In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

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.

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

This paper proposes a somatosensory stimulation system for the improvement of postural stability using vibration as somatosensory stimulation. This system consists of vibratory stimulation and postural response measurement. To evaluate this system, the center of pressure(COP) was closely observed in turn with simultaneous or separate mechanical vibratory stimulations to flexor ankle muscles (tibialis anterior, triceps surae) and two plantar zones on both feet while standing on a stable and an unstable support. The simultaneous vibratory stimulations cleared influenced postural stability and the effects of vibrations were higher with the unstable support. In separate vibratory stimulations, the extent of the COP sway reduced when the direction of the vibratory stimulations and that of the inclination of body coincided for flexor ankle muscle stimulations. In the contrary, the extent of the COP sway increased when the direction of the stimulations and that of body inclination coincided for plantar zone stimulations. These results can be useful for the development of rehabilitation systems that utilizes somatosensory inputs for postural balance.

This paper deals with the problem of designing a linear sliding surface design for a class of uncertain systems with mismatched unstructured uncertainties. The uncertain system under consideration may have mismatched parameter uncertainties in the state matrix as well as in the input matrix. In terms of linear matrix inequalities (LMIs), we give a sufficient condition for the existence of linear sliding surfaces guaranteeing the asymptotic stability of the sliding mode dynamics. We show that our LMI condition can be less conservative than the existing conditions and our result supplement the existing results. Finally, we give a numerical example showing that our method can be better than the previous results.

This paper presents a robust tracking controller design method for the track-following system of an optical recording device. A tracking loop gain adjustment algorithm is introduced to accurately estimate the tracking vibration quantity in spite of the uncertainties of the tracking actuator. A minimum tracking open-loop gain is calculated by the estimated tracking vibration quantity and a tolerable limit of tracking error. A robust tracking controller is designed by considering a robust $H_\infty$ control problem with the weighting function of a slightly larger gain than the minimum tracking open-loop gain. The proposed controller design method is applied to the track-following system of an optical recording device and is evaluated through the experimental result.

A spreader pose control system using dual-electric compasses has been implemented by measuring the skew angle of the spreader with dual-electric compasses. In the conventional spreader pose measurement, CCD cameras, laser sensors or tilt sensors are mostly used. However those sensors are not only sensitive to the weather and disturbances but also expensive to build the system. To overcome the shortcomings, an inexpensive and efficient system to control the spreader pose has been implemented using the dual-magnetic compasses. Since the spreader iron-structures are noise sources to the magnetic compass, it is not considered to use the magnetic compass to measure the orientation of the spreader. An algorithm to eliminate the interferences of metal structures to the dual compasses has been developed in this paper. The 10:1 reduction model of a spreader control system is implemented and the control performance is demonstrated to show the effectiveness of the dual-magnetic compasses proposed in this research.

This paper proposes an efficient partial matching system and representation by using a region-based method for 2D image, and we applied to an extraction of the ROI(Region of Interest) according to its matching score. The matching templates consist of the global pattern and the local one. The global pattern can make it by using region-based relation between center region and its rest regions in an object. And, the local pattern can be obtained appling to the same method as global, except relation between objects. As the templates can be normalized, we use this templates for extraction of ROI with invariant to size and position. And, our system operates only one try to match, due to normalizing of region size. To use our system for searching and examining if it's the ROI by evaluating the matching function, at first, we are searching to find candidate regions with the global template. Then, we try to find the ROI among the candidates, and it works this time by using the local template. We experimented to the binary and the color image respectively, they showed that the proposed system can be used efficiently for representing of the template and the useful applications, such as partially retrievals of 2D image.

In order for a vehicle to follow a predetermined trajectory accurately, its position must be estimated accurately and reliably. In this thesis, we propose trajectory tracking control methods for unmanned vehicle and a positioning system using ultrasonic wave. The positioning problem is an important part of control problem for unmanned navigation of a vehicle. Dead Reckoning is widely used for positioning of vehicle. However this method has problems because it accumulates estimation errors. We propose a new method to increase the accuracy of position estimation using the Ultrasonic Satellite System (USAT). It is shown that we will be able to estimate the position of vehicle precisely, in which errors are not accumulated. And proposed trajectory tracking control methods include both a new path planning method and a lateral control method for vehicle. The experimental results show that the proposed methods enables exact vehicle trajectory tracking even under various environmental factors.

