• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.832-837
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for 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 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 a reinforcement learning.

• Current Optics and Photonics
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• v.1 no.6
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• pp.642-648
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• 2017

Involuntary hand tremor has been a serious challenge in micromanipulation tasks and thus draws a significant amount of attention from related fields. To minimize the effect of the hand tremor, a variety of mechanically assistive solutions have been proposed. However, approaches increasing human awareness of their own hand tremor have not been extensively studied. In this paper, a head mount display based virtual reality (VR) system to increase human self-awareness of hand tremor is proposed. It shows a user a virtual image of a handheld device with emphasized hand tremor information. Provided with this emphasized tremor information, we hypothesize that subjects will control their hand tremor more effectively. Two methods of emphasizing hand tremor information are demonstrated: (1) direct amplification of tremor and (2) magnification of virtual object, in comparison to the controlled condition without emphasized tremor information. A human-subject study with twelve trials was conducted, with four healthy participants who performed a task of holding a handheld gripper device in a specific direction. The results showed that the proposed methods achieved a reduced level of hand tremor compared with the control condition.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1008-1016
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• 2015

This paper proposes a method for detecting the front side of vehicles. The method can find the car side with a license plate even with complicated and cluttered backgrounds. A convolutional neural network (CNN) is used to solve the detection problem as a unified framework combining feature detection, classification, searching, and localization estimation and improve the reliability of the system with simplicity of usage. The proposed CNN structure avoids sliding window search to find the locations of vehicles and reduces the computing time to achieve real-time processing. Multiple responses of the network for vehicle position are further processed by a weighted clustering and probabilistic threshold decision method. Experiments using real images in parking lots show the reliability of the method.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.621-626
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• 2015

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.398-404
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• 2015

In this paper, we describe the design and performance of a prototype multi-rotor unmaned aerial vehicle( UAV) platform featuring an inertial measurement unit(IMU) based autonomous-flying for use in bluetooth communication environments. Although there has been a fair amount of study of free-flying UAV with multi-rotors, the more recent trend has been to outfit hexarotor helicopter with gimbal to support various services. This paper introduces the hardware and software systems toward very compact and autonomous hexarotors, where they can perform search, rescue, and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices or vision system. The proposed system comprises the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling and simulation in the helicopter. Furthermore, the hexarotor helicopter with implemented IMU is connected with a micro controller unit(MCU)(ARM-cortex) board. The micro-controller is able to command the rotational speed of the rotors and to get the measurements of the IMU as input signals. The control simulation and experiment on the real system are implemented in the test platform, evaluated and compared against each other.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.34-40
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• 2008

This paper presents a vision based walking robot control system using hand shape recognition. To recognize hand shapes, the accurate hand boundary needs to be tracked in image obtained from moving camera. For this, we use an active contour model-based tracking approach with mean shift which reduces dependency of the active contour model to location of initial curve. The proposed system is composed of four modules: a hand detector, a hand tracker, a hand shape recognizer and a robot controller. The hand detector detects a skin color region, which has a specific shape, as hand in an image. Then, the hand tracking is performed using an active contour model with mean shift. Thereafter the hand shape recognition is performed using Hue moments. To assess the validity of the proposed system we tested the proposed system to a walking robot, RCB-1. The experimental results show the effectiveness of the proposed system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.384-389
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• 2005

The objective on this project is to develop a cooperative Field Robot (FR), by using a customize Open Control Platform (OCP) as design and development process. An OCP is a CORBA-based solution for networked control system, which facilitates the transitioning of control designs to embedded targets. In order to achieve the cooperation surveillance system, two FRs are distributed by navigation messages (GPS and sensor data) using CORBA event-channel communication, while graphical information from IR night vision camera is distributed using CORBA Asynchronous Method Invocation (AMI). The QoS features of AMI in the network are to provide the additional delivery method for distributing an IR camera Images will be evaluate in this experiment. In this paper also presents an empirical performance evaluation from the variable chunk sizes were compared with the number of clients and message latency, some of the measurement data's are summarized in the following paragraph. In the AMI buffers size measurement, when the chuck sizes were change, the message latency is significantly change according to it frame size. The smaller frame size between 256 bytes to 512 bytes is more efficient fur the message size below 2Mbytes, but it average performance in the large of message size a bigger frame size is more efficient. For the several destination, the same experiment using 512 bytes to 2 Mbytes frame with 2 to 5 destinations are presented. For the message size bigger than 2Mbytes, the AMI are still able to meet requirement far more than 5 clients simultaneously.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.