neural network and finally validated its efficiency by a set of real field data.

Biometric and Identity Reference Protection

This paper describes how to protect the personal information of a biometric reference provider wherein biometric reference and personally identifiable information are bounded in a biometric system. To overcome the shortcomings of the simple personal authentication method using a password, such as identify theft, a biometric system that utilizes physical and behavioral characteristics of each person is usually adopted. In the biometric system, the biometric information itself is personal information, and it can be used as an unique identifier that can identify a particular individual when combining with the other information. As a result, secure protection methods are required for generating, storing, and transmitting biometric information. Considering these issues, this paper proposes a method for ensuring confidentiality and integrity in storing and transferring personally identifiable information that is used in conjunction with biometric information, by extending the related X9.84 standard. This paper also outlines the usefulness of the proposition by defining a standard format represented by ASN.1, and implementing it.