In this work, we present the development of a patrol robot which is intended to navigate outdoor rough terrain. Proposed mechanism consists of six legs for overcoming an obstacle, and six wheels for traveling. Also, in order to absorb vibration in rough terrain effectively, the slide-spring system and tubed type tire are adopted to each leg and each wheel. The control system of robot consists of several imbedded boards for management of lots of diverse devices such as sensors designed for rough terrain, motor controllers, camera, micro controller and so on. And the base system of the robot is designed to operate in real time and to surveille in the vicinity of the robot, and the robot system is controlled by wireless LAN connected to GUI-based remote control system, while CAN communication connects the control board and the device controllers for sensors and motor controllers. For operating this robot system efficiently, we propose the control algorithms for autonomous navigation using GPS, stabilization maintenance by posture control, obstacle-avoidance by impedance control, and obstacle-overcoming with interference-avoidance between wheels. The performance of the robot and the proposed algorithms are tested and proved by a set of experiments in outdoor rough terrain.

This paper proposes a vision-based kinematic control method for mobile robots with camera-on-board. In the previous literature on the control of mobile robots using camera vision information, the forward velocity is set to be a constant, and only the rotational velocity of the robot is controlled. More efficient motion, however, is needed by controlling the forward velocity, depending on the position in the corridor. Thus, both forward and rotational velocities are controlled in the proposed method such that the mobile robots can move faster when the comer of the corridor is far away, and it slows down as it approaches the dead end of the corridor. In this way, the smooth turning motion along the corridor is possible. To this end, visual information using the camera is used to obtain the perspective lines and the distance from the current robot position to the dead end. Then, the vanishing point and the pseudo desired position are obtained, and the forward and rotational velocities are controlled by the LOS(Line Of Sight) guidance law. Both numerical and experimental results are included to demonstrate the validity of the proposed method.

Mobile Robots are increasingly being used to perform tasks in unknown environment. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. To achieve autonomous mobile robot navigation, efficient path planner and accurate localization technique are the fundamental issues that should be addressed. This paper presents mobile robot localization using IRID(InfraRed IDentification) as artificial landmarks. IRID has highly deterministic characteristics, different from RFID. By putting several IRID emitters on the ceiling, the floor is divided into many different sectors and each sector is set to have a unique identification. Dead-reckoning provides the estimated robot configuration but the error becomes accumulated as the robot travels. IRID information tells the sector the robot is in, but the size of the uncertainty is too large if only the IRID information is used. This paper presents an algorithm which combines both the encoder and the IRID information so that the size of the uncertainty becomes smaller. It also introduces a framework which can be used with other types of the artificial landmarks. The characteristics of the developed IRID and the proposed algorithm are verified from the simulation results and experiments.

In this paper, an experimental study is performed to adjust position of compliance center of Elastomer Shear Pad Remote Center Compliance (ESP RCC) device, which is used on precise peg in hole process. In the study, variation of the lateral/axial stiffness of the ESP is proposed as a control parameter to adjust the position of compliance center of the ESP RCC. The variation of the stiffness of the ESP is achieved by controlling the shear stress of the ESP. To control the shear stress of the ESP, position of top side of the ESP is changed while remaining bottom side of the ESP is fixed on the RCC plate. To evaluate effect of the proposed idea, stiffness variations of the ESP on various shear stresses are measured, and variation of the compliance center is measured with the ESP RCC that can control the position of compliance center by using the shear stress. The measured data shows unique characteristics that have not been shown in other types of ESP VRCCs.

This paper proposed a transponder detection method to reduce recognition time in RFID system. It's also shown that conventional procedure of communication in the system could cause a waste of time when a reader recognizes a transponder. The reduction of recognition time can be obtained by developing a circuit to detect a transponder actively. Detecting a transponder is achieved by using the voltage variation of reader antenna voltage that happens when a transponder approaches to the vicinity of magnetic field formed by the reader. By adding a comparator to the antenna receiver of a reader, the reader can perceive approach or existence of a transponder. A reader for experiment is made using the MFRC500 by Phillips that supports ISO/IEC 14443 protocol. Comparing the proposed method with the conventional methods by experiment, there are 47.5ms reduction of recognition time maximally and 12ms in average.

Establishing a fixed path for the message delivery through a wireless network is impossible due to the mobility. Among the number of routing protocols that have been proposed for wireless ad-hoc networks, the AODV(Ad-hoc On-demand Distance Vector) algorithm is suitable in the case of highly dynamic topology changes, along with ZigBee Routing(ZBR), with the exception of route maintenance. Accordingly, this paper introduces a routing scheme focusing on the energy efficiency and route discovery time for wireless alarm systems using IEEE 802.15.4-based ZigBee. Essentially, the proposed routing algorithm utilizes a cluster structure and applies ZBR within a cluster and DSR (Dynamic Source Routing) between clusters. The proposed algorithm does not require a routing table for the cluster heads, as the inter-cluster routing is performed using DSR. The performance of the proposed algorithm is evaluated and compared with ZBR using an NS2 simulator. The results confirm that the proposed Cluster-based AODV (CAODV) algorithm is more efficient than ZBR in terms of the route discovery time and energy consumption.