Ho-Duck Kim

Genetic Algorithm Based Feature Selection Method Development for Pattern Recognition

An important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, principal component analysis has been usually used and SFS (sequential forward selection) and SBS (sequential backward selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it genetic algorithm feature selection (GAFS) and this algorithm is compared to other methods in the performance aspect

SLAM of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors

In the moving of the mobile robot, the mobile robot acquires a map of its environment while simultaneously localizing itself relative to the map. Simultaneous localization ad mapping (SLAM) problems arise when the robot does not have access to a map of the environment, nor does it know its own pose. In this paper, we study the SLAM of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors. Digital Magnetic Compass has a strong feature against interference in the indoor environment better than compass which is can easily be disturbed by electromagnetic sources or large ferromagnetic structures. Ultrasonic Sensors are cheap and can give relatively accurate range readings. Autonomous mobile robot is aware of robots moving direction and position by the restricted data. Also robot must localize as quickly as possible. As application for the SLAM on the autonomous mobile robot system, robot can find the localization and the mapping and can solve the Kid Napping situation for itself. Especially, in the Kid Napping situation, autonomous mobile robot use Ultrasonic sensors and Digital Magnetic Compass(DMC)s data for moving. When robot receives the similar data by sensors, robot uses Computation Intelligence(CI) for perceiving in the robots position.

Simultaneous Localization and Map building using Vision Camera and Electrical Compass

Simultaneous localization and map building (SLAM) problem is occurred under the situation that a robot is moving on the unknown place. In this paper, We study the SLAM of the mobile autonomous robot system. We assume that the moving space of robots is the Ideal indoor space. Also, we suggest the data format to build the map. The data format is only used for this robot system. But it may be useful to the other robot systems. Most important thing that we present in this paper is using the electrical compass. We can get the points of the electrical compass. That point represents an original direction of the robot. Precisely, in this research, the robot uses the electrical compass and vision camera to solve the kidnapping. The robot has to deal with the points of the compass as the map building parameters. As an application for the SLAM on the autonomous mobile robot system, this study is aimed to optimize the performance of the robot navigation on the 2-D space with vision camera and electrical compass. So this paper suggests the new data format to solve the SLAM problem. Moreover, how the robot can find its own position in the kidnapping situations for itself. The robot has to be more intelligent and smart. The method is sufficient to the robot applications.

Human Emotion Recognition using Power Spectrum of EEG Signals : Application of Bayesian Networks and Relative Power Values

Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

Simultaneous Localization and Mapping of Mobile Robot using Digital Magnetic Compass and Ultrasonic Sensors

Digital Magnetic Compass(DMC) has a robust feature against interference in the indoor environment better than compass which is easily disturbed by electromagnetic sources or large ferromagnetic structures. Ultrasonic Sensors are cheap and can give relatively accurate range readings. So they ate used in Simultaneous Localization and Mapping(SLAM). In this paper, we study the Simultaneous Localization and Mapping(SLAM) of mobile robot in the indoor environment with Digital Magnetic Compass and Ultrasonic Sensors. Autonomous mobile robot is aware of robot`s moving direction and position by the restricted data. Also robot must localize as quickly as possible. And in the moving of the mobile robot, the mobile robot must acquire a map of its environment. As application for the Simultaneous Localization and Mapping(SLAM) on the autonomous mobile robot system, robot can find the localization and the mapping and can solve the Kid Napping situation for itself. Especially, in the Kid Napping situation, autonomous mobile robot use Ultrasonic sensors and Digital Magnetic Compass(DMC)`s data for moving. The robot is aware of accurate location By using Digital Magnetic Compass(DMC).

Strategy of Object Search for Distributed Autonomous Robotic Systems

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize their surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

Emotion Recognition Method of Facial Image using PCA

A research about facial image recognition is studied in the most of images in a full race. A representative part, effecting a facial image recognition, is eyes and a mouth. So, facial image recognition researchers have studied under the central eyes, eyebrows, and mouths on the facial images. But most people in front of a camera in everyday life are difficult to recognize a fast change of pupils. And people wear glasses. So, in this paper, we try using Principal Component Analysis(PCA) for facial image recognition in blindfold case.

Emotion Recognition Method for Driver Services

Electroencephalographic (EEG) is used to record activities of human brain in the area of psychology for many years. As technology developed, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection (IFS) for the feature selection whose method is based on the reinforcement learning.

Emotion Recognition Method using Physiological Signals and Gestures

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study emotion recognition method which uses one of physiological signals and gestures in the existing research. In this paper, we use together physiological signals and gestures for emotion recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

Brain-wave Analysis using fMRI, TRS and EEG for Human Emotion Recognition

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.