Youn Tae Kim

An accurate localization for mobile robot using extended Kalman filter and sensor fusion

This paper presents an accurate localization scheme for mobile robots based on the fusion of an ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve an accuracy of less than 100 mm. The INS consists of a yaw gyro and two wheel-encoders, and the U-SAT consists of four transmitters and a receiver. Besides the proposed localization method, we will fuse these in an extended Kalman filter. The performance of the localization was verified by simulation and two actual data sets (straight and curve) gathered from about 0.5 m/s of actual driving data. The localization methods used were general sensor fusion and sensor fusion through a Kalman filter using data from the INS. Through simulation and actual data analysis, the experiment shows the effectiveness of the proposed method for autonomous mobile robots.

Reliable data selection with fuzzy entropy

In this paper, the selection of a data set from a universal set is carried out using a fuzzy entropy function. According to the definition of fuzzy entropy, the fuzzy entropy function is proposed and that function is proved through definitions. The proposed fuzzy entropy function calculates the certainty or uncertainty value of a data set; hence we can choose the data set that satisfies certain bounds or references. Therefore a reliable data set can be obtained using the proposed fuzzy entropy function. With a simple example we verify that the proposed fuzzy entropy function selects the reliable data set.

Smart Cargo Monitoring System Based on Decision Support System for Liquid Carrier Tanker

In this paper, we constructed the advanced cargo monitoring system for liquid cargo tankers which embedded the Decision Support System (DSS) based on the International Ship Management Code (ISM Code). To make this system, we first organized a base of experts knowledge concerning liquid tanker operations that largely affect ocean accidents. We can find out the knowledge via inference method which simply imitates the fuzzy inference method. Based on this experts knowledge, we constructed the DSS that provides a code of conduct for operating cargo tanks safely. The proposed monitoring system could eliminate human error when confronting dangerous situations, so the system will help sailors to operate cargo tanks safely.

Movement Pattern Recognition of Medaka for an Insecticide: A Comparison of Decision Tree and Neural Network

Behavioral sequences of the medaka (Oryzias latipes) were continuously investigated through an automatic image recognition system in response to medaka treated with the insecticide and medaka not treated with the insecticide, diazinon (0.1 ㎎/l) during a 1 hour period. The observation of behavior through the movement tracking program showed many patterns of the medaka. After much observation, behavioral patterns were divided into four basic patterns: active-smooth, active-shaking, inactive-smooth, and inactive-shaking. The “smooth” and “shaking” patterns were shown as normal movement behavior. However, the “shaking” pattern was more frequently observed than the “smooth” pattern in medaka specimens that were treated with insecticide. Each pattern was classified using classification methods after the feature choice. It provides a natural way to incorporate prior knowledge from human experts in fish behavior and contains the information in a logical expression tree. The main focus of this study was to determine whether the decision tree could be useful for interpreting and classifying behavior patterns of the medaka.

Application of time-series data mining for fault diagnosis of induction motors

The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces a technique to detect faults in induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is used to display the signals. Fourier transform is used to convert the signals onto frequency domain. After the signals have been converted, the features of the signals are extracted by the signal processing methods like the wavelet analysis, spectrum analysis, and other methods. The discovered features are entered to a pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, or other models. This paper describes the results of detecting fault using Fourier and wavelet analysis.

Fault diagnostic of induction motors for equipment reliability and health maintenance based upon Fourier and wavelet analysis

The motor is the workhorse of industry. The issues of preventive and condition-based maintenance, on-line monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detection signal features.

Indoor Localization for Mobile Robot using Extended Kalman Filter

This paper is presented an accurate localization scheme for mobile robots based on the fusion of ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve enough accuracy less than 100 mm. The INS consist of a yaw gyro, two wheel-encoders. And the U-SAT consist of four transmitters, a receiver. Besides the localization method in this paper fuse these in an extended Kalman filter. The performance of the localization is verified by simulation and two actual data(straight, curve) gathered from about 0.5 m/s of driving actual driving data. localization methods used are general sensor fusion and sensor fusion through Kalman filter using data from INS. Through the simulation and actual data studies, the experiment show the effectiveness of the proposed method for autonomous mobile robots.

Fault Diagnosis of AC Servo Motor with Current Signals Based on Wavelet Decomposition and Template Matching Methods

This paper presents a diagnosis technique to detect and identify faults in AC servo motors. The first phase of stator currents among three phases is digitized and stored in the time domain. Wavelet transform is employed to convert the signals onto time-frequency domain because the time domain based approach is not suitable for detecting state features of the current signals. Pre-processing algorithms that includes a kind of mean-filtering, synchronization with Hilbert transform and difference are consecutively performed to the raw signal to determinate features. Wavelet decomposition is applied to the difference values by the optimally selected mother wavelet and the features are calculated from the transformed signals. The extracted features are compared with the motor fault templates for the template matching method. The results based on real data show that the proposed approach is very useful to extract features of the signals for fault diagnosis.

Fault diagnosis of identical brushless DC motors under patterns of state change

In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the change of state model (CSM). Based on a proposed CSM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that CSM method could be a useful tool for diagnosing the condition of identical BLDC motors.

An Emotion Recognition Technique using Speech Signals

In the field of development of human interface technology, the interactions between human and machine are important. The research on emotion recognition helps these interactions. This paper presents an algorithm for emotion recognition based on personalized speech signals. The proposed approach is trying to extract the characteristic of speech signal for emotion recognition using PLP (perceptual linear prediction) analysis. The PLP analysis technique was originally designed to suppress speaker dependent components in features used for automatic speech recognition, but later experiments demonstrated the efficiency of their use for speaker recognition tasks. So this paper proposed an algorithm that can easily evaluate the personal emotion from speech signals in real time using personalized emotion patterns that are made by PLP analysis. The experimental results show that the maximum recognition rate for the speaker dependant system is above 90%, whereas the average recognition rate is 75%. The proposed system has a simple structure and but efficient to be used in real time.