Eui-Sun Kim

A neural network model of electric differential system for electric vehicle

This paper describes a neural network model for an electrical differential system for electric vehicle. When a vehicle drives along curved road lane, the speed of the inner wheel has to be different from that of the outer wheel in order to prevent the vehicle vibrating and traveling an unsteady path. Because each wheel of this electrical vehicle has an independent driving force, an electrical differential system is required to replace a gear differential system. However, it is difficult to analysis the nonlinear behavior of the differential system in relation to the vehicle speed and steering angle, as well as vehicle structure. Therefore, a neural network is used to learn the relationships. To realize the neural network model, the speed data was acquired for the inner wheel and outer wheel, using an experimental electric vehicle at various speeds and steering angles. With this information, the differential system can be controlled using a neural network model of the nonlinear relationships.

Design of magnet based position sensing system for autonomous vehicle robot

In this paper, a position sensing system using magnet and magnetic sensor for autonomous vehicle robot is designed. Magnetic sensing is a reliable technology that has been developed for the purposed of position measurement and guidance, especially for applications in autonomous vehicle robot. The magnetic fields from a sample magnet are first measured to determine the characteristics of the field patterns as the basis for detection and position identification. A discussion of a position sensing system with a rejection algorithm of background field is presented. The position sensing technique was implemented in the guidance of autonomous vehicle robot. The results of lateral motion control show that position-sensing system can be useful for an autonomous vehicle robot.

Visual control of an autonomous vehicle using neural networks

This paper describes a control scheme for on autonomous vehicle with visual sensors, which uses visual information to guide itself along roadways. The control system integrates visual data into the servo process directly, instead of subdividing the process by reconstructing three-dimensional shape from two-dimensional image data, and relying on geometric reasoning for a vehicle-centered representation of the road. An artificial neural network system is used for determining the steering angle required to move the vanishing point and vanishing line of the road to the desired position in the camera image. The validity and the effectiveness of the proposed control scheme are verified by a computer simulation of the autonomous vehicles performance.

A System Modeling and Controller Design Method Using Discrete Fourier Transform

This paper describes system modeling and controller design method in the measured signal by discrete Fourier transform. Transfer function of the second order system is estimated by the dominant parameter which is computed in the magnitude and the phase of Fourier spectrum of the measured signal. In addition, the controller was designed by the estimated transfer function, and the results were compared. The proposed estimation method of transfer function contains only a very simple mathematical process. Therefore, it is effective to design the controller in the measured signal when the output of the system contains the characteristics of complex exponential functions case. The proposed method was applied on Op-Amp system to verify the efficiency and the reliability. The results show that the proposed algorithms are highly applicable to the system modeling and controller design in the measured data.

Cancellation of background field using magnetic compass sensor for magnetometer based autonomous vehicle

In this paper, a cancellation method of background field using a magnetic compass sensor is proposed for a magnetometer based autonomous vehicle. The magnetic fields from sample magnets are first measured to determine the characteristics of the field patterns as the basis for detection and position identification. The properties of the earths magnetic field on the recognition of magnet field patterns are discussed. Also presented in the paper is a discussion of a position sensing system with a cancellation of the earths field. The position sensing technique was implemented in the guidance of autonomous vehicle. By the experiment, it is verified that the proposed cancellation method of background field can be useful for an autonomous vehicle.

Fill Factor of Rotor of Single Phase Induction Motor for the Capability Improvement of 1/4 HP Compressor

This paper discusses to the capability improvement of the 1/4 HP compressor. And, for efficiency improvement of the single phase induction motor, this paper has proposed a casting method to improving the fill factor of the rotor of the single phase induction motor. First, by using FEM, it was analyzed the magnetic flux distribution and the fill factor to the SPIM rotor by the conventional method and the proposed method. And, based on the FEM analysis results, SPIMs are manufactured by the each casting process. Through experiments using dynamometer, they were compared and measured to their efficiencies. In addition, through experiments using Calorimeter, we have analyzed the refrigerating capacity of the compressor of which they were applied. Based on the results of the previous two experiments, they are to verify the validity of the proposed method.

The Position Control of DC Motor using the System Modeling based on the DFT

This study presents a new method of system modeling by using the Discrete Fourier Transform for the position control system of DC Motor. And the proposed method is similar to the method of System Identification by analysis of correlation of the measured input-output data. The measured output signals are transformed to the frequency domain using DFT. The Fourier Spectrum of the transformed signals is used for knowing to the feature of having an important effect on the system. And transfer function of the second order system is estimated by the dominant parameter which is computed in the magnitude and the phase of Fourier spectrum of the transformed signals. In addition, the output signal includes the unique feature of system. So, although the basic parameter of the system is unknown for us, the proposed method has an advantage to system modeling. And the controller is easily designed by the estimated transfer function. Thus, in this paper, the proposed method is applied to the system modeling for the position control system of DC Motor and the PD-controller is designed by the estimated model. And the efficiency and the reliability of the proposed method are verified by the experimental result.

A Method of Visualization and Fast Estimation of Parameter in Continuous Time Signal

This paper describes a method of visualization and fast estimation of parameter in continuous time signal. The parameter estimation method of this paper directly estimate the parameters on the basis of the discrete Fourier transform. Also, this paper present to efficient visualization method of dominant parameters obtained in continuous time signal. The proposed methods are applied to test functions with three dominant modes. The results show that the proposed methods are highly applicable to parameter estimation and visualization in continuous time signal.

Automatic driving of robicle(robotic vehicle) using vision based and magnet based guidance

In this paper, a new terminology, robicle, is defined. Robicle means a robotic vehicle with robotic intelligence and vehicular mobility. Technologies for vision based robicle and magnet based robicle are introduced. As applications of robicle, some developed robicles are presented. Also, the experimental setup and results of vision and magnet based robicle are described.

Internet Monitoring of Wind-Photovoltaic Hybrid Generation System

Recently, many researchers have shown great interest in wind-photovoltaic hybrid generation system which promotes electric power supply safely and progress of energy usage efficiently with complementary cooperation of a wind generation system and photovoltaic generation system. To use this hybrid generation system stably and effectively, we established a system which can acquire, analyse and save data and monitored remotely using internet. We constructed the signal conditioning circuit and used many kinds of converters to measure physical quantities such as wind velocity, intensity of illumination and temperature as well as many kinds of voltage and current for AC and DC. we acquired data from computer with data acquisition board, developed server program and client program which can download data that is monitored and saved in realtime at remote place. We analysed the measured data in relation to many conditions such as time and weather conditions.