• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.998-1013
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• 2013

Wireless wearable sensor networks have emerged as a promising technique for human motion tracking due to the flexibility and scalability. In such system several wireless sensor nodes being attached to human limb construct a wearable sensor network, where each sensor node including MEMS sensors (such as 3-axis accelerometer, 3-axis magnetometer and 3-axis gyroscope) monitors the limb orientation and transmits these information to the base station for reconstruction via low-power wireless communication technique. Due to the energy constraint, the high fidelity requirement for real time rendering of human motion and tiny operating system embedded in each sensor node adds more challenges for the system implementation. In this paper, we discuss such challenges and experiences in detail during the implementation of such system with wireless wearable sensor network which includes COTS wireless sensor nodes (Imote 2) and uses TinyOS 1.x in each sensor node. Since our system uses the COTS sensor nodes and popular tiny operating system, it might be helpful for further exploration in such field.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.94-101
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• 2007

Streaming video is expected to become a key service in the developing heterogeneous wireless network. However, sufficient quality of service is not offered to video applications because of bursty packet losses. An effective solution for packet loss in wireless network is to perform a proper concealment at the receiver. However, most concealment methods can not conceal effectively the consecutively damaged macro blocks, since the neighboring blocks are lost. In the previous work, bidirectional motion vector tracking (BMVT) method has been proposed which uses the moving trajectory feature of the damaged macro blocks. In this paper, a channel-adaptive redundancy coding method for the better BMVT error concealment is presented. The proposed method provides enhanced video quality at the cost of a little bit overhead in the wireless error-prone network.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.206-212
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• 2010

In this paper, we introduce the concept of robotic zigbee network as a necessary network to provide an application service of robot in the ubiquitous environment and propose an application scenario using the concept of robot Zigbee network. We have performed experiments on the network connection and data transmission which are basic of proposed an application scenario. Through the result of the experiments, we provide basis for development of robot localization and tracking algorithm which minimizes the localization error using robot Zigbee network in the future.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.11a
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• pp.225-229
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• 1996

In this study, in order to develop outdoor insulating materials, SIN(simultaneous interpenetrating polymer network) was introduced to Epoxy resin and the environment resistance was investigated. The single network structure specimen(E series) formed of Epoxy resin alone and simultaneous interpenetrating polymer network specimen (EM series) in which epoxy resin was taken as the first network and methyl methacrylate resin as the second network were manufactured. Ten kinds of specimens were manufacture by filler (SiO$_2$) content. SEM were utilized in order to confirm their network structure changes, and AC voltage dielectric strength was measured. Also, UV-test and tracking test were carried out investigate the environment resistance characteristic. Therefore the variations of network structure were happened as a result of SEM test, and it was confirmed that simultaneous interpenetrating polymer network specimens were more excellent than single network structure specimens.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.522-527
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• 2022

Object tracking is a field of signal processing that sequentially tracks the location of an object based on the previous-time location estimations and the present-time observation data. In this paper, we propose an adaptive scaling neural network that can track and adjust the scale of the input data with three recursive neural network (RNN) submodules. To evaluate object tracking performance, we compare the proposed system with the Kalman filter and the maximum likelihood object tracking scheme under an one-dimensional object movement model in which the object moves with piecewise constant acceleration. We show that the proposed scheme is generally better, in terms of root mean square error (RMSE) performance, than maximum likelihood scheme and Kalman filter and that the performance gaps grow with increased observation noise.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.986-993
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• 2017

Object Tracking is a technique for tracking moving objects over time in a video image. Using object tracking technique, many research are conducted such a detecting dangerous situation and recognizing the movement of nearby objects in a smart car. However, it still remains a challenging task such as occlusion, deformation, background clutter, illumination variation, etc. In this paper, we propose a novel deep visual object tracking method that can be operated in robust to many challenging task. For the robust visual object tracking, we proposed a Convolutional Neural Network(CNN) which shares weight of the convolutional layers. Input of the CNN is a three; first frame object image, object image in a previous frame, and current search frame containing the object movement. Also we propose a method to consider the motion of the object when determining the current search area to search for the location of the object. Extensive experimental results on a authorized resource database showed that the proposed method outperformed than the conventional methods.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.641-643
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• 2000

CATV Network Management system of Korea is used for mainly monitor forward broadcasting signal because of the difficulty of tracking, measuring and control reverse path nosie. Thereby Purpose of this Structure is removing return Path noise of CATV Network for maintaining two way Netowrk Service of the Highest quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.85-88
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• 2000

In this paper, the ultra precision positioning system for piezoelectric actuator using hysteresis compensation has been developed. Piezoelectric actuators exhibit limited accuracy in tracking control due to their hysteresis nonlinearity. The main purpose of the proposed controller is to compensate the hysteresis nonlinearity of the piezoelectric actuator. The controller is composed of a PD, hysteresis compensation and neural network part in parallel manner, at first, the excellent tracking performance of the neural network controller was verified by experiments and was compared with the classical PD controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.294-300
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• 2009

The neural network with radial basis function is introduced for position tracking control of AC servo drive with the existence of system uncertainties. An adaptive robust term is applied to overcome the external disturbances. The proposed controller is implemented on a high performance digital signal processing DSP TMS320C6713-300. The stability and the convergence of the system are proved by Lyapunov theory. The validity and robustness of the controller are verified through simulation and experimental results

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.